Spectral Reflectance-Based Estimation of Vegetation Biophysical Parameters Using Sentinel-2 images in Malegaon, Maharashtra, India (20.60°N, 74.52°E)

Abstract

The spectral reflectance from vegetation canopies is a surrogate measure of vegetation biophysical parameters, and hence can be used to monitor the health and function of vegetation. This study explores how satellite images can be used to understand the health and condition of crops in the Malegaon region of Nashik district, Maharashtra, India. The main aim is to estimate important crop features — known as biophysical parameters — such as chlorophyll content, leaf area, canopy water content, fractional vegetation cover, fraction of absorbed photosynthetically active radiation, etc, by studying the way crops reflect sunlight, which is technically called spectral reflectance. We used data from the Sentinel-2 satellite, which captures high-resolution images in multiple bands of light, including both visible and invisible (infrared) ranges. The analysis was carried out using SNAP software, developed by the European Space Agency (ESA). To make the study more reliable, we also collected field data manually from selected crop fields, known as ground truth data. The study was carried out in the winter season, November 2024, when three major crops commonly grown in the region — Wheat (Triticum aestivum), fodder (Sorghum bicolor), and Guava (Psidium guajava L.) were selected for analysis. These crops were studied during their active growth stages when chlorophyll levels are naturally high. The model used here is the PROSAIL model, which is among the best transfer models for computing biophysical parameters. Our findings show that the chlorophyll levels estimated from satellite data closely match the values measured directly from the field. This proves that satellite-based remote sensing is a practical, low-cost, and time-saving way to monitor crop health, especially in regions where regular physical surveys are difficult. This approach is highly beneficial for farmers, agricultural officers, and researchers. It allows for early detection of crop stress due to drought, pests, or poor soil, helping farmers take corrective action on time. Overall, the study promotes the use of precision farming in semi-arid areas like Malegaon and can contribute to sustainable agricultural practices in India.