Remote Sensing Applications-Society and Environment最新文献

{"title":"Remote-sensing methods for mapping eucalypt dieback: A systematic review","authors":"Weerach Charerntantanakul ,&nbsp;John T. Burley ,&nbsp;Marta Yebra ,&nbsp;Adrienne Beth Nicotra ,&nbsp;Saul Alan Cunningham ,&nbsp;Matthew Theodore Brookhouse","doi":"10.1016/j.rsase.2026.101908","DOIUrl":"10.1016/j.rsase.2026.101908","url":null,"abstract":"<div><div>Eucalypts dominate Australia's native forests and are important to global wood and fibre plantations. Both native and plantation eucalypt forests are subjected to diverse pests and diseases that act as proximal causes of canopy decline, known broadly as dieback. Remote sensing provides effective tools for monitoring tree health and mapping canopy dieback, offering valuable information for forest management. This review synthesises remote-sensing methods for mapping eucalypt dieback, with objectives to identify promising approaches and current research gaps. We assessed 32 studies from six countries and found research activity increased since 2016, with a transition of remote-sensing platforms from manned aircrafts to satellites and unmanned aerial vehicles (UAV). Insect pests specialised on eucalypts were the most frequently reported causes of dieback. Most studies mapped variables linked to overall tree health, defoliation, or damage. Multispectral and RGB sensors were most commonly used and both achieved high mean accuracy. Machine-learning and deep-learning algorithms were preferred analytical methods, but their accuracies are not significantly superior to parametric methods, especially for regression problems. Further studies are needed to evaluate hyperspectral and LiDAR sensors, especially when integrated with UAV-based high-resolution data. These are highly promising approaches that require further validation. Future research should also explore dense time series and individual tree-based approaches to strengthen the connection between remote-sensing signals and physiology-based understanding of dieback processes. This review synthesises current approaches and outlines key directions for advancing remote-sensing methods in mapping eucalypt dieback.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"41 ","pages":"Article 101908"},"PeriodicalIF":4.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147395887","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A metaheuristic-driven deep feature selection approach for hyperspectral image classification","authors":"Sneha Raina ,&nbsp;Ajay Kaul","doi":"10.1016/j.rsase.2026.101916","DOIUrl":"10.1016/j.rsase.2026.101916","url":null,"abstract":"<div><div>Hyperspectral imaging is increasingly vital across domains such as land-use classification, defense, and environmental monitoring. Its rich spectral information, surpassing that of multispectral imaging, enables precise material identification but also introduces challenges, including overfitting, extended training time, and dependency on large labeled datasets. This study proposes a metaheuristic-driven deep feature selection framework, enhancing Hyperspectral image classification by integrating transfer learning with nature-inspired optimization algorithms. In one approach, features extracted via transfer learning are directly classified; in another, a metaheuristic algorithm selects salient features before classification. Evaluations on three benchmark datasets demonstrate that our hybridized approach outperforms existing methods in both accuracy and computational efficiency. The results underscore the promise of transfer learning and metaheuristic optimization in mitigating traditional limitations of hyperspectral image analysis.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"41 ","pages":"Article 101916"},"PeriodicalIF":4.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147395890","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"LiDAR-based shade mapping: Decomposition and multi-resolution analysis in a 3D digital twin","authors":"Roshan Saud ,&nbsp;Steven M. Richter","doi":"10.1016/j.rsase.2026.101918","DOIUrl":"10.1016/j.rsase.2026.101918","url":null,"abstract":"<div><div>Quantifying urban shade is essential for mitigating the Urban Heat Island effect, yet existing approaches struggle to represent shade in complex three-dimensional urban environments and across spatial scales. This study presents a LiDAR-based 3D digital twin framework that systematically decomposes urban shade by source (Tree, Building, and Combined), Orientation (Roof, Facade, and Ground), and spatial resolution, providing a replicable approach for urban-scale analysis. High-resolution airborne LiDAR data were used to generate elevation models. Combining elevation models with other datasets, 3D representations of terrain, buildings, and vegetation were generated. Vertical and horizontal shade were modeled under consistent solar geometry. Shade was evaluated across multiple resolutions (2m, 5m, 10m, and 30m) along an urban-to-suburban transect to assess resolution sensitivity and scalability. Results reveal that in the Urban Core Zone, building shade is predominant and occurs primarily on facades, whereas in suburban landscapes, tree shade dominates, mostly covering ground surfaces—illustrating a contrasting spatial pattern. Outside of Urban Core and Suburban Zones, all Transect Zones have minimal Roof shade. Sensitivity analysis of model resolution indicates that shade detection is consistent through 5m with minor discrepancies (under 10%) emerging at 10m. At 30m, significant deviations occur, particularly for ground and roof surfaces. Roof shade in all scenarios and ground shade in the Building Scenario are the most sensitive to resolution. These findings provide insights for urban planners and geospatial scientists by improving understanding of how 3D shade distribution varies with urban morphology and spatial resolution.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"41 ","pages":"Article 101918"},"PeriodicalIF":4.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147395965","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Random forest-based species distribution modeling reveals intensifying multi-species invasion risks of alien plants in Ethiopia under climate change","authors":"Kalid Hassen Yasin ,&nbsp;Diriba Tulu ,&nbsp;Tadele Bedo Gelete ,&nbsp;Beyan Ahmed Yuya ,&nbsp;Anteneh Derribew Iguala ,&nbsp;Kiya Adare Tadesse ,&nbsp;Erana Kebede","doi":"10.1016/j.rsase.2026.101869","DOIUrl":"10.1016/j.rsase.2026.101869","url":null,"abstract":"<div><div>Invasive alien plant species (IAPS) are increasingly threatening biodiversity conservation during rapid climate change, especially in vulnerable regions, such as Ethiopia. This study aimed to model current and future habitat suitability for four major IAPS (<em>Prosopis juliflora</em>, <em>Parthenium hysterophorus</em>, <em>Lantana camara</em>, and <em>Acacia</em> spp.) and assess multi-species invasion risks across Ethiopia's 136 protected areas (PAs), using Random Forest (RF)–based species distribution modeling (SDM). The analysis integrated 27 environmental predictors with downscaled climate projections from the Coupled Model Intercomparison Project Phase 6 (CMIP6), under Shared Socioeconomic Pathway (SSP) 2-4.5 (moderate) and SSP5-8.5 (high-emission) scenarios. Findings revealed distinct ecological niches among the four major species, with <em>P. hysterophorus</em> currently exhibiting the largest area of suitable habitat (175,743 km²). All species demonstrated significant expansion potential under climate change, with <em>P. juliflora</em> showing the most dramatic relative increase (up to 106.25 % by 2070 under SSP5-8.5). Niche overlap analysis indicated high environmental similarity between <em>L. camara</em> and <em>P. hysterophorus</em> (Hellinger's <em>I</em> = 0.8826), suggesting potential for co-occurrence in invasion hotspots concentrated in the southwestern highlands and Rift Valley. Most critically, the PA vulnerability assessment revealed that smaller protected areas (&lt;100 km²) face significantly higher potential invasion pressure (mean suitable area: 41.5 %) than larger ones (≥5000 km²: 25.3 %), with <em>P. hysterophorus</em> posing the broadest spatial threat affecting 37 PAs with &gt;50 % habitat suitability. Future projections indicate 206–247 % increases in invasion threats across PAs by mid-century, with eastern and southeastern conservation landscapes facing a disproportionate rise in risk. These findings present the first national, evidence-based spatial prioritization framework for climate-adaptive IAPS management in Ethiopia, demonstrating that conservation planning should integrate dynamic invasion risk assessments to safeguard biodiversity amid global climate change.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"41 ","pages":"Article 101869"},"PeriodicalIF":4.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926032","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatiotemporal dynamics of NO2 concentration data (2018–2025) in Casablanca-Settat region, Morocco: A satellite-based assessment for urban air quality management","authors":"Abdelhalim Miftah","doi":"10.1016/j.rsase.2026.101870","DOIUrl":"10.1016/j.rsase.2026.101870","url":null,"abstract":"<div><div>The Nitrogen dioxide (NO₂) is one of the main pollutant gases in the atmosphere with known harmful effects on human health, caused by rapid urbanization, traffic, and industrialization. In the fast-growing urban areas of North Africa, NO₂ spatiotemporal variability analysis is hampered by the sparse distribution of ground stations. In neighboring Morocco, particularly in the Casablanca-Settat region, research that utilizes satellite data analysis in combination with geospatial statistical analysis is also limited. The purpose of this study is to fill the scientific gap in spatial variability analysis of NO₂ concentrations from 2018 to 2025 in a specific region in Casablanca-Settat in Morocco. The NO₂ concentration in the tropospheric region using the Sentinel-5P (TROPOMI) satellite sensor was analyzed using geospatial analysis tools. The hotspots and cold spots of NO₂ were determined using the Getis-Ord Gi∗ statistic at a statistically significant confidence level. The annual average NO₂ concentration varied between 6.3 μg/m³ in rural areas in southern provinces to 11.3 μg/m³ in Casablanca, showing sharp differences between the urban, industrial areas, and rural areas. The cities of Casablanca, Mediouna, and the Nouaceur province showed higher NO₂ concentrations than other cities in any country in the world; they often exceeded the annual average recommended by the World Health Organization at 10 μg/m³. However, southern provinces, such as Settat, Sidi Bennour, and El Jadida, had smaller NO₂ concentrations that were less variable. A marked decrease in NO₂ was observed during the COVID-19 lockdown period, followed by a stabilized period in 2022–2023 and a decline in NO₂ thereafter. The NO₂ hotspots were in the Casablanca-Beth Mohammedia-Mediouna-Berrechid region at a 99 % confidence level, which was a hotspot region, but in southern areas, it was a cold spot region.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"41 ","pages":"Article 101870"},"PeriodicalIF":4.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145977874","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Monitoring urban green space for climate-resilient development in the face of rapid urbanization: A tale of two Vietnamese cities","authors":"Leon Scheiber ,&nbsp;Vera Zühlsdorff ,&nbsp;Duong Huu Nong ,&nbsp;Thanh Son Ngo ,&nbsp;Nigel K. Downes ,&nbsp;Felix Bachofer ,&nbsp;Hong Quan Nguyen ,&nbsp;Matthias Garschagen ,&nbsp;Andrea Reimuth","doi":"10.1016/j.rsase.2025.101820","DOIUrl":"10.1016/j.rsase.2025.101820","url":null,"abstract":"<div><div>Urban green space (UGS) contributes to sustainable and climate-resilient urban development by providing ecosystem services and enhancing public health. In rapidly urbanizing cities, UGS is compromised by expanding built infrastructure, leading to loss and fragmentation of green areas. This study employs a resource-efficient remote sensing approach for monitoring UGS dynamics in two examples of rapid urbanization, Hanoi and Ho Chi Minh City (HCMC) in Vietnam. The approach identifies UGS by applying a ground-truthed threshold to Normalized Difference Vegetation Index quartile maps (NDVI–P75) from nine years of open-access Sentinel-2 imagery before blending it with national census data. The results indicate a pronounced spatial heterogeneity in UGS distributions, with low densities in urban cores and greater availability in the peripheral districts of both metropolises. The temporal analysis shows diverging trends: while UGS areas in Hanoi are relatively stable overall but declining per capita due to ongoing urbanization, HCMC experiences a general decline in both UGS indicators. The findings emphasize the urgent need for implementing integrated UGS strategies that account for the diverse socio-economic drivers of UGS loss. By offering a robust and reproducible methodology for monitoring UGS, this research highlights the potential of remote sensing tools to inform urban planning and policy development. This approach is highly transferable to other urban contexts globally, demonstrating an effective and transparent pathway to foster climate-justice and “sustainable cities and communities” in line with the United Nations’ Sustainable Development Goal No. 11.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"41 ","pages":"Article 101820"},"PeriodicalIF":4.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145738344","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing SMAP for enhanced wildfire danger prediction in boreal-Arctic ecosystems","authors":"Laura L. Bourgeau-Chavez ,&nbsp;Chelene Hanes ,&nbsp;Michael Billmire ,&nbsp;Karl Bosse ,&nbsp;Michael J. Battaglia ,&nbsp;Andreas Colliander","doi":"10.1016/j.rsase.2026.101881","DOIUrl":"10.1016/j.rsase.2026.101881","url":null,"abstract":"<div><div>In boreal and Arctic regions, where organic soils act as wildfire fuel, NASA’s SMAP soil moisture products offer strong potential to improve drought and fuel (organic soil) moisture assessment beyond point-based weather station fire danger models. Since SMAP is not calibrated for organic soils, we evaluated its suitability using a network of fuel moisture stations we established across three SMAP grid cells, as well as fire weather station data across the North American boreal and Arctic. Comparison of SMAP products, brightness temperature, and reflectivity with <em>in situ</em> fuel moisture measurements revealed SMAP products to be dry-biased with low dynamic range (r = −0.03 to 0.40). In contrast, SMAP reflectivity showed good relationships to <em>in situ</em> fuel moisture at 6 cm depth in the Alaska tundra site (r = 0.62), and 10–18 cm depth for the Alberta (r = 0.46) and Ontario (r = 0.62) boreal sites. SMAP soil moisture products were then used to develop a statistical model to predict Drought Code (DC), a weather-based index of fuel availability in the deeper (10–20 cm) organic soil layers. The model, created using hundreds of weather stations across boreal and Arctic regions, explained 63 % of overall deviance (range 28–86 %). Additionally, incorporating SMAP retrievals flagged for dense vegetation increased spatial coverage without compromising model performance. These results indicate that an operational SMAP-derived deep organic fuel moisture (e.g. DC) product is feasible if future retrievals account for soil organic content. This would enhance fire danger monitoring and decision support across boreal and Arctic regions.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"41 ","pages":"Article 101881"},"PeriodicalIF":4.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146077860","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mapping of sun-induced fluorescence (SIF) in kiwifruit canopy using a 3D radiative transfer modeling and airborne hyperspectral imaging","authors":"Reddy R. Pullanagari ,&nbsp;Mohammad Hossain Dehghan-Shoar ,&nbsp;Junqi Zhu ,&nbsp;Alvaro A. Orsi ,&nbsp;Ian J. Yule","doi":"10.1016/j.rsase.2025.101840","DOIUrl":"10.1016/j.rsase.2025.101840","url":null,"abstract":"<div><div>Sun-induced chlorophyll fluorescence (SIF) has emerged as a valuable proxy for estimating plant physiological activity. While empirical and one-dimensional (1D) radiative transfer models (RTMs) have shown reasonable success in quantifying SIF at the canopy scale using hyperspectral sensors, they face challenges in addressing complex, heterogeneous canopy structures, weak signals, and intricate sun-to-sensor geometries. In recent years, three-dimensional (3D) RTMs have made significant progress in overcoming these challenges.</div><div>This study employed multiple RTMs, such as PROSPECT-PRO, FLUSPECT, and LESS, to investigate SIF in kiwifruit orchards. High-resolution hyperspectral imagery and LiDAR data were collected over the orchards, along with ground-truth measurements. A 3D kiwifruit canopy was reconstructed using functional-structural plant modeling (FSPM) based on LiDAR point cloud data. Utilizing the LESS RTM, thousands of reflectance spectra were simulated based on the given leaf and soil optical properties and the 3D canopy structure.</div><div>A kernel ridge regression (KRR) algorithm was trained on these simulations in the SIF region (650–810 nm) and validated with the ground-truth measurements. This hybrid (3D RTM-KRR) model demonstrated a high correlation with the ground-truth data, outperforming empirical models (such as Fraunhofer line discrimination methods). This indicates its capability to extract SIF from coarse-resolution airborne and satellite-based hyperspectral missions (e.g., PRISMA and EnMAP). This approach offers a promising avenue for improving our understanding of plant physiological processes and their interactions with the environment at larger scales. This research provides a significant advancement for precision agriculture in orchards, proving the practical value of 3D RTM for heterogeneous canopies.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"41 ","pages":"Article 101840"},"PeriodicalIF":4.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145840886","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multivariate statistical analysis of rainfall variability in Brazil: Assessing climatic and environmental drivers of precipitation","authors":"Arthur Amaral e Silva ,&nbsp;Leonardo Campos de Assis ,&nbsp;Laura Coelho de Andrade ,&nbsp;Juliana Ferreira Lorentz ,&nbsp;Júlio César de Oliveira ,&nbsp;Maria Lucia Calijuri ,&nbsp;Italo Oliveira Ferreira","doi":"10.1016/j.rsase.2025.101849","DOIUrl":"10.1016/j.rsase.2025.101849","url":null,"abstract":"<div><div>This study develops a remote sensing-based, multivariate analytical framework integrating Principal Component Analysis (PCA) and CLARA clustering to investigate rainfall variability and its climatic and environmental drivers across Brazil. High-resolution datasets from over 900 rainfall stations were processed to link precipitation patterns with vegetation dynamics (NDVI), evapotranspiration, temperature, and topography. Methodologically, PCA reduced data dimensionality, isolating dominant factors controlling rainfall seasonality, while CLARA clustering classified stations into environmentally and climatically coherent groups. PCA results show that atmospheric moisture transport systems, the Flying Rivers and the South Atlantic Convergence Zone (ZCAS), dominate wet-season precipitation, explaining over 40 % of variance in January and February. NDVI and evapotranspiration contributed up to 30 % of variance in the secondary component, reflecting vegetation–climate feedbacks. During the dry season, temperature became the leading driver, negatively correlated with rainfall and intensifying drought risk. CLARA clustering identified distinct seasonal regimes, with humid zones linked to high NDVI and evapotranspiration, arid regions characterized by low rainfall (&lt;75 mm) and high temperatures (&gt;28 °C), and transitional areas sensitive to land-use change. Orographic effects further enhanced precipitation in elevated landscapes, while deforestation in the Amazon disrupted atmospheric moisture fluxes, reducing rainfall connectivity across southeastern Brazil. By integrating dimensionality reduction with spatiotemporal clustering, this research offers a scalable, data-driven framework for understanding rainfall dynamics, supporting climate adaptation, hydrological modeling, and sustainable land-use strategies in tropical regions under environmental pressure.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"41 ","pages":"Article 101849"},"PeriodicalIF":4.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145840887","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A geo-spatial assessment of desert locust risk over India during summer 2020 using GEO-LEO satellite observations and weather forecast","authors":"Rahul Nigam,&nbsp;Bimal K. Bhattacharya,&nbsp;Ayan Das,&nbsp;Mukesh Kumar,&nbsp;Prashant Kumar","doi":"10.1016/j.rsase.2025.101831","DOIUrl":"10.1016/j.rsase.2025.101831","url":null,"abstract":"<div><div>Desert Locust (DL) infestations pose a significant threat to food security in arid and semi-arid regions, particularly in East Africa, Central Asia, and the Indian subcontinent. In 2020, during the COVID-19 pandemic, India witnessed an unprecedented upsurge of DL activity during the summer (zaid) season (April–June), severely impacting Rajasthan, Gujarat, and neighbouring states. This study investigates the environmental drivers of the DL outbreak and assesses crop damage using geospatial datasets, reanalysis products, and numerical weather models. Fifteen grid cells (100 km × 100 km) along the DL-prone corridor from East Africa to India were analyzed for environmental suitability, with seasonal Spearman correlation analysis applied to identify significant factors influencing locust activity. In winter, locust activity was significantly positively correlated with rainfall (ρ = 0.47, p = 0.021), dew point temperature (ρ = 0.76, p = 0.01), and soil moisture (ρ = 0.50, p = 0.05), highlighting the importance of moisture and temperature conditions in facilitating locust presence. In spring, significant positive correlations were observed with air temperature (ρ = 0.56, p = 0.027), soil temperature 1 (ρ = 0.65, p = 0.01), and a very strong correlation with soil temperature 2 (ρ = 0.73, p = 0.002). These findings showed the crucial role of temperature and moisture during the winter and spring seasons as key drivers of locust behaviour. The Linear Discriminant Analysis (LDA) model shows potential in locust presence prediction, though challenges remain due to data limitations. Crop damage was quantified using Normalized Difference Vegetative Index (NDVI), showing severe vegetation loss in affected areas (NDVI &lt;0.3) and degradation due to locust feeding. The study further integrates weather forecast wind patterns, MODIS Leaf Area Index (LAI), and soil moisture from SMAP to track locust migration. Wind patterns, particularly westerly and south-westerly winds, guided the locusts' entry into western India. Despite moderate LAI values, the vegetation cover in central and western India provided sufficient sustenance for the locusts. Soil moisture from SMAP consistently supported locust dispersal across northern Rajasthan, central India, and parts of Uttar Pradesh. The integration of these environmental factors offers a comprehensive understanding of DL behaviour, enhancing early warning and control efforts.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"41 ","pages":"Article 101831"},"PeriodicalIF":4.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145791462","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}