Wei Zhang

Yong He

Fei Liu

Congcong Miao

Shitao Sun

Chengfeng Liu

Jianfang Jin

Research on WSN channel fading model and experimental analysis in orchard environment

Keywords: Wireless sensor networks (WSN), channel fading model, precision agriculture, orchard

The wireless sensor networks used in large-scale orchard must be able to run in various environments including different heights and densities of fruit trees. Moreover, as the radio channels have different propagation characteristics in different environments, it is important to study the propagation characteristics of wireless channel in orchards environments. This paper studied the propagation characteristics of wireless channel of 2.4GHz and 433MHz in orchard with different peach tree heights, respectively, and the channel fading and PLR (Packet Loss Rate) were tested. After that, the transmission range and PL (Path Loss) of wireless channel with different antenna heights were analyzed, and the communication channel fading model was also established. It can be observed that antenna height influence on the frequency of 433MHz is slightly larger, but the initial path loss of 2.4GHz wireless channel is greater. And it also can be found that the antenna height of 3.5 meters is better.

INTRODUCTION

Favorable orchard environment is the basis for fruit production. A continuous monitoring on the orchard environment and the growth information of fruit trees was an important technical method to improve agricultural land productivity and resource utilization efficiency, to increase crop production management level and to develop modern agriculture [START_REF] Wei | The design of precision irrigation system based on wireless sensor network and fuzzy control[END_REF][START_REF] Feng | Preliminary study on precision irrigation system based on wireless sensor networks of acoustic emission technique for crop water stress[END_REF][START_REF] Xujie | The design of wireless intelligent irrigation system based on ZigBee technology[J][END_REF][START_REF] Hui | Development of farmland soil moisture and temperature monitoring system based on wireless sensor network [J][END_REF] . Using WSN technology to solve related issues has become a hot research focus currently [START_REF] Li | the effect of antenna height of wheat field on 2.4GHz radio channel propagation characteristics[END_REF][START_REF] Pierce | Regional and on-farm wireless sensor networks for agricultural systems in Eastern Washington[END_REF] . The research on the propagation characteristics of wireless channel provides important guidance for its application in orchard environment [START_REF] Jing-Jing | Wireless Sensor Network-Based Topology Structures for the Internet of Things Localization[END_REF] .

PRINCIPLES AND METHODS

Path Loss Model

By subtracting received power (in dBm) from transmitted power (in dBm), the path loss in dB is resulted.

Radio signal meet the formula in propagation:

PR=PT / dn . ( 1
)
Where d is the distance between the sending and receiving node; n is propagation factor, whose value depends on the propagation environment of wireless signal.

Taking the logarithm on both sides of the formula: 10nlgd=10lg(PT / PR).

(

Using least-squares fitting the measured power attenuation data in Matlab based on the approximate logarithm distance path loss model, we found the data met Model (3):

PL = k+10nlgd. ( 3
)
Where n is the power decay index; K is the model parameters.

Considering that the antenna height h had a greater link with loss index n, Formula (3) could be written as:

PL= k+10n(h)*lgd. ( 4
)
Parameter k and n determined the relationship between received signal strength and signal transmission distance.

Experimental Device

The experiments selected CC2430 nodes and 1100SE RF module(CC2430 sends signal of 2.4GHz while RF1100SE sends 433MHz), and DS18B20 Digital Temperature Sensor covered by stainless steel was connected to send real-time temperature information to the receiving node. The transmitting node and receiving node were required to be consistent to ensure that no additional loss occurred to the system. Moreover, the node path loss less demanding, it could be powered by a 3V battery. The following is a brief description of the node.

CC2430 is TI's new ZigBee wireless RF module. It can work in the 2.4 GHz band, requiring low voltage (2.0-3.6 V) power supply with low power consumption (27mA when receiving data and 25mA when sending). Its sensitivity reaches up to -90dBm, and its maximum output is +0.6dBm at the maximum transfer rate of 250 kbps.

The micro-power wireless transmission module RF1100SE adopts high performance radio chip CC1100. Its maximum transmission rate can be up to 500kbps, and the baud rate can be modified using software. In addition, it can be wake up with wireless. With sensitivity up to-110dBm and high reliability, it is widely used in various short-range wireless communications.

Experimental Methods

Antenna of transmitting and receiving nodes must be consistent, so that the antenna gain would no be generated. The nodes send real-time temperature information by the temperature sensor DS18B20, then sent out with wireless signal through the antenna, monitor the changes of the wireless signal with wirelessmon [START_REF] Pierce | Regional and on-farm wireless sensor networks for agricultural systems in Eastern Washington[END_REF] software on a real-time basis. This software can monitor the received radio signal strength(RSSI), show receive rate(can easily figure out packet loss rate), draw graphics and record data.

When deploying sensor nodes in the orchard, T -R distance d and the antenna height h (distance between receiving and sending node and the antenna height) are two important factors leading to large-scale channel fading [START_REF] Kong | Analysis of low-channel's impact on wireless sensor networks[END_REF] . Through experiments in the orchard, we analyzed the attenuation of energy under different antenna heights, different distances between receiving and sending nodes and different wireless channels, respectively. Given the fact that the peach trees were measured to be about 2.5m high on average, we selected antenna heights (above ground) of 1.5, 2.5, 3.5 and 4.5m, respectively; chose 2.4GHz and 433MHz wireless channels; unified transmit power as 0.6dbm, which was easier for the comparison between two types of nodes. And the receive range was chosen to be 10, 20, 30m…, until the received signal was very weak, which was the case with packet loss rate of 1. The PLR (packet lost rate) was defined as the ratio of the loss of data frame bytes to the number of complete data frame bytes. If the data frame received by receiving node was not complete, then there was packet loss.

RESULTS AND DISCUSSIONS

Path Loss Model

Table 1 and Table 2 show the path loss parameters in the band of 433MHz and 2.4GHz, respectively. From the table it can be found that path loss index increased as the antenna height decreased, while the model parameter k appeared in two situations: as the antenna height increased, 2.4GHz wireless channel of the path loss index became smaller, but that for 433MHz gradually became larger. This raised the first doubt in the experiment. If one considered the causes such the differences between the nodes in terms of sensitivity and frequency, and discarding the zero, so the initial path loss is not the same.

The effects of different antenna heights on the 2.4GHz band

Fig. 1 and Fig. 2 show the curve of the 2.4GHz wireless channel with the four different antenna heights, the relation between the transceiver distance and the path loss and the packet loss rate, respectively. It is more clearly seen from Fig. 1 that the height of the antenna had a greater impact on path loss, and the different antenna heights in the decreasing order of the amount of path loss were: h = 1.5m> h = 2.5m> h = 3.5m> h = 4.5m. The packet loss rate curve in Fig. 2 also confirms that when the packet loss rates at different antenna heights were 100%, the different antenna heights in an increasing order of transceiver distance were: h = 1.5m (60m) <h = 2.5m (140m) <h = 3.5 m (180m) <h = 4.5m (200m), the bracket were the transceiver distance. In addition, one can find that under the condition that the antenna height was 3.5m and 4.5m, the channel fading and loss rates were relatively similar. It could be concluded from the experiment that the antenna height should be set as 3.5m in 2.4GHz band.

The effects of different antenna heights on 433MHz channel

Fig. 3 and Fig. 4 show the curves of 433MHz wireless channel with the four different antenna heights, respectively, the relation between the transceiver distance and the path loss and the packet loss rate. It is clear from Fig. 3 that the height of the antenna had a greater impact on path loss, and the different antenna heights in the decreasing order of amount of path loss are: h = 1.5m> h = 2.5m> h = 3.5m> h = 4.5m. The packet loss rate curves in Fig. 4 also confirmed that when the packet loss rated for four different antenna heights were 100%, the four different antenna heights in the increasing order of transceiver distance were: h=1.5m(190m)<h=2.5m(280m)< h=3.5m(360m)< h=4.5m(370m), the bracket were the transceiver distance. Nodes can be found in the positioning of distance, because the frequency of 433MHz was smaller. At the same time it also had the advantages of less loss of communication channel, and greater penetration. It could be also found that with the antenna height of 3.5m and 4.5m, the situation of the packet loss rate and the path loss were more similar. Thus it could be concluded from the experiment above that the antenna height should be set as 3.5m for 433MHz channel. This section described the differences between 2.4GHz and 433MHz namely: the different node receiver sensitivities, -90dbm for 2.4GHz while -110dbm for 433MHz. In Fig. 5, the 2.4GHz's energy quickly reached to 63dbm, then gradually slow down the rate of decay, and ultimately rounded up at 90dbm; for 433MHz, reach to 50dbm quickly, began to be more slow rate of decay, and finally tend to 110dbm.

Fig. 6 shows that the 3.5m antenna height could be positioned under the two frequency ranges. For 2.4GHz, the packet loss occurring from the 110m and in the 180m at 100% packet loss rate. For 433MHz, appear to 360m from 320m at the end. And packet loss rate curve was steeper, after pass the receiver sensitivity of the node, the packet loss rate rapidly transition from 0 to 1. It could be known from the above experiment summary, it can give the result that lower path loss has been 433MHz, positioning a wider range, and a more suitable for the complex environment of peach orchards.

CONCLUSIONS

The impact of the antenna height

Based on the above experimental results and analysis of Fig. 1 and Fig. 3, it could be found that influence of antenna height on 433MHz wireless channel was slightly larger, but the initial path loss of 2.4GHz wireless channel was greater.

The suitable antenna height

Through experimental results, it could be found that the antenna height of 3.5 meters was better. While the path loss and the packet loss rate at the height antenna of 4.5m was smaller in the same distance, there was little difference at the antenna height of 3.5m and 4.5m. The higher node height itself would result in the increased costs and higher layout requirements. Therefore, the optimal antenna height was set as 3.5m.

Fig. 1 .Fig. 2 .

 12 Fig.1. Transceiver distance and path loss in different antenna heights curves 0 20 40 60 80 100 120 0 50 100 150 200 250 transceiver distance/m packet loss rate/%

Fig. 3 .Fig. 4 . 3 . 4

 3434 Fig.3. Transceiver distance and path loss in different antenna heights curves

Fig. 5 .Fig. 6 .

 56 Fig.5. Curves of two different frequencies and path loss at antenna height of 3.5m.

Acknowledgements

This work was financed by 863 National High-Tech Research and Development Plan (2011AA100705), the Natural Science Foundation of Zhejiang Province of China (No. Y107761), and the Science & Technology Program of Jiaxing City of Zhejiang Province of China (NO. 2009AY2016 and 2010BY6016).

Wireless channel frequency selection

From the comparison between different channels, it could be found that the optimal radio channel frequency was 433 MHz. At the same distance and the same antenna height, the path loss and the packet loss rate of the channel were relatively lower than those of 2.4GHz wireless channel.

Suggestions on wireless node localization

When the antenna height was 3.5m, wireless channel frequency was chosen as 433MHz, and the optimal path loss model of the system was PL=21.139lgd+25.0325. This finding can provide guidance for the determination of the position of wireless nodes in large-scale WSN system.


Hong-Bin Zhang

Bao-Rui Chen

Gang Li

Xiao-Ping Xin

Professor {xin Xiaoping

Spatial information sharing technology based on Grid

Keywords: Grid, Information Sharing, metadata, geospatial information

This paper analyzes the status of spatial data sharing technology. The grid technology based spatial information sharing platform has been adopted to solve the problem of "Isolated Information Islands". A Grid technology-based information sharing system is proposed and analyzes key technologies and processing mechanism.

Introduction

Sharing of Geospatial information has great positive effect to economy and social development. But the data management systems built by different technique systems can not be accessed by each other, thus a lot of "isolated information islands" come into being. How to integrate these heterogeneous data sources distributed in different sites and to realize management and sharing quickly have become research focus at present.

Present Situation and Analysis of Geospatial Information Sharing

Present Situation of Sharing Geospatial Information

Under the promotion of application demands based on Geospatial information and with the development of Internet technology, relative research organizations and institutions promote one after another information publishing platforms and sharing systems. The main typical systems are as follows.

TerraServer: is an aerial images and satellite images publishing system designed and developed by Microsoft. Users can browse and download images in designated area by Internet.

TerraShare: is a production of Z/I imaging company. It is modularized and client/server mode geography images management and publishing system. It can deal with remote sensing images and altitude modules and digital grid images.

Google Earth: is invented earth software developed by Google Company. It can publish images and vector data based on three-dimensional model and provide popular GIS functions such as inquiry, measurement, path query and so on. The systems of the kind include World Wind, Visual Earth and so on.

The research work in spatial information sharing of our country includes two aspects: Scientific data sharing engineering: Science and Technology Department began to implement scientific data sharing engineering from 2002 and in 2003 started up to build sharing centre of mapping and earth system scientific data and sharing service experimental unit and to provide data query and download service to public.

Chinese Spatial information Network: Chinese Remote Sensing Centre has already taken advantage of advanced information technique fully to built large scale and comprehensive spatial information professional website-"Chinese Spatial Data Network"based on spatial information sharing work and experience in recent years, emphasized to set up data sharing mechanism, focused on sharing spatial information. The network realized spatial information directory query and content query and download orders.

Existed problems

Compared with advanced countries in the world, our country fall behind in spatial information development, utilization, obtaining, management, updating and services.

① Lack of integration of spatial information resources ② Short of standardized state spatial information resources ③ Backward management and low sharing level ④ Low level of automatic obtaining spatial information ⑤ Backward infrastructure of spatial information resources

Traditional geospatial information sharing

At present traditional spatial information sharing methods includes: ①data format conversion mode②data direct access mode ③data mutual operation mode ④Web GIS data sharing mode。But the traditional methods can not realize mutual operation between heterogeneous spatial data and different Web GIS platforms.

Analysis of Implementation Technology

Comprehensive analysis of features and techniques in spatial information publishing systems based on Internet, there are two sharing methods.

①Users obtain needed spatial information through spatial meta data query and share them by storing in physical mediums or downloading from internet. This method can be called off-line manual sharing mode. The technique matter is to publish and browse spatial information with the aid of web technique and to provide downloading service through HTTP and FTP agreements, such as TerraServer and Chinese Spatial information Network.

②Publishing remote sensing images and providing simple applications of query and browse by special clients or browse plug-ins. The technique essence is that clients adopt special agreements to obtain data from servers. The servers usually adopt HTTP service or web services such as Google Earth.

Analysis above two methods, we can see that sharing methods and their technique means in present systems have two aspects shortages.

①Present sharing methods can not eliminate "isolated information islands" truly The development of internet technique already can resolve information publishing easily, but can not resolve information sharing well. Storage systems used incompatible access methods however, thus remote sensing resources become isolated to users. With the building of network mapping service systems in different technique structures, the isolated information islands are not only not decreased but also increased.

From the technical means to analyze, that is because an easy sharing environment is wanting in internet based on TCP/IP agreement to resolve sharing issues involved in distributed and heterogeneous system, such as resources management, service publishing and safety problems.

②Can not share stored resources Spatial information has the characteristic of mass memory. In a variety of application systems providing high speed and stability of the stored data service have a larger proportion of cost. Obviously, browse, download off the line of the way not only can not share storage resources, but also create the great wastage of storage resources in data circulation renew.

From the technical means to analyze, mainly because the technical level has not yet been able to direct support for remote sensing data owners to provide a summary way, that will be able to at a lower cost to wan other users to provide real-time online, stable and reliable data access service. On the other hand, internet will not be able to provide convenient integration service directly for data users. Difficulty lies in how to publish the service of information, how to realize data query, how to integrate services in application systems and how to control the data access and so on.

Emerge of grid technique proved chance to resolve above questions.

Grid is the third generation of internet application following by Internet and www.

Traditional internet realizes union of computer hardware. Web realizes union of web pages. But grid tries to realize complete union of all resources on the Internet and provides weldless and compositive calculation and cooperation environment and provides fictitious and limitless calculation and distributed data resources and realizes resources sharing of fictitious organization and question solution.

Based on the research work and experience of key theories and techniques on grid calculation including resources management, information safety, information services, data management and application development environment, America Argonne national lab developed Globus Toolkit. It is grid calculation tool software which can running in every platform. At the same time, it is a grid application environment and development platform. Globus toolkit of development based on the grid application has provided on several areas of support.

①Grid safety. It can assure grid calculation environment to run regularly. Globus mainly combines sophisticated distributed safety technologies and expands to some extent in order to adapt to features of grid calculation environment.

②obtain and publish of grid information. How to publish, query and search resources information in grid calculation environment are preconditions for utilization resources effectively.

③Grid resources management. Because resources in grid environment mainly located in wan wide area network environment, frequently-used lan local area network resources management technologies can not be used effectively. So Globus realizes higher level resources management. Under support of information services, it can manage resources in wan effectively.

④Grid long distance data transmission. To realize high speed and reliable data transmission in wan and transparent long distance file I/O access of applications.

To sum up, the core of grid conception is resources sharing and cooperation work. Grid users can use all resources including calculation, storage and data conveniently, but it is not need to know who provides the services. On demands is the aim that grid tries to realize. Some person sum up the function of grid technology is to provide the most important resources for the most needed persons in the most appropriate time.

Advance and development of grid technology raise the hopes to solve difficulties in spatial information sharing and services fundamentally. Introduction grid technology to spatial information utilization and application has become important task at present for us.

Grid technique is a new generation of internet. Resources sharing is the most important mode in grid technique application. With the aid of grid, combined with spatial information sharing situation, study spatial data sharing and services based on grid in order to perfect spatial information sharing and application environment.

Acknowledgments

This work is supported by National Natural Science Foundation of China (Grant No: 41101383) and the Key Scientific Project of Inner Mongolia (Grant no.20091403) and Scientific Project of Agriculture(Grant no. 201003019,201003061,200903060) and Meteorological science and technology of China(Grant No: GYHY200906029-2)

Spatial Information Sharing Service Based on Grid

technical Line

Spatial information grid technology is based on basic grid technology. The basic frame and function component of spatial information sharing platform is set up based on the technical frame, resources management, information services, safety architecture provided by basic grid laying the foundation for spatial information grid application system. The spatial information grid system architecture is as follow.

Geospatial Data Grid Application System

Geospatial Data Grid Technology Basic Grid Technology Geospatial Data Grid Application System Geospatial Data Grid Technology Basic Grid Technology Fig1.Spatial information grid

Sharing platform Frame

Grid middleware is adopted to eliminate isolated information islands. Spatial information sharing platform frame based on grid middleware is designed to realize transparent integration of spatial information resources. The grid middleware is the score to realize sharing spatial information taking the charge of transparent and dynamic access distributed spatial information resources. In the frame, there are several parts. Fig2.Grid-based Geospatial sharing frame (1)Grid data resources. That is rock-bottom distributed spatial information resources which can be accessed by internet. Mainly include two parts: ①space source, that is the basic unit of information stored and it can be database or distributed heterogeneous spatial data source; ②information search service, mainly mean through spatial information source to establish an index and to provide index service to upper.

(2)Task scheduling service, this part is mainly responsible for transferring request from spatial information portal, decomposition calls for resources, processing tasks and management of scheduling tasks.

(3)Meta data collection and conversion. According to the results of task scheduling, information collecting task service is localization converted to meet distributed spatial information resources format.

(4)Spatial information resources control service. This part is the control centre of the whole frame and makes every grid component in the reunification of the standard and includes system configuration and index services.

key technologies

(1) Presentation of information and meta data Presentation of information reflects in two aspects: the first presentation is storing information in database or other storage medium; the second presentation is interface presented to users. Meta data abstracts description of data objection in order that information can be expressed by relationship of elemental attribute and value. We call the mate data as middle layer of data presentation. Generally speaking, data presentation can set up several middle layers and every application program logic layer has corresponding data presentation middle layer. Presentation of meta data can take advantage of semantics web technology fully.

(2) Task scheduling and resources management Grid resources is complicated, numerous, varied and large. So how to manage all resources in the grid effectively is a painstaking task. Task scheduling mainly take the charge of resolving requirements transferred from spatial information portal, and decomposing of resources scheduling demands and scheduling tasks and management scheduling tasks. Task scheduling can dynamic decompose and schedule tasks according to every site disposition situation in grid in order to improve running efficiency. So scheduling algorithm will affect system efficiency directly.

(

3) Safety technique

The aim of grid is to connect all the information service sites in the internet in order that all users can use information services. Because sites of grid located in different places, how to share data resources safely between sites and how to assure integrity of sharing data are questions to be solved at present. Grid safety authentication mechanism mainly includes identification authentication, information access authentication, audit mechanism. When setting up perfect safety mechanism, it should be avoided that safety authentication will waste too much processing capacity. If system resources used up by safety authentication is more than calculation resources saved by system, grid will lose itself true means. Fault-tolerant and automatic crash recovery should also take into consideration in grid system.


Cunjun Li

Qian Wang

Jihua Wang

Yan Wang

Xiaodong Yang

Xiaoyu Song

Wenjiang Huang

Wheat grain protein content estimation based on multi-temporal remote sensing data and generalized regression neural network

Keywords: wheat, grain protein content, multi-temporal, Landsat, generalized regression neural network

Monitoring grain protein content in large areas by remote sensing is very important for guiding graded harvest, and facilitates grain purchasing for processing enterprises. Wheat grain protein content (GPC) at maturity was measured and multi-temporal Landsat TM and Landsat ETM + images at key stages in 2003, 2004 growth stages were acquired in this study. GPC was estimated with multi-temporal remote sensing data and generalized regression neural network (GRNN) method. Results show that the GPC prediction accuracy of the GRNN model is higher, with the average relative deviation of self-modeling, average relative deviation of cross-validation as 0.003%,0.321%;4.300%, 7.349% for 2003 and 2004 respectively. GRNN method proves to be reliable and robust to monitoring GPC in large areas by multi-temporal and multi-spectral remote sensing data.

Introduction

Grain protein content (GPC) is a key indicator of wheat quality, which determines the usage of wheat for making bread, cookie or noodle. In world wheat trade, the higher the GPC is, the higher price grain production manufactures will pay for wheat farmers. Monitoring GPC aids to harvest wheat according to grain protein content, and facilitates processing enterprises to purchase graded grains. It becomes possible to monitor the quality of crops using quantitative remote sensing technology development in recent years.

Many scholars have monitored wheat protein content with remote sensing technology. Wright D.L et al. (2003) found that a good relationship existed between measured wheat organ nitrogen content and vegetation indices derived from airborne near infrared image and Quickbird image at heading stage, which can be applied to guide the fertilization at anthesis. The GPC was improved after twice fertilization for wheat under nitrogen stress. But poor relationships were found between vegetation indices derived from only the image derived from heading stage. Badri etc (2003) found that, vegetation indices derived from Landsat satellite images were greatly significantly correlated with wheat GPC (r > 0.75) two weeks before anthesis. Reflectance of near-infrared wavelengths of Aster image were significantly related to GPC three weeks after flowering with lower correlation coefficient (r < 0.5). Preliminary explanations were made for monitoring mechanism. Mieke [START_REF] Reyniers | Comparison of an aerial-based system and an on the ground continuous measuring device to predict yield of winter wheat[END_REF] used color infrared aerial image and Cropscan geophysics spectrometer to predict wheat GPC a month ahead of wheat harvest with prediction accuracy as 90%. Petterson (2007) got satisfied prediction GPC using TCARI index in planting. [START_REF] Huang | Application of red edge variables to nutrition diagnosis and grain quality forecast of winter wheat[END_REF] studied correlations between total wheat leaf nitrogen and grain quality components during late maturation period. The results showed that total nitrogen can predict GPC and gluten content. [START_REF] Wang | Prediction of grain protein content in winter wheat (Triticum aestivum L.) using plant pigment ratio (PPR)[END_REF] used PPR to monitor GPC based on field plots. Hansen et al. (2003) conducted plot experiment and measured groundbased multi-spectral, wide band (visible -near-infrared wavelengths) data. Wheat yield and protein partial least squares models were established. The high yield prediction accuracy is in contrast to low protein prediction accuracy. [START_REF] Song | Preliminary application of ASTER images in winter wheat quality monitoring[END_REF] monitored wheat protein content based on nitrogen translocation in wheat plant and Aster images at anthesis. [START_REF] Zhao | Predicting grain protein content of winter wheat using remote sensing data based on nitrogen status and water stress[END_REF] predicted winter wheat grain protein content with water vegetation indices derived from TM image acquired at anthesis. Analyzed relationships between GPC and nitrogen transport process and moisture stress.

Monitoring winter wheat grain protein content at field scale experiment with different level of nitrogen fertilization at the key growing stage is very common. However, at large scale wheat GPC will be affected by soil, weather conditions, management and other factors. These factors along with cultivars together contribute spatial variability of GPC. Only one remote sensing image at certain period can't untangle all the factors aforementioned. Multi-temporal images overcome the shortcomings of single image and can monitor the major factors that cause the spatial variety of wheat grain protein content. Therefore, in this paper we attempt to use multi-temporal remote sensing images and GRNN method to monitor GPC.

2

Material and Method

Research region

More than twenty wheat fields, distributed randomly, flat, uniformly grown, with areas more than 50 acres, were selected for this study in Beijing rural area, China in 2003, 2004. Regular management were adopted on these fields. Local popular varieties were planted on the fields, such as Jingdong 8, Jing 9507 and so on. The fields are shown in figure 1.

Data acquisition and preprocessing

One month after wheat maturity, GPC was tested with NIR (Foss1241,Foss Tecator, Sweden). The time and image type of the satellite data obtained include Landsat TM and Landsat ETM+ in wheat growing season of 2003 and 2004, which are shown in table 1. The radiation correction adopts Empirical experience linear method (Liang, 2003). High radiation body and low radiation body with flat land were determined. Through experience linear spectral reflectance method and remote sensing image transformation, DN values of target true reflectance were calculated. Low radiation body and high radiation body were Ming tombs reservoir and drying yard of Xiaotangshan precision agriculture demonstration base respectively.

Geometric correction of satellite data includes the following steps. First of all, 1:100,000, topographic map in the study area was used as base map. Second, the April 7th, 2003 Landsat image was corrected with control points of the base map. Finally, the error should be controlled within a pixel. Other images were also geometrically corrected in the same way. Beijing 54 coordinate system was adopted for projection with the center longitude as 117 °.

2.3

Data analysis

Extraction of remote sensing spectral index

According to Landsat band set and previous studies, seven multi-spectral vegetation indices were selected to monitor wheat nitrogen content, biomass, leaf chlorophyll density, and leaf equivalent water thickness. NDVI 21, NDVI 42 are sensitive to chlorophyll density, nitrogen content. NDVI47, RVI43 are sensitive to the biomass and leaf equivalent water thickness.

Generalized regression neural network

Neural network is a modeling method in recent years. Generalized regression neural network (GRNN) is a memory-based feed forward network.

The GRNN consists four layers: input layer, pattern layer, summation layer and output layer [START_REF] Specht | A General Regression Neural Network[END_REF][START_REF] Chtioui | A generealized regression neural network and its application for leaf wetness prediction to forecast plant disease[END_REF][START_REF] Haidar | Transient stability evaluation of electrical power system using generalized regression neural networks[END_REF]. Figure 2 shows a schematic diagram of generalized regression neural network architecture. GRNN has a special property in which no iterative training of the weight vectors is required. That is, any input-output mapping is possible by simply assigning the input vectors to the centroid vectors and fixing the weight vectors between the radial basis function units and outputs identical to the corresponding target vectors. This training algorithm is much better than the back propagation training, which involves long and iterative training as well as facing the problem of local minima. Moreover, a special property of GRNN is that it enables users to flexibly configure the network suitable for real hardware implementation, by adjusting only two parameters which are the center and width. Since the radial basis function acts as a detector for different input vectors, the weight vectors are computed accordingly and there is no need to train the network. Thus, the GRNN is more straightforward and does not require a training process. In GRNN, If ) , ( y X f represents the known joint continuous probability density function of a vector random variable, X , and a scalar random variable, y , the conditional mean of y given X (also called the regression of y on X) is given by

           dy y X f dy y X yf X y E ) , ( ) , ( | (1)
Where f (x,y) is the joint density and can be estimated by using Parzen's nonparametric estimator. Substituting Parzen's nonparametric estimator for f (x,y) and performing the integrations leads to the fundamental equation of GRNN:

        n i i n i i i D D Y X Y 1 2 2 1 2 2 ) 2 exp( ) 2 exp( ) (   ( 2
)
Where  is the smoothing parameter (sigma weight) and

) ( ) ( 2 i T i i x x x x D    .
Training the GRNN involves finding the optimal values for the  parameters in eq [START_REF] Chtioui | A generealized regression neural network and its application for leaf wetness prediction to forecast plant disease[END_REF], whose optimal value is often determined experimentally (Kim,2003). In this research, Matlab was used to train GRNN for protein estimation using mutitemporal Landsat TM/ETM+ data.

Model validation

The small number of selected sampling fields was about 20 in the study due to the large area, leading to some verification problems. Cross-validation method is used in this study. .Assume the number of all the samplings is n. Each time a sampling is left input layer pattern layer summation layer output layer out to validate the models established on the other n-1 sampling. In this way, all the n samplings are used to validate corresponding models. Deviation is calculated as: bias = | predicted value-actual value| / actual value× 100%. So according to the deviation the predictive power of modeling can be compared.

Results

Multi-temperal Landsat TM/ETM+ image from 2003 and 2004 were chosen as source data to estimate wheat grain protein content by GRNN method. Figure 3 shows the measured grain protein content versus predicted values derived from multi-temporal, multi-spectral parameters of all samples in 2003 with GRNN method.. Table 3 is the validation results. As shown in figure 3 and table 3, the GRNN method has potential to simulate GPC for multi-temporal and multi-spectral parameters of all samples in 2003, with high consistency and little deviation between measured and simulated ones. The average relative deviation of self-modeling and cross-validation were 0.003% and 4.300% respectively. The low deviation shows that the GRNN method has powerful ability to construct models and is a robust method to predict grain protein content.

Figure 4 shows the measured GPC versus the simulated values in 2004. 22 samples were collected in 2004, table 4 shows 22 cross-validation results for corresponding models. 4 shows that GRNN method has the potential to predict GPC with multi-temporal and multi-spectral parameters in 2004. The average relative deviation of self-modeling and cross-validation are 0.321% and 7.349% respectively. The high prediction accuracy shows that GRNN is suitable to simulate GPC.

By comparing the prediction results in 2003 and 2004, the former was better than the latter. This is perhaps because that only one image (May 8th during late period of wheat growth) was acquired in 2004, while three images including May 1st, May 9th and May 25th, were acquired during wheat growth in 2003. This is consistent with the previous conclusions that images for wheat anthesis and grain filling periods are of great importance to predict GPC (Wang, 2004).

Conclusion and Discussion

In this study, GRNN method proves to accurately predict GPC in large areas.

Although wheat GPC were affected by soil, weather conditions, management and other factors, multi-temporal images acquired during critical growth development can overcome the low accuracy problems caused by single image. However the distinguished weather conditions between 2003 and 2004 make it difficult to apply the models established on one year's data to predict the other year's grain protein content. Besides, it is necessary to combine meteorological data to establish prediction model in the future studies.

Fig. 1 .

 1 Fig. 1. Study area and the distribution map of wheat plot surveyed

Fig. 2 .

 2 Fig. 2. Schematic diagram of GRNN architecture.

Fig. 3 .

 3 Fig. 3. Measured GPC versus predicted GPC in 2003

Fig. 4 .

 4 Fig. 4. Measured GPC versus predicted GPC in 2004

Table 1 .

 1 The satellite data Obtained.

	YEAR	DATE	TYPE
	2003	April 7,	ETM+
		May 9,	ETM+
		May 17,	TM
		May 25	ETM+
	2004	April 1	TM
		April 17,	TM
		May 18	TM

Table 2 .

 2 Senven muti-spectral vegetation indices.

	Abbreviation	Index	Formula
	NDVI42	NDVI of 4 and 2	NDVI42=(B4-b2)/ (B4+b2)
	NDVI43	NDVI of 4 and 3	NDVI43=(B4-b3)/ (B4+b3)
	NDVI47	NDVI of 4 and 7	NDVI47=(B4-b7)/ (B4+b7)
	NDVI21	NDVI of 2 and 1	NDVI21=(B2-b1)/ (B2+b1)
	NDVI57	NDVI of 5 and 7	NDVI57=(B5-b7)/ (B5+b7)
	RVI43	RVI of 4 and 3	RVI43=B4/B3
	RVI54	RVI of 5 and 4	RVI54=B5/B4
	*1,2,3…7 refer to band of LANDSAT TM/ETM+

Table 3 .

 3 Cross validation of predicted wheat GPC using muti-temporal airborne Landsat mutispectral data and GRNN method in 2003

	Group	Deviation	Average	Group	Deviation	Average
		of	deviation		of	deviation
		cross-validation	of self-modeling		cross-validation	of self-modeling
	1	0.995	0.002	12	6.459	0.001
	2	2.336	0.001	13	6.733	0.001
	3	7.919	0.001	14	7.541	0.001
	4	2.291	0.001	15	1.676	0.001
	5	6.429	0.002	16	2.613	0.001
	6	6.041	0.001	17	3.914	0.007
	7	1.643	0.001	18	0.688	0.001

Table 4 .

 4 Cross validation of predicted wheat GPC using muti-temporal Landsat mutispectral data and GRNN method in 2004

	Group	Deviation	Average	Group	Deviation	Average
		of	deviation		of	deviation
		cross-validation	of self-modeling		cross-validation	of self-modeling
	1	6.660	0.085	13	10.188	1.044
	2	0.659	0.152	14	13.359	0.491
	3	22.515	1.502	15	3.697	0.098
	4	2.628	0.126	16	10.975	0.043
	5	6.703	0.005	17	7.446	0.13

Cunjun Li, Post address: P.O.Box

2449-26,Beijing, 100097, P.R.China, Phone: +86-10-51503692, Fax: +86-10-51503750, E-Mail: lcj963@163.com

Acknowledgements

. The project was supported by Beijing Natural Science Foundation (Grant No. 4092016) , Beijing Nova Program (Grant No.2008B33) and National Natural Science Foundation, China(40701120).


Jiao Li

Wei Guo

Su

Ruofei Zhong

Jianxi Huang

Jiangxia Wei

Qin Li1

Jiao Guo1

Wei Su

Large-Scale Microwave Remote Sensing of Retrieving Surface Multi-parameters Using Active and Passive Satellite Data: In the Tibetan Plateau Region of Maqu

Keywords: AMSR-E, QuikSCAT/Seawinds, Surface temperature, Soil moisture, Roughness

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Bare ground or sparse vegetation in surface conditions, AMSR-E inversion of soil surface roughness and time-sharing need to consider the effect of temperature. Traditional methods for estimating the roughness of the signal for the major impact of microwave radiation is used calibration method or empirical method, but the drawback of the calibration methods influenced by the sample with the measured roughness no definite link, although the empirical method related to the measured roughness, but the local applicability. AIEM model simulation analysis can be used to know: when the frequency similar to the initiative of the roughness of the sensitivity of the microwave signal was significantly higher than the passive microwave signal, active and passive with a more accurate estimate of roughness, which could improve the accuracy of soil moisture. Inversion of a variety of active and passive sensors SMAP soil moisture is the core mission plan.

The current study of active and passive joint inversion of soil moisture remote sensing data sharing, targeted active and passive sensors are mostly equipped with a satellite, such as TRMM, PALSAR, etc. (Wu, etc., 2006;Chen, etc., 2008), mainly in order to obtain synchronous data. Equipped with different sensors on the satellite joint inversion of the current post less, mainly due to different satellites through the same area of the transit time differences (in hours), in this time interval the surface with a pixel parameter information has changed. In addition, despite recent high spatial resolution of radar data inversion for soil water into a hot spot, but the large-scale spatial and temporal distribution of soil moisture monitoring for global change research still has an irreplaceable position. ZHONG et al ( 2006) with the AMSR-E and Quikscat / SeaWinds joint inversion of soil moisture research work directly to the scattering of dollars to a VV polarization channel radiometer with two low-frequency channels in the multi-input inversion parameters of the inversion, ignoring the two sensors is not at the same time to obtain the surface information, and the results show that soil moisture sensor for two different sensitivity in the AMSR-E on the basis of observations by adding Quikscat / SeaWinds of channel has not improved with the iterative inversion accuracy. Some researchers are using experimental data analysis Quikscat / SeaWinds on soil moisture sensitivity, they noted that: Quikscat / SeaWinds Only in very dry and very wet soil conditions are more sensitive to soil moisture, and the results of extracting soil moisture information still not ideal (Scipal etc., 2002;Mladenova, etc., 2009;Zyl, etc.). These studies show that: the above two use Quikscat / SeaWinds collaborative way and can not get a good retrieval of soil moisture inversion. Not a long period of time, except for special circumstances such as rainfall, that the surface roughness remains constant, this study based on this proposed new program: First, use the AIEM model simulations Quikscat / SeaWinds two different incidence angles different polarized backscattering coefficient and the roughness of the relationship, and then extract the roughness of the information and provide it to AMSR-E, further inversion of soil moisture and surface temperature.

Study region and data

This study area latitude and longitude of 33 ° 30'-34 ° 15'N, 101 ° 38'-102 ° 45'E, ground-based data acquisition time for July 2008 to August, on-board data selection is Quikscat / Seawinds and AMSR-E passive microwave sensors to specific areas of Qinghai-Tibet Plateau to obtain the joint surface soil moisture data, Maqu mostly open grassland areas(Fig. 1), vision, set in Maqu region collected 20 soil sites, (Fig. 2) collecting data, including temperature , soil moisture, soil texture and so on.

As the sensor is relatively large field of view, this research work will be on a pixel is within the scope of the average number of sites, just remove the anomaly is equal to the minimum or maximum value, the last for the entire study area for all the pixels as a whole is to the simulation, choose the model chosen for the Qp model of radiation scattering model AIEM model, based on data measured on the ground to complete the forward simulation, and compared with actual observations. According to this article, active and passive joint inversion scheme, first, using simulation databases established the relationship between the ratio of RMS height and the scattering coefficient and the difference of scattering plan two channels decibels, and then use Maqu region HH and VV scattering meter data to estimate the joint roughness slope information, as known parameters input to the microwave radiation model, which complete inversion of soil moisture. Here in order to eliminate measurement noise, the use of AMSR-E data in the 6.6GHZ frequency and 10.9GHZ two four-channel inversion, inversion of surface parameters to soil moisture and surface temperature inversion iterative algorithm for the LM .

3.

Physical models and retrieval algorithm In AMSR-E parameter conditions, with AIEM model to bare landmark Fresnel reflectivity research, there are two points found:

1. The effective reflectivity of surface with the frequency changes are caused by water in different frequency dielectric constant differences ,but the roughness at different frequencies to the effective reflectivity of the image is relatively stable, does not change with increasing frequency and strong .

2. Compared to the flat case, H polarization in the rough surface will reduce the reflected energy, or the emission energy increases. In other words, the rough surface effective reflectivity H polarization is always lower than the corresponding smooth surface of the Fresnel reflectivity. But V polarization is opposite.

So it can be a direct result of the two as follows: 1. You can use a frequency the roughness of the parameters of the other frequency rough parameters.

2. Polarization of the surface effective reflectivity of the rough calculation there are effects, not on H, V polarization of the two kinds of roughness parameters equated.

Then, Shi and others with AIME, respectively, and Q / H model, Hp model and the Qp model 10.6GHz, 55-degree angle under the conditions of the simulation, and compare the results.

In the three semi-empirical model simulation results, Qp model and the best fit model results AIME, Q / H model followed, Hp model the worst. Investigate is reason, Hp model did not consider the error from the incoherent part, and in the case of highfrequency, non-coherent part of the effective reflection on the surface of the dominant. Q / H model error from the H, V two polarization roughness parameters under the same treatment.

Therefore, in this study, the use of parameters and roughness parameters Qp (s / l) relationship with the Qp model to simulate AMSR-E forward radiation reflectivity, enabling researchers to use a simple, single parameter roughness of the surface effective reflectivity model simulation and inversion.

Scattering parameters of the model sensitivity analysis

AIEM model can be used to simulate the exposed surface of the scatter meter data, but also to simulate bare surface radiometer data. Through model simulations, can be found: in the soil moisture, surface temperature, surface roughness and other parameters, the scatter meter data for roughness of the most sensitive, the sensitivity of other surface parameters is small; radiometer is most sensitive to soil moisture, surface temperature of the second, less sensitive to the roughness. Soil type and other soil parameters affecting radiation scatter meter data, but little impact. Also, because the number of surface parameters, changes over time, soil moisture, surface temperature was greater than that roughness is essentially the same, in this study the use of active and passive microwave sensors to obtain data in the study area are not synchronized, Therefore, with the actual situation, the study of passive joint inversion method of soil moisture: the first with a scatter meter to estimate roughness slope, then the inversion of radiometric information with other surface roughness parameters, including soil moisture.

4.

DATAINVERSION

Forward modeling and calibration of radiation

According to previous analysis of the applicability of the model, this study used a direct simulation of the model AIEM simulate the observations of scatter meter, the inversion may not process the general operation of the "model parameter calibration" work.

Simulated on a single site, there is insufficient data to use, and the method that points represent the surface of the simulation is not science. The research work will be on a pixel is within the scope of the average number of sites, just remove the anomaly equal to the maximum or minimum value, and finally the entire study area for all pixels whole forward simulation by the entire study area all the pixels in the four channels on the forward simulation results (Fig. 3).

Fig.3.

The study area all pixels in the four channels on the forward simulation results

Scatter meter to estimate roughness

Quikscat / SeaWinds which was launched by FAA work in the Ku band, is different from operating frequency of ESA's wind scatter meter (ESA C-band).In this study, AMSR-E and Quikscat / SeaWinds combined inversion process, the bare surface conditions in the reflectivity of the passive and active backscattering coefficient of each surface parameter sensitivity analysis and found that Quikscat / SeaWinds on the surface the sensitivity of surface roughness parameters was significantly higher than the other, and AMSR-E on the surface roughness of the sensitivity is very low, so try to combine such a passive way: the use of Quikscat / SeaWinds inversion of surface roughness, and then AMSR-E soil moisture and surface temperature inversion. This study analyzed the surface temperature (st), soil volumetric water content (sm), soil bulk density (bd), roughness (RMS height (sig) and surface correlation length (cl) changes in the value of the latter to scattering coefficient.

The results showed that: the temperature from 0 ° to 30 °, backscattering coefficient of variation less than 0.05dB; soil volumetric water content from 0.02 to 0.50, the backscatter coefficient of variation less than 2.00dB; soil bulk density from 0.2 to 1.2, backscattering coefficient of variation less than 1.00dB; and can change the roughness backscattering coefficient of variation even up to tens of dB.

Based on the above analysis of the results, you can ignore the temperature change caused by changes in the backscattering coefficient, where the fixed temperature of 15 ° C, soil bulk density of 0.8 g/cm3, a second simulation. The simulation data according to different surface RMS height and correlation length of the configuration group, each change in soil moisture from 0.02 to 0.50, backscattering coefficient, including a 46 ° angle of incidence for the HH polarization and incidence angle of 54 ° the VV polarization, a total of 168 groups. Analysis have shown that, in most cases, the decibel value of two scattering data curves under different soil moisture conditions remain parallel. Due to space limitations, the following list only one of the two-group analysis chart (Fig. 4).

Use the second simulation analysis of law get, by two different incidence angles and different polarization of the decibel value of the difference to eliminate the impact of soil moisture and roughness changes to inversion. According to our understanding of the Qinghai-Tibet Plateau region Maqu ground measurement data, that the soil moisture range of 0.2 to 0.5, roughness, slope (sig / cl) range from 0.04 to 1.0, select the corresponding simulated data set to analyze the data obtained RMS height and backscatter coefficient and the ratio of two-channel scatter meter decibel difference between the diagram(Fig. 5).

According to the variation in Figure 6, the establishment of sig / cl and σ (dBHH)-σ (dBVV), exp (σ (dBHH)-σ (dBVV)) relation by regression analysis of the data to establish the following formula (3):

            109 . 0 HH VV 0034 . 0 HH VV exp 4231 . 0        dB dB dB dB cl sig     (3)
Correlation coefficient is 0.9692, standard error is 0.0342, regression analysis of fitting errors shown in Figure 6.

Here we take the 2008-07-02 global scattering plan the roughness of the estimate slope for example, Let's use of global HH, VV polarization scatter meter images to estimate the joint roughness slope, then Maqu region of the HH and VV scattering count data to estimate the roughness of the slope of the joint (Fig. 7).

It can be seen from the figure, Maqu region in July 2 to estimate the roughness of the slope ranges from 0 to 0.5, consistent with the scope of information. Follow the same steps can be estimated from 2008-07-01 to 2008-08-29 Maqu region a total of 60 days roughness slope. It can be seen from the results, small changes over time, slope roughness, according to the average cycle of a certain treatment, such as ten days, in this time range roughness slope is very small fluctuations over time

Conclusions

Several conclusions can be drawn from our study.

(1) Inversion results has a high fitting accuracy with the ground measurements, indicating that the proposed method can take advantage of the complement of the active and passive microwave data to a certain extent. The scatter meter is more suitable for estimating the impact of surface roughness, and the low-frequency channel of the radiometer is the best choice for SM inversion, and the highfrequency of the radiometer is more relevant to the surface temperature and thus is fit for inversion of ST. Based on these above analysis, this research selects the best combination of observations to retrieve land surface parameters, to achieve a high retrieval accuracy.

(2) The other advantage of importing the Qp model is the active model and the passive model can be linked. This dealing is convenient to make full use of the active/passive's fittest channel to retrieve their most sensitivity land surface parameters.

(3) The proposed method in this research is for the bare soil surface or sparse vegetation cover surface, the surface of higher vegetation cover does not apply to.

Fig. 1 .Fig. 2 .

 12 Fig.1. Typical landscape Maqu region: mountain (upper left), grassland and bare soil (upper right), Yellow (medium), Yellow (bottom left), wetlands (bottom right)

3 . 1

 31 Applicability of the radiation model Used in this study taking into account the characteristics of active and passive sensors, and according to Shi Jiancheng's research team conducted in recent years in the Tibetan Plateau soil moisture inversion of the published research literature and related information, choice of radiation model for the Qp model, scattering model AIEM model. AIEM model does not consider part of coherent radiation or scattering, but because of Quikscat / Seawinds the angle of incidence of 46 and 54.1, relatively large, even in large areas of undulating terrain, it is hard for the local angle of incidence close to 0 °, so the coherent scattering from formula to analyze, you can not consider it part of coherent scattering. Therefore, the model can be used to establish AIEM Quikscat / Seawinds analog observation database. In the inversion of passive microwave soil moisture for bare surface of a variety of semi-empirical model, Shi and other articles will be in 2006, divided into three categories: Q/H model (1), Hp model (2) and Qp model (3). They form are: can see, Hp model is Q / H model is based on the hypothesis Q = 0 obtained, Qp model at Q / H model based on the hypothesis H = 1 to get. Qp model of Hp compared with Q / H model, the advantage is that they considered the different polarization by roughness on the reflectivity of the impact.

Fig. 4 .Fig. 5 .Fig. 6 .Fig. 7 .

 4567 Fig.4. Different roughness conditions the difference between the backscattering coefficient and soil moisture changes in the relationship between

Fig. 8 .

 8 Fig.8. The inversion of all pixels and averaging the results of ground truth comparison chart

Table 1 :Table 1

 11 Here is AIEM model simulation Quikscat / SeaWinds bare surface conditions in the backscattering coefficient of a database to analyze its relationship with surface roughness, trying to build a function.AIEM model simulation with Quikscat / SeaWinds scattering database parameters in Input parameters setting for AIEM

	Sensor	Frequency/(GHz)	Incient angle and polarization	bd/( g/cm 3 )	Surface correlation function
	QuikSCAT/Seawinds	13.4	46°HH、 54°VV	1.3	Gaussian
	ST/(°C)	SM/(m 3 /m 3 )	cl/(cm)	sig/(cm)	Soil texture
	Range: 0~40, Interval:5	Range: 0.02~0.44, Interval:0.02	Range: 2.5~30, Interval: 2.5	Range: 0.25~3.5, Interval:0.25	Sand: 47.4%, clay:5%

ACKNOWLEDGMENT

The work of this paper is supported by the National Natural Science Foundation of China (NO.40701127) and the Beijing Municipal Commission of Education (NO: KM201010028016). The authors wish to thank Dr.Tang bohui of the Institute of Geographic Sciences and Natural Resources Research, CAS for his data and useful comment.