Global soils are under extreme pressure from various threats due to population expansion, economic development, and climate change. Mapping of land degradation vulnerability (LDV) using geospatial techniques play a significant role and has great importance, especially in semi-arid climates for the management of natural resources in a sustainable manner. The present study was conducted to assess the spatial distribution of land degradation hotspots based on some important parameters such as land use/land cover (LULC), Normalized Difference Vegetation Index (NDVI), terrain characteristics (Topographic Wetness Index and Multi-Resolution Index of Valley Bottom Flatness), climatic parameters (land surface temperature and mean annual rainfall), and pedological attributes (soil texture and soil organic carbon) by using Analytical Hierarchical Process (AHP) and GIS techniques in the semi-arid region of the Bundi district, Rajasthan, India. Land surface temperature (LST) and NDVI products were derived from time-series Moderate-Resolution Imaging Spectroradiometer (MODIS) datasets, rainfall data products from Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), terrain characteristics from Shuttle Radar Topography Mission (SRTM), LULC from Landsat 9, and pedological variables from legacy soil datasets. Weights derived for thematic layers from the AHP in the studied area were as follows: LULC (0.38) > NDVI (0.23) > ST (0.15) > LST (0.08) > TWI (0.06) > MAR (0.05) > SOC (0.03) > MRVBF (0.02). The consistency ratio (CR) for all studied parameters was <0.10, indicating the high accuracy of the AHP. The results show that about 20.52% and 23.54% of study area was under moderate and high to very high vulnerability of land degradation, respectively. Validation of LDV zones with the help of ultra-high-resolution Google Earth imageries indicates good agreement with the model outputs. The research aids in a better understanding of the influence of land degradation on long-term land management and development at the watershed level.

Wind erosion is a major natural disaster worldwide, and it is a key problem in western Rajasthan in India. The Analytical Hierarchy Process (AHP), the Geographic Information System (GIS), and remote sensing satellite images are effective tools for modeling and risk assessment of land degradation. The present study aimed to assess and model the land degradation vulnerable (LDV) zones based on the AHP and geospatial techniques in the Luni River basin in Rajasthan, India. This study was carried out by examining important thematic layers, such as vegetation parameters (normalized difference vegetation index and land use/land cover), a terrain parameter (slope), climatic parameters (mean annual rainfall and land surface temperature), and soil parameters (soil organic carbon, soil erosion, soil texture, and soil depth), using the Analytical Hierarchical Process (AHP) and geospatial techniques in the Luni River basin in Rajasthan, India. The weights derived for the thematic layers using AHP were as follows: NDVI (0.27) > MAR (0.22) > LST (0.15) > soil erosion (0.12) > slope (0.08) > LULC (0.06) > SOC (0.04) > soil texture (0.03) > soil depth (0.02). The result indicates that nearly 21.4 % of the total area is prone to very high degradation risks; 12.3% is prone to high risks; and 16%, 24.3%, and 26% are prone to moderate, low, and very low risks, respectively. The validation of LDV was carried out using high-resolution Google Earth images and field photographs. Additionally, the Receiver Operating Characteristic (ROC) curve found an area under the curve (AUC) value of 82%, approving the prediction accuracy of the AHP technique in the study area. This study contributes by providing a better understanding of land degradation neutrality and sustainable soil and water management practices in the river basin.

Understanding the dynamics of vegetative greenness and how it interacts with various hydroclimatic factors is crucial for comprehending the implications of global climate change. The present study utilized the MODIS-derived normalized difference vegetation index (NDVI) to understand the vegetation patterns over 21 years (2001–2021) in Rajasthan, India. The rainfall, land surface temperature (LST), and evapotranspiration (ET) were also analyzed. The changes, at a 30 m pixel resolution, were evaluated using Mann–Kendall’s trend test. The results reveal that the NDVI, ET, and rainfall had increasing trends, whereas the LST had a decreasing trend in Rajasthan. The NDVI increased for 96.5% of the total pixels, while it decreased for 3.4% of the pixels, of theh indicates vegetation improvement rather than degradation. The findings of this study provide direct proof of a significant reduction in degraded lands throughout Rajasthan, particularly in the vicinity of the Indira Gandhi Canal command area. Concurrently, there has been a noticeable expansion in the cultivated land area. The trend of vegetation decline, particularly in the metro cities, has occurred as a result of urbanization and industrialization. In contrast to the LST, which has a decreasing gradient from the western to eastern portions, the spatial variability in the NDVI, ET, and rainfall have decreasing gradients from the southern and eastern to western regions. The results of correlations between the vegetative indices and hydroclimatic variables indicate that the NDVI has a strong positive correlation with ET (r² = 0.86), and a negative correlation with LST (r² = −0.55). This research provides scientific insights into vegetation change across Rajasthan, and may help the state to monitor vegetation changes, conserve ecosystems, and implement sustainable ecosystem management.

This study presents a terrain-integrated Soil Management Unit (SMU) framework for precision agriculture in semi-arid tropical basaltic soils. Using high resolution (10-ha grid) sampling across 4627 geo-referenced locations and machine learning-enhanced integration of terrain attributes with legacy soil maps, and (3) quantitative validation of intra-SMU homogeneity, 15 SMUs were delineated based on landform, soil depth, texture, and slope. Principal Component Analysis (PCA) revealed SMU11 as the most heterogeneous (68.8%). Geo-statistical analysis revealed structured variability in soil pH (range = 1173 m) and nutrients availability with micronutrient sufficiency following Mn > Fe > Cu > Zn, (Zn deficient in SMU13). Organic carbon strongly correlated with key nutrients (AvK, r = 0.83 and Zn, r = 0.86). This represents the first systematic implementation of terrain-integrated SMU delineation in India’s basaltic landscapes, demonstrating a potential for 20–25% input savings. The spatially explicit fertility-integrated SMU framework provides a robust basis for developing decision support systems aimed at optimizing location-specific nutrient and land management strategies.