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The synthesized received power for every wave ellipticity (-45°≤χ≤45°), and orientation (-90°≤ψ≤90°) angle describe the polarization signature (PS) of a target. Thus, it provides valuable information for target characterization. The PS of various crops at two frequencies (C- and L-band) is assessed in this study for various crops. It is observed that the volume scattering mechanism is mainly produced at C-band by the tall and medium height crops viz. Sugarcane, Maize and Finger Millet. The depolarized power is evident from such targets with the pedestal height (PH) (0 ≤ PH ≤ 1) values, ranging between 0.34 and 0.22. In contrast, we noticed that the surface scattering mechanism is dominant for short-height crops, such as Chilli, Turmeric and Paddy. It is also interesting to note the differential backscatter response for a few common polarization states widely varies for the two frequencies between -5.72 and -1.5 dB for Paddy, Sugarcane and Maize. [ABSTRACT FROM AUTHOR]

Dual-pol Shannon Entropy captures the dynamic crop growth parameters. It was used to evaluate biophysical parameters of important crops using Sentinel-1 data. Improved phenology information is vital input to crop-growth models. Mustard experienced increase as it advanced in phenology but for wheat, decreased due to absorption. A significant relationship at initial crop stages (height < 150 cm and biomass < 5 kg m−2) R2 =0.65 between Shannon Entropy (SE) and crop parameters, though less strong (R2=0.33) for entire mustard cycle and wheat (plant height < 80 cm and biomass < 6 kg m−2) was observed. The SE appeared sensitive to low-medium biomass and useful in monitoring crop phenology in low biomass and initial phenophases. The dynamic crop profile is manifested conjunctively as Radar Vegetation Index (RVI). Random Forest Regression (RFR) and Support Vector Regression (SVR) were evaluated to predict the phenophases. Phenology responses till peak/beyond were found to perform close to ground observations. [ABSTRACT FROM AUTHOR]

Accurate mapping of coarse cereals envisaged maize to be discriminated from co-existing Kharif crops using multiplatform, multiparametric SAR and Optical data in various polarization combination from Sentinel-1 and RADARSAT-2 in synergism with Sentinel-2, under machine learning algorithms viz. support vector machine (SVM) and random forest (RF) and knowledge-based Decision Tree (DT). SVM over-estimates the area of maize whereas RF and DT performed similarly (60–75%) but DT being closest to field conditions (68–78%). A 4 date Sentinel-1 under DT classified Soybean to 62% accuracy and Cotton to 76% on a regional scale in Maharashtra. Inclusion of 1-date optical image improved sugarcane accuracy. The similar-structured crops, such as Maize and Pearl Millet were discriminated with their unique phenological response in crop-calendar using quad-pol RADARSAT-2. With inclusion of crop responsive model-based polarimetric decomposition (Yamaguchi Volume and Double-Bounce) and Eigen-based parameters (Entropy and Alpha-Angle), maize classification accuracy elevated to 84%. [ABSTRACT FROM AUTHOR]

This study aims to assess the potential of multi-temporal Sentinel-1 and RADARSAT-2 data for pearl millet discrimination using various classifiers—machine learning and knowledge-based decision tree (m-DT and k-DT), Support Vector Machine (SVM), and Random Forest (RF). Results show that the RF classifier outperformed the other classifiers in terms of overall accuracy (OA) and kappa coefficient. The highest Overall Accuracy (OA) of 89.1% and kappa coefficients of 0.822 and 0.814 were achieved by the RF classifier for both the Sentinel-1 and RADARSAT-2 data, respectively. The Variable Importance (VI) of the RF classifier revealed that the polarimetric parameters—volume scattering, double bounce, entropy and alpha angle were crucial for pearl millet discrimination. Polarimetric parameters, when ingested with the RF machine learning classifier, achieved better classification accuracies. [ABSTRACT FROM AUTHOR]

The effect of structure and phenology of dryland crops on SAR scattering mechanisms is under explored and under reported. This study aims to understand the complex scattering mechanisms occurring in similar structured crops and to discriminate pearl-millet crop using single date Radarsat-2 SAR data. The study arrived at few significant findings that the panicle stage isthe critical stage for discriminating pearl-millet from similar structured sorghum and maize crops. Alpha angle extracted from eigen-vector based decomposition was identified as the discriminating factor. Average alpha value almost reached 55° due to dihedral scattering occurring in the prominent panicles which emerge early in pearl millet compared to maize and sorghum. Wishart H-A-α technique discriminated pearl millet, maize and sorghum crops with a producer's accuracy of about 87.8%, 71.6% and 71.4%, respectively. Paddy was classified with highest accuracy of 95.8% due to occurrence of dominant double bounce scattering in most of the fields. [ABSTRACT FROM AUTHOR]

Synergistic use of Optical and Microwave remotely sensed data for cotton condition based on biophysical traits, indices from ground measurements and satellite derived reflectance were assessed. Regression models derived for estimating LAI, biomass and VWC using ground based indices were applied to satellite products. LAI, biomass and VWC of cotton with optical VIs as input (R2= 0.42, 0.51 and 0.52) respectively were estimated. Correlation between RVI and plant height, crop age and VWC were found between 0.4 and 0.6. Fresh biomass from RVI ranged 100 –4000 gm−2, dry biomass from NDVI ranged 50–950 gm−2 and VWC 65–85%. Correlation between VIs and RVI was found to be non- significant. A multiple linear regression model using NDVI vs. LSWI and RVI were developed to assess LAI, biomass and VWC. The model predicted LAI with R2 of 0.5 but failed to estimate biomass and VWC. [ABSTRACT FROM AUTHOR]

The potential of single date fully polarimetric SAR data was brought out in discriminating different land covers at level 3 (LULC classification system NRSC) by deriving various polarimetric parameters. Various coherent and incoherent decompositions viz. Krogager, Freeman–Durden, Yamaguchi, Van Zyl, H-A-Alpha were analyzed to evaluate its potential for crop identification. Freeman-3 decomposition overestimates the volume component predominantly for high biomass crops but is sensitive across variable range of biomass. Van Zyl decomposition was not found suitable for crop studies as it overestimates surface scattering by underestimating volume scattering from canopy structure. The differencing method using various decomposition conveyed dynamic response for various crops over the individual response. The Entropy-Alpha scatter plot exhibits its capability in discriminating various land covers. Further, polarimetric signature resulted in unique response for various land covers that can be exploited for their identification. High pedestal heights were observed for high biomass crops and vice-versa. [ABSTRACT FROM AUTHOR]

Retrieval of transplantation date (TP) using RISAT-1 medium resolution SCANSAR data for operational pre-harvest crop monitoring has been demonstrated. The methodology used 3-date HH-polarized RISAT data capturing the onset of transplantation (minimal backscatter) and rises as crop grows, for the country scale within 2–4 days of planting and was found to closely agree to ground conditions. Transplantation date ranged from June in north to October in Tamil Nadu. This variation significantly reduced to a month in the summer paddy due to irrigation. Transplantation patterns were different in both the seasons' paddy. Validation exercise for TP in four major states confirmed the feasibility of retrieval datasets for transplantation date monitoring as it matched within 2–4 days in north, south India and MP but 6–8 days in Assam. The technique has reached a level of maturity and is routinely used for monitoring the transplantation date by the Department of Agriculture, India. [ABSTRACT FROM AUTHOR]

In view with the importance of coarse cereals, it was proposed to discriminate pearl millet crop in a mixed crop scenario using different combinations of time series multipolarized SAR with available optical data. Five date dual polarized Sentinel-1 DT classification was found to be optimum, as Bajra classification accuracy increased by 7.6% over four date. The improved Bajra accuracy of 85% was achieved when 2 date Sentinel-1was integrated with higher value quad polarized Radarsat-2 data. Similar structured crops like Sorghum and maize could be discriminated using difference in crop calendar and polarization response. Single date Sentinel-2 (near harvest stage as available) with 2 date Sentinel-1 yields 66% accuracy for Bajra crop but the attempt to extract temporal phenological profile was failed due to persistent cloud cover in the previous dates. Radarsat-2 data increased Bajra accuracy up to 89.5% when responsive polarimetric parameters were used in conjunction with multiple linear polarizations. [ABSTRACT FROM AUTHOR]