Downscaling of vegetation indices from multi-satellite throughout-season maize

Abstract

Abstract Phenomenology of the growing season The Normalized Difference Vegetative Index (NDVI) provided by satellites was employed as a replacement for quantifying the output of vegetative biomass. The MODIS sensors 250-m data have been utilized for terrestrial ecosystem modelling and monitoring. MODIS’s land surface data are credible and trustworthy because to their high temporal resolution and broad spectrum of wavelengths. Land cover and land change studies have used the spatially accurate data provided by the Landsat 30m to characterize human-scale processes. Sentinel-2 is a land surveillance satellite with innovative spectrum capabilities, extensive coverage, and excellent spatial and temporal resolutions. The primary purpose of this work is to create a downscaling vegetation indices (VI) database by combining MODIS, Landsat, and Sentinel data into 250m resolution. The most important NDVI indicates the maize growing season in April and August. MODIS, Landsat, and Sentinel 250m derived biophysical information deliver the same biophysical information for moderate-scale biological aspects. This multi-sensor inquiry also includes high-resolution Landsat data, which will be useful for local ecological investigations while keeping the full seasonal dynamic information given by MODIS.