Efficient discrimination of diverse kharif crops, remains crucial for crop monitoring and production forecasting, and plays a pivotal role in decision -making for food security in India. This study aims to harness temporal backscatter data from EOS-04 C -band synthetic aperture data (SAR) payload to achieve precise discrimination among six short -duration (cereal, oilseeds, fibre) and long -duration (fibre, pulses) kharif crops. The study integrates limited ground -truth polygons and a Random Forest machine learning approach for analysing EOS-04 time -series data. The classification accuracies were found to be higher than 75% across all kharif crops, with cereals exhibiting the highest accuracy, succeeded by fibre, oilseed and pulse crops. A key focus lies in identifying optimal polarization combinations for effective discrimination among diverse kharif crop types. The study reveals that the synergistic utilization of dual polarizations outperforms individual co- or cross -polarizations, notably benefiting discrimination of cotton, soybean and groundnut crops. Horizontal-vertical polarizations are found to be most effective for achieving peak accuracies in rice and red gram crops. Furthermore, the analysis indicates a promising potential for early crop assessment, presenting an opportunity to furnish precise crop estimates at least one and a half months before the harvest.

The C-band SAR on-board EOS-04 mission provides unique opportunities to characterize forest vegetation through its sensitiveness to vegetation structure and allweather imaging capability over regions of perpetual cloud cover. The present study has brought out the applications of EOS-04 data for estimation of aboveground biomass (AGB) of tropical deciduous forests and scrublands, mapping of forest cover and delineation of mangroves vegetation. The study suggested that EOS04 data can be used for mapping AGB of tropical scrublands and low density forests of AGB <= 80 t/ha. The overall RMSE for all vegetation with AGB <= 80 t/ha was 15.3 t/ha (R-2 - 0.49). It was shown that the integration of EOS-04 and Sentinel-2 data improved AGB estimates across biomass ranges of 0-245 t/ha (RMSE - 21.60 t/ha and 0.81). EOS-04 data was also found to be useful for the delineation of mangroves and forest vegetation using machine-learning algorithms. The study supports operational use of EOS-04 data for estimation of AGB over low biomass tropical forests and scrublands.

A Very Severe Cyclonic Storm ‘Yaas’ developed over the Bay of Bengal (BoB) on 23 May 2021 and crossed over the Odisha coast on 26 May with maximum sustained wind speed of 75 kts. Herein, a pathway has been developed and exemplified for ‘Yaas’ through three-stage cyclone-induced hazard tracking. Days before the cyclone formation, cyclone genesis potential parameter, sea surface temperature (SST) (> 30 °C) and tropical cyclone heat potential (anomaly of 40–80 kJ/cm 2 ) indicated a strong possibility of cyclogenesis in the BoB. A Lagrangian advection model used for its track prediction with 24-h lead-time provided an accuracy of ~ 19 km and ~ 6 h in its landfall location and time. Further, intensity prediction was done using numerical weather prediction model. Geostationary satellites, INSAT-3D/3DR, were used to visualize cyclone structure. Passing of cyclone had its reverbarations in oceans, which are observed in SST drop of ~ 3 °C, salinity and density increase by ~ 1 psu and ~ 2 kg/m 3 , respectively. During the period, 23–26 May 2021, the Ekman suction velocity and chlorophyll concentration were found significantly high at ~ 5 m/day and > 0.5 mg/m 3 , respectively. Forecast of storm surge was found to be between 3.5 and 4 m at coastal locations. Significant wave height was found to be 5.5–9.2 m. The coastal inundation forecast for 24 May 2021 provided its quantitative maximum inland extent. Finally, loss of the crop, fishery and forest areas by strong winds and inundation/ingress of saline water associated with storm surge were examined using SAR and optical data.

Abstract The study of the icebergs and their movements is one of many applications of scatterometer data in the study of the ecosystems of polar regions. SCATSAT-1 is the Indian Space Research Organisation’s (ISRO’s) Ku-band (13.515625 GHz) scatterometer. Using enhanced resolution Gamma0H (horizontally polarised incidence angle normalised backscattering coefficient) data of SCATSAT-1, we observed the movement of iceberg D28 and its interaction with wind, ocean currents and sea ice for one and a half years of its journey (JD 269, 2019 to JD 051, 2021). The data sets used are as follows: (1) SCATSAT-1 level-4 Gamma0H; (2) OSCAR (Ocean Surface Current Analysis Real-time) third-degree resolution ocean surface currents; (3) hourly wind speed data of ERA5; 4) NSIDC (National Snow and Ice Data Center) sea ice concentration data; and (5) NSIDC Polar Path-finder Daily EASE-Grid Sea Ice Motion Vectors, Version-3. For this study, we divide the continent into five different regions/sectors. It is found that the trajectory of the iceberg is influenced by the resultant of the wind and ocean current, at different scales in these regions. Moreover, sea ice motion can also change the course of iceberg. From the on-screen digitisation of the iceberg, the average area of the iceberg is found to be approximately 1509.82 km2 with approximate dimensions of 27 km × 55.5 km. We conclude that spatial and temporal behaviours of the iceberg can be ascertained from the scatterometer data.

NASA and ISRO are jointly developing a state of art L and S band space-borne Synthetic Aperture Radar (NISAR), planned for launch in 2023. NISAR, through its repeat-pass interferometric orbits, will produce global coverage of SAR data at high repeat cycle, high resolution and larger swath with capability of producing full-polarimetric data in L-band and hybrid polarimetric data in S-band. The NISAR systematic observation at L-band and S-band over Indian region will provide unprecedented time-series data for the Ecosystems applications. NISAR data will address the critical issues of forest carbon stock estimation and monitoring carbon fluxes from vegetation disturbances; changes in the alpine vegetation tree-line; agriculture crop monitoring and changing cropping patterns; spatio-temporal distribution of field-scale soil moisture and inundation dynamics of wetlands. This paper provides overview of applications opportunities in ecosystem sciences with NISAR data and describes the ecosystem science products planned by ISRO for the user community.

Supervised classification of time series image classification through state-of-the-art Machine Learning algorithms such as Random Forest demands good quality training data for achieving good classification accuracy. A pilot study has been carried out to find out the effect of training data quality on discriminability of kharif maize crop from competing crops using multi-date C-band Synthetic Aperture Radar (SAR) data. The study was done in five districts of Telangana and four districts each of Madhya Pradesh and Maharashtra. Fairly good discrimination of kharif maize using C-band SAR with average classification accuracy of 85% have been obtained where at least 30 good quality ground truth (GT) polygons and similar number of GT for competing crops were available.

The possibility of using high resolution SCATSAT-1 data for studying ice calving events in Antarctica has been explored in this study. Two recent calving events in the ice shelves of Amery (2019) and Larsen D (2020) have been observed. These gave birth to icebergs D-28 and A-69 respectively. Enhanced resolution level-4 horizontally polarized daily gamma-0 measurements are used. Canny edge detection technique has been employed to observe these events. The results obtained from the edge detection have been compared with Sentinel-1A Level-1 Ground Range Detected datasets and are found to be in good agreement. A mean difference of 0.2 km (± 3.7 km) is obtained between the two ice front edges (derived and actual).

Sea ice plays an active role in ocean circulation, weather and regional climate as well as on the salinity of icy oceans. The insulating property of sea ice reduces evaporation of underlying water and heat loss to the atmosphere. The high albedo of 0.5 to 0.7 of sea ice reduces the global temperature (National Snow and Ice Data Center, 2020). Expulsion of brine during the formation of sea ice increases salinity and density of surrounding ocean water in the polar regions (Lake and Lewis, 1970). In case of Antarctica, this dense water flows down to the continental shelf to form Antarctic Bottom Water (Dan and Robert, 2006) and in the Arctic, flows down to the ocean floor and moves to enter the Greenland and Norwegian Seas and forms Arctic Bottom Water (Glossary of American Meteorological Society, 2012). These waters lead to the formation of the thermohaline circulation, the largest ocean circulation. Polar-orbiting satellite data are useful in observing and studying long term and seasonal changes of the Arctic sea ice extent (SIE). SIE is considered as a sensitive indicator of long-term global climate change (Budd, 1975). Estimation of SIE has been carried out using active remote sensing (Remund and Long, 2014) and passive remote sensing (Cavalieri and Parkinson, 2008; Comiso et al., 1996). In this study, enhanced resolution SCATSAT-1 data are used to estimate the Arctic SIE. Image analysis technique of Principal Component Analysis (PCA) is applied to highlight different features within an image. After masking the land parts, discrimination between sea ice and ocean water is carried out using ISODATA classification. The results obtained are validated by comparing with 1) National Snow and Ice Data Centre (NSIDC) sea ice extent derived from 30 % sea ice concentration and 2) Ocean and Sea Ice Satellite Application Facility (OSI) sea ice edge. Statistical significance is calculated between the comparing data sets. 2. Data

ISRO's SCATSAT-1 is a miniature satellite which serves as a continuity mission to the bygone Oceansat-2. Three important parameters from SCATSAT-1 are made available through the Meteorological & Oceanographic Satellite Data Archival Centre (MOSDAC), Space Applications Centre-ISRO. They are the backscattering coefficient (sigma(0)), incidence angle normalized backscattering coefficient or the gamma naught (gamma(0)) and the scatterometer noise-derived brightness temperature (T-b). In this paper, an attempt has been made to use the initially available SCATSAT-1 data in native resolution of 25 km as well as the enhanced resolution at 0.225 degrees to explore the Larsen C ice-shelf, West Antarctica during the recent calving event of 2017. Moreover, a preliminary study on the plausible melt conditions over the shelf is reported.

Antarctica is the focus of scientific studies considering the largest reservoir of terrestrial water in the form of ice and doubling of ice area during winter due to sea-ice growth. The third pole - Himalaya is equally important due to the large extent of snow and ice cover outside the polar regions, which is a major source of water for the Asian countries. At present, the Ku-band scatterometer observing global cryosphere is the SCATSAT-1 launched by India. This article describes the study carried out on different cryospheric parameters using high-resolution (similar to 2.2 km) scatterometer data in the Antarctica and Himalaya. Impact of seasonal variations in snow/ice and ice calving on the backscatter over Antarctica is discussed in detail. A procedure developed for the estimation of sea-ice extent, which yielded overall accuracy of 89%, has been presented and successfully applied for daily monitoring of the Antarctic ice extent for 2017. Surface melting using backscatter and brightness temperature data has been discussed and the contrast between large-sized and small-sized Antarctic ice shelves during the austral summer period of summer 2017-18 is highlighted. The higher average surface melt observed around majority of east Antarctic ice shelves, particularly near the Indian station 'Maitri', is of particular interest. Typical surface melting patterns observed over the third largest Antarctic ice shelf, Amery, are discussed in detail. Over northwest Himalaya, derived changes in snow water equivalent (Delta SWE) shows a good correlation between observed and calculated SWE variations. The present study demonstrates that simultaneous availability of high-resolution brightness temperature and backscatter data from SCATSAT-1 provides a unique opportunity to study the polar and mountain cryosphere.

Indian Space Research Organisation’s SCATSAT-1 is a continuity mission for Oceansat-2 Scatterometer. The sensor works in a Ku-band (13.515 GHz) similar to the one flown on-board Oceansat-2. It provides backscattering coefficient over the globe and wind vector data products over the oceans that are useful for weather forecasting, cyclone detection, and tracking services. Besides backscattering coefficient (sigma nought), two other important parameters, namely, Gamma nought (obtained from backscattering coefficient) and Brightness temperature (obtained from scatterometer noise measurement) are given as the Level-4 data products archived at the ISRO’s Meteorological & Oceanographic Satellite Data Archival Centre. We used these three parameters both in horizontal and vertical polarizations for the Antarctic region (South Polar) to perform, first, a principal component analysis. Then, we used the first three principal components explaining the largest variability in the data set to perform an unsupervised ISODATA clustering classification to estimate the regions of sea ice around Antarctica. The derived sea ice extent through this method is compared with other popular sea ice extent products available elsewhere.

A huge portion of the Larsen C ice shelf (~50,000 km 2 ) in Antarctic Peninsula calved away to an iceberg of area ~6,200 km 2 between 10 and 12 July 2017. Larsen C is the fourth largest ice shelf in Antarctica, after Filchner-Ronne, Ross and Amery ice shelves. Unusual rift propagation at Larsen C ice shelf has excited the scientific community during the last six months. The calved area is ~1.6 times the area of Goa and ~4 times the area of Delhi.

Accurate measurement of surface soil moisture of bare and vegetation covered soil over agricultural field and monitoring the changes in surface soil moisture is vital for estimation for managing and mitigating risk to agricultural crop, which requires information and knowledge to assess risk potential& implement risk reduction strategies and deliver essential responses. The empirical and semi-empirical model-based soil moisture inversion approach developed in the past are either sensor or region specific, vegetation type specific or have limited validity range, and have limited scope to explain physical scattering processes. Hence, there is need for more robust, physical polarimetric radar backscatter model-based retrieval methods, which are sensor & location independent and have wide range of validity over soil properties. In the present study, Integral Equation Model (IEM) and Vector Radiative Transfer (VRT) model were used to simulate averaged backscatter coefficients in various soil moisture (dry, moist and wet soil), soil roughness (smooth to very rough) and crop conditions (low to high vegetation water contents) over selected regions of Gujarat state of India and the results were compared with multi-temporal Radar Imaging Satellite-1 (RISAT-1) C-band Synthetic Aperture Radar (SAR) data in HH and HV polarizations, in sync with on field measured soil and crop conditions. High correlations were observed between RISAT-1.sigma(circle)(HH) and sigma(circle)(HV) with model simulated sigma(circle)(HH)&(circle)(HV) based on field measured soil with the coefficient of determination R-2 varying from 0.84 to 0.77 and RMSE varying from 0.94 dB to 2.1 dB for bare soil. Whereas in case of winter wheat crop, coefficient of determination R2 varying from 0.84 to 0.79 and RMSE varying from 0.87 dB to 1.34 dB, corresponding to with vegetation water content values up to 3.4 kg/m2. Artificial Neural Network (ANN) methods were adopted for model-based soil moisture inversion. The training datasets for the NNs were obtained from theoretical forward-scattering models with controlled parameters, thus allowing the control of wide range of soil and crop parameters with which the network was trained. A preliminary performance analysis showed good results with estimation of soil moisture with RMSE better than 6%.

It is important to monitor vegetation such as forests in order to understand the impacts of global climate change on terrestrial ecosystems and agriculture crops to ensure food security to the people and livestock. Remote sensing data such as polarimetric SAR data plays a useful role in estimating total vegetation cover and biomass. In this study, a radar vegetation index (RVI) were used to separate vegetation from non-vegetated area and the same were used along with SAR backscatter values at different polarizations in C — and L-band to estimate above-ground biomass of a tropical forest. Models based on multi-frequency SAR data including X —, C — and L-band were developed to improve the estimation of forest biomass. Also, the study of agricultural crops using C — and L-band SAR data at different polarization modes revealed that C-band produced better classification results than L-band. However, L-band showed better correlation with crop growth variables. Further, full polarimetric data was found to be better than various modes of hybrid polarimetric data for crop studies.

A polarimetric model has been developed to study the temporal growth of different vegetation canopies, and their architecture. Eigen decomposition and coherency matrices are analyzed for completely polarimetric Radarsat-2 data. Polarimetric indices have been formulated using co and cross polarized backscattering coefficients, eigen values and eigen vectors. The polarization indices are used to completely understand the difference between polarized scattering signatures of vegetation in HH and VV polarizations. In this study, two decomposition techniques have been used like Freeman-Durden and H/A/α and their volume scattering and entropy components in conjunction with co and cross polarized indices are analyzed. This qualitative evaluation of vegetation parameters and growth stage are found to work better with polarimetric complex SAR data rather than using amplitude imagery.