National Remote Sensing Centre

Seismic activity can impact different layers of the Earth’s atmosphere; however, our understanding of lithosphere-atmosphere-ionosphere coupling mechanism still remains limited and is challenging. Previous studies predominantly feature seismo-ionospheric changes associated with large earthquakes/tsunamis. Seismic-induced changes in the Mesosphere-Lower Thermosphere (MLT) region have not been properly addressed and are limited to a few reports. We present, here, rare observations of anomalies in the temperature and airglow of the MLT region using Sounding of the Atmosphere using Broadband Emission Radiometry (SABER) instrument on board Thermosphere Ionosphere Mesosphere Energetics Dynamics (TIMED) spacecraft measurements for 2025 Mw 8.8 Kamchatka Peninsula Earthquake. Beginning with the Mainshock at 23:24:52 UT on 29 July 2025, over a hundred aftershocks (with a majority exceeding intensity-scale of Mw 5.0) occurred near Petropavlovsk-Kamchatsky and Severo-Kuril’sk in Russia and activity continued beyond 31 July. We found an increase in temperature in the 73–83 km range on 30 July. However, the temperature decreased in the 87–97 km range on 30 and 31 July. Further, we noted a minor increase and distinct decrease in the volume emission rate of OH airglow on 30 July over the height range of 76–82 km and 83–90 km, respectively. Comparatively, significant decrease in OH airglow was seen on 31 July in the 81–96 km height range. Unusual gravity wave (GW) activity was, also, noted with predominant presence of waves with vertical wavelength of 18 km. Similar anomalous temperature enhancement and pronounced decrease in OH airglow was seen during the 2011 Mw 9.1 Tohoku-Oki earthquake as well.

Clouds significantly influence the Earth's radiation budget and hydrological cycle, playing a key role in weather and climate. Different cloud types have unique radiative properties and often co-occur, complicating our understanding of weather dynamics. In the present study, we studied the radiative heating due to various cloud types over the Indian Summer Monsoon (ISM) region using observations from the CloudSat and CALIPSO satellite. The vertical distribution of radiative heating peaks at different altitudes for different cloud types. We quantified the cloud radiative heating rates for Cirrus (Ci), Stratocumulus (Sc), Nimbostratus (Ns), Cumulus (Cu), Altostratus (As), Altocumulus (Ac), and Deep Convective (DC) clouds. We found that the heating rates for longwave radiation peaked at 8 to 10 km in Ns and DC clouds, with maximum net heating of about 2 K/day at higher altitudes. Radiative heating rates peaked at 15 km in Central India and the Bay of Bengal, while they peaked at 12 to 13 km over the Western Ghats and Arabian Sea, and around 10 km in the Indo-Gangetic Plain. These differences are primarily attributed to the vertical development of DC clouds in these regions and are associated with varying background dynamics. Varying radiative heating at different altitudes affects the temperature gradient, alters atmospheric stability, and influences mesoscale circulation. The frequent occurrence of multi-layer clouds, associated with higher heating rates than single-layer clouds, was observed over the ISM region. Multi-layer clouds exhibited higher heating rates than single-layer clouds, particularly in Ci-Ac and Ci-Ac-Sc formations. This study analyzes the mean net cloud radiative heating due to various cloud types over the ISM for single-layer and multi-layer clouds. Our results can be applied to evaluate climate models for better understanding regional heterogeneity and provide new insights into the role of clouds in modifying the lower atmosphere's thermal structure through radiative heating.

This study presents a multi-sensor remote sensing framework for assessing agricultural damage from Severe Cyclone Remal, which made landfall on 26 May 2024 between Sagar Island (India) and Khepupara (Bangladesh). The approach integrates multi-temporal Sentinel-1 SAR, Sentinel-2 optical data, IMERG rainfall, MERRA-2 wind speed, and ground meteorological observations. Backscatter metrics (Δσ⁰VV, Δσ⁰VH, ΔCR) were used for damage assessment, while δσ⁰VV, δσ⁰VH, and δCR characterised post-event surface response across pre-event Sentinel-2 NDVI-based crop vigour classes (low, medium, high). A decision matrix combined these metrics and NDVI classes to categorise agricultural damage into least, marginally, and severely affected areas, moving beyond binary flooded/non-flooded mapping. Results show 3,243 km² inundation, including 2,154 km² cropland; medium-vigour croplands experienced the most serious proportional damage (29.9

Spatio-temporal evaluation of agricultural water use under both observed and future climate conditions is essential for identifying opportunities for water conservation and supporting sustainable farming practices. However, such basin-scale assessments across distinct agroclimatic regions remain limited. To address this gap, the present study examines variability in the water footprint (WF) under historical and projected climate conditions for an agriculture-dominated sub-basin of India's northern Indo-Gangetic Plain. The basin was categorized into four agroclimatic zones: Zone 1 (upper basin), Zones 2-3 (middle basin), and Zone 4 (lower basin). The WF was estimated for two major crops (sugarcane and wheat). The historical mean total WF was 242 m3/t for sugarcane and 1402 m3/t for wheat. A pronounced climatic gradient is evident across the basin: rainfall decreases and evapotranspiration (ET) increases from upstream to downstream, contributing to higher total WF in the lower basin. Zone 1 shows the highest wheat WF mainly because of lower crop production despite higher rainfall. Zone 4 shows high sugarcane WF because of low rainfall and high ET. The WF of both crops is dominated by blue WF (57% for sugarcane and 56% for wheat), indicating strong dependence on surface and groundwater. Future projections show a declining trend in overall WF, primarily due to improved crop yields. The spatiotemporal WF assessment shows notable variation across zones, indicating that uniform water policies are insufficient at the river basin scale under varying climate conditions. Therefore, this study emphasises and proposes zone-specific adaptation strategies to improve sustainable, climate-resilient agricultural water management.

Soil pH and Electrical Conductivity (EC) are critical indicators of soil health, influencing agricultural productivity and environmental sustainability. This study employs Machine Learning (ML) models to generate spatial prediction maps for soil pH and EC using the laboratory analyzed soil samples, Landsat 8 bands, derived spectral indices in the Google Earth Engine (GEE) platform. Five regression models namely Random Forest (RF), Classification and Regression Tree (CART), Gradient Boosting Regression (GBR), Support Vector Machine (SVM) and Extreme Gradient Boosting (XGBoost) are applied to analyze spatial variations in soil properties. This study integrates field-collected soil parameters with remotely sensed data to enhance predictive accuracy and enable large-scale soil monitoring and generates digital maps for Indukurpet madal, located in Sri Potti Sriramulu (SPSR) Nellore district of Andhra Pradesh. The performances of the models are evaluated using coefficient of determination (R2), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) for both training and testing datasets. The RF model consistently outperformed the other methods achieving the highest prediction accuracy for EC. GBR delivered the the highest prediction accuracy for pH. For EC, the RF model attained an RMSE of 0.05, R² value of 0.86, and MAE of 0.03. while for pH, GBR achieved an RMSE of 0.14, R² value of 0.769 and MAE of 0.09. This demonstrates the robustness of the models in capturing the intricate spatial diversity of soil properties.