Eulalie Boucher
email: eulalie.boucher@obspm.fr

Filipe Aires

Towards a new generation of AI-based IASI retrievals of surface temperature: Part II -Assessment

Keywords: IASI, Deep Learning, Neural Networks, Regional Biases, Land Surface Temperature, Sea Surface Temperature 1 |

When using Neural Networks (NNs), the lack of input information caracterizing the radiative transfer (RT) can result in regional biases, especially when retrieving surface properties. The part I companion paper explored localization techniques, an attempt to help the Neural Network (NN) adjusting its behaviour to local conditions. In this paper, we choose one of the proposed methods, based on novel localized-CNN model image-processing approach for the retrieval of Sea Surface Temperature (SST) and Land Surface Temperature (LST) over a fixed domain using IASI (Infrared Atmospheric Sounding Interferometer) observations. An in depth evaluation is performed. The localized-CNN architecture is a promising Deep Learning (DL) solution that provides retrievals of good quality. The results appear to be similar or better than those of EUMETSAT's PWLR3 retrieval algorithm that also uses IASI observations, collocated with microwave (MW) data too. This is an excellent achievement for such an easyto-implement method and shows the benefits of localizing the NN retrieval. Our image-processing retrieval scheme allows to interpolate the TS below the clouds and a preliminary analysis of the cloud impact on the TS is performed. The possibility to estimate retrieval uncertainties is also investigated and a practical solution for CNN architectures is proposed. The best strategy for a global scale retrieval is yet to be found for such an image-processing scheme, but potential solutions and their respective advantages and disadvantages are discussed.

inhomogeneity translates into more chaotic kernel weights. The network fetches data from neighbouring pixels and tries to make a good mix of the obtained information to retrieve the TS in the central pixel. This is precisely why regional biases are reduced.

The network really adapts its behaviour depending on the location of the pixel. Neighboring pixels act as additional information to better constrain the solution, by exploiting recurrent patterns in this location, as well as reducing the noise-to-signal ratio.

F I G U R E 1 Obtained weights of the 5 (lat) × 5 (lon) × 3 (PCA depth) local convolution filter of the CNN retrieval method, at 12 different locations in the image. From left to right, the filters for the first, second and third PCA PC of the spectral IASI information.

Four retrieval examples are shown in Figure 2. Only clear pixels are considered. One disadvantage of using a convolutional model is that this leads to a slightly smoother retrieval, especially over land. For instance, the slightly warmer pixel present in Corsica on the second row of Figure 2(a) is smoothed in the retrieval map. This phenomena is even more noticeable when using a generic CNN model (i.e. with shared weights and same spatial filters). However, the contrast between the hotter inland TS and the much cooler TS over the Alps is well respected, suggesting that the localized-CNN has indeed learned to respect big spatial patterns that are present in the domain. Comparing the reference TS (from ERA5) and the retrieved CNN-TS, we see that the retrieval is of very good quality. Over the whole ten-year period, the average STD error between the reference ERA5 TS and the retrieved CNN-TS is 0.85 K over oceanic regions, 1.83 K over land, 1.85 K over coastal regions and 2.50 K over Alpine regions in the morning. In the evening, the average STD error differs slightly with respectively 0.65 K, 2.02 K, 2.42 K and 2.00 K. Naturally, a larger error is made in problematic areas such as the Alps, especially in colder scenarios (i.e. autumn and winter seasons). This is due to the missing information that the CNN would need to perfectly explain the TB-to-TS dependencies in these areas. Overall, the retrieval is in good agreement with the ERA5 TS. Evaluation of this TS against other datasets is provided in the next section.

Figure 3 shows the full time series on the left and, on the right, a scatterplot of the true versus retrieved CNN-TS over different regions of the image. Looking at the scatterplots, it can be seen that larger errors are obtained in the extreme TS values (i.e. hot and cold extremes), especially in pixels over the Alpine region. This can be explained by the lower number of samples available to illustrate such scenarios, and/or can witness cloud contamination. In less extreme cases, the localized-CNN manages to retrieve well the expected TS. The right column of Figure 3 shows time series between January 1 st 2015 and January 31 st 2015 over the same regions in the image. This figure shows that the localized-CNN is able to follow seasonal changes in TS as well as smaller variations within each season. These smaller variations are very well captured by the retrieval, in particular in summer months. The COR (i.e. correlation between the target ERA5 and CNN TS) and the COR of anomalies (i.e. correlation between target and CNN TS after removal of the seasonal mean computed over the whole database) are given at the top left corner of each time series. As expected, the correlation naturally diminishes marginally when the seasonal mean is removed from the observations, but remains very good. In order to analyse the localized-CNN retrieval, we first compare the ERA5 target TS and the CNN-TS to the EUMETSAT TS.

81

This database is described in the first section of the Part I companion papier (Boucher et al.). The comparison with EUMETSAT 82 is very interesting because its PWLR3 algorithm shares the same objective: to train a TS retrieval using the ERA5 TS target, from 83 IASI observations. An important difference is that PWLR3 benefits from the help of microwave (AMSU and MHS) observations.

84

A comparison is made with the Land-SAF TS too because this product is relatively independent from the ERA5 and EUMETSAT 85 products since it comes from a different method and observations. 86

The Land-SAF TS product being available only over land, the comparisons with the Land-SAF database will be done 87 only on LST. In the same way, only pixels that are clear according to both EUMETSAT and Land-SAF will be used in these F I G U R E 3 Left: Time series of the target ERA5 (in red) and CNN (in grey) clear sky TS, in 2015, in pixels of different regions: over land, ocean, alps and coast. Green lines are the predicted STD uncertainty estimates from section 4. Greyed areas show the predicted ± 2STD uncertainty envelope. AM and PM retrievals were combined before plotting. Right: Scatter plot of the target ERA5 vs. CNN-TS (K). AM and PM retrievals were also combined before plotting. error term, pixel per pixel, over the full domain, and then the average is computed. Statistics obtained over land and sea are distinguished. Results are given in Table 1. Comparing the databases could have been done with the triple collocation technique (Stoffelen and Vogelzang, 2012;[START_REF] Peter | Error estimation of buoy, satellite, and model wave height data[END_REF]. However, the TS estimates must comply to the hypothesis that they are the sum of the true TS and an error term, with the error terms being uncorrelated. Given that this hypothesis does not hold (e.g. both the EUMETSAT and CNN products are trained on the ERA5 product), results would not be reliable. The RMS error can be decomposed into a bias term and a random (i.e. std) term. The three datasets are just about equivalent in terms of std error. The remaining std random error can be considered inherent to the true physical difficulty of the physical inversion process, in particular the lack of some information to describe and constrain the RT equation in specific areas. The higher std error obtained between ERA5 and the Land-SAF TS can be explained by the higher independency that exists between the Land-SAF and the other TS products. Firstly, during the morning, it can be seen that the lowest RMS error is obtained between the ERA5 TS and the CNN TS. This demonstrates a good quality retrieval on our part since the model uses only IASI TB's. However, there is almost no bias between the ERA5 and CNN TS datasets, whilst biases of -1.24 K between the ERA5 and EUMETSAT products, and -1.39 K between the ERA5 and Land-SAF datasets exist. The localized CNN imposes significant localization constraints to the retrieval, thus reducing or even suppressing regional biases. This illustrates the good functioning of the technique. EUMETSAT and Land-SAF LSTs are both warmer than ERA5 LST. The Validation Report IASI Level-2 T and Q profiles release 1 provided by EUMETSAT (doi: 10.15770/EUM_SEC_CLM_0027) compares the EUMETSAT product to the ERA-Interim TS. This comparison shows regional differences ranging between ±10 K. Notably, small differences over the oceanic region but larger differences over the land present in our domain are retrieved, with the EUMETSAT TS being warmer by about 1 to 5 K than the ERA-Interim TS. Part of the reason for this is probably that EUMETSAT has a global retrieval, which is more ambitious, but it is more difficult to achieve. The correlation coefficients between the four datasets highlight that the temporal dynamics are well captured by all datasets. Besides the EUMETSAT LST product in the evening, the correlation coefficients are all very high and similar (between 0.95 and 0.98). This indicates that all daytime products contain the dynamics of the TS.

Morning (AM)

Comparing the databases in the case of ascending orbits (i.e. in the evening) yields much different results. The systemic bias of around 1 K is still present between the Land-SAF and ERA5 LSTs, but the bias of EUMETSAT LST with the ERA5 product is of around 5K over land. Furthermore, the error std (i.e. the random error) between EUMETSAT LST and the ERA5 LST is much higher than it was during the day (i.e. 3.47 K as opposed to 1.82 K). This suggests that there is a potential error in the EUMETSAT training in the evening. Discussions with EUMETSAT confirmed this hypothesis and it is suggested not to use the Metop-1 IASI L2 night-time LST data until the next release. In terms of bias, the localization information introduced in the CNN almost completely erases again the bias errors. This reduces the overall RMS error, making the CNN TS the closest dataset to ERA5.

| Training on EUMETSAT data: Intrinsic TS information from IASI

As briefly mentioned in the Part I companion paper (Boucher et al.), the choice to use the ERA5 TS as the target for the training of the CNN is based on the will to facilitate the assimilation of IASI retrieved products later on. Training on the model has multiple advantages, one of them being the "compatibility" of the retrieved product with the model (F. [START_REF] Prigent | Soil moisture at a global scale. ii -global statistical relationships[END_REF]Kolassa et al., 2013;[START_REF] Rodriguez-Fernandez | Smos neural network soil moisture data assimilation in a land surface model and atmospheric impact[END_REF]. This does not mean that the CNN is designed to reproduce the model, in particular the temporal behavior which is the most important for assimilation because each time step is processed independently.

The goal is to extract the TB/TS sensitivities from the learning database. This is the reason why it is even possible to train a NN on an erroneous database. The NN will remove any behaviour not consistent with respect to the general sensitivities present in the database (F. [START_REF] Prigent | Soil moisture at a global scale. ii -global statistical relationships[END_REF].

To illustrate this, an experiment was conducted in which another localized-CNN model following the same architecture is trained using EUMETSAT TS targets instead of the ERA5 ones. The two retrievals are then compared. Not only is this experiment interesting to illustrate that the retrieved signal is stable (regardless of the outputs used in the training phase) but also to show the behavior of a NN when training is done on an imperfect database (i.e. on the EUMETSAT TS database in which we know there are important errors at night-time).

F I G U R E 4 Distance (defined as 1-corr) between EUMETSAT/ERA5/Land-SAF and CNN retrieval trained respectively on EUMETSAT (left) and ERA5 (right)

Figure 4 illustrates the comparison between the retrieved CNN-TS obtained with the CNNs trained on ERA5 and EUMETSAT.

The distance between two datasets is calculated by 1-corr, where corr is the correlation coefficient between the two considered datasets. The retrieved CNN-TS is slightly closer (by 0.01) to the database on which the CNN was trained. However, this is globally negligible, and it is possible to conclude that the two retrievals converge towards a unique retrieved CNN-TS. This both highlights the stability of the CNN TS and confirms that training the CNN with model outputs does not lead to a reproduction of the target dataset. The model finds the statistical relationship between the inputs an the outputs, and retrieves the TS from the signal of the inputs (i.e. IASI observations only). It is also possible to conclude that an erroneous database does not significantly mislead the CNN, the CNN is in fact able to correct these errors (F. [START_REF] Prigent | Soil moisture at a global scale. ii -global statistical relationships[END_REF].

So far, results have been analyzed and evaluated against several surface temperature databases. However, only clear pixels were considered for these evaluations, even though the IASI images were filled (interpolated), pixels below the clouds were not considered. Indeed, using infrared observations for the retrieval of surface variables is possible only for clear-sky conditions.

Nonetheless, there might be here an opportunity to investigate the cloud impact on surface temperature. We define the cloud impact as: CI = TS Cloudy -TS clear-like , where TS Cloudy is the true surface temperature on cloudy conditions, available from MW observations or numerical models. The TS clear-like is the retrieval of TS under the cloudy pixels, but based on the information of the nearby clear pixels. This clear sky based retrieval is used as a virtual TS, as if no clouds were present. Such TS clear-like can be obtained for example by retrieving the TS only over the clear pixels and then interpolating between the clear-sky pixels to estimate what would be the TS at a location if there were no clouds. However, when the input-to-output relationship (and/or the retrieval model) is non-linear, it is better to first interpolate the inputs and then apply the retrieval algorithm. This section will first show that the localized-CNN presents a true advantage to retrieve such a clear-like TS, before assessing and analyzing the cloud impact.

| Evaluating the retrieval under clouds

A clear-sky TS can be obtained with the three following techniques:

(a) Pixel-wise retrieval performed on clear-sky pixels followed by a spatial interpolation of the retrieved TS. The pixel-wise retrieval is performed by independent NNs for each pixel of the domain. This retrieval scheme and its results are detailed in the Part I companion paper.

(b) Interpolation of the clear-sky TBs followed by pixel-wise retrieval on all pixels.

(c) Interpolation of the clear-sky TBs followed by a localized-CNN retrieval on all pixels.

We use the inter-/extra-polation technique presented in the Part I companion paper, which consists in using a bilinear interpolation for small holes and a PCA-based extrapolation scheme for larger holes. The latter relies on the EOFs extracted from the clear pixels in the database. The goal is to optimize, for each image, the combination of the EOFs in such a way to stay as close as possible to the available clear pixels in the image. This way, the extrapolation benefits from the big spatial patterns present in the EOFs, constrained by the available clear pixels. 1There is a necessity to evaluate how accurately the inversion algorithms retrieves the clear-like TS. To do this, four artificial holes are inserted into each image sample of the database over the clear pixels. One hole is centered in the middle of the domain, one is added over the complex region of the Alps, one over the Atlantic coast, and another over the Mediterranean sea. It is then possible to compare the true clear-sky TS with the retrieved clear-like TS.

Figure 5 shows the RMS, std and bias errors (K) of the three retrieval methods to compare the retrieved clear-like and the true clear-sky TS, over the four artificial holes that were added in the images. Not only is it possible to assess the reliability of the retrievals obtained underneath the clouds (i.e. clear-like TS), but also evaluate the quality of the localized-CNN method compared to the other approaches. Despite the fact that the three methods use the same spatial interpolation scheme, the localized-CNN

shows results of better quality. This is because it also benefits from the use of localized spatial patterns it learnt during its training time. These localized spatial filters allow for a better interpolation than when using a pixelwise retrieval combined with an interpolation method. Figure 5(b) shows the different statistics for the case with the inter-/extrapolation is performed on the TB inputs. The pixel-wise retrieval is then performed on all pixels of the images. An average of 3.8 K of RMS error is observed on the Alps and 2.4 K over the three other holes. These error terms can be decomposed between the std and bias errors: over land, the error is mostly coming from random errors, whilst over the sea, it is mostly a systematic bias. Comparing with Figure 5(a), it can be seen that statistics are very similar. It can be concluded that in this specific application, the interpolation can equally be performed on the inputs and the outputs. The relationship between TBs and TS is simple and linear, however, using such techniques for other geophysical variables where the inversion is less straightforward could benefit from the interpolation being performed on the TBs. On the other hand, an improvement is observed when looking at Figure 5(c). In fact, the overall RMS error over the Alps is of 2.9 K, 1.8 K over the central hole, 1.13 K over the Atlantic coast and 0.6 K over the Mediterranean sea. Furthermore, the bias term does not exceed ± 0.1 K over any region. The difference between columns (b) and (c) almost only comes from the fact that the localized-CNN benefits from small localized spatial patterns that help it to adapt to local conditions. This was already shown when analyzing the results over clear-sky, but this experiment shows that this feature is also useful for the retrieval of a clear-like TS. This is the reason the land/sea frontier can be very clearly seen in the CNN retrieval over the Atlantic coast. This clearly shows the advantages of image-processing techniques that have specialized spatial filters according to the location in the image.

This presents a real advantage when trying to obtain intelligent spatial interpolations.

| Preliminary analysis of the cloud impact

The previous sub-section confirmed that the localized-CNN model retrieves a clear-like TS in cloudy pixels, with an uncertainty of approximately 2 K. It is therefore worth attempting to use the clear-like retrieval in cloudy pixels as a way to analyze the Cloud Impact (CI) on the surface temperature. As previously defined, this can be done by subtracting the clear-like TS from the cloudy TS obtained by NWP models (i.e. here the ERA5 re-analysis) or all-sky retrievals such as the one from the EUMETSAT PWLR3

Algorithm. The impact of clouds on surface temperature depends on many parameters ranging from the weather conditions, the height of the cloud to its density, including also the amount of time the cloud stays over a given pixel. It is not possible here to assess all these complex relationships, but a preliminary analysis can be undertaken to show the potential of the IASI clear-like TS.

Figure 6 represents the CI against the Cloud Fraction (obtained from EUMETSAT) and the total column Cloud Liquid

Water Content (CLWC) (obtained from the ERA5 re-analysis). The figure gives binned statistics: the mean of the difference between the ERA5 cloudy TS and clear-like TS is computed and plotted for each bin in the x-axis. Mornings and afternoons are distinguished because the CI is very dependant on the diurnal cycle. It must be noted that the TS reaches its diurnal minimum and maximum at respectively 1 am and 1 pm. Considering that IASI orbits pass over the chosen spatial domain between 9-11 am and 9-11 pm, we should be prepared to observe a lower CI than if the analysis was conducted at the peaks of the diurnal cycle. In both plots, it should be noted that when the cloud property is at zero (i.e. when there are no clouds), the CI is zero: this is a logical behaviour, but it shows the robustness of these bias measurements. Statistics computed over the mornings and over the afternoon/evenings are symmetric: During the day, clouds tend to cool the surface by reflecting a portion of the solar energy back into space; on the contrary, during the night, clouds limit the cooling of the surface since some heat is trapped and reflected back to the surface by the clouds, thus keeping the surface warmer that it would be in clear-sky conditions. These trends are illustrated in Figures 6(a) and (b) where the CI is negative in the morning and positive in the afternoon. Obtaining correct trends suggests that the information present in the clear-like TS has a potential to be exploited.

Looking more closely at Figure 6(a) reveals that the higher the Cloud Fraction, the higher the cloud impact on the TS (whether that be positive or negative). The same trend is observed in Figure 6(b) when plotting the cloud impact against the total column CLWC. Again, the most impact is observed when CLWC is highest. However, a saturation can be seen when the CLWC reaches 1 kg/kg.

However, there are many sources of uncertainties in the cloud-to-TS relationship: First, there are large uncertainties on the cloud property products from EUMETSAT and ERA5. Secondly, the CI is calculated by the difference between the ERA5 re-analysis cloudy TS and the "clear-sky" TS obtained by the CNN, both having large uncertainties, propagating to the CI value.

Lastly, there is also a large uncertainty on the existing relationship between the cloud properties and the CI. The CI depends on many parameters. Simple and averaged cloud properties, such as the two considered here, can hardly fully describe the complexity of clouds, so a one-to-one relationship does not exist.

A physical algorithm using the land surface energy balance model has been used to derive an all-sky retrieval of surface temperature [START_REF] João | An all-weather land surface temperature product based on msg/seviri observations[END_REF]. It would be feasible to construct a NN to estimate the CI from the cloud properties. If precise enough, this estimate could then be used to correct the clear-like TS, turning it into an all-sky estimate of the TS, based solely on infrared IASI observations and the description of the cloud properties. However, as mentioned above, relationships between cloud properties and the CI are affected by many sources of uncertainties which would result in a very large uncertainty on the CI. A neuronal modeling would retrieve the deterministic statistic, but not its spread due to uncertainties. This experiment is left for future studies.

| ESTIMATING CNN RETRIEVAL UNCERTAINTIES

Estimating CNN retrieval uncertainties is not an easy task, however it is a necessary one if we want to make the most out of Machine Learning techniques. This is especially true in a NWP assimilation context. In fact, uncertainties of retrieved products are important not only to assess the quality of the retrievals but also to assimilate them into NWP frameworks. We use here a simple and easy-to-implement scheme based on the partitioning of the input space, originally proposed in (Aires and Pellet, 2021). We adapt the approach to make it suitable for the localized-CNN architecture, but the algorithm could easily be applied to all the NN methods presented in the Part I companion paper.

| Methodology

We begin by considering the case of only one pixel of the domain for illustrative purpose. Figure 7(a) represents the scatter plot of the target ERA5 TS against the the first principle component (PC1) of the IASI TBs that includes the most information on TS.

The relationship between the two variables is very strong, almost linear in fact. The horizontal axis is divided into 5 bins based on the quantiles of the distribution, and the mean TS for each bin is represented as a black square. This represents what a NN would try to learn with this monovariate input-to-output configuration. We also see a vertical dispersion, by plotting the ± STD envelope around the mean. This represents the missing information to retrieve perfectly the TS only from the TB PC1. However, part of this dispersion is in fact explained by the two other PCs used in the inputs of the NN. This can be seen by looking at the retrieved TS, plotted in blue. The remaining vertical spread (i.e. the part of the vertical spread that is not explained by the other PCs and thus not present in the retrieval) describes the inherent difficulty of the inversion problem that we would like to estimate during the retrieval itself. This difficulty comes from several sources of errors (Abdelaziz et al., 2015). Firstly, from the IASI instrument itself. Indeed, the TBs measured by IASI are contaminated by some instrumental noise. This error also propagates into the spectral PCA and thus into the principle components used here, although the PCA considerably reduces this noise (Aires et al., 2002a,c,b). As it is shown in the part I companion paper, using only the TBs (or PCs) may not be sufficient to obtain a perfect retrieval across the entire database, as some physical information might be missing such as the surface emissivity. In our case, the target TS comes from the ERA5 model and has been put into coincidence with the IASI measurements, so collocation errors may also be present. The ERA5 TS is also not the truth. All of these error sources, alongside the error of the CNN itself and any other sources of error which may have been omitted here, add up to the remaining vertical spread, or retrieval errors shown in Figure 7(b). It is seen in Figure 7(a) that the retrieval is not able to completely explain the vertical spread in the cold extremes, however, a lot of the vertical spread in hot extremes is well retrieved by the CNN. This explains the structure of the CNN retrieval error in Figure 7(b).

If we consider that this CNN retrieval error is a summary of all the sources of uncertainty, we can extract two types of information about it: The Bias: B = mean( T S -T S ), where T S is the surface temperature retrieval from the localized-CNN;

and the Error STD: STD = STD( T S -T S ). We concentrate on estimating only the error STDs. The method proposed here to estimate the error STDs is however applicable to other error statistics, as it is done in (Aires and Pellet, 2021). To estimate this error STD, we must define sets of samples in which we can actually empirically calculate the STD diagnostic. We must define these sets of samples in such a way that (1) they are adapted to each location of the image and (2) they can be obtained from the information that is available at the time of the retrieval, namely the TBs. We choose to create five bins based on the TB distribution quantiles. It is considered here that it is sufficient to consider the bins on the first TB PC only since it represents more than 95% of the input information.

The problem becomes more complex when the partitioning has to be done on a multivariate input space, but a solution for the multivariate case is also proposed in (Aires and Pellet, 2021) using a clustering instead of a binning approach. Since uncertainties vary with the location in the domain, the binning is done independently for each one of the 2640 pixels of the chosen spatial domain. In each of these bins, the STD of the error obtained with the localized-CNN is calculated, as shown for one pixel in Figure 7.

We therefore obtain images in which we represent the error STD for each pixel. We can then utilise these error STD images to train a second NN. We use a Localized-CNN following the same architecture as the one previously used for the TS retrieval, except it is asked to retrieve simultaneously the TS and its associated error STD. Although the Localized-CNN is trained with targets on only five discrete error STD values, it actually simulates a continuous mapping between the inputs and the retrieval error STDs, so continuous STDs are obtained when the CNN is used in operational mode. This process is shown in Figure 7(b):

For this specific pixel, the CNN is asked to retrieve the black square for all samples in each bin, and the CNN learns a continuous mapping, by interpolating the binned values.

| Results

The left column in Figure 8 shows the target, empirical error STD in red and the estimated error STD in grey. These error STD are represented in Kelvins (K) and plotted against the first TB PC. Results are shown over four representative pixels. As expected, larger errors occur in the more complex parts of the image, namely the Alpine region and inland. Retrieval errors over sea and coastal regions are smaller and relatively stable, respectively ranging between 0.8-1 K, and 2.2-3 K. The network predicts well the general behaviour of the uncertainties: it is continuous, and the lower the TB PC1, the higher the error. Also, looking back at the left column of Figure 3, it can be seen that the prediction, plotted as the grey line, lies mostly in the ±2 STD uncertainty envelope. The green line shows the predicted error STD throughout the 2015 year. The fact that this line is varying suggests that the error estimate is state-dependant, which is a necessary feature. This is also shown in Figure 9.

Fundamentally, the retrieval errors (or uncertainties) are a distribution2 . Here, the CNN is asked to only retrieve the STD of the error distribution. It is therefore important to check whether the predicted STD is in fact the STD of the empirical errors. For this, we compare the number of times the empirical error is inside the predicted STD and 2STD envelopes. The empirical error is inside the 1STD uncertainty envelope around 80 % of the time (80 % for a Gaussian) , and inside the ±2STD uncertainty envelope around 96 % of the time (95 % for a Gaussian), depending on the localization. This gives an indication that the predicted STD describes relatively well the uncertainties of the CNN retrieval, since error PDFs can depart from a Gaussian distribution.

The predicted error STD is a coherent measure of the retrieval uncertainty. The method here proves to be robust to correctly predict the STD of the error distribution.

LAND SEA

| DISCUSSION ON THE GLOBAL RETRIEVAL STRATEGY

The experiments and methods presented in both Part I and Part II papers concentrated on a fixed spatial domain. However, IASI is placed on-board the MetOp polar orbiting satellites and the raw data are under a much more complex format than simple images over a chosen domain. The part I companion paper and the first sections of this paper have shown how complicated it can be to process orbit data over a fixed domain. It is even more complicated to proceed at the global scale. However, it is a necessity.

The next step of this study is therefore to discuss the potential strategies to setup a global retrieval from IASI observations using image-based NN approaches.

| Pixelwise approaches

Currently, global retrieval using IASI observations is performed at the pixel level, for both statistical inversion methods [START_REF] Bouillon | Time evolution of temperature profiles retrieved from 13 years of iasi data using an artificial neural network[END_REF] and physical inversion algorithms. This has proven to be efficient, especially with coarser resolution instruments like IASI. This approach should not be neglected, despite the rise of image-processing approaches as described in this paper, because numerous practical difficulties do not appear in a pixelwise approach.

| A unique global MLP

In global retrieval schemes, it is very natural to use a unique global MLP, expected to retrieve the geo-physical variable globally [START_REF] Bouillon | Time evolution of temperature profiles retrieved from 13 years of iasi data using an artificial neural network[END_REF][START_REF] Safieddine | Artificial neural networks to retrieve land and sea skin temperature from iasi[END_REF]Aires et al., 2002d;[START_REF] Claudia Parracho | IASI-derived sea surface temperature dataset for climate studies[END_REF]. For this approach, the database consists of pixelwise inputs (namely TBs) located over the whole globe. A unique MLP model is then trained on samples covering the entire globe, and it is expected to retrieve the pixelwise TS globally. This approach is referred to as "a unique global MLP"

(Figure 10 (a))
This approach is theoretically what the community would like to use because it is very simple to implement and to use in operational mode. Missing pixels can be omitted and there is no need for missing data filling. In the context of global assimilation, it is also very practical when retrieved variables are directly assimilated into NWP models, which is still rare [START_REF] Rodríguez-Fernández | Smos neural network soil moisture data assimilation in a land surface model and atmospheric impact[END_REF]. The task asked to the MLP to perform well, over very diverse environments, and often with limited available information, is however complex and ambitious. The quality of the results depend strongly on the variable to retrieve. For surface properties such as the TS, this strategy can lead to the model having to make significant compromises. To facilitate the MLP task, it is possible to introduce localization variables into its inputs, helping to adjust its behaviour to local conditions. This improves the results by reducing regional biases. Global pixelwise MLP retrieval can also be helped by using CDF-matching (Aires et al., 2021b), another form of localization. This is useful, especially for assimilation of the retrieved data because it calibrates the CDF of the satellite-based estimate towards the CDF of the corresponding variable in the model. The variables are therefore harmonized and there is increased compatibility between the model and the retrieved variable.

However, using localization and/or CDF-matching often does not resolve all the problems the unique MLP model must face, especially if not enough information is provided to it to fully constrain the RT.

| Independent MLPs for each pixel

An alternative for pixelwise retrieval is to use independent models for each pixel of the domain (see section 3.3.1 in (Boucher et al.)). In this approach, every pixel is considered independently, and an independent MLP model is trained over each pixel.

This means that for each pixel on the globe, a separate database is built and a MLP model is trained only for this location. Each model is then asked to retrieve the TS only for its specific location. We refer to this technique as "independent MLPs for each pixel", see Figure 10 (b).

In this technique, each MLP model focuses on the input-to-output relationship over a specific location only. Therefore, a very simple MLP is sufficient to capture the dependencies over each pixel, providing un-biased results of good quality. Each model requires very little parameters, imagining a global processing scheme where each pixel of the globe is characterized by its own model does not imply a huge amount of learnable parameters and the computation time is neglectable, especially because this processing can easily be vectorized. This technique is also very simple to implement and again, missing data can easily be omitted from the processing scheme.

However, the training of MLPs can be unstable on certain locations, and using independently trained models can increase the granularity of the retrievals and may introduce some noise in the retrieved product. The impact this may have on the quality of the results depends on the variable to retrieve.

| Image-processing approaches

This two-parts paper aims at finding which image-processing approaches has the potential to be used for remote sensing.

Although the benefits were illustrated, image-processing approaches come with costs, ranging from the creation of images from IASI orbit data to dealing with missing values. Solutions were proposed to solve these operational problems, but only over a fixed domain. The required pre-processing steps that were proposed are harder to overcome when dealing with global scale retrieval as it is the case in an assimilation context. This section aims to discuss several possibilities available to use image-processing techniques globally and expose their respective advantages and draw-backs.

| Varying spatial domains

The most logical technique when using image-processing is to use images covering varying spatial domains. This is the standard approach for most uses of CNNs (Aires et al., 2021a). This would consist in fixing an image size and extracting images of such size all over the globe. A unique CNN model would then be trained on generic images from all around the world (Aires et al., 2021a). This CNN is expected to retrieve the TS for images over the entire globe. We refer to this approach as "varying spatial domain", see Figure 10 (c).

Regular sized-images must then be extracted directly from the orbits. It is however necessary to deal with missing data since CNN models require complete images. This step can be difficult, especially for infrared images that are very sensitive to clouds.

Theoretically, the stacking of many convolutional layers should allow the CNN to learn to detect spatial patterns that are general enough to help the retrieval on any of the spatial domains present in the database. In this case, the strategy is close to the unique global MLP because a general model is asked to perform well in very different environments. The difference is that instead of a pixel information, the CNN uses a local spatial domain and pertinent local spatial patterns helping the retrieval must be found during the training.

This technique was tested in an SST retrieval context (Aires et al., 2021a). It is complicated for the CNN to retrieve spatial patterns because the images represent much contrasted environments, it is in contradiction with the localized-CNN strategy since the domain is not stationary. A localized-CNN has the potential to find specific spatial patterns for specific parts of the images, however, if the observation position varies from image to image, this is a useless feature. Therefore, using a CNN in this context would require to be very deep and contain many convolutional layers to be able to cover all kind of spatial patterns present in the database (i.e. over the entire globe) which is sometimes too computationally expensive. It is however the classical approach used in most CNN applications. As any-image processing approach, its application is practically made complicated by the need to preprocess the input images to fill in the missing data.

| Multiple static domains

Another solution is to consider multiple static domains over the globe, and to train independent CNN models on each of the static domains as it was done in this two-parts paper. Each CNN model is expected to retrieve the TS only over its static domain.

This approach is referred to as using "several static domains". Figure 10 (d) illustrates this approach.

This approach allows to use both generic CNNs and a more localised version in which filters specialize to certain parts of the images (see section 3.3.2 in (Boucher et al.)). This allows obtaining results of better quality, by localizing in terms of spatial domain and inside the domains themselves. Depending on the size of the chosen static domains, pre-processing steps such as filling missing data can be more or less easy to implement. If domains are of similar size to the one used in this study (i.e. 44 × 60 on a 0.25 • regular grid), inter-/extra-polation algorithms based on EOFs can be developed for each domain, and cloudy pixels can be filled. The limit of the approach that was proposed in (Boucher et al.) is to not being able to include too independent weather phenomena inside the same domain. However, the main disadvantage of this method is the potential discontinuities between each domain, and this could cause problems for example in the assimilation process. This problem could however be resolved by considering overlapping domains.

| Global domain

A straight-forward approach for global retrieval would be to consider the global domain as the input for the CNN. In this approach, images covering the whole globe will be considered, and a single CNN (localized or not) would be expected to retrieve the TS globally. We refer to this approach as the "global domain". Figure 10 (e) illustrates this approach.

Treating such large images should not be a problem. Image processing approaches are often used on similarly-sized images coming for high resolution images. This approach would not lead to any discontinuities between domains.

However, one of the challenges of using polar-orbiting satellites is the impossibility to obtain a global image at the same observation time. Obtaining a global image would require concatenating several orbits, and some points would be retrieved several times, at different times of the day, contradicting the concept of images. Spatial patterns will be harder to extract from such images. Additionally, this technique does not allow for real-time processing since it is necessary to wait for global coverage before processing an image.

| Orbit domains

Finally, an alternative solution would be to view orbits in an unconventional manner. Orbits can be seen as a matrix of pixels connected to one another, just like an image. Therefore, it is possible to process orbits in the same way images can be processed.

This approach will be referred to as "orbit domains". Figure 10 (f) illustrates this approach.

The IASI instrument has a swath width of 2200 km, decomposed into 30 field of views each. Each of the four pixel collects the radiance spectrum formed of the 8461 IASI channels. The number of swaths m depends on the orbits. Each IASI orbit can therefore be considered as an image of size 2m × 60. Two orbits can be considered the same if they observe the same pixels at the same time of the day. IASI has a revisit period of 29 days. With 14 orbits a day, there are therefore 14 × 28 = 392 different orbits possible. Each orbit can be treated with a separate model, in a similar way as when working with static domains (section 5.2.2). A considerable advantage of treating orbits directly lies in the capacity to treat the incoming IASI data in near real-time.

When the orbit is received by the server, it can be fed to the trained CNN and temperature retrievals will be available almost immediately. This is not the case when dealing with global images since the global coverage is needed before being able to process the data. Speaking in terms of assimilation, NWP models are fed with data in the form of orbits. Processing IASI data directly under its primary form is therefore a pertinent choice. Secondly, there is no need to consider and/or specify the viewing angle. Since each orbit is processed independently, within one dataset, each orbit will see the same pixel with the same viewing angle. Thus, the relationship between the TBs and the retrieved variable for one specific pixel will not be affected by a varying viewing angle.

The downside of using such a technique mostly lies within the filling of the missing data. Using an EOF-based inter-/extrapolation method is possible, but undergoing a PCA covering the whole orbit domain comes with difficulties: in fact, it is problematic to use a spatial pattern in the form of EOFs that will link phenomena in the north and south pole. Therefore, a new filling strategy should be put in place to pre-process these orbits.

| CONCLUSION

This part II paper focused on the localized-CNN retrieval approach. This choice was motivated by the goal to better understand and analyse DL techniques, and more particularly image-processing approaches for the processing of remote sensing with coarse resolution instruments such as IASI. It was shown that the localized-CNN architecture is a promising DL solution that provides TS retrievals of good quality. Overall, an average STD error of 0.82 K is obtained over the sea (i.e. for the retrieval of SST), and 1.90 K over land (i.e. for the retrieval of LST) in comparison to the ERA5 re-analysis. These statistics appear to be an improvement compared to the EUMETSAT PWLR3 retrieval that benefits from additional MW observations. The retrieval also seems to compare to the Land-SAF in a similar way than ERA5 does, witnessing of the quality of the Localized-CNN retrieval.

The localized-CNN architecture uses the spatial dependencies to improve the retrieval in pixels that benefit from it, mixed with a more pixelwise approach in complex areas where little or no spatial coherency is found. Contrarily to more generic CNNs, localized-CNNs benefit from the specialized filters as opposed to the general ones found by a generic CNN. This feature is a true advantage when trying to exploit spatial dependencies. The training is capable of finding the TBs-to-TS dependencies present in the IASI observations, in relatively independent way from the ERA5 targets it was trained on. This is confirmed when comparing results produced by the same CNN trained on another target database. When working with NNs (both MLPs and CNNs), it is useful to obtain an estimate of the retrieval uncertainties, especially for data assimilation where the model versus observations compromise is weighted depending on them. An innovative solution is proposed here to estimate a state-dependent retrieval uncertainty.

Perspectives are numerous for this work. The next step is to manage to retrieve a global scale estimate of the TS using DL image-processing techniques, and then to build a long-term record from IASI observations into an operational framework.

EUMETSAT continually updates its processing algorithms for IASI radiances. Recently, a new processing of the IASI TBs between 2007 and 2017, aiming to homogenize past observations with recent ones, was released, and there is a real interest in producing the associated homogeneous geo-physical variable dataset [START_REF] Bouillon | Time evolution of temperature profiles retrieved from 13 years of iasi data using an artificial neural network[END_REF][START_REF] Safieddine | Artificial neural networks to retrieve land and sea skin temperature from iasi[END_REF]. The last section of this paper begins to answer the question of how to proceed for a global scale retrieval. All options should be considered. We believe there is a true potential in treating IASI orbits as static images. It is with this processing scheme that we will benefit the most from image-processing approaches, and from the localization, like it was done in this paper. Creating a set of NNs that are capable of retrieving the TS over the entire globe in near-real time would also present a real advantage if retrieved products were to be assimilated into NWP models.

Perspectives also include further assessing the capability and utility of image-processing techniques. Differences in local conditions will be more noticeable and introducing localization into global NN schemes will allow for interesting comparisons.

Extending this work to the retrieval of other surface properties (e.g. emissivities) or atmospheric variables (e.g. temperature and humidity profiles) is also one of the future works we would like to put in place. Atmospheric variables offer a possibility to work with 3D spatial patterns, and CNNs could become an asset for this type of data. Studying vertical profiles also allows to experiment with deeper networks since a lot more IASI channels will need to be considered than for TS retrieval. PCAs are often used to reduce the depth of the NN inputs (Aires et al., 2002a,c,b;[START_REF] Pellet | Bottleneck Channels Algorithm for Satellite Data Dimension Reduction: A Case Study for IASI[END_REF], but this could potentially be integrated directly into the CNN: Resulting spectral filters would therefore be non-linear [START_REF] Pellet | Bottleneck Channels Algorithm for Satellite Data Dimension Reduction: A Case Study for IASI[END_REF], data-driven, and flexible. This could also be accompanied by the introduction of constraints directly into the CNN such as a priori knowledge or smoothing constraints. More generally, these experiments will evaluate the usefulness of image-processing approaches for IASI retrievals, and for similar instruments at a greater extent.

The work on estimating retrieval uncertainties is far from finished. Perspectives include estimating uncertainties for the multivariate case such as temperature and humidity profiles for CNN retrievals [START_REF] Gal | Uncertainty in Deep Learning[END_REF], in which uncertainties for each variable is needed, as well as correlation of errors. Ultimately, we would like to manage to retrieve the error covariance matrices.

This would facilitate the assimilation process (Aires and Pellet, 2021).

To go further, we would like to try to exploit the synergy between IR and MW observations to obtain better retrieval results [START_REF] Aires | Measure and exploitation of multisensor and multiwavelength synergy for remote sensing: 1. Theoretical considerations[END_REF][START_REF] Aires | Measure and exploitation of multisensor and multiwavelength synergy for remote sensing: 1. Theoretical considerations[END_REF][START_REF] Aires | Synergistic multi-wavelength remote sensing versus a posteriori combination of retrieved products: Application for the retrieval of atmospheric profiles using metop-a[END_REF]. One of the main difficulties when putting in place synergic schemes is the collocation of the respective IR and MW observations. The IASI instrument is onboard the MetOp satellites, on which also reside the AMSU instruments. Using the AMSU MW observations will therefore make this step much simpler. Although algorithms exploiting the synergy between IR and MW have already been put into application, we believe image-processing approaches like CNNs should help further exploiting these dependencies.

AC K N O W L E D G E M E N T S

The authors would like to thank EUMETSAT, more specifically Marie Doutriaux-Boucher, for providing the IASI data and data

1 |

 1 U R E 2 Example of the ERA5 TS in column (a), the retrieved CNN-TS in column (b); followed by the difference in column (c). The date and time of the orbit is mentioned in column (a). The first two lines are examples from the morning and the two last lines are from the evening. Comparison with EUMETSAT and Land-SAF TS 80

  88comparisons. The overall RMS error, bias, standard deviation and correlation coefficient between the four datasets (ERA5, 89 EUMETSAT, Land-SAF, CNN retrievals) are computed over the 2015 year. These statistics are obtained by first calculating the ALPS

  Pixel-wise retrieval, interpolation on outputs (TS) (b) Interpolation on inputs (TBs) and pixel-wise retrieval (c) Interpolation on inputs (TBs) and localized-CNN retrieval F I G U R E 5 From top to bottom: RMS, std and bias error (K) between the true clear-sky TS and the retrieved clear-like TS over the four artificial holes. The interpolation of the artificial holes follow the aforementioned (a) (b) and (c) methods.

  0.25 0.50 0.75 1.00 1.25 1.50 Total Column Cloud Liquid Water Content (kg/kg) U R E 6 Cloud Impact (CI) calculated by the difference between the ERA5 cloudy TS and the IASI clear-like TS (K). The average CI is plotted for each bin of the Cloud Fraction (a) and Total Column Cloud Liquid Water Content (CLWC) (b). In blue is plotted the morning data (AM), and in red the afternoon data (PM).

  U R E 7 (a) The red dots show the ERA5 TS against the TB PC1. The TB PC1 space has been divided into 5 bins according to the quantiles of the TB PC1 distribution. The black squares represent the average TS in each bin, and the two black dots are ± STD. Blue dots are the CNN-Retrieved TS against the TB PC1. (b) The red dots show the TB PC1 versus the CNN retrieval error (ERA5 TS -CNN TS). Black squares are the error STD per bin and the blue dots are the CNN estimate of the error STD.

  U R E 8 Scatter plot of empirical error STD (in red) and predicted error STD (in grey) vs. the TB PC1. Results over four pixels of different regions are shown. Empirical error STD (K) (b) Predicted error STD (K) F I G U R E 9 Example of the empirical (a) and predicted (b) error STD. The first row is an example of morning data, the second row is an example of afternoon data.

  domains F I G U R E 1 0 Considered strategies for global retrievals. Rows (a) and (b) are pixelwise approaches, while rows (c) to (f) are image-processing approaches.

  reading software. Thank-you to Cathy Boonne for helping to access IASI data on the AERIS data center. Thanks also to the ECMWF for the provision of the ERA5 Skin Temperature data and especially to Patricia de Rosnay and to Tony McNally for fruitful discussions. We would also like to thank the Centre National d'Etudes Spatiales and Thalès, in particular, Clémence Pierangelo, Clément Luitot and Laure Chaumat for engaging discussions that have lead to the funding of Eulalie Boucher's PhD that began in November 2021.

More details are provided in section 4 of the Part I companion paper.

Some techniques such as Quantile Regression Neural Networks (QRNN) exist to approximate this distribution[START_REF] Pfreundschuh | A neural network approach to estimating a posteriori distributions of bayesian retrieval problems[END_REF].

C O N FL I C T O F I N T E R E S T

None.

R E F E R E N C E S

Ahmed Hussen Abdelaziz, Shinji Watanabe, John R. Hershey, Emmanuel Vincent, and Dorothea Kolossa. Uncertainty propagation through deep neural networks. September 2015. URL https://hal.inria.fr/hal-01162550.

F. Aires, W. B. Rossow, N. A. Scott, and A. Chédin. Remote sensing from the infrared atmospheric sounding interferometer instrument 2. simultaneous retrieval of temperature, water vapor, and ozone atmospheric profiles. Journal of Geophysical Research: Atmospheres,


Marco E Mechan-Llontop

John Mullet

Ashley Shade
email: ashley.shade@cnrs.fr

Genome-sequenced bacterial collection from sorghum aerial root mucilage

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

Announcement

Plant phyllosphere exudates harbor microbial communities that can engage with the host plant, for example by supporting plant growth or resilience to various stresses [START_REF] Galloway | Sticky mucilages and exudates of plants: putative microenvironmental design elements with biotechnological value[END_REF]. The heat and drought-tolerant crop bioenergy sorghum (Sorghum bicolor L. Moench) secretes abundant polysaccharide-rich mucilage on aerial roots (2) that selects for specialized microbiome members that are expected to have plant-beneficial functions [START_REF] Mechan-Llontop | Phyllosphere exudates select for distinct microbiome members in sorghum epicuticular wax and aerial root mucilage[END_REF]. As a resource for the genomic and functional diversities of bacteria associated with phyllosphere exudates, we present a collection of 47 bacterial strains isolated from sorghum aerial root mucilage. These strains represent 17 families and include 21 genera with putative plant-beneficial capabilities, including nitrogen fixation, phosphate solubilization, resistance to terpenoids, use of methanol as a carbon source, and tolerance to desiccation, based on the culture media in which the strains were originally isolated.

Bacterial strains were enriched from the mucilage of aerial roots of bioenergy sorghum cultivar TAM 17651 grown at the Great Lakes Bioenergy Research Center (GLBRC), as part of the Biofuel Cropping System Experiment (BCSE) in Hickory Corners, Michigan (42 o 23'41.6" N, 85 o 22'23.1" W). Aerial roots were harvested from sorghum plants grown on nitrogen-fertilized plots on September 25, 2020.

We used sterile razor blades to collect between 3 to 5 aerial roots per plant into sterile 50 ml Eppendorf tubes. Within 2 hours of collection, the samples were transported to the laboratory on ice and stored at -80 o C for 18 days until processing. Sorghum mucilage was first fully hydrated with sterile water, removed from roots, and then collected into sterile 1.5 ml Eppendorf tubes. Mucilage collected from different plants was pooled and serially diluted from 10 -1 to 10 -4 . To capture a diversity of bacteria from the rehydrated mucilage, we used a variety of cultivation media (Table 1). To select for anaerobic bacteria, plates were placed in anaerobic jars containing three bags of anaerobic gas generator (Mitsubishi AnaeroPack System). All plates were incubated at 25 o C and 37 o C for up to 14 days. Isolated colonies were transferred on new plates with the same medium as used for isolation to confirm purity. Bacteria were grown overnight on R2A broth at 28 o C with shaking at 200rpm and glycerol stock (25% v/v) of pure isolates was stored at -80 o C. Prior to performing whole genome sequencing, we performed full-length 16S rRNA gene sequencing, with universal primers 27F (5′-AGAGTTTGATCCTGGCTCAG-3′) and 1492R (5′-TACGGTTACCTTGTTACGACTT-3′) (4), of each isolate using the Sanger protocol to link them to corresponding 16S rRNA gene amplicon dynamics in a field microbiome study that was executed at the same field site at Kellogg Biological Station (3).

Individual isolates or the full cryopreserved collection is available. Cultures can be regrown using standard microbiological procedures to transfer a sterile inoculation loop of freezer stock onto the original isolation medium and incubation time (Table 1).

The high molecular weight genomic DNA of each bacterial isolate was extracted using a phenol/chloroform extraction protocol [START_REF] Kutchma | Small-Scale Isolation of Genomic DNA from Streptomyces Mycelia or Spores[END_REF]. The genomic DNA of each strain was submitted to the U.S.

Department of Energy Joint Genome Institute (DOE-JGI), for library preparation and sequencing with the Pacific Biosciences (PacBio) platform [START_REF] Eid | Real-Time DNA Sequencing from Single Polymerase Molecules[END_REF]. DNA was sheared to 10kb using g-TUBE columns (Covaris) and subjected to library preparation using the SMARTbell Express Template prep 2.0 kit and sequenced on the PacBio Sequel platform. Samples that failed DNA quality control required by the PacBio pipeline were instead sequenced using the NovaSeq S4 Illumina platform [START_REF] Bennett | Solexa Ltd[END_REF]. High fidelity CCS reads >5kb were assembled with Flye 2.8.3 (8) using default settings. Raw Illumina sequences were quality-filtered using BBTools [START_REF] Bushnell | BBTools software package[END_REF]. Artifact filtered and normalized Illumina reads were assembled with SPAdes v3.15.3 (10) (phred-offset 33 -cov-cutoff auto -t 16 -m 64 -careful -k 25,55,95), and contigs were discarded if the length was <1kb (BBTools reformat.sh: minlength=1000 ow=t). Genome completeness and contamination was estimated with CheckM v1.2.2 [START_REF] Parks | CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes[END_REF]. The assembly was annotated using Prokka 1.14.6 [START_REF] Seemann | Prokka: rapid prokaryotic genome annotation[END_REF] and the phylogenetic tree was inferred using Orthofinder 2.5.5 [START_REF] Emms | OrthoFinder: phylogenetic orthology inference for comparative genomics[END_REF] and edited with iTOLs v.6.5.8 (14) (Figure 1). were also included. The species Tree was inferred from unrroted orthogroups gene trees using STAG and then rooted using the STRIDE algorithms implemented in OrthoFinder. Species tree was annotated with iTOLs.

FiguresFigure 1 .

 1 Figures

TABLE 1 .

 1 - Taxonomy, colony phenotype, and genome characteristics of 49 bacterial isolates from sorghum aerial root mucilage, as described in this study. SRA is the sequence read archive of the U.S. National Institutes of Health.

	Isolate ID	Bacterial species	Initial isolation media	Temperature, oxygen availability	Colony phenotype	Sequencing Platform	SRA accession number	GenBank accession	Genome length (bp)	N o contigs	Coverage	GC %	Genome completeness
	SORGH_AS_0335 Acidovorax wautersii	R2A	25	o C, anaerobic Small, white PacBio	SRP437222	JAVIZX000000000	4,571,377		70.4 X	68.30% 99.52%
	SORGH_AS_0440 Agrobacterium sp.	M9 + 0.4% sucrose 25	o C, aerobic	White	PacBio	SRP437134 JAVIYR000000000	5,277,481		166.0 X	59.30% 99.04%
	SORGH_AS_0745 Agrobacterium sp.	M9 nitrogen-free + 1% xylose	37	o C, aerobic	White, EPS production	PacBio	SRP437174	JAUTBI000000000	5,670,289		177.6 X	58.60% 99.88%
	SORGH_AS_0749	Agrobacterium tumefaciens	M9 nitrogen-free, Carbon-free	37	o C, aerobic	White, EPS production	PacBio	SRP437121	JAURUU000000000 5,669,583		151.8 X	58.40% 99.93%
	SORGH_AS_0212 Arthrobacter sp.	Jensen	25	o C, aerobic	White, EPS production	PacBio	SRP437201	JAUTAA000000000	4,369,871		117.7 X	66.10% 99.71%
	SORGH_AS_0304 Atlantibacter sp.	Jensen	25	o C, anaerobic White	PacBio	SRP437126 JAVLWF000000000 4,876,478		140.5 X	56.20% 99.39%
	SORGH_AS_0510 Bacillus sp.	R2A + 0.1% linalool 37	o C, aerobic	White	PacBio	SRP437241	JAUTAU000000000 4,828,420		171.6 X	38.90% 98.52%
	SORGH_AS_0407 Bacillus thuringiensis	R2A + PEG6000 -0.73MPa	25	o C, aerobic	White	PacBio	SRP437118	JAURUT000000000 5,894,929		106.5 X	35.40% 99.34%
	SORGH_AS_0431	Brevundimonas vesicularis	M9 + 0.4% sucrose 25	o C, aerobic	pink	PacBio	SRP437147	JAUTAQ000000000 3,191,065		68.5 X	66.20% 99.05%
	SORGH_AS_0447 Chryseobacterium sp.	TSA	25	o C, aerobic	Yellow	PacBio	SRP437232	JAUTAR000000000 4,374,394		132.6 X	39.90% 99.51%
	SORGH_AS_0776 Curtobacterium sp.	M9 nitrogen-free + 1% xylose	37	o C, aerobic	Yellow	PacBio	SRP437163	JAVIYU000000000	3,877,562		164.5 X	71.30% 98.99%
	SORGH_AS_0287 Enterobacter sp.	Jensen	37	o C, anaerobic	White, EPS production	PacBio	SRP437233	JAVIZW000000000 4.947,816		108.4 X	54.60% 99.97%
	SORGH_AS_0622 Flavobacterium sp.	MMS methanol	25	o C, aerobic	Orange yellow	PacBio	SRP437242	JAUTAX000000000 5,243,061		137.1 X	33.80% 98.94%
	SORGH_AS_0428 Microbacterium sp.	M9 + 0.4% sucrose 25	o C, aerobic	Light yellow, EPS	PacBio	SRP437182	JAVIZT000000000	3,300,177		89.6 X	69.60% 98.23%
						production
	SORGH_AS_0445	Microbacterium foliorum	TSA	25	o C, aerobic	Light yellow PacBio	SRP437225	JAVIZQ000000000	3,850,168		187.5 X	67.90% 99.14%
	SORGH_AS_0209	Microbacterium proteolyticum	Jensen	25	o C, aerobic	White, EPS production	PacBio	SRP437239	JAUSZZ000000000	3,767,688		122.8 X	70.20% 98.48%
	SORGH_AS_0344 Microbacterium sp.	R2A	37	o C, aerobic	Pink	PacBio	SRP437155	JAUTAD000000000 3,750,535		78.7 X	70.20% 97.98%
	SORGH_AS_0421 Microbacterium sp.	M9 + 0.4% sucrose 25	o C, aerobic	Orange	PacBio	SRP437226 JAUTAO000000000 3,450,105		228.2 X	69.50% 97.72%

Acknowledgments

This work was supported by the Great Lakes Bioenergy Research Center, U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research under Award Number DE-SC0018409. Support for field research was provided by the Great Lakes Bioenergy Research Center, U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research (Awards DE-SC0018409 and DE-FC02-07ER64494), by the National Science Foundation Long-term Ecological Research Program (DEB 1637653) at the Kellogg Biological Station, and by Michigan State University AgBioResearch. The work conducted by the U.S. Department of Energy Joint Genome Institute, a DOE Office of Science User Facility, is supported under Contract No. DE-AC02-05CH11231.

This data was generated for JGI Proposal #508590.

The authors declare no conflict of interest.

Data availability statement

Raw sequencing data are available at the Joint Genome Institute's GOLD database under Study ID Gs0157305, as well as with the National Institutes of Health Sequence Read Archive (see Table 1).

Genome sequencing assembly data has been deposited in the GenBank with accession numbers listed in

Table 1. The bacterial collection is available at The Great Lakes Bioenergy Research Center at Michigan State University, USA (glbrc@msu.edu).