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Surface soil moisture has vital role in water energy balance, climate change and agriculture mainly for crop water requirements and irrigation scheduling. Microwave remote sensing with its unique characteristics of high penetration and sensitivity towards dielectric constant, has enabled the researchers to explore various techniques for soil moisture estimation. With the launch of Sentinel-1 (A&B) Synthetic Aperture Radar (SAR) satellites, the hindrance in accessing high spatial and temporal resolution data is eliminated. The current study focuses on surface soil moisture estimation for bare agricultural fields in the semi-arid region. Field soil moisture up to 5 cm depth using HydraGo Probe sensor and surface roughness synchronizing with satellite pass dates were collected from total 102 locations spanning four dates. Volumetric and sensor-based soil moisture are well correlated with R² = 0.85. The Modified Dubois Model (MDM) was applied to obtain the relative permittivity of the soil for the backscattering coefficient (σ°) for VV polarization, which is used as one of the inputs in universal Topp's model for soil moisture calculation. Model derived soil moisture is well correlated with ground-based soil moisture for the entire range of the soil moisture (0.02-0.18 m³m-3) with R² = 0.85 and RMSE=0.005. The entire soil moisture was categorized in three soil moisture ranges to evaluate the sensitivity. The highest correlation was observed for 0.06-0.1 m³m-3 with R² = 0.73 and RMSE=0.003 followed by 0.015-0.6 m³m-3 with R² = 0.81 and RMSE=0.001 and 0.11-0.18 m³m-3 with R² = 0.48 and RMSE=0.019 which is significantly low. Performance accuracy of MDM is encouraging for bare soil moisture estimation for even the lower range of surface soil moisture. [ABSTRACT FROM AUTHOR]

The potential of dual-polarization Sentinel-1 polarimetric decomposition parameters i.e., entropy, anisotropy and alpha angle, to monitor the biophysical parameters of rice crop namely, fresh biomass, dry biomass, vegetation water content (VWC) and plant height is investigated in this study. Multi-temporal Sentinel-1A dataset during critical growth stages of rice was considered for regression analysis between the polarimetric decomposition parameters and the biophysical parameters using linear and logarithmic models. The best correlations are obtained at early vegetation stages of the crop i.e. between tillering to booting stages. The maximum value of Pearson correlation (R) is 0.82, found for VH polarization and plant height and maximum R² value obtained is 0.6 for linear regression of entropy and VWC followed by other biophysical parameters. The correlation of polarimetric decomposition parameters with biophysical parameters is not as high as that of VH backscatter coefficient. Multiple regression using various Sentinel-1 parameter yields a better correlation than regression using individual parameters. The maximum R2 value of 0.765 is obtained for the multiple linear regression for VWC. Multiple regression using the backscatter coefficients and polarimetric decomposition parameters together gives a better correlation than that using only the backscatter coefficients. This indicate that the polarimetric decomposition parameters are sensitive to biophysical parameters and can be used as additional parameters along with the backscatter parameters for rice crop monitoring. [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 present study focuses on the soil moisture estimation using dual polarimetric RISAT-1, C-band SAR data in HH and HV polarization. The semi empirical approach derived by Dubois and the same modified and proposed as modified Dubois model (MDM) is worked for winter wheat crop from initial vegetative to maturity stage. Surface roughness model derived roughness is replaced with 's' parameter for retrieval of dielectric constant which is further used in Topp's model to derive soil moisture. Model derived soil moisture and ground measured soil moisture demonstrates pragmatic correlation after performing linear regression. The behaviour of inversion model varies at different crop stages depending on the soil exposure and crop cover. Normalized difference vegetation index from moderate resolution imaging spectroradiometer has also been evaluated to monitor crop status. Performance of MDM is promising for early and maturity stages of crop for soil moisture estimation. [ABSTRACT FROM AUTHOR]

The fragmented nature of arable landscapes and diverse cropping patterns often thwart the precise mapping of crop types. Recent advances in remote-sensing technologies and data mining approaches offer a viable solution to this mapping problem. We demonstrated the potential of using hyperspectral imaging and an ensemble classification approach that combines five machine-learning classifiers to map crop types in the Anand District of Gujarat, India. We derived a set of narrow/broad-band indices from the Airborne Visible Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) imagery to represent spectral variations and identify target classes and their distribution patterns. The results showed that Maximum Entropy (MaxEnt) and Generalised Linear Model (GLM) had strong discriminatory image classification abilities with Area Under the Curve (AUC) values ranging between 0.75 and 0.93 for MaxEnt and between 0.73 and 0.92 for GLM. The ensemble model resulted in improved accuracy scores compared to individual models. We found the Photochemical Reflectance Index (PRI) and Moment Distance Ratio Right/Left (MDRRL) to be important predictors for target classes such as wheat, legumes, and eggplant. Results from the study revealed the potential of using one-class ensemble modelling approach and hyperspectral images with limited field dataset to map agricultural systems that are fragmented in nature. [ABSTRACT FROM AUTHOR]

Surface soil moisture (SSM) at high spatial resolution is an essential land parameter for agricultural applications like irrigation mapping, scheduling, crop water stress assessment, etc. However, available satellite derived soil moisture products are inadequate for meeting the requirements of agricultural applications due to coarse scale soil moisture (~10–40 km). In this article, we developed an operational framework for first of its kind sub-km (~500 m) operational soil moisture product over India by utilizing ISRO’s EOS-04 C-band synthetic aperture radar (SAR) data based on active-passive approach. The potential of EOS-04 SAR for sub-km scale is demonstrated and tested over major cropland sites covering highly heterogeneous and dynamic crop conditions in different agro-climatic regions over India which shows a good agreement with in situ datasets with mean ubRMSE, ranging from 0.051 to 0.078 m³ /m³. [ABSTRACT FROM AUTHOR]

This study examines the MODIS time series NDVI datasets to detect greenness regeneration over Western Rajasthan and the Gujarat region of India. Time series analysis was applied to 17-years (2000-2016). MODIS NDVI satellite data product. Rainfall data for the same period were also analyzed to understand its impact over vegetation. NDVI time series datasets of MODIS 16-day composite proved sufficient for deriving statistically significant trend values for identifying areas of change in vegetation cover. Areas showing positive changes in NDVI trend was clearly correlated with areas which were brought under irrigational network over these areas, indicating an increase in vegetation, due to availability of water supply. Trends in NDVI were also compared with the trends in rainfall over the selected locations from Gujarat and Western Rajasthan. NDVI was positively correlated with the rainfall in both the regions. The NDVI time series trend analysis successfully detected the changes in greenness regeneration areas in Gujarat and western Rajasthan. [ABSTRACT FROM AUTHOR]

The current study investigated the potential of multi-temporal polarimetric Synthetic Aperture Radar (SAR) data of RISAT-1 for monitoring and discrimination of cotton and groundnut area where special emphasis was given to the signature analysis of the crop for two consecutive years viz., kharif2013-14 and 2014-15. The study area was selected in major cotton and groundnut producing districts of Gujarat state which accounts for 85-90% of total area. The SAR (C -band) backscatter is the function of temporal behavior due to interaction of microwave radiation with soil moisture, soil roughness, soil type and other crop parameters including crop canopy and volume scattering increasing from the early vegetative stage to harvesting stage and the backscattering of different region for both HH and HV polarizations. The change in backscatter of the order of 2 to 4 dB in C-band was observed. Cotton and groundnut crops were classified based on backscatter values and cotton was further classified in desi (indigenous) cotton, early sown, timely sown and late sown category for hybrid cotton depending on its time of sowing. Variation in backscatter profile with respect to sowing time was observed depending on rainfall availability in different regions. Hierarchical decision rule model based on the temporal evaluation of SAR backscatter was attempted to classify cotton, groundnut and other land covers achieved about 89 per cent accuracy in the case of cotton and about 85 per cent for groundnut. This output was very consistent in the irrigated tracts of Gujarat for cotton crop. This methodology would be crucial for monitoring and early estimation of cotton area in the country and cotton-groundnut discrimination when cloud free optical data is a major hindrance to the crop inventory. [ABSTRACT FROM AUTHOR]