Soybean, a pivotal global source of oil and protein, exhibits heightened sensitivity to soil moisture conditions throughout its growth cycle. Accurate monitoring of soil moisture (SM) in soybean fields during the growing season is indispensable for optimizing yields and forecasting sustainable agricultural practices. Leveraging advancements in remote sensing technology, passive microwave soil moisture retrieval has emerged as a crucial tool for large-scale precision agriculture and enduring environmental monitoring. However, challenges in the L-band Microwave Emission of the Biosphere (L-MEB) model, particularly in the computation of vegetation transmissivity, may compromise the accuracy of soil moisture retrieval. In this study, we improved the Beer-Lambert law to more accurately quantify the attenuation effect of the vegetation layer on microwave signals, aiming to ameliorate the inherent limitations in the L-MEB model. The proposed soil moisture retrieval method, primarily validated in soybean fields, was also subjected to supplementary experiments in canola and wheat fields to further assess its effectiveness and generalizability. The proposed method integrates passive microwave and optical data, demonstrating a substantial improvement in accuracy. Experimental results reveal that our enhanced method significantly outperforms the L-MEB model in soybean fields: Pearson correlation coefficients of soil moisture, derived using vegetation water content and leaf area index, are 0.712 and 0.692 respectively. Furthermore, root mean square errors have decreased to 0.056m3/m3and 0.050 m3/m3, a reduction of 39.78% and 19.35%, respectively. In canola and wheat fields, the method exhibited an approximate 10% enhancement in retrieval accuracy. This advancement not only furnishes novel technical support for water management in soybean cultivation but also contributes theoretical and technical insights to the domain of passive microwave soil moisture retrieval. Index Terms-L-MEB model, passive microwave, soil moisture retrieval, vegetation transmissivity.

Synthetic aperture radar (SAR) and optical image fusion can leverage the strength of both data sources for improved ground object identification. However, existing fusion methods often encounter challenges such as spatial information loss and spectral distortion, and few studies address scenarios where fused images struggle to distinguish between distinct ground objects simultaneously, such as crops at different growth stages and low woodland. To address these challenges, we propose a hybrid fusion method for Sentinel-1/2 images and construct a temporal phenology feature for improved crop mapping. To efficiently preserve spatial and spectral information, our method combines principal component analysis (PCA) transform, and utilizes convolution sparse representation (CSR) and generative adversarial network (GAN) to fuse the high- and low-frequency coefficients of nonsubsampled shearlet transform (NSST), respectively. Furthermore, leveraging the annual normalized difference vegetation index (NDVI) curve to capture the crop growth cycle, we analyze ground object change patterns across a growth cycle and construct temporal phenology feature based on fused images to enhance crop classification accuracy. Results demonstrate that our method outperforms six classical methods and five state-of-the-art deep learning methods across both subjective and objective evaluation performance. When compared with independent SAR, optical, and fused images, the combination of fused images and temporal phenology feature improves the distinction between cropland and woodland and achieves the highest classification accuracy. We also expand the application scenarios to a total of twelve agricultural regions in China, Canada, the United States and France, yielding satisfactory results.

Leaf area index (LAI) serves as a key ecophysiological parameter for assessing plant growth and is particularly vital for crop monitoring. Using unmanned aerial vehicle (UAV)-based point cloud data generated through photogrammetry techniques offers valuable structural insights into crops, facilitating LAI retrieval. This study introduces a method for estimating LAI from 3-D point clouds. By employing spherical voxel partitioning, the vegetation gap fraction is computed based on the spatial distribution of point clouds. Furthermore, the leaf inclination angle is determined through triangular patch collections reconstructed from 3-D point clouds. Projection functions, accounting for varying zenith perspectives, are developed considering the leaf inclination angle. Subsequently, the combination of vegetation gap fraction and projection functions is used within the Beer–Lambert law framework to calculate LAI. Validation against ground measurements demonstrates a strong correlation between measured and retrieved LAI ($R^{2} = 0.64$, RMSE = 0.43), affirming the effectiveness of the proposed method in estimating LAI using UAV-based structure from motion (SfM) point cloud data.

Leaf area index (LAI) and fractional vegetation cover (FVC) are two essential vegetation parameters for ecological and climate studies. The Chinese Gaofen-1(GF-1) wide field view (WFV) satellite data is a valuable data source for LAI and FVC retrieval at high spatio-temporal resolution. Like its name, GF-1 WFV has very large view angle ranging from 0° to 48°, which can impact the accuracy of vegetation parameter retrieval. The primary aim of the study was to develop an angular effect correction (AFX-fix) method that can effectively normalize GF-1 WFV data. Our objective was to enhance the applicability of the corrected data in retrieving LAI and FVC. The AFX-fix method used angular index, anisotropy flat index (AFX), and a fixed set of bidirectional reflectance distribution function (BRDF) parameters. LAI and FVC were retrieved from the GF-1 WFV reflectance data using the PROSAIL model combined with a random forest method. Results showed that the accuracy of LAI and FVC retrieval in wheat and corn from angular corrected GF-1 WFV data was improved with a decrease in root mean square error (RMSE) by 0.66 for LAI and 0.03 for FVC compared to that based on the original data. We anticipate that this new method will help improve the performance of LAI retrieval of these crop types using WFV data.

Cropland classification can be used to monitor cropland distribution and its change over time. In this letter, a new superpixel-based cropland classification method is proposed for synthetic aperture radar (SAR) imagery through the integration of statistical texture, polarization, and spatial information. First, the method combines random forest algorithm and superpixels, which are generated using simple linear iterative clustering algorithm with polarization features of Pauli decomposition and spatial information. Superpixel-based spatial context information is used to reduce the influence of coherent speckle and misclassification in cropland blocks. Second,$G^{0}$statistical texture feature is used to reduce the interference of background targets such as woodland in cropland classification. Comparison experiments of different methods using C-band airborne SAR (AIRSAR) polarimetric data acquired in early July show that the proposed method has better classification performance, with an overall accuracy of 88.62%. The classification accuracy of corn and soybean is above 95% and 91%, respectively. The$G^{0}$statistical texture feature is helpful to eliminate woodland that may cause crop misclassification using single-date SAR image.

Information on soil available nutrient (SAN) at key crop growth stages is critical to generate prescription maps for implementing variable rate fertilization (VRF). Our previous study showed that integrating time-series remote sensing (RS) data with the modified World Food Studies (WOFOST) crop model provides a useful approach (preliminary RS-WOFOST-based method) to acquiring information on field SAN; however, the estimation accuracy was low for VRF application. In this paper, three steps were proposed to further improve the SAN estimation accuracy. At the first step, the rapid-nutrient assimilation (RNA) method was used to optimize the crop growth simulation process. Compared with the ensemble Kalman filter (EnKF) method, the RNA method showed an improved performance in estimating soil available nitrogen (N), phosphorus (P), and potassium (K) content [EnKF: R2= 0.48 (N), 0.37 (P), 0.15 (K); RNA: R2= 0.59 (N), 0.46 (P), and 0.18 (K)]. The improved K estimation at the first step was clearly lower than that of N and P; hence, the K content estimation was further optimized at the second step and the accuracy was improved (R2= 0.27) by using the estimated N as an input variable during the K estimation. At the third step, an iteration algorithm was implemented based on the first two steps, and the final R2= 0.71 (N), 0.58 (P), 0.49 (K); root-mean-square error = 14.35 (N), 3.70 (P), and 14.87 (K). In general, the optimized approach can overcome the limitations of the preliminary RS-WOFOST-based method and improve the SAN estimation accuracy.

This reported study was intended to advance the retrieval of leaf area index (LAI) using synthetic aperture radar (SAR) data. A novel method was proposed by introducing the vegetation coverage into the Water Cloud Model (WCM) to improve the retrieval accuracy of the LAI. LAI is a strong indicator of crop productivity, and vegetation coverage has a strong relationship with the LAI (R2=0.9733), a function can be created to express their relation. Finally, the accuracy in this innovative LAI retrieval method were evaluated. The results showed that the accuracy of estimation was improved greatly (R2was increased to 0.6055 and 0.6422 from 0.3491 and 0.3561 in VH and HH polarization). Thus, the method has operational potential for the LAI retrieval of crop in agriculture regions.

A simple target scattering model is developed integrating the shape factor and geometric randomness that are less influenced by the SAR configuration, dielectric properties of crop, and the underlying soil conditions, to describe the shape and statistical distribution of the targets, respectively. 47 fully polarimetric RADARSAT-2 images in FQ1W, FQ6W, FQ10W, FQ15W and FQ19W modes with different incidence angles acquired over Temiskaming Shores, Ontario, Canada in 2015 are adopted for the validation. Comparisons of the geometric features with H, α, and RVI, demonstrate that geometric features of wheat, oat, alfalfa, and barely show less fluctuation and more consistent with their growing stages over time till their harvest, but crops with broad leaves such as corn and soybean still show some fluctuations with high geometric randomness. In addition, the plots of n and δ of different crops also shows their potential on the crop classification.

Cropland productivity, characterized by crop yields, is determined by soil and meteorological conditions as well as management practices, e.g., crop types and their associated phenological cycles. As canopy spectral reflectance is governed by vegetation photosynthetic activities and is indicative of primary productivity, we investigated the potential of using time-series NDVI for mapping spatial variability of cropland productivity in south-western Ontario, Canada. NDVI was derived from the 8-day composite 250-m MODIS surface reflectance data, using a general cropland mask and crop specific masks, respectively. It was observed that for the three major annual crops (corn, soybean and winter wheat), using a general cropland mask, the strongest positive linear correlation between county level crop yield and NDVI was reached between the end of July and early August; whereas using crop specific masks the time of strongest linear correlation for wheat was shifted to between mid-May and early June. Large differences in phenological patterns and interleaved spatial distribution of these different crops led to difficulties for yield estimation using low resolution remote sensing data in this region.

This paper examines the potential of Radarsat-2 C-band synthetic aperture radar (SAR) data for quantifying the spatial variability of soil moisture during the agriculture growth period. To remove the effect of crop within total backscattering, a method that adequately represents the scattering behavior of vegetation-covered area by defining the scattering of the vegetation and underlying soil was developed. The Dubois model was employed to determine the backscattering from the underlying soil. The modified Water Cloud Model was used to reduce the effect of backscattering caused by the vegetation. Soil moisture was derived by the inversion scheme which uses of the dual polarizations (HH and VV) available from the quad polarization Radarsat-2 data.

In this letter, we attempt to improve existing model-based decomposition methods to estimate the soil moisture for C-band RADARSAT-2 data. An adaptive two-component decomposition (ATCD) is developed that considers the surface and volume scattering caused by the soil and crop canopy, respectively. The surface scattering adopted is an X-Bragg scattering, with the orientation angle induced by the azimuthal slope under a zero-mean normal distribution function, whereas the volume scattering model is constructed based on the nth power of sine and cosine probability distribution functions. Five sets of fully polarimetric RADARSAT-2 data acquired, in 2013 and 2015, over two study areas, were used to demonstrate the proposed technique, showing that the volumetric soil moisture derived from the ATCD is more consistent with the verifiable ground conditions compared with other model-based decomposition methods.

Many research studies have investigated surface parameter inversion for bare soils. This paper attempts to take into account the agricultural fields with crop residues and fields under low vegetation cover in addition to bare soil fields. An integrated surface parameter inversion scheme (ISPIS) is proposed to invert surface parameters in these agricultural fields based on the analysis of H-α parameters at the early crop growing stages, in which the calibrated integral equation model (CIEM) is adopted to invert surface parameters for bare soils, and an adaptive two-component decomposition combined with the CIEM and a simplified adaptive volume scattering model is developed for fields with crop residues and under low vegetation cover. Fully polarimetric RADARSAT-2 data with ground truth collected on April 29 and May 9 in 2013 and from May to June in 2014 are used for validation. Compared with other methods, the derived volumetric soil moisture (MV) and surface roughness (KS) of all agricultural fields are consistent with verifiable observations with the lowest overall root mean square error: 6.12 [vol.%] and 0.48, respectively, when all sample sites are considered.

Many research studies have investigated the retrieval of surface parameters for bare soils. This study attempts to take into account agricultural fields with crop residues and low vegetation cover. An adaptive two-component decomposition (ATCD) method is developed in this paper to invert surface parameters in these fields. The study area is located in Southwestern Ontario, Canada where two wheat fields and two corn-residue fields are selected as sample sites. Three fully polarimetric RADARSAT-2 data, which were acquired in 2013 and 2014 respectively, are employed for the validation. The ATCD has 100% inversion rate compared with the Bragg and X-Bragg models, while its root mean square errors (RMSE) of the soil moisture and roughness have the lowest value of 6.72% and 0.33, respectively. The derived soil moisture in wheat field also has the lowest RMSE value of 3.97% compared with the Y-CIEM.

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.

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 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.

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.

Acid mine drainage (AMD), caused by mine tailings, poses an environmental threat. AMD control is a major challenge facing the mining industries worldwide. An important initial step towards the reclamation of mine tailings sites is to identify the presence of sulphide-rich minerals and their spatial distribution. This study investigated the potential of hyperspectral PROBE data for mine tailings characterization over the Copper Cliff's tailings site in northern Ontario, Canada. The results indicated that PROBE data could provide information on locating oxidation zonations of the tailings. More importantly, it revealed that library mineral spectra could replace the scene-derived endmember spectra to unmix the PROBE image.