Rice is the most essential and nutritional staple food crop worldwide. There is a need for accurate and timely rice mapping and monitoring, which is a pre-requisite for crop management and food security. Recent studies have utilized Sentinel-1 data for mapping and monitoring ricegrowing areas. The present study was carried out in the Google Earth Engine (GEE), where the Sentinel-1data were used for monitoring the rice-growing area over Kulithalai taluk of Karur district, located along the Cauvery delta region. Normally, the production of rice in the study area starts in the late Samba Season where the long duration variety Cr1009 (130 days) is extensively grown. The results exhibit low backscattering values during the transplanting stage of VV and VH polarization (−15.19 db and −24 db), whereas maximum backscattering is experienced at the peak vegetation stage of VV and VH polarization (−7.42 and −16.9 db) and there is a decrease in the backscattering values after attaining the maturity stage. Amongst VH and VV polarization, VH polarization provides a consistently increasing trend in backscatter coefficients from the panicle initiation phase to the early milking phase, after which the crop attains its maturity phase, whereas in VV polarization, an early peak of backscatter coefficients is seen much earlier during the flowering phase itself. Thus, in this study, VV polarization gives better interpretation than VH polarization in the selected rice crop fields. The obtained results were cross-validated by collecting the ground truth values during the satellite data acquisition time, throughout the crop growing period from the selected rice fields.

Rice is an important staple food crop worldwide, especially in India. Accurate and timely prediction of rice phenology plays a significant role in the management of water resources, administrative planning, and food security. In addition to conventional methods, remotely sensed time series data can provide the necessary estimation of rice phenological stages over a large region. Thus, the present study utilizes the 16-day composite Enhanced Vegetation Index (EVI) product with a spatial resolution of 250 m from the Moderate Resolution Imaging Spectroradiometer (MODIS) to monitor the rice phenological stages over Karur district of Tamil Nadu, India, using the Google Earth Engine (GEE) platform. The rice fields in the study area were classified using the machine learning algorithm in GEE. The ground truth was obtained from the paddy fields during crop production which was used for classifying the paddy grown area. After the classification of paddy fields, local maxima, and local minima present in each pixel of time series, the EVI product was used to determine the paddy growing stages in the study area. The results show that in the initial stage the pixel value of EVI in the paddy field shows local minima (0.23), whereas local maxima (0.41) were obtained during the peak vegetative stage. The results derived from the present study using MODIS data were cross-validated using the field data.

Soil erosion is a serious environmental threat amongst the prevailing major natural hazards which affects the livelihood of millions of people around the world. The deterioration of nutrient-rich topsoil can affect the sustainability of agriculture and various ecosystems by decreasing soil productivity. Conservation measures should be implemented in those regions which are critical to soil erosion. The identification of areas susceptible to soil erosion through prioritization of watershed can help in proper planning and implementation of suitable conservational measures. Therefore, in this study, the prioritization of 23 micro-watersheds present in the Dnyanganga watershed of Tapti River basin is carried out based on morphometric parameters and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). TanDEM-X 90m openly accessible DEM generated from SAR interferometry, obtained through DLR, is used for determining the morphometric parameters. These parameters are grouped into linear, areal and relief aspects. Initially, the relative weights of various morphometric parameters used in TOPSIS were determined using Saaty's Analytical Hierarchy Process (AHP). Thereafter, the MCDM package in R software was utilized to implement TOPSIS. The micro-watersheds were classified into very high (0.459-0.357), high (0.326-0.240), moderate (0.213-0.098), and low (0.096-0.088) prioritization levels based on the TOPSIS highest closeness (Ci + ) to ideal solution. It is evident from the results that microwatersheds (MW10, MW18, MW19, MW2, MW11, and MW17) are highly susceptible to soil erosion and thus, conservation measures can be carried out in these micro-watersheds with the priority to ensure the sustainability of future agriculture by preventing excessive soil loss through erosion.

Among the cereal grains, rice is staple food for ½ of the world population. In Asian countries as population increases, the demand for rice also increases. For this future demand rice, biophysical variables are monitored for agricultural management and yield prediction using space borne satellite platform. While in satellite remote sensing there are numerous trouble in mapping and monitoring rice field, particularly in multi season paddy in rainy season, incorporating the changes in crop phenology, the impact of climate and farmland variability. To determine these issues Sentinel-1 was launched and provide opportunity to monitor rice crop, in 10m spatial resolution, C-band, dual polarization image with 12 days revisit. A rice field in Kulithalai, Tamilnadu is utilized as examination. In this study Sentinel-1 data, which can recognize little vegetation difference at firmly found ground truth (GT) point. While observing the different growing stages of rice, the volume scattering segment proportion was increased though the surface scattering segment proportion for the most part diminished. During rice growing stages, surface scattering component ratio decreased, while volume scattering ratio will be increased. This study describes how multi temporal observation by SAR has extent capacity for estimating rice growing land and monitoring growing stages and also interpret σ 0 VV and σ 0 VH back scattering co-efficient. These are examined by knowing crop parameters like leaf area index, plant height, no. of panicle/m2 and no. of grains/m 2.1.

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