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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. [ABSTRACT FROM AUTHOR]

Sequences of multi-temporal synthetic aperture radar (SAR) images are routinely used for land-use land-change (LULC) applications, allowing the retrieval of accurate and up-to-date information on the state of the Earth's surface and its temporal variations. Change detection (CD) methods that rely on the exploitation of SAR data are, generally, made of three distinctive steps: (1) pre-processing of the SAR images; (2) comparison of the pairs of SAR images; and (3) the automatic extraction of the "changed areas", employing proper thresholding algorithms. Within this general framework, the reduction in speckle noise effects, which can be obtained by applying spatial multi-looking operations and ad hoc noise filters, is fundamental for the better detecting and classifying of changed regions. Usually, speckle noise filters are singularly and independently applied to every SAR image without the consideration of their inherent temporal relationships. In particular, most use local (spatial) approaches based on determining and averaging SAR backscattered signals related to neighboring SAR pixels. In this work, conversely, we explore the potential of a joint 3D space-time non-local mean filter (NLMF), which relies on the discrimination of similar features in a block of non-local SAR pixels extracted from the same or different SAR images. The theory behind non-local-mean filters is, first, shortly revised. Then, the developed space-time NLMF is applied to a real test case for the purposes of identifying flooded zones due to the massive inundations that hit the Kerala region, India, during the summer of 2018. To this aim, a set of 18 descending SAR images collected from the European (EU) Copernicus Sentinel-1 (S-1) sensor was exploited. The performance of the developed NLMF has also been assessed. It is worth remarking that the proposed method can be applied for the purposes of analyzing a heterogenous set of natural and/or artificial disastrous conditions. Further, it can also be helpful during the pre-processing stages of the sequences of SAR images for the purposes of CD applications. [ABSTRACT FROM AUTHOR]

Spatially explicit measurement of Above Ground Biomass (AGB) is crucial for the quantification of forest carbon stock and fluxes. To achieve this, an integration of Optical and Synthetic Aperture Radar (SAR) satellite datasets could provide an accurate estimation of forest biomass. This will also help in removing the uncertainties associated with the single sensor-based estimation approaches. Therefore, the present study attempts to integrate Sentinel-2 optical data with Sentinel-1 SAR dataset to estimate AGB in the Shoolpaneshwar Wildlife Sanctuary (SWS), Gujarat, India. In this study, two non-parametric machine learning algorithms viz Support Vector Machines (SVMs) with different kernel functions—linear, sigmoidal, radial and polynomial and Random Forest (RF) were employed for the prediction of AGB using different combinations of VV, VH, Normalized Difference Vegetation Index (NDVI) and Incidence Angle (IA). Ground based AGB was estimated through allometric equation at 35 sampling sites with the help of tree height and Diameter at Breast's Height (DBH). Standalone collinearity analysis among different parameters resulted in poor correlation of AGB with VH (r = 0.05) and IA (r = 0.015), whereas a significantly good correlation with NDVI (r = 0.80) and VV (r = 0.74) were observed. Inclusion of NDVI with VV and VH together also resulted in a better correlation (r = 0.85) than other combinations. The SVM with linear kernel utilizing parametric the combinations of VV + VH + NDVI and VV + VH + NDVI + IA were found to be best performing on the basis of evaluation metrics. The outcome of this study highlighted the significance of machine learning techniques and synergistic use of different remote sensing data for an improved AGB quantification in tropical forests. [ABSTRACT FROM AUTHOR]

The demarcation of linear geomorphological features due to fracture systems, underlying or exposed faults, drainage lines etc. from satellite images has been one of the most widely used applications in geological studies. Synthetic Aperture Radar (SAR) data has been recognized as an effective tool for lineament extraction due to its unique sensitivity to subtle topography, soil moisture and texture. However, the typical SAR sensor parameters such as frequency, polarization and look direction plays a vital role in enhancing the performance for a specific application. Therefore, the study aims to analyse the multi-frequency (C- and L-band), multi-polarization (like and cross) and multi-look direction SAR data for geological lineaments extraction. In this paper, Sentinel-1 and Advanced Land Observation Satellite (ALOS)-2 Phased Array type L-band Synthetic Aperture Radar (PALSAR)-2 datasets acquired over a part of the Indo-Burma ranges of Manipur region (northeastern part of India) are considered for analysis. The lineaments extracted from SAR datasets are compared with reference to lineaments provided by the Geological Survey of India (GSI) through their portal. The study observes that the like-polarization C-band SAR data provide optimal results for this application. It is also observed that the integration of lineaments extracted from multi-look SAR data can significantly improve the performance than that derived from single-look data. Overall, the study also focuses on the spatial distribution of lineaments with respect to the local structural regime for a better understanding of the regional tectonic setup. [ABSTRACT FROM AUTHOR]

The characterization of ground targets from a remotely sensed Synthetic Aperture Radar (SAR) image is addressed by polarimetric decomposition. The polarimetric SAR (PolSAR) decomposition measures the contribution of total backscatter from different scattering mechanisms using SAR images. The ambiguities present in the retrieval of scattering are the major problems associated with the model-based decomposition which could be reduced with a combination of interferometric coherence and PolSAR backscatter. The objective of this study is to improve the polarimetric decomposition model for identifying the scattering mechanisms based on the Polarimetric SAR Interferometry (PolInSAR) coherence for natural and manmade features. In this paper, we have proposed a model-based polarimetric decomposition using PolInSAR decorrelation. The PolInSAR decorrelation is exploited here to distinguish the time-varying and invariant scatterers present in the ground. The volume scattering power was calculated using the proposed decorrelation parameter which is the combination of PolInSAR coherence and decorrelation. The proposed algorithm has been tested on spaceborne multifrequency SAR datasets consisting of X-band TerraSAR-X and TerraSAR-X add-on for Digital Elevation Measurement (TanDEM-X), C-band Radarsat-2, and phased array L-band synthetic aperture radar-2 (PALSAR-2) data of advanced land observing satellite-2 (ALOS-2) PolInSAR data for the Dehradun region, India. The results show that there is a remarkable reduction in the ambiguities present in the identification of the scattering mechanism from the SAR image by using the proposed decorrelation-based decomposition model. Moreover, the algorithm is tested on X-band TerraSAR-X and TanDEM-X data of the Haridwar area and Rudrapur area, Uttarakhand, India to analyse the potential of the proposed decomposition technique in representing different manmade and natural features. [ABSTRACT FROM AUTHOR]

Strong bidirectional internal solitary waves (ISWs) generate from a shallow channel between Car Nicobar and Chowra Islands of Nicobar Islands, India, and propagate toward the Andaman Sea (eastward) and Bay of Bengal (westward). Batti Malv Island separates this shallow channel into two ridges, north of Batti Malv (NBM) and south of Batti Malv (SBM). First, this study identifies the prominent mode-1 and mode-2 ISWs emerging from NBM and SBM using synthetic aperture radar images and then explores their generation mechanism(s) using a nonlinear, unstructured, and nonhydrostatic model, SUNTANS. During spring tide, flow over NBM is supercritical with respect to mode-1 internal wave. Model simulations reveal that mode-1 ISWs are generated at NBM by a "lee wave mechanism" and propagate both in the east and west directions depending on the tidal phases. However, the flow over SBM is subcritical with respect to mode-1 internal wave. The bidirectional propagating mode-1 ISWs evolve from a long-wave disturbance induced by "upstream influence." But, during spring tide, with an increased tidal flow over SBM, it is observed that the westward propagating ISWs are formed by a dispersed hydraulic jump observed over the ridge. Moreover, the bidirectional mode-2 waves from SBM are generated by a lee wave mechanism. An energy budget comparison reveals that the region surrounding NBM is efficient in radiating low-mode baroclinic energy (0.98 GW), while SBM is highly efficient in converting barotropic to baroclinic energy (4.1 GW). [ABSTRACT FROM AUTHOR]

An optimal model was developed for accounting forest carbon stock from synergistic use of optical data from Landsat TM and synthetic aperture radar (SAR) data from COSMO-Skymed (X-band), Radarsat-2 (C-band) and ALOS PALSAR (L-band) sensors over a tropical deciduous heterogeneous forest of India. The best-fit integrated multiple linear regression model had a model accuracy of 83%, r2 = 0.96, root mean square error = 10.02 Mg/ha and Willmott's index of agreement of 0.98. The model further validated using chi-squared and t-test. Results of models for calculating the aboveground biomass (AGB) were converted to C and CO2 using conversion factors. Average AGB, C and CO2 were 70.5, 35.26 and 130.89 Mg/ha, respectively. The synergistic use of optical and multi-frequency SAR data enhanced the AGB saturation threshold to about 150 Mg/ha for tropical deciduous mixed forests. Hence, the synergistic use of this data is suggested for large-scale AGB and C estimations for tropical forests. Optical remote sensing sensors are extensively used due to greater data availability despite their poor sensitivity toward forest parameters. In contrast, SAR signals are highly sensitive toward forest biophysical and structural parameters, providing a better alternative. This unique integrated approach provides valuable information regarding the spatial distribution and quantification of forest biomass and carbon. [ABSTRACT FROM AUTHOR]

Coal mines are the major source of energy in industries and are important indicators of the economic growth of a country. Excessive withdrawal of coal from underground coal mines without stowing leads to surface deformation. In this study, Synthetic Aperture Radar Technology has been used to identify the surface deformation in the Korba Coal Mines of India. Korba coal mines are major coal-producing mines in India that come under South Eastern Coalfields Limited (SECL). It covers three underground mines. Depillaring has been done through the caving method, which may lead to the deformation of the area and its surroundings. Deformation may have devastating consequences if overlooked. So, it requires continuous monitoring of the area. Space-based SAR interferometry is the most preferred technique to monitor any mining area over a period of time. In this technique, known as the DInSAR technique, a deformation map can be generated from images of the same area acquired over different points of time. In the present study, images of the month of February of the years 2015, 2016, 2017, and 2019 have been successfully utilized for DInSAR analysis over the Rajgamar area. The Interferogram allows analysis of an entire mine site including pits and their surrounding area. High precision monitoring over wide coverage enables to bring out accurate deformation results due to the presence of coherent targets and monitoring risk across the study area. Maps generated from data were scrutinized and interpreted. Consequentially, 2015–2016 shows greater deformation than the other two corresponding maps generated 2015–2017 and 2015–2019 data. Results were validated through ground observations. Overall the area of the Rajgamar coal mine is getting deformed at a slow rate. • Rajgamar coalmine is an underground mine which area due to excavation of coal. • The DInSAR technique helps to predict the Deformation with low cost and time. • It helps to identify the affected area with the help of less no. of images with suitable parameters. • This helps to predict the most hazardous zones in the area and helps in planning and management of identical area. [ABSTRACT FROM AUTHOR]

Glacier velocity is one of the important parameters to know about glacier dynamics, its mass and health. Understanding glacier velocity also assists in the analysis of ice thickness, ice flux and mass balance. Furthermore, basin/sub-basin wise studies are helpful to understand the climate change over that region instead of monitoring individual glaciers. In the current study, the spatial distribution of glacier velocity is generated for the entire Chandra and Bhaga sub-basins, in the Lahaul-Spiti district of Himachal Pradesh, India, using Advanced Land Observing Satellite-2/Phased Array type L-band Synthetic Aperture Radar-2 (ALOS-2/PALSAR-2) differential interferometry pair images with a 14-days temporal gap. The glaciers in Chandra sub-basin are found to have a high flow rate as compared to the glaciers in Bhaga sub-basin. Local slope and ice thickness, which decrease towards the terminus in general, control the movement of valley glaciers, and lower movement rate is observed for most of glaciers towards the terminus/frontal area. However, a few glaciers of Bhaga sub-basin demonstrate an accelerated movement in their frontal areas. This irregular behaviour is also studied in this work and it is discovered that terrain slope and influx of melting water from adjacent glaciers are the major causes for such behaviour of these glaciers. They are, therefore, prone to accelerated mass loss compared to other glaciers in the sub-basin. Another key finding of the anomalous glacier dynamics study is the disintegrating behaviour of glaciers, which is also identified by interpreting the responses of physical scattering mechanisms retrieved from glaciers using fully polarimetric ALOS-2/PALSAR-2. Warming temperatures are expected to cause glacier retreat and separation of tributary glaciers from the main glacier body. Melt water from tributary glaciers can further accelerate glacier flow and ice melt in lower part of glacier trunk. This can lead to formation of dead ice zones and disintegration of Himalayan glaciers, which are supported by numerous tributary glaciers. Therefore, we expect that the loss in glacier area due to warming will be larger than the present prediction, where mass loss is considered from conventional way. • DInSAR glacier velocity spatial distribution is generated for Chandra and Bhaga basins, Himalaya. • Anomalous glacier dynamics information is discovered using DInSAR. • Influx of melting water from adjacent glaciers is major cause for anomalous dynamics of glaciers. • Disintegrating behaviour in glaciers is also observed due to influx of melting water. • Melt water from tributary glaciers accelerates glacier flow and bottom ice melt of glacier trunk. [ABSTRACT FROM AUTHOR]