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.

Periodic crop condition monitoring is of prime importance in cotton belt of western India for water stress management. In this article, vegetation water content (VWC) is assessed using Radar Vegetation Index (RVI) derived from the RISAT-1 data during July to September, vegetative to first picking phase, for utilizing its potential for large area cotton condition assessment. The RVI estimation from dual-polarized data has been demonstrated for regional applications. Prediction models of VWC for cotton crop using RVI and in situ ground measurements depicts significant relationship, with R² varying from 0.5 to 0.6 and RMSE of 0.3–0.7 kg m⁻². High correlation exists between RVI with crop age and crop biomass with R² varying from 0.55 to 0.7, this proves useful for sowing date prediction. The results showed good validation (R² = 0.8) for operational applications. The estimated VWC was found with 30–35% error above 4 kg m⁻² biomasses as compared to 20–25% in lower ranges.

Crop phenology monitoring is a necessary action for precision agriculture. Sentinel-1 and Sentinel-2 satellites provide us with the opportunity to monitor crop phenology at a high spatial resolution with high accuracy. The main objective of this study was to examine the potential of the Sentinel-1 and Sentinel-2 data and their combination for monitoring sugarcane phenological stages and evaluate the temporal behaviour of Sentinel-1 parameters and Sentinel-2 indices. Seven machine learning models, namely logistic regression, decision tree, random forest, artificial neural network, support vector machine, naïve Bayes, and fuzzy rule based systems, were implemented, and their predictive performance was compared. Accuracy, precision, specificity, sensitivity or recall, F score, area under curve of receiver operating characteristic and kappa value were used as performance metrics. The research was carried out in the Indo-Gangetic alluvial plains in the districts of Hisar and Jind, Haryana, India. The Sentinel-1 backscatters and parameters VV, alpha and anisotropy and, among Sentinel-2 indices, normalized difference vegetation index and weighted difference vegetation index were found to be the most important features for predicting sugarcane phenology. The accuracy of models ranged from 40 to 60%, 56 to 84% and 76 to 88% for Sentinel-1 data, Sentinel-2 data and combined data, respectively. Area under the ROC curve and kappa values also supported the supremacy of the combined use of Sentinel-1 and Sentinel-2 data. This study infers that combined Sentinel-1 and Sentinel-2 data are more efficient in predicting sugarcane phenology than Sentinel-1 and Sentinel-2 alone.

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.

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.