Bioenergy Resource Mapping of Rice Paddy Fields across the Korean Peninsula Using MODIS Satellite Data and AI-based Crop Models

This study quantitatively estimates biomass resources in rice cultivation areas using MODIS satellite imagery and artificial intelligence, and evaluates the theoretical bioenergy potential across the Korean Peninsula.Vegetation indices were derived from MODIS data, and a machine learning model was used to estimate leaf area index, which was subsequently integrated into a remote sensing-integrated crop model to simulate biomass accumulation across different rice growth stages.Based on the estimated grain yield, conversion factors for agricultural byproducts (rice straw, 1.02; rice husk, 0.177) and heating values (rice straw, 15.3 MJ/kg; rice husk, 14.2 MJ/kg) were applied to calculate the bioenergy potential in terms of tons of oil equivalent.Geographic information system (GIS)-based spatial analysis was conducted to produce administrative-level bioenergy resource maps.The results indicate that Gimje City exhibited the highest productivity among the analyzed South Korean regions.In North Korea, Pyongyang showed lower productivity and greater interannual variability than the selected South Korean study region.This study evaluates the spatial and temporal variability of agricultural byproduct biomass resources using a satellite-based approach, providing a quantitative dataset for regional renewable energy policy planning and spatial analysis.