Early-season crop type classification provides crucial information for monitoring in-season crop growth and predicting crop diseases, supporting global food security. In remote sensing practices for agriculture, it is technically challenging to perform early-season prediction mainly due to factors such as significant soil interference to crops on the ground and limited availability of remote sensing data. In this work, we propose a time series based machine/deep learning approach which is able to utilize all useful features of satellite images captured by multiple sensors (RCM, Sentinel-1, and Sentinel-2) in the early season. The algorithm is implemented iteratively on data sequences such that when a new satellite image becomes available, new features will be appended to existing time series data, thus providing a more accurate prediction map in updated regions. In the study, four (4) machine/deep learning algorithms, including XGBoost, CNN, and LSTM, are evaluated for their performance in terms of prediction accuracy and computational complexity. The proposed approach is tested in three study areas in the Canadian Prairie provinces in the 2021 season. Crop type mapping results are obtained from the available early-season images from June 10 to June 30, 2021, with extended results to July 31, 2021. The validation results on independent crop survey data show that the proposed algorithms can achieve crop type classification accuracy around 85% at the end of June and above 90% at the end of July.

A plethora of studies have empirically demonstrated and linked canola heat stress susceptibility to substantial yield losses. Such biophysical phenomenon underscores the importance of timely and accurate monitoring of crop phenological events. Optical and Synthetic Aperture Radar (SAR) data, to meet such a requirement, have aided in developing diverse yet complementary information for crop monitoring. In this study, we investigate 47 satellite-based Land Surface Parameters (LSPs) from Sentinel-1 and -2 imagery to monitor canola phenology across arable lands in Saskatchewan, Canada. Daily ground reference phenological data were collected using trail-cameras installed across 28 canola fields. We constructed daily time-series profiles for each LSP by coupling a cubic interpolation algorithm with a Savitzky-Golay filtering. LSPs were correlated with ground reference data to investigate temporal trends and sensitivity of specific patterns to phenological events. Preliminary results indicate that SAR-based LSPs were most sensitive to canola bolting and pod maturity, while flowering and associated stages are mapped efficiently through optical indices.

A changing climate is bringing uncertainty to agricultural production and methods that can deliver assessments of crop condition will help mitigate impacts. Agriculture and Agri-Food Canada is developing a vegetation index based on Synthetic Aperture Radar (SAR) to monitor crops. In this research, machine learning algorithms are used to relate SAR parameters from RADARSAT-2 fully polarimetric and Sentinel-1 dual-pol (VV-VH) Single Look Complex data to optical Normalized Difference Vegetation Index values. For four crops (canola, corn, soybeans, wheat) Random Forest Regressors and Least-squares Boosting were able to create strong models using multiple polarimetric parameters. Coefficients of determination (R2) ranged from 0.91 to 0.84 depending on the crop type, sensor and model. Errors for oats and barley were higher due to a more limited training dataset.

Monitoring how farmers till their fields can provide important information in estimating the contributions of the agriculture sector towards soil carbon sequestration and reductions in greenhouse gas emissions. Agriculture and Agri-Food Canada (AAFC) is testing the use of Coherent Change Detection, applied to Sentinel-1 Synthetic Aperture Radar (SAR) data to identify when fields are tilled. Data have been collected in sites in eastern Canada and results to date have been positive. By monitoring the temporal change in coherence, fields that were tilled were successfully flagged using this approach. A more comprehensive data set is currently being collected in order to extend validation of this method and to test if type of tillage can also be identified.

The RADARSAT Constellation Mission (RCM) is equipped with three SAR satellites (RCM1, RCM2 and RCM3) flying in a constellation configuration. Each SAR is equipped with Compact polarimetry (CP) capabilities. It is now admitted that the actual SAR technology does not permit the generation of a perfectly circular polarization and this may significantly affect CP information [1], [2]. Recently, a new CP calibration model [2], which explicitly takes into account the non-circularity of transmitted polarization, in addition to polarimetric antenna distortion matrix and channel imbalances, was introduced and validated using ALOS2 CP data. In this study, the Touzi CP calibration model [2] is extended to the RCM. The high isolation of RCM antennas (cross-talk lower than -40 dB) permits a simplification of the Touzi CP model and its use as the basis of a convenient method for measurement of RCM CP axial ratio (AR) using Amazonian rainforests. The Touzi CP calibration model is also used to setup the requirements on CP calibration. These new requirements on CP calibration lead to the conclusion that the current RCM calibration meets the CP requirements for all the RCM beams with AR lower (or equal) than 0.5 dB (5m-CP2-to-14, and 30mSC beams within the incidence angle range 20° -to- 43°). A new calibration method based on the Touzi RCM CP-calibration model is developed (and validated) for the correction of the non-circularity of RCM CP beams with AR larger than 0.5dB.

The European Space Agency (ESA) launched the SMOS (Soil Moisture Ocean Salinity) mission in 2009; currently, multiple global soil moisture (SM) products are based on the measurements of its L-band (1.4 GHz) radiometer. We compared four SMOS products with each other: Level 2, Level 3, IC (INRA-CESBIO), and Near Real Time products. The comparisons focused on core validation sites (CVS), whose spatial representativeness errors allow the estimation of the SM product performance for bias-insensitive metrics (unbiased root mean square error (ubRMSE) and correlation (R), and anomaly R) with negligible uncertainty and for bias-sensitive metrics (mean difference (MD) and root mean square difference or RMSD) with acceptable uncertainty. When the products were compared with CVS independently, the results showed that the ubRMSE, R, and anomaly R of the IC product were better than those of the other products, while the MD was larger. However, the differences between the performances were smaller when the products were assessed using only the data points when each product had a valid retrieval. This indicates that the algorithms have similar performance and that data screening and quality flagging of the retrievals markedly affects the performance. The NASA Soil Moisture Active Passive (SMAP) mission produces a similar SM product as SMOS using an L-band radiometer. The closeness of the ubRMSE, R, and anomaly R performance of the IC product and the SMAP product (0.039 m3/m3vs. 0.041 m3/m3, 0.80 vs. 0.81, and 0.75 vs. 0.75) demonstrate that the SMOS and SMAP radiometers can achieve similar SM sensitivity.

Coherent Change Detection (CCD) is applied to exact repeat passes of Synthetic Aperture Radar (SAR) images to identify subtle changes in targets and surfaces. When farmers till their fields, the soil is disturbed and this disturbance can be detected and sometimes measured by SARs. CCD is being investigated as a technique to detect tillage in the Canadian Lake Erie Basin. In this study C-band data from 12 passes of the RADARSAT Constellation Mission (RCM) are processed and compared to field observations. RCM CCD pairs are helpful to distinguish when change happens (due to harvest, tillage and chemical termination of crops). However other scattering parameters, such as volume scattering from the m-chi decomposition, will likely be required to separate harvest from tillage events.

The RADARSAT Constellation Mission (RCM) is able to acquire imagery over large swaths in Compact Polarimetric (CP) modes. The wide area coverage of CP, and revisit with this three satellite constellation, is of potential benefit for operational crop mapping carried out by Agriculture and Agri-Food Canada. This research examined the accuracy of Stokes parameters and m-Chi decomposition parameters, derived from CP data, for identifying crops with a Random Forest (RF) classifier. Stokes S1and S2parameters were plotted on the Poincaré sphere and interpreted as a function of crop phenology. High overall classification accuracies (>90%) were reported when either Stokes vectors or m-Chi decomposition parameters were used in the RF classifier. The Stokes parameters also revealed that the ellipticity, orientation and handedness of scattering varies considerably as crops undergo changes in phenology.

Ongoing evaluation of the soil moisture active passive (SMAP) soil moisture products has utilized validation networks distributed in several regions around the world. Thein situreference used for validation of the soil moisture retrieval algorithm is associated with measurements from soil moisture probes typically located at 5 cm beneath the soil surface; however, some networks also consider a vertically oriented probe that measures from 0 to 5 cm. In this study, we compare the correlation and unbiased root mean square error (ubRMSE) from the SMAP L2 radiometer soil moisture product when compared toin situmeasurements taken at 5 cm (approximately 3.5–6.5 cm) below the surface and measurements taken as an integrated measure from 0 to 5.7 cm. The data were obtained from two SMAP validation networks in Canada: the Kenaston network in Saskatchewan and Carman network situated in Manitoba. At both sites, correlations between thein situand the SMAP L2 product were consistently higher with vertically oriented probes following rain events. With respect to the ubRMSE, the vertically oriented probes at the Carman site had lower ubRMSE with the SMAP product than the horizontal probes that are currently used for validation activities. In some cases, vertical probe information should be considered in validation approaches when this data is available and could be considered in the design ofin situcalibration/validation networks. These results may be useful in design considerations of networks for upcoming soil moisture product validation.

Agriculture and Agri-Food Canada (AAFC) delivers annual maps of crops grown across Canada, operationally, using Synthetic Aperture Radar (SAR) and optical satellite data. This study applies the AAFC methodology to sites in Latin America to test performance and adaptability to these cropping systems, using TerraSAR-X and RADARSAT SAR data. Overall classification results are promising (79.4% to 86.0%), but improvements will occur with better matching of SAR collection dates to local growing seasons, and by acquiring more robust field observations. These improvements will be the subject of additional research by AAFC and partner organizations, in this region.

In this study, we propose a sequence-to-sequence neural network architecture to jointly estimate the plant area index (PAI) and wet biomass of canola and soybean. The PAI and wet biomass have considerable importance for crop growth stage mapping and monitoring. RADARSAT-2 quad-pol data along within situmeasurements of canola and soybean obtained from the SMAPVEX16 campaign over Manitoba, Canada, are utilized for evaluating the efficiency and accuracy of the proposed estimation methodology. The analysis indicates promising results for the two crops with a correlation coefficient$(r)$in the range of 0.69–0.87. The results also confirm intercorrelation between the PAI and wet biomass for canola and soybean.

In this study, we propose a new vegetation index (DpRVI) for dual polarimetric synthetic aperture radar (SAR) data. The evaluation of this new index is performed with a particular attention towards the preparation of the NASA-ISRO SAR (NISAR) L-band system science objective. The proposed vegetation index is derived for two dual-pol (HH-HV and VV-VH) modes obtained through a simulation from L-band full-pol UAVSAR data. Time-series simulated NISAR data are obtained from the UAVSAR data acquired during the SMAPVEX12 campaign over the CAL/VAL test site in Winnipeg (Canada), to assess the proposed vegetation index. The temporal trend of DpRVI follows the growth stages of canola with a promising correlation of DpRVI with several biophysical variables. Correlation analysis indicates that DpRVI derived for VV-VH mode correlates better with the canola biophysical parameters than the HH-HV mode.

Crop growth monitoring using compact-pol synthetic aperture radar (CP-SAR) data is gaining attention with the rapid advancements toward operational applications. In this article, we propose a vegetation index for compact polarimetric (CP) SAR data [compact-pol radar vegetation index (CpRVI)]. The CpRVI is derived using the concept of a geodesic distance between the Kennaugh matrices projected on a unit sphere. This distance is utilized to compute a similarity measure between the observed Kennaugh matrix and the Kennaugh matrix of an ideal depolarizer (a realization of vegetation canopy). The similarity measure is then modulated with a scaled quantity derived from the scattering power ratio of the same and opposite sense polarization with respect to the transmitted circular polarization. In this article, we utilize time-series-simulated RADARSAT Constellation Mission (RCM) compact-pol SAR data (RH-RV) obtained from the full-pol RADARSAT-2 observations during the soil moisture active passive (SMAP) validation experiment 2016 (SMAPVEX16-MB) campaign in Manitoba, Canada, to assess the proposed vegetation index. Among the various crops grown in this region, in particular, we analyze the growth stages of wheat and soybean due to their different canopy structures. A temporal analysis of the proposed CpRVI with crop biophysical parameters [the plant area index (PAI) and vegetation water content (VWC)] at different phenological stages confirms the trend of CpRVI with the plant growth. Nevertheless, variations of CpRVI values are apparent with different plant densities for both the crop types. Also, the linear regression analysis confirms that the CpRVI values significantly correlate with PAI (r = 0.72 and 0.85) and VWC (r = 0.62 and 0.75) for both wheat and soybean. We observed good retrieval of PAI and VWC for both wheat and soybean.

The NASA SMAP (Soil Moisture Active Passive) mission provides a global coverage of soil moisture measurements based on its L-band microwave radiometer every 2-3 days at about 40 km resolution. The soil moisture retrieval algorithms model the brightness temperature as a function of soil moisture, surface conditions and vegetation. External data sources inform the algorithms about the surface conditions and vegetation, which enable the retrieval of soil moisture. The inversion process contains uncertainties related to radiometer measurements, forward model assumptions and ancillary data sources. This study focuses on the uncertainties that depend on the seasonal evolution of the surface conditions and vegetation. The study compares the SMAP and core validation site (CVS) soil moisture values over a period of four years to extract the evolution of performance metrics over time. The analysis showed that most CVS that include managed agriculture exhibit significant time-dependent seasonal bias. This bias was linked to seasonal temperature cycle, which is a proxy to several features that can cause seasonally dependent errors in the SMAP product.

This work deals with the classification of wheat phenology by regressing the synthetic aperture radar (SAR) backscatter coefficients (VV, VH) to vegetation water content (VWC) and plant area index (PAI) through a representation learning network. The representation network architecture consists of a pair (VV, VH) of two regression layers (VWC, PAI) which finally converge to a classification (crop phenology) layer. The study was conducted with the Sentinel-1 C-band SAR data acquired during the SMAPVEX16 campaign in Manitoba, Canada. Using this framework, the wheat phenology was classified to an accuracy of 86.67%. However, in comparison, the classification accuracy reduced by ~ 20% while using only the backscatter coefficients of (VV, VH) polarization channels. The results obtained from this study justifies the potential of using a representation learning scheme for crop phenology classification with SAR data.

Leaf area index (LAI) measurements acquired during the SMAP Validation Experiment 2016 in Manitoba (SMAPVEX16-MB) field campaign were used to validate LAI estimates from Sentinel-2/MSI data using The Simplified Level 2 Product Prototype Processor (SL2P) processor and LAI estimates obtained from locally calibrated vegetation indices (VI) models. Results showed that performances of LAI/SL2P estimates (RMSE = 0.98, bias = -0.37, slope = 0.70), when compared to in-situ data, are lower than performances of LAI/VI estimates (RMSE = 0.38, bias = 0.19, slope = 0.75) when compared to the same in-situ data.

The Water Cloud Model (WCM) has been widely used for estimation of Leaf Area Index (LAI) from Synthetic Aperture Radar (SAR). In different studies, it was demonstrated that this model performs well if it is calibrated well. However, calibration of this model requires access to both LAI and soil moisture for the calibration points. An alternative, if the soil moisture data are not available, is Machine Learning (ML) algorithms. However, ML methods are highly dependent on the number of calibration points. In this study, 6 different ML algorithms including Neural Network (NN), Support Vector Machine (SVM), Ensemble of Trees (ET), Regression Tree (RT), Radial Basis Function (RBF) and Gaussian Process Model (GPM) are used and compared with the WCM model for estimation of LAI over corn fields. This comparison was done using different numbers of calibration points. The results demonstrated that when a lower number of calibration points are used, WCM outperformed some of the ML algorithms including NN, SVM and ET algorithms. But with more calibration points, all machine learning algorithms outperformed the WCM. The highest accuracies were from the GPM model with a correlation coefficient (R) of 0.93, Root Mean Square (RMSE) of 0.56 m2m-2and Mean Absolute Error (MAE) of 0.38 m2m-2. Theses results were derived using the data collected during the SMAP Validation Experiment 2012 (SMAPVEX12) that was conducted in Manitoba, Canada. Further testing and comparison of the ML algorithms and WCM model using data from other Joint Experiment for Crop Assessment and Monitoring (JECAM) sites are ongoing.

In this study, we propose a vegetation index for compact polarimetric (CP) SAR data (CpRVI) using a geodesic distance between two Kennaugh matrices projected on a unit sphere, as given in Ratha et. al. This distance is utilized to compute a similarity measure between the observed Kennaugh matrix and the Kennaugh matrix of an isotropic depolarizer. The proposed vegetation index is compared with the Radar Vegetation Index (RVI) obtained from RADARSAT-2 full-polarimetric SAR data. We use a time series of simulated compact-pol SAR data (RH-RV) obtained from the RADARSAT-2 data acquired during the SMAPVEX16-MB campaign over the Joint Experiment for Crop Assessment and Monitoring (JECAM) test site in Manitoba, Canada to assess the proposed vegetation index. Among the various crops grown in this region, only the growth stages of soybean are analyzed in this work. The temporal trend of CpRVI follows the growth stages of soybean. Regression analysis shows that CpRVI correlates better with the Plant Area Index (PAI) and Vegetation Water Content (VWC) than RVI.

In preparation for Canada's launch of the RADARSAT-Constellation, this study examines the use of VV-VH intensities to estimate the Leaf Area Index (LAI) of corn. LAI is indicative of crop productivity. Two implementations of the Water Cloud Model performed equally well in estimating corn LAI over sites in Poland and Canada with correlation coefficients over 0.8 and Root Mean Square Errors and Mean Average Errors of 0.72-0.73 m2m-2and 0.47-0.54 m2m-2, respectively. This research will continue to pull in data from other international sites. If results remain robust, a strong case can be made to use an integration of Sentinel-1 and RCM for operational crop condition monitoring.

Annual and within-season crop type monitoring and mapping is an important ongoing consideration for governments, global agricultural monitoring organizations and private interests worldwide. Successful country-wide operational remote sensing-based inventories are well-established utilizing optical-only and optical/single frequency Synthetic Aperture Radar (SAR) combinations of data. However, the drawbacks of these data combinations are the requirement of multiple sources of imagery throughout the entire growing season, which impedes within-season analysis, and cloud cover effects on the optical data. Currently, C-band SAR data are available with continuous global coverage from Sentinel-1A & B, RADARSAT-2 and from the expected launch of the RADARSAT Constellation Mission (RCM). With current and expected launches of several other frequency (L-, P-, etc.) SAR missions over the next few years (SAOCOM, NISAR, etc.) the opportunity for continuous, multi-frequency SAR coverage edges toward reality. The JECAM SAR Inter-Comparison Experiment is a multi-year, multi-partner project that aims to compare global methods for SAR-based crop monitoring and inventory. The third component of this experiment is the assessment of multi-frequency SAR data for crop classification and mapping. Earth observation (EO) data acquisitions of Sentinel-1A & B, RADARSAT-2/RCM, ALOS-2, SAOCOM1 and TerraSAR-X/TanDEM-X have been planned and requested for the 2019 growing season to complement within field surveys being conducted across the globe. In preparation for these data, this research analyzed ALOS-2, TerraSAR-X and RADARSAT-2 data for crop mapping at the JECAM Canada-Carman site using two dates of multi-frequency SAR data, in comparison to a traditional full season optical/SAR dataset. The multi-frequency data had similar overall accuracies as the optical/SAR dataset, and improved on several individual agricultural class accuracies.

Agriculture and Agri-Food Canada (AAFC) has combined RADARSAT-2 C-band dual polarization data with optical imagery to map crop types across the agricultural extent of Canada yearly since 2009. In preparation for the launch of the RADARSAT Constellation Mission (RCM) primary research has been focused on incorporating similar-mode dual polarization RCM data in the operational system. The availability of the compact polarimetry (CP) mode with continuous coverage has important implications for crop mapping and inventory. CP mode on RCM has a circular transmit and two orthogonal linear receive structure and maintains phase information. The addition of CP data to AAFC's operational crop type mapping will expand the information that the current dual polarization Synthetic Aperture Radar (SAR) component provides. This will increase the SAR contribution from the simple intensity of backscatter to capturing the scattering characteristics of the target. There are many parameters and decompositions that can be derived from CP data. Many of these parameters are highly correlated or may not provide information that are important for crop type identification. The goal of this research was to derive twenty-four CP parameters and decompositions for a growing season of SAR imagery and to assess the importance and contribution of these features to an overall classification of crops in southern Manitoba, Canada.

Cloudy conditions impede and reduce the utility of optical imagery. With the launch of Sentinel-1A and B, the ongoing availability of RADARSAT-2 imagery, and the expected launch of the RADARSAT Constellation Mission (RCM), dense time series of C-band Synthetic Aperture Radar (SAR) data will now be readily available. For crop classification and mapping, SAR imagery has yet to be used to its full potential and has generally been combined with optical imagery. The JECAM SAR Inter-Comparison Experiment is a multi-year, multi-partner project that aims to compare global methods for SAR-based crop monitoring and inventory. Sets of dense time-series SAR imagery which include RADARSAT-2 and Sentinel-1 data were prepared for this experiment. AAFC's operational Decision Tree (DT) and newly implemented Random Forest (RF) classification methodologies were applied to these SAR only data-stacks, and to optimized, traditional data-stacks of optical/SAR combinations. This paper outlines the results of these dense time-series classifications and how these results were affected by changing numbers of agriculture classes, numbers of available SAR imagery and numbers of training and validation data points for individual crop types. In general, for the dense time-series SAR stacks, overall accuracies of greater than 85%, a typical operational goal, were obtained for 6 of 12 sites. These results have important operational implications for particularly cloudy regions where the availability of optical imagery is limited.

The objective of this study is to investigate the retrieval of crop phenology using polarimetric decompositions and Random Forest (RF) algorithms. To realize this objective, we used multi-temporal RADARSAT-2 data and ground measured vegetation characteristics acquired during the SMAPVEX16-MB (Soil Moisture Active Passive Validation Experiment 2016 in Manitoba) campaign in Canada. Polarimetric parameters with the potential to quantify the volume scattering mechanism were extracted, and then analyzed with respect to ground identified phenology for different crop types. The RF algorithm was subsequently trained based on 60% of the data, and validated using the remaining data. Results show that the crop phenology can be monitored, through the combination of multiple polarimetric parameters to build different decision trees in the RF algorithm. By averaging the estimates from multiple decision trees, the complex relative patterns between the polarimetric parameters and crop phenology were recognized, leading to appropriate estimations on crop phenology. The obtained spearman correlation coefficients between the retrieved and ground identified crop phenology were 0.94, 0.91, 0.81 and 0.89 for canola, corn, soybean and wheat, respectively. This study also suggests suitable polarimetric parameters for a timely monitoring of crop phenology.

In order to calibrate and validate the SMAP soil moisture products, networks of ground-based soil moisture sensors have been deployed. Measurements collected from the networks must be upscaled to the radiometer footprint scale (30-40 km) for comparison with the SMAP radiometer-based retrievals. The upscaling is typically performed as a weighted average of individual sensor measurements within the SMAP grid. Since different weighting schemes have been found to result in different upscaled soil moisture estimates, an independent method of assessing soil moisture estimation biases is needed. We therefore present a method for calculating estimation biases at each SMAP Core Validation Site (CVS). The estimation was enabled by networks of enhanced soil moisture sampling that were deployed at four CVSs for a limited time. Based on Random Forests, our method offers a straightforward, systematic, and unified approach to bias estimation across a variety of sites. The method was applied to estimate biases at the four SMAP CVSs.

In this letter, we propose a novel vegetation index from polarimetric synthetic-aperture radar (PolSAR) data using the generalized volume scattering model. The geodesic distance between two Kennaugh matrices projected on a unit sphere proposed by Ratha et al. is used in this letter. This distance is utilized to compute a similarity measure between the observed Kennaugh matrix and generalized volume scattering models. A factor is estimated corresponding to the ratio of the minimum to the maximum geodesic distances between the observed Kennaugh matrix and the set of elementary targets: trihedral, cylinder, dihedral, and narrow dihedral. This factor is then scaled and multiplied with the similarity measure to obtain the novel vegetation index. The proposed vegetation index is compared with the radar vegetation index (RVI) proposed by Kim and van Zyl. A time series of RADARSAT-2 data acquired during the Soil Moisture Active Passive Validation Experiment 2016 (SMAPVEX16-MB) campaign in Manitoba, Canada, is used to assessing the proposed RVI.

In this study we evaluate a Random Forest (RF) model for characterizing the spatial variability of soil moisture based on model derived from in situ soil moisture samples, geophysical data and RADAR observations. The RF model is run with and without C-band SAR backscatter to understand the importance of the inclusion of SAR data for mapping of soil moisture at field scale. The inclusion of SAR data in the RF resulted in a modest improvement however the geophysical parameters (e.g. soil types and terrain properties) were of greater importance.

In this study, a cross-calibration approach was applied to combine RADARSAT-2 and RapidEye sensors for biomass monitoring over corn fields. First, RapidEye and RADARSAT-2 sensors were compared in terms of biomass estimation. Then the estimated biomass from RADARSAT-2 was cross-calibrated with respect to the biomass estimated from RapidEye. Combination of the optical and cross-calibrated Synthetic Aperture Radar (SAR) derived biomass was proposed to have higher temporal resolution biomass maps. Vegetation indices including normalized difference vegetation index (NDVI), red-edge triangular vegetation index (RTVI), simple ratio (SR) and red-edge simple ratio (SRre) were used for modeling of biomass estimation from RapidEye. Water Cloud Model (WCM) was also used for biomass estimation from RADARSAT-2. Data collected during SMAP Validation Experiment 2012 (SMAPVEX12) field campaign was used for validation. The results demonstrate that the accuracies of biomass estimations from RapidEye and RADARSAT-2 are close. For RapidEye, the highest accuracies derived from RTVI index with correlation coefficient (R) of 0.92 and Root Mean Square of (RMSE) of 118.18 gr/m2. The R values derived from RADARSAT-2 is 0.83 and its RMSE is 171.93 gr/m2. After cross-calibration of the biomass derived from RADARSAT-2 versus those derived from RapidEye, the RMSE of estimates dropped by 18.86 gr/m2.

In this paper, a multi-target inversion scheme is adopted for joint estimation of crop biophysical parameters from dual-pol SAR data. The single-output support vector regression (SVR) method is extended to a multi-output support vector regression (MSVR) method to estimate biophysical parameters. The MSVR is implemented for simultaneous retrieval of plant area index (PAI) and crop biomass from the Sentinel-l C-band dual-pol (VV + VH) data. In this particular study, the inversion algorithm is trained and validated for the canola crop using in-situ measurements collected during the Soil Moisture Active Passive Validation Experiment 2016 (SMAPVEX16-MB) Manitoba campaign. The validation results indicate a good correlation coefficient (r) of 0.72 and 0.85, with a RMSE of 0.35 m2m-2and 0.48 kgm-2for PAI and wet biomass respectively. In addition, the mapped PAI and wet biomass values at flowering stage of canola capture the variability in crop growth from Sentinel-l data.

Soil moisture content is an essential climate variable that is operationally delivered at low resolution (e.g. 36-9 km) by earth observation missions, such as ESA/SMOS, NASA/SMAP and EUMETSAT/ASCAT. However numerous land applications would benefit from the availability of soil moisture maps at higher resolution. For this reason, there is a large research effort to develop soil moisture products at higher resolution using, for instance, data acquired by the new ESA's Sentinel missions. The objective of this study is twofold. First, it presents the validation status of a pre-operational soil moisture product derived from Sentinel-1 at 1 km resolution. Second, it assesses the possibility of integrating Sentinel-2 data and additional ancillary information, such as parcel borders and high resolution soil texture maps, in order to obtain soil moisture maps at “field scale” resolution, i.e. ~0.1 km. Case studies concerning agricultural sites located in Europe are presented.

In preparation for Canada's launch of the RADARSAT-Constellation, Agriculture and Agri-Food Canada examined whether Compact Polarimetry (CP) would be suitable for estimation of surface soil moisture. In this study, CP RH and RV intensities were simulated from RADARSAT-2 Quad Pol data. A simple transfer function was developed between RH and HH intensity, as well as RV and VV. These HH-and VV-like intensities were then used with the Integral Equation Model to estimate soil moisture. These retrievals were validated against in situ networks, demonstrating the potential that RCM CP is likely to present for soil moisture monitoring.

Utilizing Synthetic Aperture Radar (SAR) sensors for crop inventory and condition monitoring offers many advantages, particularly the ability to collect data under cloudy conditions. The JECAM SAR Inter-Comparison Experiment is a multi-year, multi-partner project that aims to compare global methods for SAR crop monitoring and inventory, and Leaf Area Index (LAI) and biomass retrieval. Data sets of SAR imagery including RADARSAT-2 and Sentinel-1 are being prepared for this experiment and it is important to develop best practices to ensure consistency across the data sets. This paper outlines the speckle filter testing results based upon changing filter types and window sizes in comparison with changing field size. In general, it was found that the adaptive Touzi filter resulted in the highest overall classification accuracies for all field sizes. It was also found that there may be importance to speckle filter window size in relation to agriculture field size for other filter types.

The Advanced Microwave Scanning Radiometer 2 (AMSR2) is part of the Global Change Observation Mission-Water (GCOM-W) mission. AMSR2 fills the void left by the loss of the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) after almost 10 years. Both missions provide brightness temperature observations that are used to retrieve soil moisture. Merging AMSR-E and AMSR2 will help build a consistent long-term dataset. Before tackling the integration of AMSR-E and AMSR2 it is necessary to conduct a thorough validation and assessment of the AMSR2 soil moisture products. This study focuses on validation of the AMSR2 soil moisture products by comparison with in situ reference data from a set of core validation sites. Three products that rely on different algorithms were evaluated; the JAXA Soil Moisture Algorithm (JAXA), the Land Parameter Retrieval Model (LPRM), and the Single Channel Algorithm (SCA). Results indicate that overall the SCA has the best performance based upon the metrics considered.

Since the beginning of its routine science operation in March 2015, the NASA SMAP observatory has been returning interference-mitigated brightness temperature observations at L-band (1.41 GHz) frequency from space. The resulting data enable frequent global mapping of soil moisture with a retrieval uncertainty below 0.040 m3/m3at a 36 km spatial scale. This paper describes the development and validation of an enhanced version of the current standard soil moisture product. Compared with the standard product that is posted on a 36 km grid, the new enhanced product is posted on a 9 km grid. Derived from the same time-ordered brightness temperature observations that feed the current standard passive soil moisture product, the enhanced passive soil moisture product leverages on the Backus-Gilbert optimal interpolation technique that more fully utilizes the additional information from the original radiometer observations to achieve global mapping of soil moisture with enhanced clarity. The resulting enhanced soil moisture product was assessed using long-term in situ soil moisture observations from core validation sites located in diverse biomes and was found to exhibit an average retrieval uncertainty below 0.040 m3/m3. As of December 2016, the enhanced soil moisture product has been made available to the public from the NASA Distributed Active Archive Center at the National Snow and Ice Data Center.

NASA's SMAP (Soil Moisture Active Passive) calibration and validation program revealed that the soil moisture products are experiencing difficulties in meeting the mission requirements in certain agricultural areas. Therefore, the mission organized airborne field experiments at two core validation sites to investigate these anomalies. The SMAP Validation Experiment 2016 included airborne observations with the PALS (Passive Active L-band Sensor) instrument and intensive ground sampling. The goal of the PALS measurements are to investigate the soil moisture retrieval algorithm formulation and parameterization under the varying (spatially and temporally) conditions of the agricultural domains and to obtain high resolution soil moisture maps within the SMAP pixels. In this paper the soil moisture retrieval using the PALS brightness temperature measurement in SMAPVEX16 is discussed in relation to in situ and SMAP soil moisture.

The systematic retrieval of near surface soil moisture (SSM) fields at high resolution (e.g., 0.1-1.0 km) is a challenging task that requires the exploitation of new retrieval algorithms and SAR data with advanced observational capabilities (in terms of spatial/temporal resolution, radiometric accuracy, very large swath, long-term continuity and rapid data dissemination). The launch of the Sentinel-1 (S-1) constellation provides these capabilities and calls for the development and validation of pre-operational SSM products at high resolution. The objective of this paper is to present and initially assess a SSM retrieval algorithm developed in view of S-1 data exploitation. The activity is supported by a large scientific community engaged in fostering a more effective interaction between researchers working in the field of high and low resolution SSM retrieval.

Adoption of optical vegetation indices for local, national, and global crop condition monitoring is wide spread. Given that cloud cover impedes acquisition of these data, this research examines whether Synthetic Aperture Radar (SAR), specifically a compact polarimetric (CP) configuration, could augment these operational initiatives. Encouraging statistical correlations are reported between several CP parameters and the Normalized Difference Vegetation Index (NDVI). These early results suggest that further development is warranted to integrate a SAR-based index with optical-NDVI particularly considering the configuration of future Canadian satellite systems.

NASA's Soil Moisture Active Passive (SMAP) mission launched on January 31, 2015 into a sun-synchronous 6 am/6 pm orbit with an objective to produce global mapping of high-resolution soil moisture and freeze-thaw state every 2-3 days. The SMAP radiometer began acquiring routine science data on March 31, 2015 and continues to operate nominally. SMAP's radiometer-derived standard soil moisture product (L2SMP) provides soil moisture estimates posted on a 36-km fixed Earth grid using brightness temperature observations and ancillary data. A beta quality version of L2SMP was released to the public in October, 2015, Version 3 validated L2SMP soil moisture data were released in May, 2016, and Version 4 L2SMP data were released in December, 2016. Version 4 data are processed using the same soil moisture retrieval algorithms as previous versions, but now include retrieved soil moisture from both the 6 am descending orbits and the 6 pm ascending orbits. Validation of 19 months of the standard L2SMP product was done for both AM and PM retrievals using in situ measurements from global core cal/val sites. Accuracy of the soil moisture retrievals averaged over the core sites showed that SMAP accuracy requirements are being met.

GCOM-W AMSR2 provides continuity following AMSR-E and the opportunity to generate a global long-term satellite soil moisture data record from the same instrument type. Various soil moisture products are being developed using AMSR observations. The JAXA soil moisture along with the Single Channel Algorithm (SCA) product were evaluated using in situ observations from different geographical domains. Both the JAXA and SCA soil moisture estimates capture the overall climatological features and the overall spatial structure of the two products is similar. The JAXA soil moisture product shows a lower dynamic range in the retrieved soil moisture. The SCA performs well over low and moderately vegetated areas. This study focuses on the development of the AMSR2 soil moisture product. Validation results using in situ observations from diverse climate and land cover conditions will be presented.

NASA's Soil Moisture Active Passive (SMAP) mission launched on January 31, 2015 into a sun-synchronous 6 am/6 pm orbit with an objective to produce global mapping of high-resolution soil moisture and freeze-thaw state every 2-3 days using an L-band (active) radar and an L-band (passive) radiometer. The SMAP radiometer began acquiring routine science data on March 31, 2015 and continues to operate nominally. SMAP's radiometer-derived soil moisture product (L2_SM_P) provides soil moisture estimates posted on a 36 km fixed Earth grid using brightness temperature observations from descending (6 am) passes and ancillary data. A beta quality version of L2_SM_P was released to the public in September, 2015, with the fully validated L2_SM_P soil moisture data expected to be released in May, 2016. Additional improvements (including optimization of retrieval algorithm parameters and upscaling approaches) and methodology expansions (including increasing the number of core sites, model-based intercomparisons, and results from several intensive field campaigns) are anticipated in moving from accuracy assessment of the beta quality data to an evaluation of the fully validated L2_SM_P data product.

The National Aeronautics and Space Administration (NASA) Soil Moisture Active Passive (SMAP) satellite mission was launched on January 31, 2015. The observatory was developed to provide global mapping of high-resolution soil moisture and freeze-thaw state every two to three days using an L-band (active) radar and an L-band (passive) radiometer. After an irrecoverable hardware failure of the radar on July 7, 2015, the radiometer-only soil moisture product became the only operational soil moisture product for SMAP. The product provides soil moisture estimates posted on a 36 km Earth-fixed grid produced using brightness temperature observations from descending passes. Within months after the commissioning of the SMAP radiometer, the product was assessed to have attained preliminary (beta) science quality, and data were released to the public for evaluation in September 2015. The product is available from the NASA Distributed Active Archive Center at the National Snow and Ice Data Center. This paper provides a summary of the Level 2 Passive Soil Moisture Product (L2_SM_P) and its validation against in situ ground measurements collected from different data sources. Initial in situ comparisons conducted between March 31, 2015 and October 26, 2015, at a limited number of core validation sites (CVSs) and several hundred sparse network points, indicate that the V-pol Single Channel Algorithm (SCA-V) currently delivers the best performance among algorithms considered for L2_SM_P, based on several metrics. The accuracy of the soil moisture retrievals averaged over the CVSs was 0.038 m3/m3unbiased root-mean-square difference (ubRMSD), which approaches the SMAP mission requirement of 0.040 m3/m3.

In this letter, it is shown that spaceborne observations made by the European Space Agency's Soil Moisture and Ocean Salinity (SMOS) satellite agreed closely with the Passive Active L-band System (PALS) brightness temperature acquisitions during the Soil Moisture Active Passive (SMAP) Validation Experiment 2012. The difference between the SMOS and PALS measurements was less than 5 K and 6 K for vertical and horizontal polarizations, respectively, over the relatively homogeneous agricultural areas. These values are less than the SMOS subpixel variability determined from the PALS measurement. This result demonstrated that the measurements obtained in the experiment are scalable to spaceborne brightness temperature observations, are representative of the expected SMAP observations, and will be of value in the development of soil moisture algorithms for spaceborne missions.

The National Aeronautics and Space Administration (NASA) Soil Moisture Active Passive (SMAP) satellite is scheduled for launch in January 2015. In order to develop robust soil moisture retrieval algorithms that fully exploit the unique capabilities of SMAP, algorithm developers had identified a need for long-duration combined active and passive L-band microwave observations. In response to this need, a joint Canada-U.S. field experiment (SMAPVEX12) was conducted in Manitoba (Canada) over a six-week period in 2012. Several times per week, NASA flew two aircraft carrying instruments that could simulate the observations the SMAP satellite would provide. Ground crews collected soil moisture data, crop measurements, and biomass samples in support of this campaign. The objective of SMAPVEX12 was to support the development, enhancement, and testing of SMAP soil moisture retrieval algorithms. This paper details the airborne and field data collection as well as data calibration and analysis. Early results from the SMAP active radar retrieval methods are presented and demonstrate that relative and absolute soil moisture can be delivered by this approach. Passive active L-band sensor (PALS) antenna temperatures and reflectivity, as well as backscatter, closely follow dry down and wetting events observed during SMAPVEX12. The SMAPVEX12 experiment was highly successful in achieving its objectives and provides a unique and valuable data set that will advance algorithm development.

The Soil Moisture Ocean Salinity (SMOS) mission was launched in 2009 and provides derived soil moisture globally using a forward modelling approach that incorporates a number of auxiliary data sets. By default, the SMOS mission uses global land cover and soils data sets to run the soil moisture retrieval models. This study examines the use of national data sets from Agriculture and Agri-Food Canada (AAFC) to determine if improvements in land cover and soils accuracies achieved using these national data sets can provide an improvement in SMOS soil moisture retrieval. Results show that changing the land cover produced the greatest differences, with a reduction in the fraction of the land area identified as forest, but also an increase in the number of failed model retrievals. The use of the AAFC soils resulted in a greater fraction of clay in the surface soil layer, but this did not have a large impact on the overall retrieval accuracy at the study sites. This suggests that the default SMOS parameterization can provide adequate estimation of soil moisture over most sites, but areas where forest, wetland or urban land cover may be over or underestimated should be more closely evaluated.

Passive microwave derived satellite soil moisture data was evaluated over in situ monitoring sites in Canada from two L-Band sensors. Soil moisture data from the Soil Moisture and Ocean Salinity (SMOS) and the Aquarius mission were used, which collect data at different spatial resolutions and using different retrieval models. Both sensors tend to underestimate soil moisture, with the underestimation from SMOS much more pronounced. Correlation coefficients show a reasonably good correspondence with in situ data, and this correlation tends to be better at sites where sub-grid soil moisture variability is represented in the in situ measured data. This highlights the importance of distributed in situ networks.

This paper uses RADARSAT-2 QUADPOL fully POLarimetric Inteferometric Synthetic Aperture Radar (POLInSAR) data to detect agriculture crop fields changes using coherence optimization method. The RADARSAT-2 POLInSAR data, acquired in July and September 2010, contains wheat, corn and soybean fields. Interferogram and coherence images were generated using single polarimetric data and fully polarimetric data. The coherence optimization method was carried out by maximizing the complex Lagrangian function. The optimized coherence image from the largest eigenvalue can correctly detect changes in the agricultural fields which cannot be detected using single coherence image. The results were validated using ground truth information.

This paper presents a multi-step framework for classification and crop mapping using several polarimetric features, extracted from multitemopral Synthetic Aperture Radar (SAR) imagery. The multi-temporal data classification, not only improves the overall retrieval accuracy, but also provides more reliable crop discrimination in comparison to single-date data [1]. This is mainly because various phenogical stages of crops can contribute discrimination and classification of agricultural lands. The proposed framework in this paper consists of three main steps: a) data preprocessing, b) processing, and c) classification and evaluation. Several polarimetic features are extracted from preprocessed data, including the coherency and/or the covariance matrixes. Polarimetry decompositions then can allpy to ectract the statistical or physical based polarimetric components. Support vector machines' (SVM) classifier is employed for classification of these features. In addition, different kinds of kernel functions are used to evaluate the performance of SVM for classification. The method is applied to several UAVSAR L-band SAR images acquired over an agricultural area near Wennipeg, Manitoba, Canada. in summer of 2012. The experimental tests show that using two data data increases the overall accuracy of the classification up to 14%, and using an aditional date, i.e. three multitemporal datasets, increases the overall accuracy about 9% in comparing to two date imagery. The effect of multi-temporal data in crop classification is much more than even using more training data, which sometimes is expensive and time consuming.

This paper studies the feasibility of monitoring crop growth based on a trend analysis of three elementary radar scattering mechanisms using three consecutive years (2008–2010) of RADARSAT-2 (R-2) Fine Quad Mode data. The polarimetric synthetic aperture radar analysis is based on the Pauli decomposition. Multitemporal analysis is applied to RGB images constructed using surface scattering, double-bounce, and volume scattering, along with intensity analysis of these scattering mechanisms. The test site is located in Eastern Ontario, Canada where the cropping system is dominated by corn, spring wheat, and soybeans. Each crop has unique physical structural characteristics which provide different responses for these scattering mechanisms. Significant changes occur in these scattering mechanisms as the crops move from one phenological stage to the next. By monitoring these changes over the season, the crop growth cycle from emergence to harvest can be observed. When harvest occurs, the backscatter intensities change significantly, and these changes aid in identifying crops. The temporal evaluation of the intensity of the scattering mechanisms generally track the measured leaf area index and observed phenological plant development. Changes in growth stage are crop type specific. Thus, to monitor changes in crop phenology and the occurrence of harvest activities, knowledge of the crop grown in any particular field is required. To accommodate this requirement, a maximum likelihood classification was performed on the R-2 data to produce a crop map. An overall classification accuracy of 85$\%$was achieved.

The Canadian Experiment for Soil Moisture in 2010 (CanEx-SM10) was carried out in Saskatchewan, Canada, from 31 May to 16 June, 2010. Its main objective was to contribute to Soil Moisture and Ocean Salinity (SMOS) mission validation and the prelaunch assessment of the proposed Soil Moisture Active and Passive (SMAP) mission. During CanEx-SM10, SMOS data as well as other passive and active microwave measurements were collected by both airborne and satellite platforms. Ground-based measurements of soil (moisture, temperature, roughness, bulk density) and vegetation characteristics (leaf area index, biomass, vegetation height) were conducted close in time to the airborne and satellite acquisitions. Moreover, two ground-based in situ networks provided continuous measurements of meteorological conditions and soil moisture and soil temperature profiles. Two sites, each covering 33 km × 71 km (about two SMOS pixels) were selected in agricultural and boreal forested areas in order to provide contrasting soil and vegetation conditions. This paper describes the measurement strategy, provides an overview of the data sets, and presents preliminary results. Over the agricultural area, the airborne L-band brightness temperatures matched up well with the SMOS data (prototype 346). The radio frequency interference observed in both SMOS and the airborne L-band radiometer data exhibited spatial and temporal variability and polarization dependency. The temporal evolution of the SMOS soil moisture product (prototype 307) matched that observed with the ground data, but the absolute soil moisture estimates did not meet the accuracy requirements (0.04 m3/m3) of the SMOS mission. AMSR-E soil moisture estimates from the National Snow and Ice Data Center more closely reflected soil moisture measurements.

The availability of advanced satellite radar sensors (C-band RADARSAT-2 and X-band TerraSAR-X) provides significant opportunities for timely monitoring of crop growth. Recent studies revealed that many polarimetric SAR parameters are sensitive to crop Leaf Area Index (LAI). However the reduced swath coverage of fully polarimetric SAR limits the operational application of these modes for large regional monitoring activities. Compact polarimetry mode, on the other hand, permits much larger swath coverage than fully polarimetric SAR. This study investigates the sensitivity of compact polarimetry SAR parameters to crop LAI using simulated data from RADARSAT-2 imagery collected in Canada over two growing seasons. Results revealed that compact polarimetric decomposition parameters associated with volumetric scattering are well correlated with crop LAI. This suggests that compact polarimetric SAR can be an important data source for large scale crop growth monitoring.

Monitoring the amount of moisture held in the soil is critical in the management of risk for the agriculture sector. Extremes in soil moisture can lead to devastating consequences. Agriculture and Agri-Food Canada has been acquiring RADARSAT-2 data since 2008 to evaluate the accuracy with which this sensor can provide soil moisture to assist with implementing risk reduction strategies for the Canadian agriculture sector. Using the calibrated Integral Equation Model, and a total of 16 data sets, field level soil moisture was estimated to a mean average error of 7.7%. At a regional scale, mean errors fell to 3.23%. The model was also able to detect wetting and drying events with inputs of RADARSAT-2 data.

The purpose of this study is to evaluate the capability of the Oh backscattering model in combination with the Freeman Durden decomposition to estimate soil moisture over agricultural fields from fully polarimetric RADARSAT-2 C-band SAR responses. Initially, soil moisture multi-polarization retrieval was accomplished by using a look-up table (LUT) approach applied to the Oh model. Two methods were considered: the multi-polarization method and the one-unknown configuration. Of the two methods, results showed that the HH-HV inversion provided the best estimates. In the second phase, the Freeman Durden decomposition was applied to the polarimetric data. The conceptual approach for retrieving soil moisture using the surface scattering component of the total power was implemented in a LUT inversion. The algorithm attempts to minimize the difference between measured single scattering power obtained by applying the Freeman Durden decomposition and simulated total power using Oh model. When compared with the multi-polarization approach, this polarimetry-based method improves the accuracy of soil moisture estimates.

This research outlines a preliminary assessment of the use of TerraSAR-X data for classifying agricultural crop land in Canada. X-Band data were able to identify crops (pasture-forage, soybeans, corn and wheat) to accuracies of 95% once a post-classification filter was applied. These accuracies were achieved using six TerraSAR-X images from 2008 and a decision-tree classification algorithm. Acquisitions began only mid-season and consequently a second full season TerraSAR-X data set is being collected in 2009. C-Band classification accuracies were about 10% lower in comparison. These results clearly demonstrate the potential of X-Band data for crop identification.

Agriculture plays an important role in the global economy, and sustainability of this sector is critical for world food security. Annual information on agricultural land use (crop inventory) would permit efficient and effective delivery of agricultural programs that support sustainability of this resource. Previous research has revealed encouraging results on using space borne satellite data (Landsat, SPOT) for crop mapping at the regional scale. Given Canada's large land mass, for operational crop monitoring satellite data with a wide swath and moderate spatial resolution are needed. This study presents the results on integrating RADARSAT-2 ScanSAR data with AWiFS data to improve crop identification. This study demonstrates that multi-temporal AWiFS data can produce an adequate crop classification, with an overall accuracy of 83%. The addition of ScanSAR data increases the overall classification accuracies. The radar contribution is most pronounced during the earlier season.

Mapping and monitoring changes in the distribution of cropland provide information that aids sustainable approaches to agriculture and supports early warning of threats to global and regional food security. This paper tested the capability of Phased Array type L-band Synthetic Aperture Radar (SAR) (PALSAR) multipolarization and polarimetric data for crop classification. L-band results were compared with those achieved with a C-band SAR data set (ASAR and RADARSAT-1), an integrated C- and L-band data set, and a multitemporal optical data set. Using all L-band linear polarizations, corn, soybeans, cereals, and hay-pasture were classified to an overall accuracy of 70%. A more temporally rich C-band data set provided an accuracy of 80%. Larger biomass crops were well classified using the PALSAR data. C-band data were needed to accurately classify low biomass crops. With a multifrequency data set, an overall accuracy of 88.7% was reached, and many individual crops were classified to accuracies better than 90%. These results were competitive with the overall accuracy achieved using three Landsat images (88.0%). L-band parameters derived from three decomposition approaches (Cloude-Pottier, Freeman-Durden, and Krogager) produced superior crop classification accuracies relative to those achieved using the linear polarizations. Using the Krogager decomposition parameters from all three PALSAR acquisitions, an overall accuracy of 77.2% was achieved. The results reported in this paper emphasize the value of polarimetric, as well as multifrequency SAR, data for crop classification. With such a diverse capability, a SAR-only approach to crop classification becomes increasingly viable.

Information on agricultural land use (crop inventory) is needed by various organizations on an annual basis. To meet this operational requirement, Agriculture and Agri-Food Canada (AAFC) has carried out a multi-year (2004 - 2007), multi-sensor (Landsat TM, SPOT, RADARSAT-1, ASAR), and multi-site (five provinces: Ontario, Saskatchewan, Alberta, Manitoba, P.E.I.) research activity to develop a robust methodology to inventory crops across Canada's large and diverse agricultural landscapes. Results clearly demonstrated that multi-temporal satellite data can successfully classify crops for a variety of cropping systems across Canada. Overall accuracies of at least 85% were achieved. When available, multi-temporal (2 to 3 scenes acquired at different growth stages) optical data are ideal for crop classification. However due to cloud and haze interference, good optical data are not always obtainable. A SAR-optical combination offers a good alternative. This research has found that when only one optical image is available, the addition of two ASAR images acquired in VV/VH polarization will provide acceptable accuracies. Of particular interest is the observation that with the incorporation of radar, crop inventories can be delivered earlier in the growing season.

The outcome of a multi-year project carried out across sites within Canada has been the development of a method to deliver crop inventories using the integration of SAR and optical satellite data. Although multi-temporal optical imagery can classify crops at the target accuracy, SAR data will be valuable in boosting accuracies and ensuring operational delivery of this annual inventory.

Abstract - Because soil moisture as well as surface roughness affect radar backscatter of bare soils, any practical application of radar must be able to account for these two target properties. The new generation of SAR satellites like ENVISAT, RADARSAT-2 and ALOS permit the acquisition of two or more images with different sensor configurations over the same target. Using this capacity, it is possible to retrieve both of these soil surface parameters simultaneously from a combination of images with different polarizations and/or different incidence angles. This study addresses the problem of the estimation of bare soil surface parameters (roughness and moisture). It applies the inversion algorithms of backscattering models to this estimation based on multi polarization and multi angle SAR data. As our preliminary results are suggesting, the accuracy of the estimated surface parameters strongly depends on the performance of backscattering models and data configuration. Therefore, the best fitted model with the best sensor configuration are being chosen and tested over several experimental sites in order to estimate soil moisture and surface roughness.

In this paper, we briefly preview and demonstrate how the technical improvements included in RADARSAT-2 will impact the system's potential utility for 32 applications in the fields of agriculture, cartography, disaster management, forestry, geology, hydrology, oceans, and sea and land ice.

Agriculture and Agri-Food Canada is developing a national land use and agricultural management practices inventory. This inventory is envisioned as a representative, statistical database of land use and management information. Considering the magnitude of this project, the use of remotely sensed earth observation data is a key source of information. This project is exploring the use of optical imagery for crop residue mapping and the use of SAR data for establishing changes in surface roughness, which occur as a result of tillage. It is anticipated that land management classes can be determined through the integration of the crop residue and surface roughness products. This paper discusses the project, summarizes the data acquisition and analysis plan and provides some preliminary results

This paper focuses on a comparative study between a semi empirical model, the Modified Soil Adjusted Crop Residue Index (MSACRI), and artificial neutral networks (ANN) for estimating crop residue cover on agricultural fields using hyperspectral imagery. The results indicate the ANN method is more accurate and more representative of the ground reference information than the MSACRI.

This study focused on the demonstration of hyperspectral Probe-1 data for the discrimination of crop residue from bare soil. The results indicated that independently of the crop residue cover and the optical properties of the bare soil, the best combination of bands to distinguish crop residue from bare soil was band 36 (943 nm) versus band 115 (2303 nm).

The Canada Centre for Remote Sensing conducted an intensive field campaign in 2000 at an agricultural test site at Indian Head, Saskatchewan (Canada). Airborne C-band fully polarimetric SAR data were acquired over the site. The supporting ground information is being used to establish the sensitivity of several polarimetric parameters to variations in crop condition. Preliminary results suggest that several polarizations are sensitive to variations in crop growth and crop stress, particularly in small grain crops like wheat. Information on crop growth gathered from sensors, like RADARSAT-2, could aid in strategies to better manage nutrient applications on agricultural fields.

In this paper, we preview and demonstrate how the technical improvements included in RADARSAT-2 will impact the system's potential utility for 32 applications in the fields of agriculture, cartography, disaster management, forestry, geology, hydrology, oceans, and sea and land ice.

Surface roughness has a significant effect on the relationship between radar backscatter and soil moisture. In order to use existing radar satellite data for soil moisture, roughness effects must be corrected. A technique is presented that utilizes the data bases from soil erosion studies and soil moisture remote sensing investigations to provide first order estimates of the roughness parameters.