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Research Article Open access CC BY 4.0

Climate Variability and Crop Land Degradation Assessment Using a Remote Sensing and GIS in the Nashik Division of Maharashtra, India

P. P. Patil, J. D. Jadhav, S. V. Bagade, S. M. Apturkar, B. K. Gavit, S. K. Upadhye, V. A. Sthool

International Journal of Environment and Climate Change · pp. 663–674 · Published 21 May 2026

10.9734/ijecc/2026/v16i55465

Abstract

In India's semi-arid areas, agricultural sustainability is becoming exponentially susceptible to unpredictable monsoon patterns and environmental instability. This study focuses across the Nashik division of Maharashtra to assess the combined effects of climatic variability on soil health and agricultural output. The study used the non-parametric Mann-Kendall test and Sen's slope estimator to identify temporal climatic trends by combining 33 years (1991–2023) of gridded IMD meteorological data with multi-temporal satellite images from Landsat 7/8 and Sentinel-2. Additionally, high-resolution satellite imagery and spectral indices, such as the Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), and Normalized Difference Salinity Index (NDSI), which were validated with significant ground-truth data, were used to assess Land Use Land Cover (LULC) dynamics. The results show an effective regional warming indication, with annual minimum temperatures (T_min) increasing by around 0.02°C to 0.03°C annually. A significant rise of late-season rainfall in September and a reduction of the early monsoon in June were indicative of a major seasonal change. The increase of fallow lands and irreversible urbanization caused a 3% net loss in agricultural land between 2018 and 2023, according to remote sensing studies. The rabi season NDVI showed a notable "browning" pattern, indicating prolonged post-monsoon water stress, but the kharif season showed positive greening behaviors.  The loss of a protective plant canopy is the main cause of soil salinity and land degradation, as confirmed by strong negative relationships between NDSI and both NDVI and SAVI. This study offers a spatially explicit framework for climate-smart agriculture, highlighting the critical need to adopt climate-resilient cultivars and put "Drought-Escape" methods into practice in order to protect regional food security.

Agricultural meteorology climate variability geographic information system land degradation nashik division remote sensing

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