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In the following study, an attempt is made for crop classification of rainy season through analyzing time-series Sentinel-1 SAR data of May 2020 to September 2020. The SVIDP index derived from dual-pol (VV and VH) bands consisting of NRPB ( σ 0 vh ij - σ 0 vv ij / σ 0 vh ij + σ 0 vv ij ), DPDD (σ 0 vh ij + σ 0 vv ij ) / √ 2 ), IDPDD ( σ 0 vv (m a x) - σ 0 vv ij ) + σ 0 vh ij / √ 2 ), and VDDPI (σ 0 vh ij + σ 0 vv ij / σ 0 vv ij) ratios are utilized for discriminating inter-vegetative boundaries of crop pixels. This study was conducted near Karnal city region, Karnal district, Haryana, India. The Sentinel-1 data has the capability to penetrate thick cloud cover and provide high revisit frequency data for rain-fed crops. Obtained classification achieved higher accuracy in both RF (93.77%) and SVM (93.50%) classifiers. Obtained linear regression statistics of mean raster imagery reveals that IDPDD index is much sensitive to other crop which has highest standard deviations in σvh° and σvv° bands throughout the period, and high R2 with σvh° (0.70), VV (0.58), NRPB (0.693), and DPDD (0.697) indices. In contrast to this, IDPDD index has least correlation (< 0.289) with σvh°, σvv°, EVI 2, NRPB, and DPDD indices for water body which has smooth surface and lowest SAR backscattering with minimum standard deviations in the same period. [ABSTRACT FROM AUTHOR]

Generation of spatio-temporal information such as land use system and management practices is one of the key ingredients for carrying out the regional level agro-ecosystem modelling. However, at the regional level availability of such data is scarce, where analysis of a cropping system is essential and a pre-requisite for studying the overall sustainability of the agricultural production system. The present investigation was carried out to identify the actually practised cropping pattern and their mapping in Hisar district of Haryana (India) using Multi-Data Approach (MDA). Multi-date sentinel-1 for the rabi season of 2019 was classified using multi-phase unsupervised classification approach and classes of interest were identified. Finally, classified images were subjected to logical combinations which helped in generating crop classification maps and statistics. Results showed that cropping pattern of the district exhibited huge variations and area under wheat was observed to be highest (204.76 thousand ha) in comparison to mustard crop (64.42 thousand ha) and least was under the sugarcane crop (0.97 thousand ha). Some other crops like vegetables and horticultural crops were also identified during this period, but the major crops that were identified during rabi 2019 were wheat and mustard. Hence, regional crop classification using sentinel-1 data appears to be a valuable tool for predicting a specific regions cropping pattern, which is considered to be the most significant aspect of an agricultural production system. [ABSTRACT FROM AUTHOR]