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Sentinel-1 SAR data preprocessing is essential for several earth observation applications, including land cover classification, change detection, vegetation monitoring, urban growth, natural hazards, etc. The information can be extracted from the 2x2 covariance matrix [C2] of Sentinel-1 dual-pol (VV-VH) acquisitions. To generate the covariance matrix from Sentinel-1 single look complex (SLC) data, several preprocessing steps are required. The ESA SNAP S-1 toolbox can be used to preprocess the data to generate a [C2] matrix. The polarimetric analysis in respective application fields often starts with the covariance matrix. However, due to limited availability of Sentinel-1 SLC data preprocessing workflow standards for polarimetric applications in contemporary research methods, downstream applications unable to comply with these workflows directly. In this paper, we propose a couple of generic practices to preprocess Sentinel-1 SLC data in SNAP S-1 toolbox, which would be beneficial for the radar remote sensing user community. Single and multi-date data preprocessing workflow for Sentinel-1  A generic workflow to obtain dual-pol covariance matrix elements from SLC products  Accurate sub-pixel level coregistration of multi-date data

The future projections of climate change envisage a global increase in extreme precipitation events and subsequent flooding. The reliable and rapid flood maps are the critical parameters in preparing the disaster management plans. This study demonstrated an effective flood mapping framework using freely available multi-temporal Earth Observation (EO) images, including C-band Sentinel-1A & 1B Synthetic Aperture Radar (SAR) images and optical WorldView-3 images, for analyzing the 2018 flood event of Kerala, India. Two Change Detection (CD) techniques, i.e. Ratio Index (RI) and Normalized Change Index (NCI) combined with semi-automatic thresholding are implemented on temporal descending pass SAR images for flood identification. For ascending pass SAR images, the statistical-based thresholding method is implemented. The results indicate that combined use of ascending and descending pass SAR images contributed to a better understanding of flood conditions. It is also inferred that the use of a pre-flood image can enhance flood area estimation and helps in minimizing the overestimation errors. The results also found that NCI outperforms RI for Kerala flood event. Flood area extracted from these techniques is plotted against the Indian Meteorological Department (IMD) rainfall datasets, which showed a similar trend. Field photographs and optical images are used for validation purposes.

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<p><strong>Abstract.</strong> Climatological variables such as rainfall, temperature have been extensively used by researchers for drought monitoring at a larger spatial region. These variables have a direct influence on the soil moisture which in turn extends the application of soil moisture in drought assessment. With the advancement of technology, various satellites provide soil moisture data at different spatio-temporal resolutions. In this article, soil moisture obtained from Soil Moisture Ocean Salinity (SMOS) is used to analyze the drought condition over Latur district in Maharashtra, India. The monthly soil moisture derived by averaging the daily data for the years 2010 to 2015 is compared with two drought indices, i.e. Standardized Precipitation Index (SPI) calculated for years 2010 to 2015 and Standardized Precipitation-Evapotranspiration Index (SPEI) calculated for years 2010 to 2013. Even though the overall correlation among the indices with the soil moisture is not significant, the seasonal (summer) correlation is significant. From the results, it is identified that SMOS derived soil moisture can be used as a potential parameter in drought assessment.</p>