Satellite-Based Assessment of Crop Gross Primary Productivity and Yield Across Maharashtra, India, Using MODIS Data
Ravi Sharma, Kaywan Othman Ahmed, Cheikh Faye
Journal of Computing Systems & Softwares · Vol. 1, No. 1 · pp. 3–26 · Published 2026-11-07
DOI: https://doi.org/10.68857/jcss.2026.v1i1-02
Abstract
atellite remote sensing data, combined with carbon cycle processes, has led to the development of several models for estimating vegetation productivity. There is a growing interest in accurate crop yield assessments using these methods for county observing and agricultural resiliency with food safety. The existing methods used to estimate vegetation productivity from satellite data at a global scale may not accurately account for the differences in crop growth patterns and variability within agricultural land. A recent report estimated crop yields in six administrative divisions of Maharashtra State, India, including Amravati, Aurangabad, Konkan, Nagpur, Nashik, and Pune. The study used MOD17A2H.006: Terra Gross Primary Productivity 8-Day Global 500m data to estimate gross primary productivity (GPP) over croplands at 500-m spatial resolution. These datasets are created using the radiation-use efficiency idea and may be utilized as input data to models for calculating hydrological cycle systems and biogeochemistry of flora. Notably, there are no limitations on the utilization, commercialization, or distribution of the data and products obtained from the LP DAAC, specifically those derived from MODIS. The research utilized a method of estimating crop yields by multiplying crops' calculated Gross Primary Productivity (GPP) during the growing season with a standard Harvest Index (HI) at a resolution of 500 meters. This approach successfully captured the typical productivity patterns and seasonal changes in cropland, providing reassurance about the accuracy of the study's methods. The study concludes that its techniques and outcomes are appropriate for practical crop yield monitoring on a local scale. The findings also highlight the potential for utilizing global satellite interpretations to enhance agricultural management, inform policy decisions, and improve the food security of regions and the world.
Keywords: Crop yield, Gross primary productivity (GPP), MODIS, Google earth engine (GEE)
Copyright & Licence: © 2026 Ravi Sharma, Kaywan Othman Ahmed, Cheikh Faye. Published by Chirenanya International Publications. All Rights Reserved / Institutional Dissemination