DOWNSCALING OF VEGETATION INDICES FROM MULTI-SATELLITE THROUGHOUT-SEASON MAIZE Muhammad Iqbal Habibie(a*) and Nety Nurda (b)
a) National Research and Innovation Agency (BRIN), Cibinong, Indonesia
*iqbalhabibie0684[at]gmail.com
b). National Resilience Institute of the Republic of Indonesia (LEMHANAS), Jakarta, Indonesia
Abstract
The growing season phenology Normalized Difference Vegetation Index (NDVI) collected from satellites has been used as a proxy for vegetation biomass production. The MODIS sensors 250-m data have been utilized for terrestrial ecosystem modeling and monitoring. MODIS land surface products are resilient and trustworthy because to their high temporal resolution and wide range of wavelengths. The Landsat 30m produce spatially detailed information for defining human-scale processes and have been employed in studies of land cover and land change. 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 250 m MODIS, 30 m Landsat, and 10 m Sentinel 2 data. The most important NDVI indicates the maize growth season in April and August. MODIS, Landsat, and Sentinel 250m derived biophysical information delivers the same biophysical information for moderate-scale biological aspects. This multi-sensor study preserves the comprehensive seasonal dynamic information recorded by MODIS while also using high-resolution data from Landsat, which will be beneficial for regional ecosystem studies.
Keywords: MODIS- Landsat- Sentinel 2 - growing season - moving average - NDVI- downscaling- revisit time- vegetation index- 250m
