domain: sdu sde spi

text:
Mathieu Le Breton

Frédéric Liébault

Laurent Baillet

Arthur Charléty

Éric Larose

Smail Tedjini

DENSE AND LONG-TERM MONITORING OF EARTH SURFACE PROCESSES WITH PASSIVE RFID -A REVIEW

Billions of Radio-Frequency Identification (RFID) passive tags are produced yearly to identify goods remotely. New research and business applications are continuously arising, including recently localization and sensing to monitor earth surface processes. Indeed, passive tags can cost 10 to 100 times less than wireless sensors networks and require little maintenance, facilitating years-long monitoring with ten's to thousands of tags. This study reviews the existing and potential applications of RFID in geosciences. The most mature application today is the study of coarse sediment transport in rivers or coastal environments, using tags placed into pebbles. More recently, tag localization was used to monitor landslide displacement, with a centimetric accuracy. Sensing tags were used to detect a displacement threshold on unstable rocks, to monitor the soil moisture or temperature, and to monitor the snowpack temperature and snow water equivalent. RFID sensors, available today, could monitor other parameters, such as the vibration of structures, the tilt of unstable boulders, the strain of a material, or the salinity of water. Key challenges for using RFID monitoring more broadly in geosciences include the use of ground and aerial vehicles to collect data or localize tags, the increase in reading range and duration, the ability to use tags placed under ground, snow, water or vegetation, and the optimization of economical and environmental cost. As a pattern, passive RFID could fill a gap between wireless sensor networks and manual measurements, to collect data efficiently over large areas, during several years, at high spatial density and moderate cost.

1 Why passive RFID in geosciences?

What is RFID: Back to basics

Radio Frequency Identification (RFID) is a set of technologies that enables to detect and identify a target using a wireless radiofrequency communication channel. Active RFID uses a conventional radiofrequency communication, in which a device with a battery emits its own radiofrequency wave to an interrogator (many protocols exist such as Bluetooth, LoRA or LTE) in order to communicate its identifier. In this review, we focus instead on passive RFID, where only the interrogator emits a radiofrequency wave. A passive target, called "tag," receives this wave, modulates it to embed information, and reflects the modulated wave towards the interrogator. Backscatter communication is a method in which a passive RFID tag receives a radiofrequency excitation from an interrogator, and backscatters a modulated signal to send back a response to the interrogator.

Historically, backscattered communication was first patented as a preliminary concept to communicate between an active and a passive radiotelegraphy station [START_REF] Brard | Process for radiotelegraphic or radiotelephonic communication[END_REF]. Later, [START_REF] Stockman | Communication by Means of Reflected Power[END_REF] explained the physics behind the communication by reflected powers. He concluded that "considerable research and development work has to be done before the remaining basic problems in reflected-power communication are solved, and before the field of useful applications is explored." At the same time, Theremin secretly released in 1945 a fully passive device for wireless microphone spying (see [START_REF] Nikitin | Leon Theremin (Lev Termen)[END_REF], called the "Thing." It consists of a simple electromagnetic cavity, with one of its walls replaced by a vibrating membrane sensitive to sound waves. Under the illumination by a radiofrequency signal at its resonant frequency, the "Thing" backscattered a signal modulated in amplitude by the ambient sound waves. It is probably the very first passive wireless sensor and is arguably the ancestor of the RFID tag. On the other hand, the radar systems which began to develop also during the 1940s are among the most relevant and useful techniques exploiting backscatter signals. It gave rise to the development of the Identify Friend or Foe System very widely used by the allies during the Second World War (see [START_REF] Obe | Air Defence 1940-1970/Military IFF and Secondary Radar -A Historical Review[END_REF]. With the development of electronic systems and the advent of semiconductor materials and integration technologies in the 1950s, it was possible to develop integrated and compact systems like the modern labels. The best-known device inherited from the 1960s is the Electronic Article Surveillance method, which enabled the first commercial application of RFID [START_REF] Minasy | Method and apparatus for article theft detection[END_REF]. The method is effective for preventing shoplifting from stores or item removal from buildings.

Today, industrial tags mostly use microelectronic chips. Such tags are essentially an antenna or a coil, connected to an application-specific integrated circuit (the RFID chip). The chip can switch its input impedance between two states, thus alternating the radiofrequency power reflected by the tag between two levels (see Fig. 1). This alternation will be interpreted by the reader as a digital signal, containing data such as the identifier of the tag stored in the chip memory. Physically, today's tags send data back to the reader either by magnetic coupling through a coil (e.g., Low-Frequency tags at 125 kHz and High Frequency tags at 13.56 MHz) or by backscattering through an antenna (e.g., ultrahigh frequency tags at 866-960 MHz) (see Fig. 2). Besides, many examples in this review use batteryassisted passive (BAP) tags (also called semi-passive). These are essentially passive tags, whose chip receive extra power from an external battery, to increase the chip capabilities and its reading distance. While passive tags (either batteryless or battery-assisted) use the backscattering communication, active tags use an RF front-end architecture for communication. Research also investigates and develop chipless tags, to reduce the tags cost by encoding information physically on the tag instead of using a microelectronic chip. While promising, chipless RFID is not yet widespread in the industry; therefore it is still too preliminary to be usable in real operations for geoscience. Overall, the different technologies produced by the RFID industry today offer new opportunities for sensing the earth surface, discussed in the next section.

The interest of passive RFID in geosciences

Passive RFID is widely used to identify goods for logistics, transportation and retail. Today, the implementation of ambitious concepts such as the Internet of things [START_REF] Xu | Internet of Things in Industries: A Survey[END_REF] is opening the technology to completely new applications [START_REF] Duroc | RFID: A key technology for Humanity[END_REF]. Indeed, passive RFID tags can now offer, besides the identification function, new functions of sensing and of centimetric localization [START_REF] Zannas | From Identification to Sensing: Augmented RFID Tags[END_REF]Li et al., 2019), whose research is continuously increasing since 2003 (Fig. 3). The addition of sensing an external parameter to tag identification capabilities enables the concept of ubiquitous sensing [START_REF] Want | Enabling ubiquitous sensing with RFID[END_REF] in many sectors, such as food transport [START_REF] Bibi | A review: RFID technology having sensing aptitudes for food industry and their contribution to tracking and monitoring of food products[END_REF], health care [START_REF] Bianco | A Survey on Radio Frequency Identification as a Scalable Technology to Face Pandemics[END_REF], industry [START_REF] Occhiuzzi | RFID Technology for Industry 4.0: Architectures and Challenges[END_REF], agriculture [START_REF] Maroli | Applications of IoT for achieving sustainability in agricultural sector: A comprehensive review[END_REF], smart cities [START_REF] Fahmy | Role of RFID Technology in Smart City Applications[END_REF] civil engineering [START_REF] Zhang | A Review of Passive RFID Tag Antenna-Based Sensors and Systems for Structural Health Monitoring Applications[END_REF][START_REF] Duan | Emerging RFID technology in structural engineering -A review[END_REF], and geoscience (covered by the present review). In geosciences, the augmented functions of passive RFID tags offer new ways to deploy and operate sensors. The data from RFID tags is collected either with fixed or mobile readers (Fig. 4, e-f). Fixed readers can continuously interrogate the tags that are within its reading area. Readers can read tags passing near a portal (e.g., placed at a river section), or tags staying within a larger zone (e.g., on a landslide). However, using fixed readers requires to install and maintain a permanent infrastructure, and one reader can cover only a limited area. On the contrary, mobile readers can be moved around to scan a large area, either manually or with a vehicle, a robot or an unmanned aerial vehicle (UAV). Therefore, RFID can complement wired sensors, wireless sensor networks (WSN), remote sensing and manual operation of sensors (Fig. 4, a-d).

WSN specifically, in which sensing nodes communicate wirelessly one to another up to a central node, play a major role in the Internet of things. WSNs have become a mature technology in earth science both for operational and research monitoring (e.g., global positionning systems (GPS), microseismicity, tilting, soil moisture, strain). WSNs are also evolving towards lower cost of materials and lower energy consumption [START_REF] Wixted | Evaluation of LoRa and LoRaWAN for wireless sensor networks[END_REF]: they reach material costs of typically 200-2000€ per node with battery lifetime of 1 -10 years. Compared to WSNs, RFID sensors offer a longer lifetime and a lower cost per sensor, but has smaller read distance (see 6.3), fewer functionalities, and less standardization regarding sensing techniques [START_REF] Atzori | The Internet of Things: A survey[END_REF]. As an example, WSNs deployed and maintained on geotechnical sites consists typically of 5 -50 nodes. Yet, operating a large-scale WSN with hundreds of nodes during decades remains expensive due to the cost of material and maintenance. Passive RFID appears more competitive under these conditions due to its low-cost both in materials and maintenance. Besides, conventional RFID is well standardized and obeys several regulations and norms (sometime specific to geographic regions). This favors the interoperability of RFID components from different providers and the rapid implementation of RFID in real applications Passive sensors in general-including RFID-allow affordable deployment of many sensors for long duration, up to decades. Sensors operated manually or with remote sensing (e.g., laser reflectors, radar reflectors, and simple visual sensors) also present low-cost, zero-energy consumption, and reduced maintenance. However, their data collection requires either a manual reading or complex remote sensing instruments. Compared to other passive sensors, RFID tags exhibit several advantages. First, they are produced industrially at a large scale, allowing for reliable, interoperable and low-cost tags. Second, their standard communication protocols ease the data collection of hundreds of tags almost simultaneously. Third, unique identification number of each tag eases the data inventory of large-scale networks. Fourth, RFID tags offer the capability to switch easily from manual data collection to automatic continuous monitoring (see Fig. 4); in geotechnics for example, it is common to run manual surveys over a large area when the risk is low, and to install locally a continuous monitoring if the risk increases [START_REF] Burland | ICE manual of geotechnical engineering[END_REF]. Finally, RFID also allows for quick data collection by an operator, with a result that is independent of his skills.

Geoscientists have used RFID progressively, following the technologies available on the market (see Fig. 5). First, they inserted low-frequency tags (LF: 125 kHz) into pebbles, to trace their mobility during flow events in rivers. The tags are later detected with either manual surveys or permanent detection portals at short distance from the interrogator (<1 m) [START_REF] Lamarre | Using Passive Integrated Transponder (PIT) Tags to Investigate Sediment Transport in Gravel-Bed Rivers[END_REF][START_REF] Schneider | Studying sediment transport in mountain rivers by mobile and stationary RFID antennas[END_REF], to measure their motion between surveys. More recently, RFID tags have been used to measure the displacement of slow-moving landslides (0.01-10 m/year) during several months, with a centimetric accuracy (Le Breton et al., 2019). The tags worked at ultra-high frequency (UHF, 868 MHz) by means of backscattering waves, at a long distance from the interrogator, up to 60 m in 2018 with commercial off-the-shelf devices, and currently growing (see 6.3). Augmented tags can, in addition, provide information on their environment [START_REF] Zannas | From Identification to Sensing: Augmented RFID Tags[END_REF]. They were used to monitor soil temperature [START_REF] Luvisi | RFID temperature sensors for monitoring soil solarization with biodegradable films[END_REF], soil moisture [START_REF] Pichorim | Two Solutions of Soil Moisture Sensing with RFID for Landslide Monitoring[END_REF]J. Wang et al., 2020;[START_REF] Deng | Novel soil environment monitoring system based on RFID sensor and LoRa[END_REF], displacement of rock structures (Le [START_REF] Breton | Monitoring rock displacement threshold with 1-bit sensing passive RFID tag[END_REF], and snow water equivalent of dry snow (Le Breton, 2019). Also, potential applications on rock slopes could use sensors developed for measuring crack opening [START_REF] Caizzone | Wireless Passive RFID Crack Width Sensor for Structural Health Monitoring[END_REF], vibrations [START_REF] Jayawardana | Measurement System With Accelerometer Integrated RFID Tag for Infrastructure Health Monitoring[END_REF][START_REF] Jayawardana | RFID-Based Wireless Multi-Sensory System for Simultaneous Dynamic Acceleration and Strain Measurements of Civil Infrastructure[END_REF] or tilt [START_REF] Vena | Passive UHF RFID Sensor to Monitor Fragile Objects during Transportation[END_REF]. Finally, RFID tags can be deployed in wider areas thanks to an increasing reading range [START_REF] Durgin | RF thermoelectric generation for passive RFID[END_REF][START_REF] Amato | RFID Backscattering in Long-Range Scenarios[END_REF], the interrogation by an unmanned aerial vehicle [START_REF] Ma | Drone Relays for Battery-Free Networks[END_REF][START_REF] Buffi | A SAR-Based Measurement Method for Passive-Tag Positioning With a Flying UHF-RFID Reader[END_REF][START_REF] Wang | A New Vision for Smart Objects and the Internet of Things: Mobile Robots and Long-Range UHF RFID Sensor Tags[END_REF], and the deployment of tags in harsh areas such as buried under the ground [START_REF] Abdelnour | Passive Harmonic RFID System for Buried Assets Localization[END_REF], or below snow or vegetation (Le Breton, 2019). This article presents how RFID technologies were applied for geoscientific and geotechnical monitoring, in the laboratory or in the field outdoors. Then it discusses the key technical challenges that may increase the value of the technique in regards of geoscientific applications.

Sensing tags

Numerous applications have turned tags into wireless sensors [START_REF] Marrocco | Pervasive electromagnetics: sensing paradigms by passive RFID technology[END_REF][START_REF] Costa | A Review of RFID Sensors, the New Frontier of Internet of Things[END_REF] by augmenting its capabilities [START_REF] Tedjini | Augmented RFID Tags[END_REF]. The information provided can go from simple 1-bit threshold sensing to more quantitative measurements. In our opinion, the techniques of sensing tags fall into three categories: dedicated sensors, antenna-based sensing, and recently propagation-based sensing (see Connecting a dedicated sensor to the tag is the first way to transform RFID tags into sensors. In this approach, the chip powers the sensor, acquires the data, and sends the data numerically over the RFID radio channel. This approach is the most conventional and often the most accurate. Indeed, the sensor is designed to be sensitive to the measured parameter, and should note be influenced by the quality of the communication channel (however, in practice, the received power may influence the measured value on batteryless tags). We group the dedicated sensors into three categories: the sensors built into the RFID chip, the sensors connected to an input on the RFID chip, and the sensors connected to an intermediate microcontroller (see Table 2). The sensors directly built into chips are the simplest to use but offer few options for measurements (today's chips can measure only temperature, strain or hall effect). In contrast, the sensors connected to an RFID chip input allow many more possibilities: switch and encoders can connect on anti-tampering and general-purpose input/output (GPIO) contact inputs (e.g., [START_REF] Zhu | Passive Digital Sensing Method and Its Implementation on Passive RFID Temperature Sensors[END_REF]; resistive and capacitive sensors can connect on analog inputs (e.g., [START_REF] Pichorim | Two Solutions of Soil Moisture Sensing with RFID for Landslide Monitoring[END_REF]A. Abdelnour et al., 2019); digital sensors can connect as slaves on digital communication buses such as SPI (e.g., [START_REF] Vena | Passive UHF RFID Sensor to Monitor Fragile Objects during Transportation[END_REF]. Finally, the sensors can be connected to an external microcontroller, which acquires the sensed data and writes it into the RFID chip memory through a digital communication bus. A microcontroller adds new sensing capabilities such as multisensors, better time sampling, better sensitivity or pre-processing (e.g., De Donno et al., 2014;[START_REF] Jayawardana | Measurement System With Accelerometer Integrated RFID Tag for Infrastructure Health Monitoring[END_REF]. But it also increases the complexity, cost and power consumption of the tag. Overall, dedicated sensors attached on RFID tags allow measuring many physical parameters, such as temperature, light, acceleration, dielectric constant, humidity, conductivity, strain, pressure, and much more. The counterpart is that they require to design a dedicated tag for each sensed parameter [START_REF] Węglarski | Factors Affecting the Synthesis of Autonomous Sensors with RFID Interface[END_REF]. Their exploitation to monitor natural earth processes is covered in more details in the next sections. Antenna-based sensing is the second way to transform RFID tags into wireless sensors. Indeed, any change in the shape, substrate properties, or immediate vicinity (near-field region) of the tag antenna, may also change the antenna impedance. As the impedance matching between the tag antenna and the RFID chip governs the performance of RFID tags, the modification of the antenna impedance will alter the parameter of the tag-to-reader communication. Such alteration can be measured on different communication parameters, in particular the maximum read range, the received signal strength indicator (RSSI), the resonance frequency, the phase shift [START_REF] Caccami | Phase-Oriented Sensing by Means of Loaded UHF RFID Tags[END_REF], the group delay and the power of activation. This concept of antenna sensitivity, which can provide extremely low-cost sensing tags, has been exploited in several publications. [START_REF] Bhattacharyya | Towards tag antenna based sensing -An RFID displacement sensor[END_REF] exploited the perturbation of a tag antenna by a metal plate to measure the inflection of a bridge. Other tags exploited the deformation of their antenna caused by external strain (Occhiuzzi et al., 2011a) or temperature (Bhattacharyya et al., 2010b), for instance. Occhiuzzi et al. (2011b) loaded the antenna with carbon nanotubes which are sensitive to gas concentration. Manzari et al. (2014a) added a thermistor on the antenna. [START_REF] Nguyen | Approach for quality detection of food by RFID-based wireless sensor tag[END_REF] measured the dielectric permittivity of meat in the food industry. Antenna-based RFID sensors were reviewed, for general applications [START_REF] Occhiuzzi | Passive UHF RFID antennas for sensing applications: Principles, methods, and classifcations[END_REF] and also more specifically for structural health monitoring [START_REF] Zhang | A Review of Passive RFID Tag Antenna-Based Sensors and Systems for Structural Health Monitoring Applications[END_REF]. We emphasize that most of the published papers on antenna-based RFID sensors focus on the concept of the device and on the validation of the proposed structure. The effective variation of the backscatter signal with the sensed parameters is usually demonstrated with experimental laboratory measurements. Yet, using the sensor in real applications would request to mitigate several influences. For example, the signal amplitude strongly depends on the tag orientation and on multipath interferences. Besides, the phase is sensitive to the tag position [START_REF] Nikitin | Phase based spatial identification of UHF RFID tags[END_REF] and to the environment (Le [START_REF] Breton | Outdoor UHF RFID: Phase Stabilization for Real-World Applications[END_REF]. In practice, the use of antenna-based sensing would often require calibration, obtained for example by associating some reference devices to correct the RFID measurements [START_REF] Marrocco | Pervasive electromagnetics: sensing paradigms by passive RFID technology[END_REF]. Additionally, several methods demonstrated in the laboratory, such as frequency sweeping, will also be highly restricted by regulation (e.g., ETSI-302-208 from the European Telecommunication Standards Institute). Therefore we deduce that antenna-based sensing should be limited to the use as a threshold indicator, to distinguish between two states (or three sometimes) of a system.

More recently, self-tuning RFID chips were exploited to directly measure the changes in the antenna impedance. These chips have the capability to stay tuned to the antenna impedance, and transmit to the reader a value indicating the exact amount of impedance correction to be made by the chip. We consider this recent case as antenna-based sensing because the tag antenna is the sensor, yet it also has some advantage of dedicated sensing, because the measurement is made by the chip. Since the antenna impedance is influenced by materials at its vicinity, this correction value can thus be used to estimate parameters, such as sensing moisture [START_REF] Swedberg | Smartrac's New Passive Sensor DogBone Transmits Moisture Levels[END_REF], chemical composition [START_REF] Caccami | Electromagnetic Modeling of Self-Tuning RFID Sensor Antennas in Linear and Nonlinear Regimes[END_REF],temperature [START_REF] Zannas | Self-Tuning RFID Tag: A New Approach for Temperature Sensing[END_REF], or electric potential [START_REF] Nanni | Potentiometric Sensing by means of Self-tuning RFID ICs[END_REF]. Self-tuning measurements are made directly by the tag, and therefore are more independent to unwanted influences (distance, multipath, orientation) than phase and RSSI sensing indicators, given that they receive the adequate signal power [START_REF] Caccami | Electromagnetic Modeling of Self-Tuning RFID Sensor Antennas in Linear and Nonlinear Regimes[END_REF].

Propagation-based sensing is a third way of RFID sensing. It consists in measuring the influence of the propagation environment on the RFID signal, to estimate changes of properties in the propagation channel. These changes occur through phenomena of direct wave transmission, depolarization, or multipath interferences. The first phenomenon concerns the direct wave transmission: a change in the medium placed between a tag and the interrogator antenna will modify the phase or RSSI measured by the interrogator. Such change was exploited to detect a human presence [START_REF] Hussain | A review and categorization of techniques on device-free human activity recognition[END_REF], estimate the quantity of fresh dry snow (see 5.3) (Le Breton et al., in preparation), or estimate the water content of a vegetal layer (Le Breton, 2019). The second phenomenon is the depolarization caused by scattering. For example, [START_REF] He | RFID Based Non-Contact Human Activity Detection Exploiting Cross Polarization[END_REF] proposes to cross-polarize a tag and reader antenna at a perpendicular orientation. When no scattering occurs, most of the signal is lost due to the cross polarization, which cancels the communication. However, in the presence of a scattering body nearby, the linearly polarized wave partly depolarizes into a perpendicular component, reestablishing the communication. This can detect the presence of a scattering body, such as a human body. The last phenomenon, multipath interferences, can cause changes in the signal phase and strength from only tiny changes in the surroundings of a tag. It was used to detect the vibration of an object near a tag and measure the vibration frequency [START_REF] Yang | RF-Ear: Contactless Multi-device Vibration Sensing and Identification Using COTS RFID[END_REF]. Overall, propagation-based sensing seems of particular interest for geoscience, because it can estimate average properties of a complex medium over large distances and do not require customized tags. Propagation-based sensing principles are also very similar to geophysical methods such as ground-penetrating radar, polarimetric radar or GPS interferometry [START_REF] Bradford | Complex dielectric permittivity measurements from ground-penetrating radar data to estimate snow liquid water content in the pendular regime: MEASURING SWE WITH GPR[END_REF][START_REF] Kim | A Time-Series Approach to Estimate Soil Moisture Using Polarimetric Radar Data[END_REF][START_REF] Larson | GPS interferometric reflectometry: applications to surface soil moisture, snow depth, and vegetation water content in the western United States[END_REF][START_REF] Lin | Active Microwave Scattering Signature of Snowpack-Continuous Multiyear SnowScat Observation Experiments[END_REF]; the main difference is that the reflectors (the tags) are easily identified but need to be placed in advance. Propagation-based sensing has provided a decent measurement accuracy for snow sensing and for vibration sensing. However, the tags and reader must be placed adequately, for example to allow communication across the snow, or reflections on the vibrating object.

The accuracy of RFID sensing is often a challenge compared to traditional sensing systems. Obtaining accurate measurements may require special care. For example, that often requires to mitigate the influences due to environmental fluctuations, such as temperature, rain, snow or icing [START_REF] Breton | Outdoor UHF RFID: Phase Stabilization for Real-World Applications[END_REF]Le Breton, 2019). Another care can consist in controlling the signal power delivered by the interrogating antenna (e.g., [START_REF] Caccami | Electromagnetic Modeling of Self-Tuning RFID Sensor Antennas in Linear and Nonlinear Regimes[END_REF][START_REF] Camera | Electromagnetic-Based Correction of Bio-Integrated RFID Sensors for Reliable Skin Temperature Monitoring[END_REF]. In regard to accuracy, antenna-based sensing seems the most difficult approach, because it is sensitive to many influence parameters. Therefore, in our opinion, it should be used only to determine the qualitative state of a system, for instance a binary state: on/off system, open/closed circuit or below/above a threshold. Dedicated sensing and propagation-based sensing seem the most appropriate for geoscience observations. In addition, combining different sensing approaches can increase the accuracy. For example, the measurement of phase by the reader can be calibrated against the temperature measured by the tag (Le [START_REF] Breton | Outdoor UHF RFID: Phase Stabilization for Real-World Applications[END_REF]. In addition, data provided by sensors can be calibrated against the on-chip received signal strength [START_REF] Camera | Electromagnetic-Based Correction of Bio-Integrated RFID Sensors for Reliable Skin Temperature Monitoring[END_REF].

To conclude this section, the technologies available today allow for the design of effective RFID devices that enable innovative solutions for environmental sensing. To add new application of RFID sensors and exploit the very competitive cost of RFID tags, it has been proposed early to map large spaces with numerous tags [START_REF] Capdevila | RFID Multiprobe Impedance-Based Sensors[END_REF]. Moreover, industrial companies are planning the future potential of RFID sensing, for example with the definition of the RAIN RFID standards "a wireless technology aimed at connecting billions of everyday items to the Internet, enabling businesses and consumers to identify, locate, authenticate, and engage each item" (Rain RFID eBook, 2020).

Localization of tags

In addition to sensing, tags can also be located in space, using their signal amplitude or the variations of phase delay. The simplest method is to detect a tag and conclude that it is nearby, within the detection range of the interrogator. The method has been used extensively to track the displacement of pebbles in rivers (see section 2) using low-frequency tags with sub-metric detection range. This technique has, nevertheless, a spatial resolution that depends on the detection range, of several decimeters to meters. The localization of tags at longer distances uses UHF tags [START_REF] Miesen | Where is the Tag[END_REF], and exploits the received signal strength (RSSI) or the phase of arrival.

Signal-strength-based methods have been initially introduced for tag localization [START_REF] Griffin | Complete Link Budgets for Backscatter-Radio and RFID Systems[END_REF][START_REF] Ni | LANDMARC: indoor location sensing using active RFID[END_REF], followed by phase-based methods which usually offer the best accuracy, reaching one centimeter or less [START_REF] Scherhäufl | UHF RFID Localization Based on Phase Evaluation of Passive Tag Arrays[END_REF][START_REF] Wang | TrackT: Accurate tracking of RFID tags with mm-level accuracy using first-order taylor series approximation[END_REF][START_REF] Zhou | Accurate phase-based ranging measurements for backscatter RFID tags[END_REF]. In practice the phase-based methods show the best potential for observing ground displacements (Le Breton et al., 2019). In this review we will present phase-based methods only, which we consider more adapted among other localization methods [START_REF] Balaji | A Comprehensive Nomenclature Of RFID Localization[END_REF]. Backscattering communication used in passive RFID can easily measure the phase difference arrival because the reader transmits and receives the same carrier wave, compared to active RFID which uses two distinct RF front-ends on the tag and reader.

The phase of arrival in free space relates to the delay of propagation, and depends on the speed of light in the air c, the distance between the tag and the reader r , the carrier frequency f, and an offset caused by the devices:

= - 4 + (1)
In practice, the phase of arrival measured by the reader is ambiguous, wrapped within 0-π or 0-2π (Miesen et al., 2013b), reducing its intrinsic value as a single measurement. However, the Phase Difference of Arrival [START_REF] Vossiek | The Switched Injection-Locked Oscillator: A Novel Versatile Concept for Wireless Transponder and Localization Systems[END_REF][START_REF] Nikitin | Phase based spatial identification of UHF RFID tags[END_REF] measured between two different phase readings is exploitable for localization. It relates to the change in tag-reader radial distance that occurred between the two reading positions :

= - = - 4 (2)
Where represents the measured phases at different positions related. Note that this equation holds only for phase shifts smaller than the reader measurement ambiguity ( or 2 ). Simplest usage of this approach estimate 1D distance or displacement between a tag and a station antenna (see Fig. 6). Optionally it can locate the tag in 2D or 3D using multiple station antennas and trilateration [START_REF] Scherhäufl | UHF RFID Localization Based on Phase Evaluation of Passive Tag Arrays[END_REF]. The two main phase-based localization schemes exploit either the variation of the phase in time which is a relative localization technique, or the variation of phase with signal frequency which allows for absolute ranging. The latter shows less accuracy and seems not suited to track slow displacements (Le Breton et al., 2019).

Using arrays of tags allows estimating the tilt of the array relatively to the station antenna (by comparing the phase differences between tags) (Fig. 7). For reciprocity reasons, this approach also holds for arrays of antenna facing fixed tags [START_REF] Scherhäufl | UHF RFID Localization Based on Phase Evaluation of Passive Tag Arrays[END_REF]. The approaches mentioned previously allow for a centimeter-scale error for punctual measurements as well as month-long monitoring in outdoor conditions (Charlety et al., in preparation). The synthetic aperture radar (SAR) is another use of phase measurements, used typically for satellite or ground-based radars. Applied to RFID, it implies moving one or more reader antennas along known trajectories (Fig. 8) [START_REF] Buffi | A SAR-Based Measurement Method for Passive-Tag Positioning With a Flying UHF-RFID Reader[END_REF][START_REF] Wu | A Fast UHF RFID Localization Method Using Unwrapped Phase-Position Model[END_REF], often carried by a mobile robot, a UAV (see Section 6.4). Most SAR use a similar theoretical approach, which consists in the minimization of a cost function comparing real phase measurements and simulated measurements computed from the antenna trajectory and a probable tag position [START_REF] Motroni | A Multi-Antenna SAR-based method for UHF RFID Tag Localization via UGV[END_REF]. Optimization algorithms like particle swarms or Kalman filters are often used to decrease the computational cost of such calculations [START_REF] Bernardini | Particle Swarm Optimization in SAR-based Method enabling Real-Time 3D Positioning of UHF-RFID Tags[END_REF][START_REF] Gareis | Particle Filter-Based SAR Approach and Trajectory Optimization for Real-Time 3D UHF-RFID Tag Localization[END_REF]. SAR localization has proven to reach a centimeter-scale precision. However, the shape of the reader trajectory has a strong impact on the localization accuracy, and has to be chosen carefully to locate the tag in all investigated dimensions [START_REF] Bernardini | Robot-Based Indoor Positioning of UHF-RFID Tags: The SAR Method With Multiple Trajectories[END_REF]. One of the main limitations of RFID phase localization is the phase wrapping ambiguity and the influence of multipathing [START_REF] Faseth | The influence of multipath propagation on phase-based narrowband positioning principles in UHF RFID[END_REF][START_REF] Ma | An Indoor Localization Method Based on AOA and PDOA Using Virtual Stations in Multipath and NLOS Environments for Passive UHF RFID[END_REF][START_REF] Digiampaolo | A Multiple Baseline Approach to Face Multipath[END_REF]. A way to reduce these effects is the Time of Flight method [START_REF] Arnitz | UWB ranging in passive UHF RFID: proof of concept[END_REF][START_REF] Arthaber | Spread-Spectrum Based Ranging of Passive UHF EPC RFID Tags[END_REF], which avoids ambiguity as the full-time shift is measured. It can also reduce the influence of multipath when the time of first arrival is measured, and when a wide-band pulse is used. However it requires custom devices that are not on the market nor standardized. Nonetheless, RFID localization using standard commercial equipment has proven doable and is currently a flourishing research subject, with a centimetric to decimetric spatial resolution.

To conclude, RFID localization is nowadays investigated in various fields of application and through several approaches. It shows great capability and adaptability both in indoor and outdoor environments, with multiple ways to bypass the measurement limitations. More developments in this domain are surely to come.

Mobility of coarse sediments in rivers and coasts

In earth sciences, RFID tags (also called PIT tags, for Passive Integrated Transponders) were first used to track the mobility of coarse sediments in rivers during flow events for the study of bedload transport [START_REF] Nichols | A Radio Frequency Identification System for Monitoring Coarse Sediment Particle Displacement[END_REF][START_REF] Lamarre | Using Passive Integrated Transponder (PIT) Tags to Investigate Sediment Transport in Gravel-Bed Rivers[END_REF]. Then they were rapidly applied to study the transport of coarse sediments in coasts (e.g., [START_REF] Allan | The use of Passive Integrated Transponder (PIT) tags to trace cobble transport in a mixed sand-and-gravel beach on the high-energy Oregon coast, USA[END_REF][START_REF] Curtiss | Seasonal patterns of coarse sediment transport on a mixed sand and gravel beach due to vessel wakes, wind waves, and tidal currents[END_REF][START_REF] Osborne | Measurements and Modeling of Gravel Transport under Wind Waves, Vessel-Generated Waves, and Tidal Currents[END_REF]. Other applications were reported to track the transport of woody debris in rivers [START_REF] Macvicar | Quantifying the temporal dynamics of wood in large rivers: field trials of wood surveying, dating, tracking, and monitoring techniques[END_REF][START_REF] Ravazzolo | Tracking log displacement during floods in the Tagliamento River using RFID and GPS tracker devices[END_REF] and boulders in debris flows [START_REF] Mccoy | Observations of debris flows at Chalk Cliffs, Colorado, USA: part 1, in situ measurements of flow dynamics, tracer particle movement and video imagery from the summer of 2009[END_REF][START_REF] Graff | Traçage sédimentaire d'une lave torrentielle dans le bassin de la Peyronnelle (Queyras, Alpes françaises du Sud)[END_REF]. But these applications have stayed relatively limited compared to fluvial bedload tracing and coastal sediment tracing, for which around 60 papers and 15 have been published between 2004 and 2021, respectively.

Design and implementation of RFID tracing experiment

The very first field experiments with RFID tags in rivers showed high recovery rates (>80%) for relatively large populations of deployed tracers (>100) [START_REF] Nichols | A Radio Frequency Identification System for Monitoring Coarse Sediment Particle Displacement[END_REF][START_REF] Lamarre | Using Passive Integrated Transponder (PIT) Tags to Investigate Sediment Transport in Gravel-Bed Rivers[END_REF]. Soon after these early promising results, RFID tags rapidly became the reference technology for bedload tracing in rivers, and they progressively replaced the most commonly used techniques, such as painted or magnetic stones [START_REF] Hassan | Use of tracers in fluvial geomorphology[END_REF]. We can estimate from the literature that during the last 20 years, several tens of thousands RFID tracers have been injected in rivers all around the world. This wide adoption of passive RFID tags in sediment transport studies is based on several decisive advantages: they provide low-cost (<5 €), long-lived (>10 yr), and small size (<35 mm) tracers, that can be remotely identified under several decimeters of sediments. Although buried magnetic stones can also be detected with recovery rates comparable to RFID tags, their identification is only possible after time-consuming and intrusive surveying of alluvial deposits. The only alternative solution for remote identification of tracer stones today are the battery-powered radio transmitters (e.g., [START_REF] Habersack | Radio-tracking gravel particles in a large braided river in New Zealand: a field test of the stochastic theory of bed load transport proposed by Einstein[END_REF]. However, the need for a battery reduces the duration of the experiments, and their higher cost reduces the number of tracers deployed.

The most commonly used passive RFID tags in sediment transport studies are glass-encapsulated waterproof low-frequency (134.2 kHz) tags of 23 mm or 32 mm length. Transponders are generally inserted into rocks by drilling or notching natural stones (Fig. 9a), but artificial stones can also be used (e.g., [START_REF] Schneider | Scaling relationships between bed load volumes, transport distances, and stream power in steep mountain channels[END_REF][START_REF] Olinde | Using RFID and accelerometer-embedded tracers to measure probabilities of bed load transport, step lengths, and rest times in a mountain stream[END_REF]. The range of investigated grain sizes is constrained by the size of the transponder, and the smallest tracers are generally comprised between 20 mm and 40 mm in intermediate axis (b-axis). Once equipped with a tag, RFID tracers are deployed in river channels, and may be transported by one or multiple flow events. Their displacement is surveyed after a period by the manual detection and mapping of displaced tracers using a mobile antenna and a positioning system (e.g., differential GPS, total station) (Fig. 9b). The limited detection range of low-frequency RFID tags (<1 m) implies time-consuming field surveys. Another strategy is to use a stationary antenna at a cross-section to automatically detect RFID tracers crossing the monitoring section [START_REF] Schneider | Studying sediment transport in mountain rivers by mobile and stationary RFID antennas[END_REF][START_REF] Mao | Sediment motion and velocity in a glacier-fed stream[END_REF][START_REF] Stähly | Erosion, transport and deposition of a sediment replenishment under flood conditions[END_REF][START_REF] Casserly | Coarse sediment dynamics and low-head dams: Monitoring instantaneous bedload transport using a stationary RFID antenna[END_REF]. RFID tracing experiments in rivers are typically based on hundreds of tracers surveyed at seasonal or yearly time intervals. The quality of data from tracing experiments strongly depends on the recovery rate, which is generally above 70% for small streams, but which strongly decline for large rivers (see compilations of recovery rates in [START_REF] Chapuis | Detection ranges and uncertainty of passive Radio Frequency Identification (RFID) transponders for sediment tracking in gravel rivers and coastal environments: DETECTION RANGES AND UNCERTAINTY OF PASSIVE RFID TRANSPONDERS[END_REF][START_REF] Chapuis | Coupling channel evolution monitoring and RFID tracking in a large, wandering, gravel-bed river: Insights into sediment routing on geomorphic continuity through a riffle-pool sequence[END_REF].

Sediment transport studies using RFID tags.

One of the most popular applications of RFID tags in fluvial sediment transport is the study of grain dispersion by flow events along river reaches. This grain-scale approach of bedload transport has been conceptualized by Einstein in the late 1930s, who considered bedload as a random process of individual particle displacements, resulting from a succession of step lengths and rest periods, exponentially distributed [START_REF] Einstein | Bedload transport as a probability problem[END_REF]. RFID tracers have therefore been widely used to constrain the distribution of cumulative bedload transport distances in a variety of fluvial environments, including small and medium boulder-bed streams (Lamarre andRoy, 2008a, 2008b;[START_REF] Macvicar | Sediment mobility in a forced riffle-pool[END_REF][START_REF] Phillips | Impulse framework for unsteady flows reveals superdiffusive bed load transport[END_REF][START_REF] Schneider | Scaling relationships between bed load volumes, transport distances, and stream power in steep mountain channels[END_REF][START_REF] Olinde | Using RFID and accelerometer-embedded tracers to measure probabilities of bed load transport, step lengths, and rest times in a mountain stream[END_REF], plane-bed [START_REF] Imhoff | Coarse bedload routing and dispersion through tributary confluences[END_REF]Papangelakis et al., 2019a), wandering or braided gravel-bed streams [START_REF] Bradley | Measuring gravel transport and dispersion in a mountain river using passive radio tracers[END_REF][START_REF] Liébault | Bedload tracing in a highsediment-load mountain stream[END_REF], and large rivers [START_REF] Arnaud | Monitoring gravel augmentation in a large regulated river and implications for process-based restoration[END_REF] (Fig. 10). Controlling factors of bedload dispersion have been investigated in several studies. The most commonly used hydraulic predictor for the mean displacement of the tracer population is the excess specific stream power (Lamarre and Roy, 2008b;[START_REF] Schneider | Scaling relationships between bed load volumes, transport distances, and stream power in steep mountain channels[END_REF][START_REF] Houbrechts | Evaluation of long-term bedload virtual velocity in gravel-bed rivers (Ardenne, Belgium)[END_REF][START_REF] Arnaud | Monitoring gravel augmentation in a large regulated river and implications for process-based restoration[END_REF]Papangelakis et al., 2019a;[START_REF] Gilet | Hydromorphometric parameters controlling travel distance of pebbles and cobbles in three gravel bed streams[END_REF]. It is computed by considering the difference between the peak stream power during the survey period and the critical stream power for incipient motion of bedload transport. Some time-integrated flow predictors have been also proposed, like the impulse framework, a dimensionless impulse integrating the cumulative excess shear velocity normalized by grain size [START_REF] Phillips | Impulse framework for unsteady flows reveals superdiffusive bed load transport[END_REF][START_REF] Phillips | Dynamics and Mechanics of Bed-Load Tracer Particles[END_REF][START_REF] Imhoff | Coarse bedload routing and dispersion through tributary confluences[END_REF][START_REF] Gilet | Hydromorphometric parameters controlling travel distance of pebbles and cobbles in three gravel bed streams[END_REF], or the cumulative stream energy [START_REF] Schneider | Scaling relationships between bed load volumes, transport distances, and stream power in steep mountain channels[END_REF] and the effective runoff [START_REF] Olinde | Using RFID and accelerometer-embedded tracers to measure probabilities of bed load transport, step lengths, and rest times in a mountain stream[END_REF]. Scaling laws of transport distances can be successfully fitted with flow predictors; however they remain site-specific because bedload dispersion in river channels is a complex phenomenon driven not only by the flow, but also by interactions between channel morphology and sediment transport.

RFID tags have been widely used to investigate grain-size effects on bedload dispersion in different fluvial environments, by comparing displacement lengths and/or virtual velocity of tracers of different sizes [START_REF] Macvicar | Sediment mobility in a forced riffle-pool[END_REF][START_REF] Liébault | Bedload tracing in a highsediment-load mountain stream[END_REF][START_REF] Schneider | Scaling relationships between bed load volumes, transport distances, and stream power in steep mountain channels[END_REF][START_REF] Dell'agnese | Bedload transport in a formerly glaciated mountain catchment constrained by particle tracking[END_REF][START_REF] Rainato | Near-bankfull floods in an Alpine stream: Effects on the sediment mobility and bedload magnitude[END_REF][START_REF] Mao | Controls over particle motion and resting times of coarse bed load transport in a glacier-fed mountain stream[END_REF]. This kind of analysis is particularly interesting for characterizing full and partial mobility regimes of bedload transport using data from tracer populations with wide grain-size distributions [START_REF] Wilcock | Entrainment, displacement and transport of tracer gravels[END_REF][START_REF] Vázquez-Tarrío | Particle transport in gravel-bed rivers: Revisiting passive tracer data[END_REF]. RFID tags with wide grain-size distributions also offer the possibility to test in the field physically-based formulations of incipient motion conditions for bedload transport [START_REF] Phillips | Dynamics and Mechanics of Bed-Load Tracer Particles[END_REF][START_REF] Houbrechts | Evaluation of long-term bedload virtual velocity in gravel-bed rivers (Ardenne, Belgium)[END_REF][START_REF] Petit | Dimensionless critical shear stress in gravel-bed rivers[END_REF][START_REF] Ivanov | Event-scale pebble mobility observed by RFID tracking in a pre-Alpine stream: a field laboratory[END_REF][START_REF] Roberts | Field measurement of the probability of coarse-grained sediment entrainment in natural rivers[END_REF].

Morphological controls on bedload dispersion is another key topic addressed with RFID tags. A strong difference of mobility between tracers deployed in gravel bars and low-flow channels has been observed in a wandering gravel-bed river during flow conditions of moderate intensity. That suggests that for regular flow events, the contribution of gravel bars to bedload transport is limited [START_REF] Liébault | Bedload tracing in a highsediment-load mountain stream[END_REF]. Morphological controls on bedload dispersion can also be inferred by comparing transport distances with the mean longitudinal spacing between morphological units of the channel. It has been for example shown in small boulder-bed streams that the average distance between steps and pools or between steps controls displacement lengths (Lamarre and Roy, 2008b;[START_REF] Mao | Controls over particle motion and resting times of coarse bed load transport in a glacier-fed mountain stream[END_REF]. Another approach is to combine tracer surveys with topographic monitoring and hydraulic modeling of the river channel to make inferences about links between morphodynamics and tracer entrainment and deposition [START_REF] Macvicar | Sediment mobility in a forced riffle-pool[END_REF][START_REF] Milan | Sediment routing hypothesis for pool-riffle maintenance[END_REF][START_REF] Chapuis | Coupling channel evolution monitoring and RFID tracking in a large, wandering, gravel-bed river: Insights into sediment routing on geomorphic continuity through a riffle-pool sequence[END_REF].

Another RFID application in sediment transport concerns the tracking of debris flows in headwater channels. Only few attempts have been undertaken up to now, because the processes of channel scouring and filling during debris flows are much more intense than those related to bedload transport; RFID tracers may therefore become buried under sediment deposits much thicker than the detection range of passive RFID tags. Debris flows are also known as fast-moving mass movements typically propagating over long distances along rough, steep, and inaccessible channels, making surveying with mobile antennas particularly difficult. However, a recent tracing experiment in the French Alps with RFID tags revealed a recovery rate of 31% after a debris flow, with maximum travel distances of about 720 m [START_REF] Graff | Traçage sédimentaire d'une lave torrentielle dans le bassin de la Peyronnelle (Queyras, Alpes françaises du Sud)[END_REF] (Fig. 11). Another example of RFID tracking in a very active debris-flow torrent of the Southern French Alps revealed that medium-size boulders (70 cm diameter) can be transported over distances of 1.5 km during small debris flow events [START_REF] Bel | Analysis of debris-flow occurrence in active catchments of the French Alps using monitoring stations[END_REF].

RFID tags applications in river management

Although RFID tags have been early used to assess the physical effects of hydraulic works dedicated to aquatic habitat restoration in river channels [START_REF] Carré | Flow dynamics and bedload sediment transport around paired deflectors for fish habitat enhancement: a field study in the Nicolet River[END_REF][START_REF] Biron | Sediment Transport and Flow Dynamics Around a Restored Pool in a Fish Habitat Rehabilitation Project: Field and 3d Numerical Modelling Experiments[END_REF], their diffusion in the science of river management has stayed limited until very recently. The last four years have seen the emergence of numerous RFID tracing experiments dedicated to the monitoring of river restoration projects. Most of them are addressing the question of the propagation of gravels artificially introduced in river channels (sediment replenishment) to mitigate the morphological impact of dams [START_REF] Arnaud | Monitoring gravel augmentation in a large regulated river and implications for process-based restoration[END_REF]Brousse et al., 2020a;[START_REF] Stähly | Erosion, transport and deposition of a sediment replenishment under flood conditions[END_REF]. But other tracing experiments were dedicated to the effect of weirs [START_REF] Casserly | Impact of low-head dams on bedload transport rates in coarse-bedded streams[END_REF][START_REF] Peeters | Can coarse bedload pass through weirs?[END_REF][START_REF] Magilligan | The impact of run-of-river dams on sediment longitudinal connectivity and downstream channel equilibrium[END_REF] or check-dams [START_REF] Galia | Impact of check dam series on coarse sediment connectivity[END_REF] on bedload transport, or to the comparison of restored and unrestored riffle-pool sequences in a small urbanized gravel-bed stream [START_REF] Papangelakis | Process-based assessment of success and failure in a constructed riffle-pool river restoration project[END_REF]. RFID tags have also been recently used in a restored braided river to assess the sediment continuity between the restored reach and its main upstream sediment source [START_REF] Brousse | Gravel replenishment and active-channel widening for braided-river restoration: The case of the Upper Drac River (France)[END_REF]. All these recent studies reveal that the range of applications of bedload tracing with RFID tags is very large.

Coarse sediment tracking in coastal environments

Passive RFID tags have been early used to study coarse sediment transport in coastal environments [START_REF] Allan | The use of Passive Integrated Transponder (PIT) tags to trace cobble transport in a mixed sand-and-gravel beach on the high-energy Oregon coast, USA[END_REF][START_REF] Bertoni | Radio Frequency Identification (RFID) technology applied to the definition of underwater and subaerial coarse sediment movement[END_REF][START_REF] Curtiss | Seasonal patterns of coarse sediment transport on a mixed sand and gravel beach due to vessel wakes, wind waves, and tidal currents[END_REF]. Although the number of reported field experiments (around 15) is much less than those of fluvial environments, coastal sediment transport represents an important application field of passive RFID in geosciences. PIT tags have been successfully employed to investigate longshore and cross-shore transport of cobbles along mixed sand-gravel beaches, in relation to marine currents directions, wave climates, and submerged artificial structures for beach protection [START_REF] Allan | The use of Passive Integrated Transponder (PIT) tags to trace cobble transport in a mixed sand-and-gravel beach on the high-energy Oregon coast, USA[END_REF][START_REF] Curtiss | Seasonal patterns of coarse sediment transport on a mixed sand and gravel beach due to vessel wakes, wind waves, and tidal currents[END_REF][START_REF] Bertoni | Radio Frequency Identification (RFID) technology applied to the definition of underwater and subaerial coarse sediment movement[END_REF][START_REF] Miller | Observations of coarse sediment movements on the mixed beach of the Elwha Delta, Washington[END_REF][START_REF] Bertoni | Transport trajectories of "smart" pebbles on an artificial coarse-grained beach at Marina di Pisa (Italy): Implications for beach morphodynamics[END_REF][START_REF] Dolphin | Velocity of RFID Tagged Gravel in a Non-uniform Longshore Transport System[END_REF]. Seasonal patterns of sediment transport have been documented, by comparing tracer movements induced by different forcing mechanisms, such as wind waves, tides, and wakes [START_REF] Curtiss | Seasonal patterns of coarse sediment transport on a mixed sand and gravel beach due to vessel wakes, wind waves, and tidal currents[END_REF][START_REF] Osborne | Measurements and Modeling of Gravel Transport under Wind Waves, Vessel-Generated Waves, and Tidal Currents[END_REF], or by detecting uni-or multidirectional movements as a function of incident wave angles (Miller et al., 2011, p. 20). Coastal RFID tracers also provide information on size-sorting effects and abrasion rates [START_REF] Allan | The use of Passive Integrated Transponder (PIT) tags to trace cobble transport in a mixed sand-and-gravel beach on the high-energy Oregon coast, USA[END_REF][START_REF] Dickson | Longshore transport of cobbles on a mixed sand and gravel beach, southern Hawke Bay, New Zealand[END_REF], and on morphometric indicators of sediment flux, such as beach active width and mobile layer depth [START_REF] Miller | Observations of coarse sediment movements on the mixed beach of the Elwha Delta, Washington[END_REF][START_REF] Miller | Measuring sediment transport and bed disturbance with tracers on a mixed beach[END_REF]. The tracing experiment of the Elwha River delta reported by [START_REF] Miller | Observations of coarse sediment movements on the mixed beach of the Elwha Delta, Washington[END_REF] was notably used to reconstruct volumetric sediment fluxes at two different sites with contrasting angles of breaking waves, showing more intense sediment transport for the site with less oblique angle, a field observation in contradiction with predictions from longshore sediment transport models. More recently, RFID tags have been used to study intertidal boulder transport during storm events, with direct implications for the morphological evolution of rocky coasts in the UK [START_REF] Hastewell | Intertidal boulder transport: A proposed methodology adopting Radio Frequency Identification (RFID) technology to quantify storm induced boulder mobility[END_REF][START_REF] Hastewell | Quantification of contemporary storminduced boulder transport on an intertidal shore platform using radio frequency identification technology[END_REF] and north-west Spain [START_REF] Gómez-Pazo | Tracking clast mobility using RFID sensors on a boulder beach in Galicia, NW Spain[END_REF]. Field studies reported in the UK notably revealed that very large boulders (more than 10 t) can be transported during contemporary storms of moderate intensity. Passive RFID in coastal environments was also employed to document pebble movements related to swash processes on low-energy beaches [START_REF] Bertoni | On the displacement of marked pebbles on two coarse-clastic beaches during short fair-weather periods (Marina di Pisa and Portonovo, Italy)[END_REF], and to typhoon conditions on a gravel beach [START_REF] Han | Typhoon Impact on a Pure Gravel Beach as Assessed through Gravel Movement and Topographic Change at Yeocha Beach, South Coast of Korea[END_REF]. The movement of detrital coral fragments has been also tracked along a fringing reef with passive RFID [START_REF] Ford | The application of PIT tags to measure transport of detrital coral fragments on a fringing reef: Majuro Atoll, Marshall Islands[END_REF]. Mobile RFID antennas commonly used in wadable river channels have been adapted for applications to coastal environments requiring underwater surveying. A waterproof RFID reader has notably been developed and included in a toolbox specifically designed for beaches [START_REF] Benelli | An RFID-Based Toolbox for the Study of Under-and Outside-Water Movement of Pebbles on Coarse-Grained Beaches[END_REF]. 3 Displacement of unstable terrains

Landslide and soil displacements

The displacement of landslides-typically a few centimeters to a few meters per year-is often spatially heterogeneous and irregular in time. Landslides sometimes display catastrophic acceleration phases that need to be monitored to obtain early warnings and reduce their risk to the population. Landslide monitoring greatly benefit from dense data in space and time (e.g., [START_REF] Intrieri | Forecasting the time of failure of landslides at slopescale: A literature review[END_REF][START_REF] Lacroix | Life and death of slow-moving landslides[END_REF]. Several monitoring methods already exist [START_REF] Angeli | A critical review of landslide monitoring experiences[END_REF], such as GPS [START_REF] Benoit | Monitoring landslide displacements with the Geocube wireless network of low-cost GPS[END_REF][START_REF] Gili | Using Global Positioning System techniques in landslide monitoring[END_REF], laser total stations, photogrammetry [START_REF] Travelletti | Correlation of multi-temporal ground-based optical images for landslide monitoring: Application, potential and limitations[END_REF] [START_REF] Jaboyedoff | Use of LIDAR in landslide investigations: a review[END_REF], Radar interferometry [START_REF] Herrera | A landslide forecasting model using ground based SAR data: The Portalet case study[END_REF], or local radiofrequency networks [START_REF] Kenney | Precise positioning with wireless sensor nodes: Monitoring natural hazards in all terrains[END_REF][START_REF] Intrieri | Application of an ultra-wide band sensor-free wireless network for ground monitoring[END_REF]. Among them, radiofrequency techniques (GPS, radar, wireless sensor networks) are the most reliable over the year since they can operate during rainfall, snowfall, heavy fog, or in the presence of vegetation cover. Reducing the cost of reliable monitoring techniques is a challenge, emphasized in recent reviews related to landslide monitoring [START_REF] Intrieri | Forecasting the time of failure of landslides at slopescale: A literature review[END_REF][START_REF] Lacroix | Life and death of slow-moving landslides[END_REF]. For example, low-cost GPS solutions were developed [START_REF] Squarzoni | Differential single-frequency GPS monitoring of the La Valette landslide (French Alps)[END_REF][START_REF] Buchli | GPS-Equipped Wireless Sensor Network Node for High-Accuracy Positioning Applications[END_REF][START_REF] Benoit | Monitoring landslide displacements with the Geocube wireless network of low-cost GPS[END_REF], but they still cost around a few k€ per measured point, which is too costly to monitor hundreds of points.

Recently, Le Breton et al. (2019) have monitored a landslide using localization methods of UHF RFID tags. The radial displacement (1D) of nineteen tags was measured relatively to a base station installed on a stable ground, continuously for 5 months, using the phase difference of arrival method [START_REF] Nikitin | Phase based spatial identification of UHF RFID tags[END_REF]. The measurements were validated against a long-range wire extensometer, and surveys using a total station. The RFID technique appears little sensitive to most environmental effects, particularly when using appropriate devices and calibration (Le [START_REF] Breton | Outdoor UHF RFID: Phase Stabilization for Real-World Applications[END_REF], as compared to the existing long-range wire extensometer. The accuracy of the RFID technique was 1 cm during normal weather and always less than 8 cm during the hardest conditions like heavy snow events, for which the long-range wire extensometer was no longer reliable. This work showed that RFID localization techniques can properly measure the ground displacements of a few centimeters up to several meters per year. Since its validation on the Pont-Bourquin landslide (Fig. 12), some of the authors then deployed the method on several additional landslides, in order to test and validate various operational configurations (Charléty et al., 2022a). Another configuration was tested on the slope above the Rieu Benoit River, next to the Valloire ski resort, French Alps. In this case the goal was to monitor different sets of tags, in different directions, from one single RFID reader. We thus deployed two antennas in two different directions, connected to the same RFID reader device.

On the Harmalière landslide, a clayey slow moving landslide displaying a regressive character, a set of 4 antennas were deployed around the same RFID reader (see Fig. 13 for field configuration) with a total opening of 5 m, to track the tag positions in 2D or 3D (Charléty et al., 2022b). In this specific configuration, the group of antennas was deployed on a stable ground, parallel to the movement of the landslide, addressing the same set of about 20 tags. The poor radial sensitivity of a single antenna for phase change detection was there compensated with the use of additional antennas installed at different positions.

The Villa Itxas Gaianas landslide (Delbreil, south of France) is also instrumented since January 2022. Continuous RFID measurements and manual GPS surveys measured a motion of to 2 meters in six months. This setup confirmed the usage RFID displacement monitoring in coastal place, subject to strong wind and corrosive sea spray.

In these use cases, the read range (the maximum tag-station distance) appears critical to monitor large landslides. Owing to cumulative development, the read range has gradually increased from 60 m (2018) to 120 m (2020), and further on (see details in 6.3).

Tilt of unstable boulders and slopes

Tilt monitoring can be valuable to provide early warning [START_REF] Intrieri | Design and implementation of a landslide early warning system[END_REF] before failure of rotational landslides [START_REF] Hungr | The Varnes classification of landslide types, an update[END_REF] and other unstable elements. The ability to deploy low-cost and low-power sensors appears critical when the zones to monitor are highly heterogeneous and need monitoring during several years. Low-cost WSNs have been proposed in pebbles [START_REF] Gronz | Smartstones: A small 9-axis sensor implanted in stones to track their movements[END_REF], on rotational landslides, on shallow landslides [START_REF] Uchimura | Simple monitoring method for precaution of landslides watching tilting and water contents on slopes surface[END_REF] and on unstable boulders [START_REF] Dini | Monitoring boulder movement using the Internet of Things: towards a landslide early warning system (other)[END_REF] (Fig. 14). The latter study highlights the need for numerous sensors, to monitor each prone-to-fall boulder independently. Yet, WSN nodes are more expensive than RFID tags. And they consume much more power than RFID tags, which is operationally challenging for continuous monitoring of a large number of nodes.

Again, RFID seems like an appealing alternative, for example to track the tilt of many boulders independently. Tilt of RFID tags can be measured either with dedicated sensors or with localization methods. Localization methods can measure a tilt from the relative displacement between several tags fixed on the same object, or from the optimal polarization of the reader antenna, in line with the tag [START_REF] Gupta | Orientation Detection Using Passive UHF RFID Technology [Education Column[END_REF][START_REF] Lai | A Novel Displacement and Tilt Detection Method Using Passive UHF RFID Technology[END_REF]. Localization has the advantage to work with any tag. But in practice the accuracy is easily deteriorated by environmental influences such as multipathing, and the measurement quality depends on the system geometry. In practice, dedicated sensors are likely to provide more reliable results with simpler installations, than propagation-based approaches.

Dedicated RFID tilt sensors use either MEMS accelerometers and switch sensors. MEMS accelerometers usually measure 3-axis acceleration, providing complete orientation of the object relatively to the vertical. They can be interfaced either directly on tags inputs [START_REF] Vena | Passive UHF RFID Sensor to Monitor Fragile Objects during Transportation[END_REF] or through a microcontroller [START_REF] Farsens | EVAL01-Kineo wireless, battery free orientation sensor tag[END_REF][START_REF] Jayawardana | Measurement System With Accelerometer Integrated RFID Tag for Infrastructure Health Monitoring[END_REF]. Unfortunately, these studies did not characterize the tilt accuracy. But general studies on ultra-low power accelerometers indicate that the accuracy should remain <1° (Łuczak et al., 2017). That is sufficient for boulder and landslide applications.

Alternatively, switch sensors consist in a continuity loop that is open or closed depending on the tag orientation. Such sensors can be connected to the tag antenna, in order to detune it when its tilt reaches a threshold [START_REF] Philipose | Batteryfree wireless identification and sensing[END_REF][START_REF] Shi | Design of A RFID patch antenna integrated with mercury switches for wireless tilt sensing[END_REF][START_REF] Ziai | Supply chain integrity tilt sensing RFID tag[END_REF]. On use cases that require permanent detection even when the tag is not interrogated, irreversible latching sensors can be used (W. Wang et al., 2020a). However, antenna-based sensing can be challenging in the field, due to its difficulty to differentiate a change of signal due to the sensor from other effects such as the tag deterioration by environmental influences. Connecting these switch sensors to tags equipped with digital input pins (see Table 2) could in practice increase the measurement reliability.

To conclude, boulders and landslides may be monitored with a tilt switch for threshold alert, or with MEMS accelerometers to quantify their rotation along time. Propagation-based and antennabased sensing allows for simple implementations, but are less reliable and accurate. In practice the usage of chips with dedicated sensor inputs seems preferable, particularly in an early-warning system that is deployed to secure the population.

Millimetric displacement of fissures

Rocks falling from cliffs present a risk to the population, which can be investigated and mitigated by monitoring rock compartments. The current monitoring techniques, reviewed by Lambert (2011), include insitu sensors such as extensometer, crackmeters, laser distance-meters or strain gauges. These reach subcentimetric to submillimetric accuracy but end up expensive to monitor large areas. The remote interferometric radar technique is well adapted to monitor large areas, but it measures mostly line of sight displacements, and its high cost dedicates it for high-risk zones only. When the risk is lower or not yet identified, passive visual targets can be installed on fissures with manual data collection, which is adequate on the ground but more difficult on a cliff. RFID sensors could allow deploying numerous passive targets, but interrogated wirelessly. They also gives the ability to switch seamlessly from manual wireless data collection over large areas (when the risk is low) to automatic continuous monitoring on a specific area (when an activity is detected). This strategy is indeed suggested for geotechnical monitoring of limited risk zones (Dunnicliff et al., 2012, p. 94).

Several tags have been designed and tested in the laboratory for measuring strain, displacement and crack formation. Antenna-based strain sensors were first developed to measure large strain up to 50% deformations [START_REF] Merilampi | Embedded wireless strain sensors based on printed RFID tag[END_REF]Occhiuzzi et al., 2011a;[START_REF] Hasani | Implementation of a Dual-Interrogation-Mode Embroidered RFID-Enabled Strain Sensor[END_REF]. Then, much smaller strains were measured with an accuracy of the order of 20 μm/m in the laboratory [START_REF] Yi | Passive wireless antenna sensor for strain and crack sensing-electromagnetic modeling, simulation, and testing[END_REF][START_REF] Yi | Sensitivity Modeling of an RFID-Based Strain-Sensing Antenna With Dielectric Constant Change[END_REF] (Fig. 15c), for structural health monitoring and crack detection [START_REF] Zhang | A Review of Passive RFID Tag Antenna-Based Sensors and Systems for Structural Health Monitoring Applications[END_REF] (Fig. 15a). In practice, however, searching for the antenna resonant frequency (Fig. 15b) is impractical out of the laboratory, particularly in regions under the European ETSI-302-208 regulation that limits the available bandwidth. Displacement sensors were also developed, to measure the distance between the tag and a metallic object at its vicinity [START_REF] Bhattacharyya | Towards tag antenna based sensing -An RFID displacement sensor[END_REF] or between two tags [START_REF] Caizzone | Wireless Passive RFID Crack Width Sensor for Structural Health Monitoring[END_REF]. To improve the accuracy, it is possible to connect dedicated strain gauge sensors on the tag [START_REF] Digiampaolo | An RFID-Enabled Wireless Strain Gauge Sensor for Static and Dynamic Structural Monitoring[END_REF][START_REF] Jayawardana | RFID-Based Wireless Multi-Sensory System for Simultaneous Dynamic Acceleration and Strain Measurements of Civil Infrastructure[END_REF]. Some RFID chips that sense their own deformation could also be used (ASYGN AS321X), given a special care on the mechanical properties of the tag, its long-term behavior, and its fixation.

Threshold displacement detection with RFID tags appear as a simple but a reliable approach. It was proposed to detect defects appearing on a surface [START_REF] Nappi | Inkjet-Printed RFID-Skins for the Detection of Surface Defects[END_REF], contact with a screw or a magnet [START_REF] Fischbacher | Localization of UHF RFID Magnetic Field Sensor Tags[END_REF][START_REF] Inserra | Screw Relaxing Detection With UHF RFID Tag[END_REF] or rupture of a part of an antenna (W. Wang et al., 2020b). Threshold displacement tags were recently deployed on a cliff to detect potential displacement of rock compartments (Le Breton et al., 2021) (Fig. 16). Each tag is connected to a simple actuator placed on the two sides of a fissure, to provide 1-bit information when displacement exceeds a threshold. Laboratory experiments demonstrated the ability to raise an alarm just after reaching a displacement threshold of 1 mm.

Ground vibrations

Vibration measurements can be used to monitor the resonant frequency of structures (civil or geological), or to detect transient vibration events that exceed a threshold (construction work, blast, earthquake). Measuring resonant frequencies exploits weak ambient vibrations and requires highaccuracy seismic-grade accelerometers providing noise levels below μg [START_REF] Scudero | MEMS technology in seismology: A short review[END_REF] (C. Wang et al., 2020). Detecting transient vibrations requires permanent monitoring, presenting a challenge of power consumption. We evaluate their feasibility for a practical usage, and the challenges to be resolved.

Resonant frequency of structures

Ambient noise seismology exploits vibration from non-controlled and diffuse sources, to characterize the subsurface and detect changes over time. In particular, arrays of seismic sensors placed at short distances (over tens to hundreds of meters) for long duration can monitor dynamic parameters of near-surface geological structures [START_REF] Larose | Environmental seismology: What can we learn on earth surface processes with ambient noise[END_REF] such as their resonant frequency. This frequency provides insights on the structure's mechanical behavior [START_REF] Del Gaudio | What we can learn about slope response to earthquakes from ambient noise analysis: An overview[END_REF][START_REF] Larose | Environmental seismology: What can we learn on earth surface processes with ambient noise[END_REF][START_REF] Kleinbrod | Ambient vibration classification of unstable rock slopes: A systematic approach[END_REF][START_REF] Colombero | Seismic Noise Parameters as Indicators of Reversible Modifications in Slope Stability: A Review[END_REF] such as the stability of a rock column [START_REF] Lévy | Analysis of seismic signals recorded on a prone-tofall rock column (Vercors massif, French Alps)[END_REF][START_REF] Bottelin | Spectral Analysis of Proneto-fall Rock Compartments using Ambient Vibrations[END_REF][START_REF] Bottelin | Monitoring rock reinforcement works with ambient vibrations: La Bourne case study (Vercors, France)[END_REF]. This section evaluates whether RFID vibration sensors could be used to monitor resonant frequency of a geological structure or not.

RFID tags were recently developed to measure the vibration modes of civil structures [START_REF] Jayawardana | Measurement System With Accelerometer Integrated RFID Tag for Infrastructure Health Monitoring[END_REF][START_REF] Jayawardana | RFID-Based Wireless Multi-Sensory System for Simultaneous Dynamic Acceleration and Strain Measurements of Civil Infrastructure[END_REF]) (Fig. 17). A tag connected to an ultra-low-power MEMS accelerometer was used to retrieve the fundamental vibration frequency of a metallic beam. This system could measure the beam's fundamental frequency within 1 -40 Hz, with a precision of ±0.01 Hz (at 6 Hz). That worked for values of acceleration within 30 -300 mg, equivalent to displacements of 0.02-0.2 mm. In 2019, the authors improved the results by adding a strain gauge to measure oscillating deformation of the beam of the order of 100-10 µε. DiGiampaolo et al. ( 2017) proposed a similar experiment, using only a strain gauge to measure the vibrations of a beam.

Simpler implementations were also proposed, using an acceleration actuator attached on the tag antenna [START_REF] Rahmadya | Measurement of a UHF RFID-Based Battery-Less Vibration Frequency Sensitive Sensor Tag Using Tilt/Vibration Switches[END_REF]. Standard tags were also used, by measuring the oscillation of the phase caused either by the displacement of the tag itself [START_REF] Yang | Tagbeat: Sensing Mechanical Vibration Period With COTS RFID Systems[END_REF] [START_REF] Li | RFID Harmonic for Vibration Sensing[END_REF], or by the displacement of an object near the tag that modifies the multipath interferences [START_REF] Yang | RF-Ear: Contactless Multi-device Vibration Sensing and Identification Using COTS RFID[END_REF]. However those simpler approaches could measure at most displacements of 2 mm, which is much less accurate than dedicated sensors.

The mentioned dedicated sensors can measure the typical 0.1-100 Hz vibration frequencies observed on rock columns [START_REF] Moore | Use of Seismic Resonance Measurements to Determine the Elastic Modulus of Freestanding Rock Masses[END_REF]). Yet, in practice the sensor's noise levels of 90 -250 µg/√Hz (see Table 4) is far greater than the ambient vibrations levels of 1 ng to 10 µg at 1 -10 Hz [START_REF] Peterson | Observations and modeling of seismic background noise[END_REF]. Therefore, today, RFID vibration sensors may offer applications of modal analysis only for measuring strong vibrations. This occurs at the vicinity of strong vibration sources (transportation, factories, explosives) or on sites that present high resonance factors (e.g., buildings, large bridges). As we expect future improvements in the sensor's sensitivity, we envision that RFID accelerometers will be applicable to more and more applications.

Accelerometer Sensor

Resolution Noise level horizontal (µg/√Hz) *

Power consumption

Used in ADLX362 1 mg ±2 g n.a. (wakeup) 550 @2g 175 @2g 0.5 µW @6 Hz 3.5 µW @100Hz 45 µW @100Hz [START_REF] Jayawardana | Measurement System With Accelerometer Integrated RFID Tag for Infrastructure Health Monitoring[END_REF][START_REF] Jayawardana | RFID-Based Wireless Multi-Sensory System for Simultaneous Dynamic Acceleration and Strain Measurements of Civil Infrastructure[END_REF][START_REF] Konstantakos | Earthquake instrumentation node with MEMS sensors[END_REF][START_REF] Vena | Passive UHF RFID Sensor to Monitor Fragile Objects during Transportation[END_REF]) Bosh BMA400 1 mg ±2 g 2600 eq. (11mg) @4g 180 @4g 1.5 µW @25Hz 26 µW continuous ST IIS2DLPC

1 mg ±2 g (5.5 mg) @2g 6.3 µW @50Hz *Noise levels are given on X and Y-axis. They are often higher (x1.5 to x3) on Z-axis.

Detection of transient vibrations and shocks

Soil vibrations are important to quantify because they can damage buildings, depending on the vibration amplitude and frequency ( Table 5). That can occur near construction works or explosive blasts [START_REF] Segarra | Performance of some coupling methods for blast vibration monitoring[END_REF]. Monitoring the vibrations ensures that buildings are not damaged following international standardization rules (ISO 4866, 2010) and can also apply to fragile or unstable geological structures [START_REF] Bottelin | Seismic study of soda straws exposed to nearby blasting vibrations[END_REF]. Ultra-low power MEMS can detect events with very little power, by providing a triggering signal only when a vibration exceeds a threshold [START_REF] Evans | Performance of Several Low-Cost Accelerometers[END_REF]. In a low-power seismic acquisition station, [START_REF] Konstantakos | Earthquake instrumentation node with MEMS sensors[END_REF] monitored vibrations using the ADLX362 sensor (Table 4) which consumed only 3 μA current (and can go down to 0.3 μA in wake-up mode). When vibration amplitude exceeds a threshold, the sensor wakes-up a complete system to properly record the event.

Detecting transient vibrations is challenging with RFID, because it requires enough power to enable permanent monitoring. RFID tags equipped with ultra-low-power MEMS accelerometers may do so, using battery assistance [START_REF] Jayawardana | RFID-Based Wireless Multi-Sensory System for Simultaneous Dynamic Acceleration and Strain Measurements of Civil Infrastructure[END_REF], energy harvesting (e.g., small solar cells), or radiofrequency energy stored temporarily in a capacitor (e.g., [START_REF] Vena | Passive UHF RFID Sensor to Monitor Fragile Objects during Transportation[END_REF], as discussed in 6.3. In terms of sensitivity, the ADLX362 accelerometer offers 1 mg sensitivity and 0.55 mg/√Hz noise level. This should detect the vibration levels that can damage a mud-brick house, of 2-3 mm/s at 5-20 Hz, corresponding to 6-36 mg [START_REF] Yan | Evaluation of human response to blasting vibration from excavation of a large scale rock slope: A case study[END_REF].

Shock detection may also be interesting, for example to detect if a part of infrastructure has suffered from a shock and needs maintenance verification. Such shocks imply strong vibrations, which should be detectable with 1-bit threshold vibration sensors attached on RFID tags. A tag equipped with a simple 2-state switch sensor could detect variations of acceleration (>0.5 g on the example) [START_REF] Occhiuzzi | Modeling, Design and Experimentation of Wearable RFID Sensor Tag[END_REF] (Fig. 18). However, such tag would need a constant interrogation to record a transient vibration: it can record vibration only when it is interrogated and it has no memory. Alternatively, shock sensing may be useful not only in real time, but also to detect if an event has occurred in the past. This may be feasible by connecting the sensor to a tag that offers tampering detection input. Alternatively, the information can be stored mechanically, requiring zero sensing power to detect the event, by attaching a bistable accelerometer to a tag [START_REF] Todd | Low-Cost RFID Threshold Shock Sensors[END_REF]. Latching bistable sensors can change their mechanical state durably when acceleration exceeds a certain threshold. The threshold values go from just above 1 g [START_REF] Mehner | A passive microsystem for detecting multiple acceleration events beyond a threshold[END_REF] to hundreds of g's [START_REF] Hansen | Plastic latching accelerometer based on bistable compliant mechanisms[END_REF][START_REF] Reddy | A zeropower sensing MEMS shock sensor with a latch-reset mechanism for multi-threshold events monitoring[END_REF] and can depend on the vibration frequency ( Fig. 19). If such a sensor were implemented on a field, the threshold value would have to be chosen in accordance with the strength of the expected events to monitor. Technical improvements of these latching sensors could improve their usability if they were implemented on RFID systems. Recent developments allow latching sensors to record the amplitude of the highest vibration event [START_REF] Mehner | A passive microsystem for detecting multiple acceleration events beyond a threshold[END_REF][START_REF] Reddy | A zeropower sensing MEMS shock sensor with a latch-reset mechanism for multi-threshold events monitoring[END_REF], or to be reset electrically to their initial position [START_REF] Mehner | A passive microsystem for detecting multiple acceleration events beyond a threshold[END_REF][START_REF] Reddy | A zeropower sensing MEMS shock sensor with a latch-reset mechanism for multi-threshold events monitoring[END_REF].

In conclusion, RFID tags can detect strong shocks (several g's) using zero-power bistable sensors. Small shocks or motions (>0.5 g) can be detected with a one-bit reversible switch sensor. Soil vibrations that are strong enough to damage buildings (>5 mg), generated for example by human activity or earthquakes, should be detectable using ultra-low-power Micro Electro Mechanical Systems (MEMS) accelerometers. Proof of concept tags have been designed in the laboratory for sensing such vibrations, and remain to be adapted for field applications.

Chemico-physical properties of geomaterials

Soil moisture

Soil moisture determination is important in the fields of agricultural, geotechnical, hydrological and environmental engineering [START_REF] Lekshmi | A critical review of soil moisture measurement[END_REF]. Three electrical methods are used today to estimate soil moisture [START_REF] Schmugge | Survey of methods for soil moisture determination[END_REF][START_REF] Lekshmi | A critical review of soil moisture measurement[END_REF]Dane and Topp, 2020, p. 4). They measure either the electrical conductivity between electrodes, the change of capacitance of a sensor, or the time delay of a wave transmitting in the soil (transmissiometry/reflectometry). Many other nonelectrical methods allow such estimation, for example through the influence of soil moisture on underground air humidity or on daily temperature fluctuations.

Soil conductivity directly depends on moisture and water salinity, and to a lesser extent on the temperature and on the soil solid phase. Therefore, the conductivity method requires regular calibration on each location to mitigate the effect of soil and salinity. The method consists in measuring the electrical resistance between two probe electrodes when two additional electrodes inject a current. That is implemented today on commercial passive RFID tags (FARSENS HYDRO H402), and on multi-sensor tags [START_REF] Cappelli | Battery-Less HF RFID Sensor Tag for Soil Moisture Measurements[END_REF].

Capacitive sensors (Fig. 20.a) are considered more accurate than conductivity sensors to estimate soil moisture, because they are sensitive mostly to the soil in-phase dielectric permittivity, which in turn depends mostly on water content for wet soils. The in-phase dielectric constant is one for air, 3 -5 for dry soil and about 80 for water at 20 °C at 1 MHz to 1 GHz frequencies. Therefore water content holds the major influence factor on dielectric permittivity in wet soils [START_REF] Peplinski | Dielectric properties of soils in the 0.3-1.3-GHz range[END_REF]. Capacitive sensors connected to analog sensing tags (SL900A) have provided quite accurate measurements in the laboratory [START_REF] Fonseca | A passive capacitive soil moisture and environment temperature UHF RFID based sensor for low cost agricultural applications[END_REF][START_REF] Pichorim | Two Solutions of Soil Moisture Sensing with RFID for Landslide Monitoring[END_REF] including during several days [START_REF] Korošak | The Implementation of a Low Power Environmental Monitoring and Soil Moisture Measurement System Based on UHF RFID[END_REF].

We consider antenna-based sensing as a group of capacitive sensors. In the early stages, such tags were demonstrated to detect the presence of liquid water near the tag (Bhattacharyya et al., 2010a), because the water strongly reduces the received signal strength. Soil moisture was then estimated using received signal strength. However the RSSI indicator is sensitive to many unwanted parameters and has a limited range of measurement. To improve the accuracy, a reference tag can be placed above the ground (J. Wang et al., 2020). To maximize the dynamic range, the tag antenna can be tuned to the permittivity of dry soil [START_REF] Alonso | UHF passive RFID-based sensor-less system to detect humidity for irrigation monitoring[END_REF]. Sweeping the carrier frequency-allowed mostly in laboratories-can instead determine the tag resonant frequency and its amplitude. The soil moisture control mostly the resonant frequency [START_REF] Kim | An RFIDenabled inkjet-printed soil moisture sensor on paper for "smart" agricultural applications[END_REF], and its salinity controls the amplitude of the resonance [START_REF] Dey | A Folded Monopole Shaped Novel Soil Moisture and Salinity Sensor for Precision Agriculture Based Chipless RFID Applications[END_REF][START_REF] Dey | Electromagnetic characterization of soil moisture and salinity for UHF RFID applications in precision agriculture[END_REF][START_REF] Hasan | Towards pervasive soil moisture sensing using RFID tag antenna-based sensors[END_REF][START_REF] Hasan | A monopole-coupled RFID sensor for pervasive soil moisture monitoring[END_REF]. Very preliminary results also investigated the usage of self-tuning tags to estimate the antenna detuning [START_REF] Stoddard | Examining the Effectiveness of Commercial RFID Tags as Soil Moisture Sensors[END_REF]. In terms of application, antenna-based moisture sensors [START_REF] Zhou | A Novel Passive Wireless Sensor for Concrete Humidity Monitoring[END_REF] monitored drying concrete and could retrieve the water content over a very large range of values, from very wet to dry. [START_REF] Aroca | Application of Standard EPC/GEN2 UHF RFID Tags as Soil Moisture Sensors[END_REF][START_REF] Aroca | Calibration of Passive UHF RFID Tags Using Neural Networks to Measure Soil Moisture[END_REF]) buried tags at different depths below the ground, and observed a loss of RSSI, caused both by propagation attenuation-as they suggest-and by the coupling of soil with the tag antenna. J. Wang et al. (2020) presented for the first time a real-world application of soil moisture sensing (Fig. 20.b), using the antenna of commercial low-cost tags for monitoring the moisture of pots in a green plant. After calibration, they could determine the soil moisture with 5% accuracy on one pot, and in a largescale experiment they could determine which pot required watering with 95% of success. They optimized the system by using several existing concepts. First, they observed that using the indicator of power of activation (i.e., the minimal power transmitted by the reader for which the tag is activated), instead of the RSSI, divides by two the influence of propagation. Then, they used a reference tag to make differential measurements independently from the pot location. Next, they averaged the measurements over long periods to eliminate rapid fluctuations caused by human activity. Finally, they calibrated the measurement against a standard measurement, for a pot on which the soil is reproducible.

The reflectometry (or transmissiometry) method measures the change in time delay of a propagated wave between two electrodes. It is among the most accurate method for measuring soil moisture [START_REF] Lekshmi | A critical review of soil moisture measurement[END_REF], because it measures the propagation over a volume of soil. Therefore it depends much less on the mechanical coupling of the sensor with the soil than with capacitive sensors. [START_REF] Fonseca | A Fully Passive UHF RFID Soil Moisture Time-Domain Transmissometry Based Sensor[END_REF] have implemented the transmissiometry method with a UHF RFID tag (Fig. 20.c): the wave propagates along a line connected to the tag, which includes a 10 MHz oscillator for measurements. The results were linear within 10-32% of moisture and up to 0.15 S/m conductivity.

Alternative indicators can be related to soil moisture. For example, an air humidity sensor buried in a cavity would measure either a low or saturated humidity in dry and wet soil, respectively [START_REF] Cappelli | Battery-Less HF RFID Sensor Tag for Soil Moisture Measurements[END_REF]. In complement, tags were also proposed to measure not only soil moisture, but temperature, air humidity, soil conductivity and luminous intensity [START_REF] Korošak | The Implementation of a Low Power Environmental Monitoring and Soil Moisture Measurement System Based on UHF RFID[END_REF][START_REF] Deng | Novel soil environment monitoring system based on RFID sensor and LoRa[END_REF][START_REF] Cappelli | Battery-Less HF RFID Sensor Tag for Soil Moisture Measurements[END_REF].

In conclusion, many approaches have been tested to measure soil moisture. Among them, transmissiometry is the most accurate because it distinguishes moisture from salinity. Antenna-based sensing has also proven efficient in an industrial green house, using a reference tag and a calibration on a reproducible soil. Most studies installed the tag antennas above ground and the sensor within the ground. One design also buried the tag, which could be promising for long-term monitoring (i.e., for agriculture), but strongly reduces the reading range (see 6.2.1).

Soil temperature

The daily and yearly temperature variations significantly influence many near-surface processes. Temperature is a very important parameter to monitor over several decades, particularly in regions sensitive to global warming. For example, in permafrost and mountainous areas, a small temperature change near 0 °C may have important consequences (e.g., [START_REF] Ravanel | Instability of a high alpine rock ridge: the lower arête des cosmiques, mont blanc massif, france[END_REF]. The temperature of soils, rocks or snow can be very heterogenous in space, particularly along depth. Monitoring temperature over long duration at low cost can be of interest, particularly with buried passive wireless systems that don't require ground wiring that can easily be damaged (agriculture, theft, animals, grass mowing…). Arrays of batteryless RFID tags could monitor the temperature at different depths and locations in soil or rock materials, during years, with little cost and maintenance, as already demonstrated in logistics [START_REF] Jedermann | Spatial temperature profiling by semi-passive RFID loggers for perishable food transportation[END_REF]. Temperature data can also rectify the thermal influences of another measurement to increase its accuracy (e.g., [START_REF] Breton | Outdoor UHF RFID: Phase Stabilization for Real-World Applications[END_REF].

Today, RFID temperature sensors are embedded in RFID chips and produced industrially (see Table 2). They offer 1-2° C accuracy, depending on the calibration procedure, the temperature range [START_REF] Occhiuzzi | Sensing-oriented RFID tag Response in High Temperature Conditions[END_REF], and the powering level on batteryless tags [START_REF] Camera | Electromagnetic-Based Correction of Bio-Integrated RFID Sensors for Reliable Skin Temperature Monitoring[END_REF]. Researchers have also developed ultra-low consumption temperature sensors for batteryless tags [START_REF] Opasjumruskit | Selfpowered wireless temperature sensors exploit RFID technology[END_REF][START_REF] Yin | A system-on-chip EPC Gen-2 passive UHF RFID tag with embedded temperature sensor[END_REF][START_REF] Vaz | Full Passive UHF Tag With a Temperature Sensor Suitable for Human Body Temperature Monitoring[END_REF][START_REF] Wang | A Passive RFID Tag Embedded Temperature Sensor With Improved Process Spreads Immunity for a $-\hbox 30^\circ\hbox C$ to 60$^\circ\hbox C$ Sensing Range[END_REF], which could reach 0.4 °C accuracy [START_REF] Tan | A Fully Passive RFID Temperature Sensor SoC With an Accuracy of ±0.4 °C ($3\sigma$) From 0 °C to 125[END_REF]. Many other original temperature sensors have been designed. They were based on antenna sensing [START_REF] Qiao | Reconfigurable Sensing Antenna With Novel HDPE-BST Material for Temperature Monitoring[END_REF][START_REF] Wang | On Remote Temperature Sensing Using Commercial UHF RFID Tags[END_REF][START_REF] Zannas | Self-Tuning RFID Tag: A New Approach for Temperature Sensing[END_REF], irreversible threshold detectors [START_REF] Bhattacharyya | RFID tag antenna based temperature sensing in the frequency domain[END_REF][START_REF] Babar | Passive UHF RFID Tag for Heat Sensing Applications[END_REF], or electromechanical encoders [START_REF] Zhu | Passive Digital Sensing Method and Its Implementation on Passive RFID Temperature Sensors[END_REF]. Among all these sensors, the embedded temperature sensors should be preferred for geoscience applications because of their advanced maturity.

Soil temperature sensing using RFID tags was first suggested by [START_REF] Hamrita | Development of a smart wireless soil monitoring sensor prototype using RFID technology[END_REF], and implemented much later on the field, to compare the soil and air temperature [START_REF] Luvisi | RFID temperature sensors for monitoring soil solarization with biodegradable films[END_REF]. The experiment buried a semi-passive tag with temperature data logging (Easy2Log RT0005, Caen RFID) buried 10 cm into the ground, read from a handheld RFID reader. The aim was to monitor the efficiency of a soil treatment for pathogens and weed, called solarization, that consists in covering the soil with a film to increase its temperature. The solarization increased the soil temperature relatively to the air temperature. Once the solarization film had broken, the resulting decrease of relative soil temperature could easily be measured thanks to the sensing tag (Fig. 21). Another implementation of buried soil temperature sensor has been also proposed by [START_REF] Deng | Novel soil environment monitoring system based on RFID sensor and LoRa[END_REF], as a proof of concept for underground communication using a robot.

In conclusion, RFID temperature sensing is a mature and easy to use integrated sensors technology, offering a 1-2°C accuracy, and a low thermal signature. It could be particularly useful to monitor materials that are sensitive to temperature fluctuations such as snow or permafrost.

Snow and ice

Snowpack monitoring is used for example to predict the quantity of snowmelt water to come in rivers for hydroelectric management or to estimate risks of an avalanche near ski resorts. The properties of the snowpack-the snow accumulated on the ground-strongly varies in time and space. Its measurement often requires the combination of multiple techniques [START_REF] Kinar | Measurement of the physical properties of the snowpack[END_REF]. Some macroscopic properties of a snowpack such as thickness, liquid water content, density, and total water equivalent content, control the snow dielectric permittivity and can be measured with microwaves [START_REF] Tedesco | Electromagnetic properties of components of the cryosphere[END_REF]. In particular, the influences of antenna coupling, transmission through snow and multipath interference are used today for snow monitoring [START_REF] Denoth | An electronic device for long-term snow wetness recording[END_REF][START_REF] Bradford | Complex dielectric permittivity measurements from ground-penetrating radar data to estimate snow liquid water content in the pendular regime: MEASURING SWE WITH GPR[END_REF][START_REF] Larson | GPS interferometric reflectometry: applications to surface soil moisture, snow depth, and vegetation water content in the western United States[END_REF][START_REF] Koch | Retrieval of Snow Water Equivalent, Liquid Water Content, and Snow Height of Dry and Wet Snow by Combining GPS Signal Attenuation and Time Delay[END_REF]. These influences similarly affect the RFID signal (Le Breton, 2019), opening the way for sensing applications.

The snow water equivalent (SWE) of a snowpack can be monitored from the variations of phase delay over time, due to changes in the wave velocity propagation (Le [START_REF] Breton | Monitoring snowpack SWE and temperature using RFID tags as wireless sensors[END_REF]. The experiment consisted in placing a tag on a support very close to the ground, and adding dry snow between the tag and antenna which increases the phase delay and allows estimating the increase of snow water equivalent. The method was first validated in the laboratory on dry snow of different densities, then outdoors in a mountainous environment during several snow event, and finally during an entire winter on Col de Porte, the national snow test site of Meteo France (see Fig. 22).

The snowpack temperature can also be monitored with RFID tags embedding a temperature sensor, like used in soil (see previous section). The concept was demonstrated at Col de Porte, simultaneously as the SWE experiment. A vertical array of 23 tags installed before the snowfall provided a continuous vertical profile of temperature data, with 1 °C accuracy, during the whole winter (Le [START_REF] Breton | Monitoring snowpack SWE and temperature using RFID tags as wireless sensors[END_REF]). It is an example of monitor simultaneously two parameters with an RFID system-the SWE and snowpack temperature-combining both propagation-based sensing and a dedicated sensor.

Frost deposition and melting on a tag also detunes its antenna, leading to variations of the phase and RSSI (Le Breton, 2019). This influence has been recently exploited to estimate the quantity of ice deposited on a tag, and to detect its melting (Wagih andShi, 2021a, 2021b).

Potential for mechanical, gas, luminosity and water sensing

Other RFID sensors that might be used to monitor near-surface processes are cited in this section. Mechanical sensors of pressure [START_REF] Salmerón | Design and Development of Sensing RFID Tags on Flexible Foil Compatible With EPC Gen 2[END_REF] or weight [START_REF] Catarinucci | IoT-Aware Waste Management System Based on Cloud Services and Ultra-Low-Power RFID Sensor-Tags[END_REF]) (e.g., FARSENS EVAL01-Zygos-RM), might be of interest for example for geotechnical monitoring [START_REF] Dunnicliff | Chapter 94 Principles of geotechnical monitoring[END_REF].

Air humidity sensing has been proposed on several research tag designs. They exploit antennabased sensing [START_REF] Siden | Remote Moisture Sensing utilizing Ordinary RFID Tags[END_REF][START_REF] Virtanen | Inkjet-Printed Humidity Sensor for Passive UHF RFID Systems[END_REF], dedicated sensing [START_REF] Pursula | High Frequency and Ultrahigh Frequency Radio Frequency Identification Passive Sensor Transponders for Humidity and Temperature Measurement Within Building Structures[END_REF][START_REF] Yu | Passive and Semi-Passive Wireless Temperature and Humidity Sensors Based on EPC Generation-2 UHF Protocol[END_REF], and sometimes multi-sensing platforms [START_REF] Cappelli | Battery-Less HF RFID Sensor Tag for Soil Moisture Measurements[END_REF][START_REF] Korošak | The Implementation of a Low Power Environmental Monitoring and Soil Moisture Measurement System Based on UHF RFID[END_REF]. Commercial tags are also available, greatly easing the ability for air humidity sensing (ASYGN AS321X Humidity; EVAL02-Hygro-Fenix-RM). In addition to humidity sensing, tags were also designed to detect other gases, such as ammonia [START_REF] Karuppuswami | RFID Coupled Passive Digital Ammonia Sensor for Quality Control of Packaged Food[END_REF]Occhiuzzi et al., 2011b;[START_REF] Yang | A Novel Conformal RFID-Enabled Module Utilizing Inkjet-Printed Antennas and Carbon Nanotubes for Gas-Detection Applications[END_REF], octane, ethanol (Manzari et al., 2014b), and methane [START_REF] Zhang | Methane Gas Density Monitoring and Predicting Based on RFID Sensor Tag and CNN Algorithm[END_REF]. These gas sensors might find applications for example in underground mines or on geothermal and volcanic areas.

Luminous intensity sensors have been developed for research [START_REF] De Donno | RAMSES: RFID Augmented Module for Smart Environmental Sensing[END_REF][START_REF] Falco | Fully Printed Flexible Single-Chip RFID Tag with Light Detection Capabilities[END_REF][START_REF] Salmerón | Design and Development of Sensing RFID Tags on Flexible Foil Compatible With EPC Gen 2[END_REF] and commercial tags (FARSENS, AS321X-EVAL-Light). Multispectral sensing tag may even quantify the luminosity in different bandwidth, within the visible spectrum only [START_REF] Boada | NFC Battery-Less Colour Sensor and its Applications[END_REF] or also in the infrared and ultraviolet spectra [START_REF] Escobedo | Passive UHF RFID Tag for Multispectral Assessment[END_REF]. This might be useful for example to estimate the effect of solar radiation energy on soils, rocks, or on the snowpack.

Water can also be monitored using RFID, with tags that measure the level of water (Bhattacharyya et al., 2010a;[START_REF] Capdevila | Passive RFID based sensing[END_REF][START_REF] Melia-Segui | Ubiquitous moisture sensing in automaker industry based on standard UHF RFID tags[END_REF], the pH [START_REF] Kassal | Wireless smart tag with potentiometric input for ultra low-power chemical sensing[END_REF][START_REF] Nanni | Potentiometric Sensing by means of Self-tuning RFID ICs[END_REF] and the concentration of electrolytes [START_REF] Rose | Adhesive RFID Sensor Patch for Monitoring of Sweat Electrolytes[END_REF][START_REF] Occhiuzzi | RFID epidermal sensor including hydrogel membranes for wound monitoring and healing[END_REF][START_REF] Kantareddy | UHF RFID tag IC power mode switching for wireless sensing of resistive and electrochemical transduction modalities[END_REF][START_REF] Hillier | A Passive UHF RFID Dielectric Sensor for Aqueous Electrolytes[END_REF]. On the field, an array of water sensing tags might serve to evaluate where groundwater may emerge at the surface, for example on a landslide, or to track pollution.

Key challenges

RFID tags may offer advantages in harsh environments: tags do not require batteries, and can be interrogated across opaque media. Their affordability allows getting them destroyed.

Bedload tracing

The short detection range of passive low-frequency RFID tags (134 kHz) typically used in bedload transport studies is one of the most limiting technical drawbacks to consider. It implies the loss of deeply buried tracers, and time expensive field surveys when tracers spread over wide or long channel reaches. Standard operating procedures for bedload tracing with RFID tags have been proposed to optimize the detection range [START_REF] Chapuis | Detection ranges and uncertainty of passive Radio Frequency Identification (RFID) transponders for sediment tracking in gravel rivers and coastal environments: DETECTION RANGES AND UNCERTAINTY OF PASSIVE RFID TRANSPONDERS[END_REF]. This study investigated parameters such as the 3D shape of antenna detection zones for different RFID tags, different orientations, different types of antennas, and different media of tag deposition (open-air, dry and saturated sediment, water). It showed a maximum detection range of 0.7 m for 23-mm-long tags that are oriented perpendicular to the loop of the antenna, and a significant decrease of recovery rate for burial depths above 0.5 m. It has been therefore recommended to insert RFID tags parallel to the short axis (c-axis) of stones to favor a perpendicular orientation of tags with respect to the channel surface for maximizing the detection range. The comparison of detection ranges in different media showed a small effect of dry or saturated granular media on the detection range, as compared with open-air conditions. However, a significant reduction of the detection range (40 to 50% decrease) has been obtained for water submergence as compared to open-air conditions. Similar experiments of PIT tag detection were implemented directly in the field using 23 mm and 32 mm transponders and different types of antenna. The experiments confirmed that a granular medium (burial in the sediment deposit) does not have a strong effect on the detection range, with a decrease of less than 10% [START_REF] Arnaud | Technical specifications of low-frequency radio identification bedload tracking from field experiments: Differences in antennas, tags and operators[END_REF]. These field experiments also showed a small effect of water submergence on tag detection, contrary to what has been observed in the sandbox experiments of [START_REF] Chapuis | Detection ranges and uncertainty of passive Radio Frequency Identification (RFID) transponders for sediment tracking in gravel rivers and coastal environments: DETECTION RANGES AND UNCERTAINTY OF PASSIVE RFID TRANSPONDERS[END_REF].

These experimental studies provide useful recommendations for the design of RFID protocols maximizing the detection range of tags. However, whatever the size and type of passive low-frequency tags and antennas, no disruptive technological step was found to increase the detection range of PIT tags. The only solution to date is to use active UHF RFID tags (e.g., COIN-ID 433.92 MHz from ELA innovation). These tags can be detected at distances up to 80 m in open-air, and 2.5 m below water or in a saturated granular medium (Cassel et al., 2017a). Another decisive advantage of these UHF tags is their anti-collision protocol (these are standard for passive UHF tags, but not LF tags) allowing the simultaneous detection of several tags close to each other. This means that active UHF RFID tags can be deployed in columns buried in the subsurface of river channels to constrain the active layer depth, as a kind of RFID scour chain. This has been recently tested in two large braided gravel-bed rivers [START_REF] Brousse | Experimental bed active-layer survey with active RFID scour chains: Example of two braided rivers (the Drac and the Vénéon) in the French Alps[END_REF][START_REF] Brousse | Gravel replenishment and active-channel widening for braided-river restoration: The case of the Upper Drac River (France)[END_REF], allowing the quantification of scour and fill depths which would have not been seen with traditional scour chains (Fig. 23). The first reported bedload tracing experiments with those tags revealed, in a large gravel-bed river, frontrunners that have traveled more than 3 km after a 5-year return period flood (Brousse et al., 2020a). The tracers were recovered at a high rate (71%). At the same site, the field-testing of several detection and positioning protocols built from groundbased and UAV surveys shows that high recovery rates can be obtained from short-time field surveys [START_REF] Cassel | Comparison of ground-based and UAV a-UHF artificial tracer mobility monitoring methods on a braided river[END_REF]. The main drawbacks of these tags compared to PIT tags are their higher cost (unit price around €40), larger size and volume (36 mm diameter and 10 mm thick). Additionally the lifespan, which depends on the chosen frequency of emission, is generally less than 5 yrs, which is relatively short. Their cylindrical shape does not facilitate their insertion in natural stones like for glassencapsulated PIT tags, and necessitates the casting of artificial stones (Cassel et al., 2017a(Cassel et al., , 2017b)). However, active UHF RFID tags might partially replace passive tags in the future, especially for bedload tracing in large rivers for short duration. Passive RFID tags may still have interest for long-term studies in small rivers. A second major challenge of bedload tracing with RFID is to be able to know the burial depth of tracers. This information is crucial for the determination of the active layer of the bed during flow events. By combining the depth of the active layer with distances of transport, it becomes possible to estimate the time-averaged bedload flux [START_REF] Haschenburger | Bed material transport estimated from the virtual velocity of sediment[END_REF][START_REF] Liébault | Evaluation of bedload yield in gravel-bed rivers using scour chains and painted tracers: the case of the Esconavette Torrent (Southern French Prealps)[END_REF][START_REF] Houbrechts | Comparison of methods for quantifying active layer dynamics and bedload discharge in armoured gravel-bed rivers[END_REF]. The burial depth of low-frequency RFID tags can be constrained at fixed locations by considering the decay of the received input voltage by an antenna [START_REF] Tsakiris | Identification of the Burial Depth of Radio Frequency Identification Transponders in Riverine Applications[END_REF]. It has been shown that this decay scales with the cube of the distance between the tag and the reader, whatever the medium (air, water, gravel, sand). However, the orientation of the tag modulates the signal received by the reader, and this strongly limits the application of the decay law for tracers buried in alluvial deposits for which the orientation is variable and unknown. To fix this problem, it has been recently proposed to use a rotating weighted inner ball, which maintains the vertical orientation of a 12 mm PIT tags into stones. It is known as the Wobblestone device (Papangelakis et al., 2019b;[START_REF] Cain | Field tests of an improved sediment tracer including nonintrusive measurement of burial depth[END_REF]. During its first field-testing, this innovative device allowed to measure the burial depth of tracers with an accuracy of around 2 cm, down to a burial depth of 15 cm [START_REF] Cain | Field tests of an improved sediment tracer including nonintrusive measurement of burial depth[END_REF]. That confirmed the great potential of this new RFID tracing device.

A third key challenge of RFID bedload tracing is to reduce the size of tags. This will allow surveying the displacement of the fine tails of bedload grain-size distribution (<15-20 mm), for which tracking solutions are still lacking today. The smallest tags that have been used up to date in bedload transport studies are the 12 mm glass-encapsulated PIT tags. However those tags have not been so much used because their detection range is strongly reduced [START_REF] Chapuis | Detection ranges and uncertainty of passive Radio Frequency Identification (RFID) transponders for sediment tracking in gravel rivers and coastal environments: DETECTION RANGES AND UNCERTAINTY OF PASSIVE RFID TRANSPONDERS[END_REF]. Any progress on the miniaturization of RFID tags will have therefore direct applications in bedload tracing. In this regards, tiny tags with a size of 1-4 mm 2 (e.g., models Murata LXMSJZNCMF-210, Hitachi PK2020B) are used today to track insects at 1-3 cm distance from the reader [START_REF] Nunes-Silva | Applications of RFID technology on the study of bees[END_REF]. Research is investigating smaller tags with 0.03mm 2 size [START_REF] Bhanushali | A 125 μ m × 245 μ m Mainly Digital UHF EPC Gen2 Compatible RFID Tag in 55 nm CMOS Process[END_REF], and even intracellular tags [START_REF] Yang | Intracellular detection and communication of a wireless chip in cell[END_REF]). Yet, smaller tags generally means smaller reading range, therefore smaller recovery rate. The strong challenge is that such smaller tags afford long-enough reading range. The last key challenge is to reduce the time required for detecting the tags. Indeed, manual surveys of pebble and cobble tracers are time consuming, requiring a compromise between the area surveyed and the spatial density of the surveying directly linked to the recovery rate. Only one study could survey a large area (80 000 m 2 ) every month, with very good recovery rate (70-80% after 3 years), using a thousand passive tags [START_REF] Dolphin | Velocity of RFID Tagged Gravel in a Non-uniform Longshore Transport System[END_REF]. To reach this performance, they used a quad with an array of detectors to scan a large area densely in a limited time. This rationalization of the surveying procedures can be a key enabler for the emergence of professional services of coarse sediments tracking.

Tags under soil, water, snow or vegetation

General sources of signal loss

Tags placed outdoors may be covered by soil, water, snow or vegetation. These media can influence both antenna parameters and wave propagation, leading to a deterioration of the reading range or loss of localization accuracy (Fig. 25). Four main causes are identified: the attenuation of the wave within the medium, the partial reflection at the air-medium interface, the near-field coupling of the tag antenna with the medium (Fig. 26). Depending on the context, multipath effect may also have to be considered. These losses occur twice, on forward and backward communication. For the attenuation, it is well established that the propagation of radiofrequency signals is very sensitive to the geometric and physical characteristics of the propagation medium. Its effect also depends strongly on operating frequency. The radiofrequency waves penetrating the ground undergo a very significant attenuation. As a rule of thumb, the penetration depth is inversely proportional to the square root of the product of the wave frequency and on the medium conductivity; the conductivity itself often depends on the liquid water content and salinity of the medium. Therefore the performances of RFID technologies will depend on the application environment and the RFID frequency (from 125 kHz to 2.4 GHz), as discussed in [START_REF] Dziadak | Model for the 3D Location of Buried Assets Based on RFID Technology[END_REF][START_REF] Kumar | A model for RFID-based 3D location of buried assets[END_REF]. Numerous examples in the literature are dedicated to the implementation of RFID solutions in severe environment from an electromagnetic point of view.

Additionally, a wave will reflect a part of its energy when at the interface between the air and the medium. The resulting reflection coefficient R depends on the dielectric permittivity of the medium, on the angle of incidence and on the wave polarization (see [START_REF] Balanis | Advanced Engineering Electromagnetics[END_REF], 5.3). The transmission coefficient ranges from 0.2 to ~1 (water to air) under the most favorable normal incidence, and decreases towards zero at parallel incidence. The reflection coefficient is minimized either for normal incidence, but also around the Brewster angle which occurs in general on vertical polarization (more exactly, on the component of the wave polarization that is perpendicular to the interface). Therefore, at least three strategies can be used to reduce the loss: normal incidence (i.e., interrogation from a UAV), Brewster's angle, or installing also the station antenna into the medium to avoid crossing the interface.

The coupling of the tag antenna at the proximity of the medium affects the antenna behavior in several ways. On the one hand, the medium will detune the antenna, leading to a shift of its resonant frequency, resulting in a power loss. On the other hand, the propagation loss in the medium will also decrease the amplitude of the antenna resonance. Finally, the radiation pattern and efficiency of the antenna may also be affected when placed at the proximity of an interface.

Also, multipath and scattering may influence the wave propagation, even for a tag placed above the medium. A change in the medium, for example due to vegetation growth, or to the formation of a snow layer on the ground, can then result in a modification of the phase or signal strength. That may lead to inaccuracy of localization, antenna-based sensing of propagation-based sensing. In the following sections, we study the influence of these media caused by the effects described hereinbefore.

Buried tags

Buried wireless sensor networks [START_REF] Akyildiz | Wireless underground sensor networks: Research challenges[END_REF][START_REF] Akyildiz | Signal propagation techniques for wireless underground communication networks[END_REF] offer many potential applications for soil sensing, such as detecting pipes and leakages [START_REF] Sun | MISE-PIPE: Magnetic induction-based wireless sensor networks for underground pipeline monitoring[END_REF][START_REF] Sadeghioon | SmartPipes: Smart Wireless Sensor Networks for Leak Detection in Water Pipelines[END_REF]Raza and Salam, 2020a). However it undergoes severe bottlenecks to maintain the sensors communication and power (Raza and Salam, 2020b;[START_REF] Salam | Signals in the Soil: Developments in Internet of Underground Things[END_REF]. In particular, as a long lifetime is required for this application (more than a decade), passive and robust devices offer more efficient solutions compared to battery-powered devices. Besides, batteries can be a source of soil contamination due to the chemical substances that compose the battery. Therefore, remotely powered devices, such as passive RFID tags, are preferred. Buried RFID tags were applied for sensing soil temperature and moisture [START_REF] Deng | Novel soil environment monitoring system based on RFID sensor and LoRa[END_REF][START_REF] Luvisi | RFID temperature sensors for monitoring soil solarization with biodegradable films[END_REF], and could be useful for monitoring soil in agriculture, for hydrological monitoring, or to detect leakages in buried networks.

However, the high permittivity (up to ε=20) and conductivity of the soil are challenging for reading tags. The permittivity contrast causes antenna detuning and wave reflection at the air interface, and the conductivity causes attenuation. Both greatly reduce the depth at which buried tags can be read, particularly at UHF. Examples of different passive tags and their performances are shown on Table 6. The attenuation by the soil at UHF, observed both with buried transmitters [START_REF] Bogena | Hybrid Wireless Underground Sensor Networks: Quantification of Signal Attenuation in Soil[END_REF]) and buried RFID tags [START_REF] Elboushi | Electromagnetic Soil Characterization for Undergrounded RFID System Implementation[END_REF], depends mainly on three physical parameters of the soil: its moisture, the type of porous media, and the conductivity of the pore water. In soils with 0-30% moisture, UHF tags could be interrogated down to depth of 0.6 m [START_REF] Deng | Novel soil environment monitoring system based on RFID sensor and LoRa[END_REF]. Low-frequency tags allow potentially larger ground penetration depths, because the attenuation of the soil increases with the frequency. Low-frequency tags serve to track underground pebbles in river beds (see section 2), but also to mark buried construction assets (3M EMS) or metallic pipes [START_REF] Vyas | A Sequential RFID System for Robust Communication with Underground Carbon Steel Pipes in Oil and Gas Applications[END_REF][START_REF] Zarifi | Wireless passive RFID sensor for pipeline integrity monitoring[END_REF]. The loss due to antenna detuning is caused by a shift of the resonant frequency of the tag antenna, which can strongly reduce the performance of the tag. To mitigate this effect, it was proposed to pretune the tag antenna to the permittivity of a dry soil, instead of air [START_REF] Alonso | UHF passive RFID-based sensor-less system to detect humidity for irrigation monitoring[END_REF]. However, the soil permittivity, and therefore the tag tuning, fluctuates considerably depending on the soil type and moisture. To adapt to the different soil properties, it is proposed to use wide-band buried antennas (Mondal et al., 2018b). However, using a wide-band antenna supposes nonconductive soil, which is very rare in practice. Another solution is to sweep the emitted RFID signal frequency to communicate at the tuning frequency of the tag [START_REF] Salam | A Theoretical Model of Underground Dipole Antennas for Communications in Internet of Underground Things[END_REF]. However regulation and commercial readers prevent this. Alternatively, improvement might be attained by deploying multiple tags each pre-tuned to a given permittivity, or self-tuning tags that adapts to strong changes of medium.

The contrast of permittivity at the air-soil interface strongly increases the reflection coefficient and decreases the transmission coefficient (Mondal et al., 2018a). It is important to note that the impact of the reflection coefficient counts twice: a first time for the interrogation signal from a reader and a second time for the backscatter signal from the tag. The strong reflection coefficient can pose challenges not only for the propagation within the soil but for the tag antenna and the radiofrequency electronics. As a rule of thumb, besides the degradation of reflection coefficients, the global transmission coefficient is harder to optimize and to monitor in general. Theoretically, the reflection coefficient can be minimized by interrogating with a vertical angle of incidence, or with an angle that has a total transmission (the Brewster angle, which occurs only at a vertical polarization). But in practice, the implementation of such a technique remains hazardous and very complex to exploit. Alternatively, the negative impact of reflection at the ground-air interface may be reduced by using harmonic tags. Harmonic tags generate a response signal at the double frequency of the incoming carrier signal, which the reader can more easily distinguish from the ground reflection [START_REF] Abdelnour | Passive Harmonic RFID System for Buried Assets Localization[END_REF] (Fig. 27).

Additionally, any technique that increases the read range in the air (see 6.3), such as tags with highgain antennas [START_REF] Chang | Search, Identification and Positioning of the Underground Manhole with RFID Ground Tag[END_REF][START_REF] Hautcoeur | High gain RFID tag antenna for the underground localization applications at 915 MHz band[END_REF], should also increase the reading depth of buried tags.

Underwater tags

RFID, just like other radiofrequency technologies, is very sensitive to the high dielectric constant and ionic conductivity of water, which affects radiofrequency communication [START_REF] Che | Re-evaluation of RF electromagnetic communication in underwater sensor networks[END_REF]. While water is not a natural conductor, the presence of dissolved impurities and salts transforms water into a partial conductor, which attenuates the propagated wave. From an electromagnetic point of view, two main parameters characterize water: its relative dielectric permittivity, typically 80, and its conductivity that depends on the impurities (salinity in particular). For pure water, which can be considered as an ideal lossless medium (conductivity of the order of 10 -4 S/m), the propagation of electromagnetic signals is possible. However, there are three major sources of loss. First, the interface between air and water results in huge power reflection coefficients as high as 64%; it corresponds to a round trip air-water-air transmission penalty of 9 dB. Second, propagation of a distance equal to penetration depth does result roughly in a 9 dB attenuation. So, considering a link of length equal to the penetration depth, the penalty due to water attenuation and air-water interface reflection is approaching 20 dB. From Table 7 one can conclude that only LF and HF frequencies can be considered for the implementation of RFID in water environment (Y. Li et al., 2019). Third, antennas immersed under water will see a change in tuning frequency. Therefore underwater tags, particularly at UHF, would require an antenna that is specifically optimized for water (e.g., [START_REF] Sohrab | A UHF RFID Tag With Improved Performance on Liquid Bottles[END_REF].

The wave frequency is the main factor that impacts the link budget and the global performance. At Extremely Low Frequency (3-300 Hz), radio signals can travel in sea water over hundreds of meters. At Very Low Frequency (3-30 kHz), radio signals can penetrate sea water up to 20 meters. While these two frequency bands exhibit comfortable communication distances, they present severe technical limitations and practical challenges. In particular, their extremely long wavelengths require very bulky antennas of very large dimensions. Also, because of the narrow bandwidth, these frequencies can be used to transmit at very slow data rates only. Now considering the standardized RFID frequency bands, depending on quality of water, in particular its conductivity, the penetration depth of radio waves can be evaluated for different categories of waters and data are reported in Table 7. snow, and <1 m just below the surface at an oblique angle. In practice, other influences on the reading range, such as the multipath interferences, the 3D heterogeneity of the liquid water content and the presence of impurities in the snowpack, may require to run a more detailed estimation.

The formation of ice layers on tags also influences the RFID signal, affecting both the phase and the signal strength, as observed by Le Breton et al. (2019) and quantified by (Wagih and Shi, 2021a). Two phenomena are identified. First, the ice layer forms progressively, typically during the night. Given the low permittivity of ice, it influences the phase and deteriorates the signal strength only moderately, and progressively. Then heat provided by solar radiation in the morning can rapidly melt this layer resulting in a strong loss of RSSI and increase of phase delay, temporarily until the water evaporates.

Vegetated environments

Radiofrequency methods have the advantage of transmitting across vegetation cover, as compared to remote optical sensors such as LIDAR, tacheometers or cameras. It could be useful to use RFID tags to collect sensor data across vegetation, for example on a vegetated landslide or an agriculture field. Le Breton (2019) shows that reading RFID tags across a high-grass vegetation can slow the radiofrequency wave by 0.1 -1% and amenuate it by 0.2 -1 dB/m. These numbers strongly depend on the density and on the moisture of the vegetal cover. The interrogation and the localization of RFID tags across a few meters of vegetal cover are therefore almost not disturbed. However, the communication could decrease the performances more significantly across tens of meters, both in terms of reading range and localization accuracy. This influence could potentially be exploited for sensing vegetation properties using RFID. Indeed, monitoring the slowness and attenuation of the radiofrequency wave might provide a rough estimate of the grass moisture content (Le Breton, 2019). Monitoring large areas, of several hundreds of meters long, would particularly benefit from a dense network of low-cost sensors. The reading range, of around 50 m in recent outdoor implementations (Le Breton et al., 2019), is increasing with the improvement in sensitivity of the RFID devices. Indeed, one of the essential properties of RFID chips is their sensitivity to radiofrequency signals which corresponds to the minimum power necessary to activate the RFID chip and therefore allow the communication with an RFID reader. Modern passive RFID chips require less than -22 dBm to be activated, resulting in a 25 m read range under the strict compliance with radiofrequency emission standards (European ETSI or North American FCC). The evolution of this sensitivity and the corresponding read range since the beginning of the 21st century is summarized in Fig. 29. Today, offthe-shelf readers with the same antenna for emission and reception (called monostatic) have typically sensitivities of -80 dBm to -95 dBm. The sensitivity of batteryless integrated circuits are typically -7 to -16 dBm with sensing capabilities that consume additional power, down to -23 dBm for standard passive tags, and down to -35 dBm when adding a battery assistance. In this last case, the chip is no longer the limiting factor for the reading range, and the limit then comes from the reader. The sensitivity tends to decrease with time, both for the tags [START_REF] Nikitin | UHF RFID tag characterization: overview and stateof-the-art[END_REF][START_REF] Durgin | RF thermoelectric generation for passive RFID[END_REF] and for the readers, which will probably lead to increases in reading range in the future. Recently, research has reached reading ranges of several hundreds of meters using passive tags [START_REF] Durgin | RF thermoelectric generation for passive RFID[END_REF][START_REF] Amato | RFID Backscattering in Long-Range Scenarios[END_REF][START_REF] Qi | Breaking the Range Limit of RFID Localization: Phase-based Positioning with Tunneling Tags[END_REF], while remaining in the legislation limits, thanks to original microelectronic designs. Work still has to be performed to adapt these experimental tags to standard RFID interrogators. Another way to increase the reading range is to develop tags using directive antennas (e.g., Lin et al., 2016;[START_REF] Zuffanelli | A High-Gain Passive UHF-RFID Tag with Increased Read Range[END_REF]. Such tag antennas do not fit to the majority of the RFID use cases: they can be read only from specific directions, and they are larger and more expensive than with the typical small tags. But directive tag antennas make the monitoring of a large zone, such as a landslide, much more practical, by increasing the reading range.

Read range, duration and cost of the tags

The duration of sensing tags can be a challenge for monitoring over years to decades. The limit concerns the tags with advanced capabilities which need battery assistance, and the robustness of the tags over time. Battery assistance is often used today, to enable advanced capabilities of tags, such as permanent monitoring or longer reading range. However, batteries can sometimes be problematic in areas where maintenance is difficult. Energy harvesting could eliminate the need for battery assistance in most sensing tags, by converting small amounts of energy available in the environment into a usable electrical form [START_REF] Ferdous | Renewable energy harvesting for wireless sensors using passive RFID tag technology: A review[END_REF]. For example, energy harvesting was implemented on tags using photovoltaic cells (Abanob Abdelnour et al., 2019;[START_REF] Kantareddy | Perovskite PV-Powered RFID: Enabling Low-Cost Self-Powered IoT Sensors[END_REF][START_REF] Kantareddy | Long range battery-less PV-powered RFID tag sensors[END_REF][START_REF] Kantareddy | Long range battery-less PV-powered RFID tag sensors[END_REF][START_REF] Valentine | Design of Solar Harvested Semi Active RFID Transponder with Supercapacitor Storage[END_REF], electrochemical reactions [START_REF] Kantareddy | UHF RFID tag IC power mode switching for wireless sensing of resistive and electrochemical transduction modalities[END_REF], vibrations [START_REF] Lu | Performance Evaluation of a Long-Range RFID Tag Powered by a Vibration Energy Harvester[END_REF], heat dissipation [START_REF] Jauregi | UHF RFID Temperature Sensor Assisted With Body-Heat Dissipation Energy Harvesting[END_REF] or radiofrequency wave field [START_REF] Allane | Harmonic Power Harvesting System for Passive RFID Sensor Tags[END_REF][START_REF] Deng | Novel soil environment monitoring system based on RFID sensor and LoRa[END_REF][START_REF] Zhu | Passive Digital Sensing Method and Its Implementation on Passive RFID Temperature Sensors[END_REF]. Developments in energy harvesting could allow for monitoring earth processes with advanced sensing tags for decades. The second challenge related to duration concerns the long-term robustness of RFID tags outdoors. The longest deployment reported in the literature was six years under a bridge, and showed no apparent degradation [START_REF] Watkins | RFID Instrumentation in a Field Application[END_REF]. Accelerated aging was also studied with cycles of high temperature [START_REF] Taoufik | Fiabilité et analyse de défaillance des tags RFID UHF passifs sous contraintes environnementales sévères[END_REF] or changing humidity [START_REF] Saarinen | Reliability Analysis of RFID Tags in Changing Humid Environment[END_REF]. Low-cost tags were rapidly damaged by this accelerated aging, but rugged industrial tags were designed to resist them. In our experience, rugged tags have worked well since their installation over at least four years (2017-2020), on a harsh mountainous environment.

The low cost per tag is a key advantage of the RFID technology, particularly on price-sensitive applications (e.g., soil moisture for agriculture). Yet, the cost per tag can increase significantly on applications that require a specific new tag. Indeed, developing a new sensing tag requires to design a tag and to set up a production [START_REF] Węglarski | Factors Affecting the Synthesis of Autonomous Sensors with RFID Interface[END_REF]. This initial investment could strongly increase the cost per tag with small volumes of production (e.g., thousands of tags). In this case, it is preferable to use a sensing approach that works with existing commercial tags. For example, localization and propagation-based sensing exploit physical measurement principles that are independent from the tag. Many antenna-based sensing can also use standard or self-tuning tags available on the market (e.g., J. Wang et al., 2020). Several industrial tags also embed temperature sensors (see Table 2). Finally, demonstration tags sold by producers of sensing chips (e.g., ASYGN, FARSENS) can sense a myriad of physical parameters. More sensing tags are also likely to be produced in the future as the RFID market continues its growth, for large outdoor applications such as structural health monitoring or precision agriculture [START_REF] Zhang | A Review of Passive RFID Tag Antenna-Based Sensors and Systems for Structural Health Monitoring Applications[END_REF][START_REF] Wasson | Integration of RFID and sensor in agriculture using IOT[END_REF]. As for today, we suggest using commercial off-the-shelf tags when possible.

UAVs to access harsh fields

Unmanned Aerial Vehicles (UAVs) are now commonly used for earth surface sensing [START_REF] Bemis | Ground-based and UAV-Based photogrammetry: A multi-scale, high-resolution mapping tool for structural geology and paleoseismology[END_REF]. They can monitor long-term changes in topography with optical methods [START_REF] Niethammer | UAV-based remote sensing of the Super-Sauze landslide: Evaluation and results[END_REF][START_REF] Casagli | Spaceborne, UAV and ground-based remote sensing techniques for landslide mapping, monitoring and early warning[END_REF][START_REF] Peppa | Brief communication: landslide motion from cross correlation of UAV-derived morphological attributes[END_REF] or synthetic aperture array radar [START_REF] Li | Synthetic aperture radar imaging using a small consumer drone[END_REF]. Passive targets are sometimes placed on the ground to improve the accuracy. Surveying the earth surface using UAVs proves particularly effective to monitor zones that are not accessible by land vehicles and difficult to access on foot, such as cliffs, landslides, steep hill slopes, non-wadable rivers, highly vegetated environments. They are also helpful when access is forbidden or dangerous, such as on volcanoes or minefields. A single UAV carrying the RFID reader antenna can replace a large series of interrogators fixed on the ground, thus reducing the operation cost (for time-lapse monitoring) and allowing deployment over very large areas. It may enable the exploitation of thousands of tags over wide areas. This section presents the latest research that uses UAVs to deploy, localize and collect data from sensor networks and passive RFID tags.

UAVs have been demonstrated to collect data from passive radiofrequency sensors on the deformation of a bridge, the humidity on a tree, and the soil moisture [START_REF] Mascareñas | Wireless Sensor Technologies for Monitoring Civil Structures 5[END_REF][START_REF] Wang | A New Vision for Smart Objects and the Internet of Things: Mobile Robots and Long-Range UHF RFID Sensor Tags[END_REF] (Fig. 30). To ensure a reliable reading with standard antennas and transmit power, the UAV might need to fly a few meters away from the ground at a maximum velocity of a few m/s [START_REF] Casati | The Interrogation Footprint of RFID-UAV: Electromagnetic Modeling and Experimentations[END_REF].

Localization of RFID tags could offer important advantages compared to widely used optical methods. Indeed, provided the UAV can fly with GPS-defined itinerary, it could work 24-24 h without sun under any meteorological conditions (except high-speed wind). Tags could also be read under a forest, under a dense vegetation, or under snow, allowing measurements of places where optical acquisition are difficult, if not impossible. The UAV-RFID method would also greatly reduce the size and processing time of the raw data.

Tags have first been localized from UAV with a metric accuracy using the signal amplitude measurement (RSSI) (PINC Inc., n.d.;[START_REF] Greco | Localization of RFID tags for environmental monitoring using UAV[END_REF]. Then, centimetric accuracy has been reached using synthetic aperture array radar with phase measurements [START_REF] Buffi | SARFID on drone: Drone-based UHF-RFID tag localization[END_REF][START_REF] Buffi | A SAR-Based Measurement Method for Passive-Tag Positioning With a Flying UHF-RFID Reader[END_REF][START_REF] Ma | Drone Relays for Battery-Free Networks[END_REF] which was presented in previous sections. Best accuracy was reached with increased synthetic aperture length, and with relative ranging of an array of tags. The bottleneck to reach higher accuracy is the localization of the UAV. Outdoors, [START_REF] Buffi | A SAR-Based Measurement Method for Passive-Tag Positioning With a Flying UHF-RFID Reader[END_REF] located the antenna carried by the UAV with a differential single-frequency GPS receiver, and estimated the error as +/-2 cm. Many solutions-out of the scope of this review-exist to localize UAV with higher accuracy, such as dualfrequency GPS, optical positioning or ultra-wide band local positioning. [START_REF] Zhao | PEC: Synthetic Aperture RFID Localization with Aperture Position Error Compensation[END_REF] proposed a joint optimization algorithm for localizing tags using SAR methodology while compensating for aperture position error. The localization of the UAV itself from a grid of reference tags has also been investigated, using the signal amplitude (Longhi et al., 2018b), and suggested with using the phase [START_REF] Ma | Drone Relays for Battery-Free Networks[END_REF][START_REF] Savochkin | Directed Antennas for RFID-Based Navigation for Agricultural Unmanned Vehicles[END_REF]. As a side usage, thanks to reciprocity, it might help to navigate UAVs in areas with bad GPS reception, such as on a cliff, steep mountainous slopes, river gorges, tunnels or caves. Finally, deployment of sensors over low-accessibility terrains could be facilitated with the combination of UAV and RFID. Indeed, active wireless sensors were deployed from UAV on low accessibility terrains (e.g., [START_REF] Corke | Autonomous deployment and repair of a sensor network using an unmanned aerial vehicle[END_REF][START_REF] Bernard | Autonomous transportation and deployment with aerial robots for search and rescue missions[END_REF], to measure air conditions [START_REF] Martínez-De Dios | Combining Unmanned Aerial Systems and Sensor Networks for Earth Observation[END_REF][START_REF] Melita | Measurement and Exploration in Volcanic Environments[END_REF], seismic vibrations [START_REF] Sudarshan | A Heterogeneous Robotics Team for Large-Scale Seismic Sensing[END_REF][START_REF] Lys | METIS, a disruptive R&D project to revolutionize land seismic acquisition[END_REF] and underground humidity (Sun et al., 2018) (Fig. 31a, b). However to recharging or replacing their battery would also require a UAV which is challenging [START_REF] Bertoldo | Recharging RFID Tags for Environmental Monitoring Using UAVs: A Feasibility Analysis[END_REF][START_REF] Sheu | Design and Implementation of Mobile Robot for Nodes Replacement in Wireless Sensor Networks[END_REF]. Deploying passive RFID tags instead would present many advantages: they are low-cost, light to carry, and batteryless [START_REF] Luvisi | RFID temperature sensors for monitoring soil solarization with biodegradable films[END_REF]. Recently, UAVs were tested to deploy sensing tags high above the ground or on a water body [START_REF] Wang | A New Vision for Smart Objects and the Internet of Things: Mobile Robots and Long-Range UHF RFID Sensor Tags[END_REF]. Due to their very light weight, tags can be attached permanently on a microdrone and read from the ground (Fig. 31d). In this case, the microdrone moves to the zone of measurement, stays on this zone for the time of the monitoring, then comes back (Longhi et al., 2018b(Longhi et al., , 2018a;;[START_REF] Longhi | Ubiquitous Flying Sensor Antennas: Radiofrequency Identification Meets Micro Drones[END_REF]. The small size and low cost of microdrones make it less risky to deploy in areas with little space and high risk of a crash or collision.

Overall, UAVs could prove very useful to deploy RFID tags and collect data, over dangerous and low-accessibility terrains, such as cliffs, near-to-collapse unstable slopes, highly vegetated areas, minefields, highly contaminated sites, volcanoes, or water bodies. Multiple studies demonstrated the ability to collect sensing data and to localize tags with accuracy smaller than 10 cm, by carrying an RFID reader under a UAV. The ability for deployment is still challenging, but deploying light RFID tags should be easier than heavier and more costly active wireless sensors.

Sustainability and green RFID

The production and operation of large numbers of RFID sensors will generate electronic waste and consume energy. This challenge is tackled within the Green Internet of Things concept, which aims at reducing the waste and energy consumption of wireless sensing systems [START_REF] Zhu | Green Internet of Things for Smart World[END_REF]. RFID and WSNs are considered among the most promising wireless systems to enable Green Internet of Things [START_REF] Shaikh | Enabling Technologies for Green Internet of Things[END_REF][START_REF] Khan | Analysis of Green IoT[END_REF]. RFID tags specifically are produced with little material and energy, and their ability for batteryless sensing is essential to increase sensor lifespan and reduces toxic wastes.

Yet, RFID tags generate electronic wastes [START_REF] Condemi | Circular Economy and E-Waste: An Opportunity from RFID TAGs[END_REF][START_REF] Bukova | A Model of the Environmental Burden of RFID Technology in the Slovak Republic[END_REF] that are difficult to recycle [START_REF] Aliaga | Influence of RFID tags on recyclability of plastic packaging[END_REF]. The waste is reduced with smaller tags that use less polluting material [START_REF] Voipio | Environmental RFID: measuring the relevance in the fashion industry[END_REF] or even biodegradable materials, and by avoiding battery assistance. However, the tags deployed outdoors for years may oppose those guidelines. Indeed, the tags need a rugged protective casing to withstand harsh outdoor conditions. The tags may also require large antennas to increase the read range. Finally, they may need a battery assistance for sensing or for increasing the read range even further. Avoiding the battery assistance would both increase the tag's lifespan and reduce its toxic wastes: it appears as a priority to enable sustainable RFID applications. Removing batteries may require to reduce the energy consumption of the sensing tag and to increase the chip sensitivity, or otherwise to embed a complementary energy harvesting module [START_REF] Ferdous | Renewable energy harvesting for wireless sensors using passive RFID tag technology: A review[END_REF]. Finally, moving the reader close to the tag-for example using a UAV-also increases the energy received by the tag, allowing for smaller and batteryless tags.

In an RFID system, the reader consumes most of the energy and also generates electronic wastes. That is particularly true for permanent reading station installed on the field outdoors, that may need heavyweight batteries and powering through solar panels or wind turbines. In this case, reducing the reader power consumption strongly reduces the environmental cost of the infrastructure. The power can be reduced simply by alternating acquisition with sleeping time or by reducing the power emitted. In the future, more efficient communication protocols between the reader and tag should also reduce this energy consumption [START_REF] Su | Energy Efficient Tag Identification Algorithms For RFID: Survey, Motivation And New Design[END_REF]. Finally, using a mobile reader would again solve this issue, by avoiding to install a permanent infrastructure on each monitored site.

Today, RFID is among the most promising technology to enable green Internet of Things: tags generate much less waste than other wireless sensors. RFID systems with mobile readers or mobile tags should allow for sustainable and low-cost monitoring installations. The low cost of tags and data collection should allow to obtain data from new places where standard instrumentation was too costly. The efficiency and saving allowed by this data, such as predictive maintenance or risk reduction, is likely to overcome the environmental cost of the RFID system itself [START_REF] Bose | The Green Potential of RFID Projects: A Case-Based Analysis[END_REF][START_REF] Nasiri | Developing Disruptive Innovations for Sustainability: A Review on Impact of Internet of Things (IOT)[END_REF][START_REF] Gladysz | A Method for an Integrated Sustainability Assessment of RFID Technology[END_REF].

Conclusions

RFID research develops the ability to use passive tags as sensors or location trackers. This opens many opportunities to monitor the dynamics of the earth surface with dense networks of low-cost and long-lasting tags.

As for now, the identification function of passive RFID has been used widely for the monitoring of sediment transport in rivers (more than sixty studies) and on coasts (fifteen studies). In these studies, low-frequency tags are inserted in sediments of various sizes (from centimeters to meters long) and localized with readers from distances below 1 m. In general, passive RFID offers higher efficiency in terms of cost and operation compared with other methods (e.g., visual, radioactive, magnetic). This enables longer studies (several years), with more tracers (up to thousands), that are tracked individually. The better information allows understanding more finely the natural processes observed.

The augmented RFID technologies-localization and sensing-are more recent; their usage is now emerging in earth science. The centimetric localization of ultra-high frequency tags allowed for continuous displacement monitoring on three landslides, during years. The detection of millimetric displacement thresholds have been deployed to monitor unstable rocks on a cliff. The monitoring of underground temperature with buried tags was used to monitor the efficiency of solarization processes used in agriculture. Monitoring the phase delay on tags on the ground allowed to estimate the increase of snow water equivalent caused by dry snowfall events during a whole winter. Finally, soil moisture sensors have been deployed both in industrial greenhouse to monitor the moisture of plant pots, and outdoors using buried tags.

The variety of deployment topologies across the studies confirms that passive RFID is enabling new possibilities for wireless sensing. Surveys for sediment tracers detection were firstly operated with handheld readers carried by a human operator. More efficient surveying methods have now been developed, using unmanned aerial vehicles or land vehicles. They provide more data (larger areas, better recovery rates, more frequent surveying) which again helps to better understand the monitored processes. As an alternative to surveying, several studies deployed fixed readers monitoring infrastructure. Continuous monitoring can improve the understanding of natural processes that are time sensitive (e.g., step-by-step sediment displacement or landslide reactivation). It also opens the door for operation surveillance applications (e.g., rockfall warning). However, the reader infrastructure is more expensive than the tags, it requires maintenance, and it has limitations in the monitored area (tag-reader distance below 100 m).

Many other RFID sensing techniques have been developed and tested in the laboratory, and could be adapted to a real field application, to measure soil indicators with high spatial density over several years. On landslides, tags may monitor soil moisture and tilt. On rocky structures, tags could monitor cracks, vibration modes and vibration intensity. Tags may also monitor changes in harsh areas such as the cryosphere, volcanoes, densely vegetated areas, or shallow underground.

The low-cost, low maintenance, small size and long duration characteristics of passive wireless tags should make them effective compared to active wireless sensors, in several scenarios: mostly, pricesensitive applications that use large amounts of sensors (e.g., soil moisture); monitored objects that need small, flexible or little-intrusive devices (e.g., small pebbles, or snow); monitoring during decades; environmental concerns that require minimal waste (e.g., to avoid leaving batteries in rivers); areas with difficult access for maintenance (e.g., underground); sensors that are likely to be lost (e.g., on a landslide, on a river, in the soil); measurement that exploits the phase delay (e.g., snowpack monitoring over its volume, or localization).

The drawbacks are mainly the reading range and the sensing accuracy. Reading range reaches an order of magnitude of 1 m with low-frequency tags, 10 m with ultra-high frequency batteryless sensing tags and up to 100 m with battery-assisted tags. Fortunately, the reading range is constantly growing with technological improvements. Furthermore, the tag localization or sensor data collection by a mobile reader (including unmanned aerial vehicles) should allow to cover larger areas. The accuracy of RFID sensing tags, often lower than conventional instruments, can also limit the potential applications. The accuracy is particularly challenging on batteryless tags with dedicated sensors (due to the little energy available to power the acquisition electronics) and on antenna-based sensors (due to the combination of many influences).

In perspective, many RFID sensing and localization techniques may be mature enough to deploy comprehensive "real-world" applications, reusing and assembling concepts previously demonstrated in the laboratory. That has recently led to new applications, for monitoring landslide displacements, soil moisture, soil temperature, and snow water equivalent. Furthermore, future deployments may grow from tens to thousands of RFID sensors, similarly to the earlier growth in the application of fluvial and coastal sediment tracking. This will fully exploit the low-cost advantage of passive RFID, as compared to wireless sensor networks or manual measurements. Finally, new concepts of propagation-based RFID sensing remain to be developed and demonstrated, with the advantage that they can use any commercially available tag. These concepts could particularly work outdoors where multipathing is less challenging than indoors. Overall, the development of RFID for geoscience instrumentation will require interdisciplinary collaborations between research laboratories and private companies, and between various scientific domains (from electronics and applied mathematics to bio/geosciences) and should create many opportunities for technological innovations.

Fig. 1 :

 1 Fig. 1: When receiving an incident radiofrequency signal, Ultra-High Frequency (UHF) tags send information to the reader by switching the impedance of the RFID chip between two states: Zmatch which maximizes the absorbed power (necessary to power up the chip) and Zreflect which maximizes the reflected power (In practice, both states reflect and absorb something). Resulting alternance of backscattered power level is interpreted by the reader as a digital signal. The performance of the RFID chip depends both on its ability to power-up using the absorbed power, and on the reader's ability to distinguish between the reflected signals of the two states.

Fig. 2 :

 2 Fig. 2: Main types of passive RFID tags, working by magnetic coupling at low-frequency (LF) and high frequency (HF), or by backscattered wave propagation (UHF). Turns refer to the number of loops of the conductor for the antenna. From (Dobkin, 2008)

Fig. 3 :

 3 Fig. 3: Number of publications listed in Web of Science for RFID sensing and RFID localization. The search uses the Expanded and Emerging Citation Index, with the words [RFID+Radio frequency identification] combined with [Sensing+Sensor] or [Localization+Location+Positionning] in the title and abstract. Inspired from (Zhang et al., 2017)

  application that are very sensitive to the cost per sensor (material and long-term maintenance) should particularly benefit from passive RFID over WSNs.

Fig. 4 :

 4 Fig. 4: (a-d) Schematics ways to collect data from sensors in geosciences. Fixed monitoring infrastructure allows for continuous data, whereas mobile data collection allows for lightweight infrastructure. The blue square represents an autonomous acquisition system that stores the data or communicates it to a server over remote network (3G, Sigfox…). The sensing units are either dependent to the central acquisition device (small blue round) or autonomous in terms of data acquisition, communication, and powering (larger blue rounds). The communication is either wired or wireless communication link. The main deployment used for monitoring of earth surface processes today consists in (a) wired sensors connected to an acquisition system; (b) wireless sensor networks of autonomous nodes which communicate wirelessly one to another locally up the main node; (c) remote sensing units, either mobile or fixed, that acquire data from the environment reflections or from passive reflectors; (d) manual collection of data measured with non-communicating sensors (installed permanently or carried by an operator) that need a human operator. RFID systems allow for new deployment possibilities, with dense arrays of low-cost wireless tags and rapid data collection. RFID adds two new topologies: (e) a fixed reader which reads passive tags continuously within its read range, or (f) a mobile reader which collects data over a larger area with lightweight infrastructure that consist only in the passive tags.

Fig. 5 :

 5 Fig. 5: Overview of the RFID functions and their application to monitor earth surface dynamics, with the period of appearance in research.

Fig. 6 :

 6 Fig. 6: Schematic description of the Phase-Difference of Arrival measurement principle. A displacement ⃗ between two positions and implies a variation in the phase measurement from to linked to the radial displacement . From (Nikitin et al., 2010; Le Breton et al., 2019)

Fig. 7 :

 7 Fig. 7: Schematic of a uniform linear array consisting of 2 tags separated by a known distance . The difference between the measured phases and is related to the array angular orientation , also called tilt. This approach is used for localization and tilt measurements. Inspired from (Nikitin et al., 2010)

Fig

  Fig. 8: Principle of the Synthetic Aperture Radar localization approach. A series of phase measurements is performed along a given reader trajectory, with known displacements between consecutive measurements. This approach assumes a constant tag position. Inspired from (Wu et al., 2019)

Fig. 9 :

 9 Fig. 9: (a) Tracer gravel equipped with a glass-encapsulated low-frequency passive RFID tag and (b) mobile antenna used for the remote detection of RFID tracers in river channels. Adapted from (Liébault et al., 2012).

Fig. 10 :

 10 Fig. 10: Example of RFID tracer dispersion observed in a wandering gravelbed river during a 3-month period after which frontrunners have been located at more than 2 km from their seeding location: (A) dispersion map on hillshade view of a LIDAR digital terrain model; (B) zoom on the position of recovered RFID tags in the active channel, showing preferential deposition of tracers close to the wet channel which can be viewed as the active bedload transport zone during the investigated period; (C) picture showing the morphological configuration of the active channel (adapted from Liébault et al., 2012)

Fig. 11 :

 11 Fig. 11: An example of passive RFID tracer dispersion induced by a debris flow in a small steep-slope torrent of the French Alps (Peyronelle Torrent); the debris fan appears in green, and active channels in gray; the picture in panel B shows a debris-flow lobe deposited in a secondary channel, in which several RFID tracers were detected. From (Graff et al., 2018)

Fig. 12 .

 12 Fig. 12. Instrumentation of Pont-Bourquin landslide using RFID tags. (a) aerial view of the landslide, (b) picture of the instruments on the field, and (c) results of RFID measurements for one tag, validated by a wire extensometer and laser positioning. From Le Breton et al. (2019).

Fig. 13 :

 13 Fig. 13: Installation of RFID tags for measuring displacements of 2D on Harmalière landslide, with the cumulated displacement measured over 6 months indicated with an arrow, using a GPS as a reference. From (Charléty et al., 2022b).

Fig. 14 :

 14 Fig. 14: Example of a wireless sensor network, to monitor the tilt of boulders on a slope. Use of passive RFID tags can be envisioned on a similar use case. Modified from (Dini et al., 2020)

Fig. 15 :

 15 Fig. 15: (a) Propagation of a crack on a tag antenna. (b) The resonant frequency of the antenna, centered on UHF RFID frequencies, moves while the crack opens. (c) As a result, the normalized resonant frequency versus the crack size shows a potential accuracy <0.1 mm. From (Yi et al., 2013, 2015)

Fig. 17 :

 17 Fig. 17: (a) Experiment on a tag with an accelerometer, to measure the vibrations of a cantilever beam. (b) Transient acceleration measured after removing the load at t=19.5s, showing a resonance at 6 Hz. From (Jayawardana et al., 2016, 2019)

Fig

  Fig. 18: (left) 1-bit motion detector attached to a tag and (top right) records by a dedicated vertical accelerometer and (lower right) by the 1bit RFID sensor. From (Occhiuzzi et al., 2010)

Fig. 20 :

 20 Fig. 20: Examples of soil moisture sensor tags. (a) Dedicated capacitive sensor (Pichorim et al., 2018); (b) antenna-based sensing based on activation power using reference tags (J. Wang et al., 2020); and (c) timedomain transmissiometry using a microcontroller and an energy harvesting module (Fonseca et al., 2018)

Fig. 21 :

 21 Fig. 21: (a) Buried temperature data logging RFID tag, allowing (b) a comparison of air and soil temperature. In this example, the data allows detecting the rupture of a thermal solarization film that was keeping the soil hotter than the air. Modified from (Luvisi et al., 2016).

Fig. 22 :

 22 Fig. 22: (a) Installation of a permanent RFID reader outdoors, interrogating a tag placed just above ground and covered by snow (b) result of the SWE estimated with RFID over a full winter on Col de Porte instrument testing, compared with snow pits and cosmic ray measurements of the SWE. From Le Breton et al., 2022

Fig. 23 :

 23 Fig. 23: Example of using the anti-collision property of active UHF RFID tags to survey the active layer (blue arrows) of river channels during flow events, by inserting columns of RFID tags in the alluvial bed; RFID columns are used to detect the maximum bed scouring level, and the cross-section topographic resurvey is used to measure the thickness of sediment deposition; RFID columns have been made by superposing RFID tags and pieces of wood visible at the inset A; the May 2016 cross-section has been surveyed during the RFID column deployment; those data have been obtained in a very active braided river channel (Vénéon River, inset B), with an active layer thickness of around 1-m on the right bank during the investigated period (adapted from Brousse et al., 2018)

Fig. 24 :

 24 Fig. 24: Tracking of sediments from (a) large boulders to (b) smallest gravels. Today, the tracking of smallest sediments is limited by the size of the tag. Modified from (Hastewell et al., 2020; Curtiss et al., 2009)

(

  left) Array of four RFID readers carried by a quad which increases the velocity of surveying to 4m 2 / second. (right) The frequent surveying showed a recovery rate that oscillates yearly, with a decreasing trend. From (Dolphin et al., 2016)

Fig. 25 :

 25 Fig. 25: Major sources of signal loss identified when a tag is under a different medium than air

Fig. 26 :

 26 Fig. 26: The main type of losses into a medium. (a) Loss due to reflection at an interface 1-R 2 , that accounts twice for backward and forward propagation; (b) bulk attenuation in a snow medium depending on its density and liquid water content; (c) change of tag antenna resonance with changing humidity in soil. From (Le Breton, 2019; Dey et al., 2019)

Fig. 27 :

 27 Fig. 27: (a) Block diagram of the proposed harmonic system and (b) an image of a typical installation, buried down to 50 cm below ground. From (Abdelnour et al., 2018)

Fig. 29 :

 29 Fig. 29: Evolution of purely passive RFID chip sensitivity (in dBm, right scale) and corresponding read range corresponding to the FCC (Federal Communication Commissions, USA) legislation using an ideal semi-dipole tag antenna (in meter, left scale). Adapted from (Nikitin et al., 2012; Durgin, 2016)

Fig. 30 :

 30 Fig. 30: Bridge monitoring with an interrogator (reader antenna) attached to a UAV that powers wirelessly a passive displacement sensor. From Mascareñas et al. (2008)

Table

Table 1 : Categories of RFID sensing approaches, and their characteristics

 1

		Dedicated sensor	Antenna-based	Propagation-based sensing
			sensing
	Sensing	-Temperature	-Moisture	-Human activity
	examples	-Displacement threshold	-Chemical	-Object vibration
		-Tilt	-Proximity	-Snow density
		... many more examples	-Temperature	-Vegetation moisture
	Data used	Value in chip memory	-Received Signal strength	-Received Signal strength
			-Phase	-Phase
			-Self-tuning code
	Physical	-Analog sensor	-Material near the tag	-Attenuation in medium
	principle	(embedded sensor,	which position or	-Phase delay
		embedded analog-to-digital	permittivity is changed.	-Multipath interferences
		converter, external	-Tag antenna alteration	-Depolarization, scattering
		microcontroller)	(shape, permittivity of
		-Digital sensor (serial bus,	substrate, actuator)
		contact pins)
	Measured	Sensor vicinity	Tag vicinity (0 -0.1 m)	Far field (0.1 -100 m)
	zone
	Specific	Few chips can use sensors;	The tag antenna often	The location of tags and
	Constraints	The sensor needs extra	needs adaptation.	reader must be adequate.
		power.
	Comments	Often the most accurate.	Often less accurate, can	Remote measurement over
		More expensive tags that	provide qualitative	large areas.
		consume more energy.	insights.	Can use any commercial tag.
			More suitable for
			threshold values

Table 2 : Selection of UHF RFID chips with extra sensing functionalities

 2

		Read			Input sensing interface
	RFID Chip (launch date)	Sensitivity (dBm) Passive / BAP	Built-in Sensors and data logging	Via	microcontrol	ler	Digital	Contact	Analog	Self-tuning
	Impinj MonzaX	-19			I 2 C
	(2012)
	NXP Ucode I2C	-18	-23		I 2 C
	(2011)
	SERMA PE3001	-6			SPI
	(2009)
	Fujitsu MB97R8110	-12		Logger 28 kbit	SPI		SPI	3 pin
	(2019)
	Farsens Rocky100	-14	-35	Temperature	SPI		SPI	5 pin
	(2017)
	EM-Micro EM4325	-7	-31	Temperature	SPI		SPI* 4 pin
	(2012)
	Axzon AZN50x (2022) -14	-22	Temperature;					2 pin
				Logger 4k values
	NXP Ucode GiM+ &	-18	-27						1 pin
	GiL+ (2010-2011)
	Asygn 3213	-12	-16	Temperature; strain;					12 bit
	(2020)	-13		Hall effect.
	AMS SL900A	-7	-15	Temperature; battery					10 bit
	(2013)			level; Logger 1 kbit.					s
									(×2)
	Axzon Magnus S3	-16		Temperature; Tag RSSI							9 bit
	(2015)
	Impinj Monza R6	-22									3 bit
	(2012)
	NXP Ucode 8	-23									2 bit

Table 3 : Technical indicators on studies that tracked coastal sediments using RFID tags.

 3

  , LIDAR

	Reference	Tags	Area	Recovery	Duration of	Survey	Transport	Comments
			(m 2 )	rate (%)	the survey	repetitions	max (m)
	(Gómez-Pazo et al., 2021)	80	2 500	50-75 39 mo	6	20 Large boulders
	(Hastewell et al., 2019, 2020)	104 30 000	91 19 mo	15	21 Large boulders
	(Dolphin et al., 2016)	940 81 387	> 70 36 mo	35	1300 Motorized survey
	(Han et al., 2016)	200	2 500	37 4 mo	2	27 Uses UHF tags
	(Ford, 2014)	382 20 000		4 6 mo	1	312
	(Bertoni et al., 2013)	145	2 000	90 24 h	2	19
		78	1 500	97 24 h	2	5
	(Bertoni et al., 2012; Benelli	102	1 500	52 2 mo	1	82 Underwater
	et al., 2012)
	(Dickson et al., 2011)	180 250 000	22 7 mo	8	2 500
	(Miller et al., 2011)	128	1 500	83 12h / 12 mo	9	70
	(Bertoni et al., 2010; Benelli	96	2 500	77 2 mo	1	50 Underwater
	et al., 2012)
	(Curtiss et al., 2009; Osborne	96	3 000	~ 80 14 mo	~30	> 50
	et al., 2011)
	(Allan et al., 2006)	400	5 000	18-66 20 mo	8	57

Table 4 : Examples of ultra-low power commercial MEMS accelerometers.

 4

Table 5 : Range of structural response for various sources, that may damage structures. Adapted from (ISO 4866, 2010).

 5

Table 6 : Examples of buried passive RFID tags

 6

	Reference	Depth	Soil moisture	Where	Frequency Comments
		(m)
	(3M EMS)	1.8	Not indicated	Outdoors	169 kHz	Industrial device
	(Vyas and Tye, 2019)	1.25	None	Laboratory	105/15 kHz Sensing tag on metal
	(Luvisi et al., 2016)	0.1	10-90%	Outdoors	865 MHz	Temperature monitoring
	(Li et al., 2018)	1.2	Dry	Laboratory	915 MHz	Flood warning application
	(Deng et al., 2020)	0.6	30%	Outdoors	915 MHz	Sensing soil moisture and temperature
	(Khokhlova and	0.1	25%	Laboratory	40 MHz	The goal was to estimate tag depth.
	Delevoye, 2019)
	(Elboushi et al., 2020)	0.5	Dry	Laboratory	915 MHz	Characterize different soils
	(Abdelnour et al., 2018) 0.6	25%	Outdoor	865 MHz	Exploits frequency doubling

Table 7 : Penetration depth (distance where the electrical and magnetic fields are reduced by a 1/e factor) of radio waves into water. From (Benelli and Pozzebon, 2013)

 7

		Low Frequency (125 kHz)	High Frequency (13.56 MHz)	Ultra High Frequency (900 MHz)	Microwaves (2.45 MHz)	Microwaves (5.8 GHz)
	Fresh Water (0.003 S/m) 26 000 cm	250 cm	26.5 cm	18.6 cm	12 cm
	Fresh Water (0.2 S/m)	320 cm	30 cm	3 cm	2.3 cm	1.5 cm

Salt Water (4 S/m) 71 cm 6.8 cm 0.7 cm 0.5 cm 0.3 cm Despite these limiting effects, some studies have demonstrated that in well-controlled situations, low-frequency RFID is usable even under water [START_REF] Benelli | RFID Under Water: Technical Issues and Applications[END_REF]. Among the significant examples of underwater applications, RFID has been used for the monitoring of coastal dynamics by embedding tags into pebbles (see section 2). In addition to the river in applications, a few tests were made with HF (13.56 MHz) tags in salt water, but the read range was limited to a few cm [START_REF] Benelli | An RFID Based System for the Underwater Tracking of Pebbles on Artificial Coarse Beaches[END_REF]. The paper concluded that the results agree with the theoretical analysis i.e., the achieved read range is lower than the penetration depth. In other contexts, low-frequency tags were applied to monitoring seaweed growth and sewers [START_REF] Peres | Theoretical models for underwater RFID and the impact of water salinity on the design of wireless systems[END_REF][START_REF] Peres | Development of a Low-Power Underwater NFC-Enabled Sensor Device for Seaweed Monitoring[END_REF][START_REF] Tatiparthi | Development of radio-frequency identification (RFID) sensors suitable for smart-monitoring applications in sewer systems[END_REF], and considered for supporting the navigation of autonomous underwater vehicles [START_REF] Harasti | Underwater RFID Arrangement for Optimizing Underwater Operations[END_REF].

Snowy and icy environments

Snow covers most of mountainous and cold regions in winter. It complicates the maintenance of wireless sensor networks supplied by wind turbines or solar panels. RFID tags have the advantage to work when covered by snow, without the need to be powered. However, very little literature deals with using RFID tags in snowy conditions. [START_REF] Nummela | Passive UHF RFID tags in arctic environment[END_REF] showed that tags could continue to operate when covered with a few millimeters of snow. Le Breton (2019) has demonstrated that tags can be interrogated across several meters of snow. A snowpack on the ground affects the radiofrequency signal mostly through transmission in snow and at interfaces, interference from waves reflected at the snow surface, and proximity coupling with the antennas(Le Breton et al., in preparation). These influences depend strongly on the properties of the snowpack, particularly the snow depth, dry density, and liquid water content, which strongly vary in time and space. A transmitted wave can be slowed by a factor of 0-80%, and the signal loss attenuated by 0 -12 dB per meter of transmission in snow. The influences on multipath interferences are of the order of ±2 rad (equivalent to ±5 cm of propagation at 866 MHz in the air) and ±12 dB on the amplitude. As a consequence, snowfall events have shown to induce a reversible and temporary error of tag localization outdoors of the order of ±8 cm (Le Breton et al., 2019). The reading range of tags under the snowpack or at <10 cm of its surface may also reduce drastically (Le Breton et al., in preparation) The reading range with a tag under snow depends mostly on the snow liquid water content and on the angle of incidence with the snowpack surface. With vertical incidence and dry snow, the propagation is negligible (-1 to -2 dB). The tag antenna might be detuned by dry snow at its vicinity (the Confidex Survivor B rugged tags, with patch antennas, suffered no significant loss, yet other tags designs may experience a loss). With wet snow however (e.g., a liquid water content of 10% on the surface and 5% on average, with 500 kg/m 3 density), the snowpack would attenuate the power by -3 dB/m (-6 dB/m both ways) and the tag's performance would fall (typically -10 dB to -20 dB based on the water's effect [START_REF] Occhiuzzi | Passive UHF RFID antennas for sensing applications: Principles, methods, and classifcations[END_REF]. To provide orders of magnitude, a system with a 20m read range in the air would read a tag across 15-20m of dry snow at vertical incidence, only 2 m in wet

Acknowledgment

We acknowledge funding and support from the project RISQID granted by the Région Auvergne-Rhône-Alpes region, from the French National Agency for research (ANR) through the LABCOM Geo3iLab project (ANR-17-LCV2-0007-01), from the Labex OSUG@2020, and from the companies Géolithe and Géolithe Innov. We thank D. Jongmans, P. Benech, E. Rey, F. Guyoton and the engineers from Géolithe company for fruitful discussions.