Abstract
The remote sensing-integrated crop model (RSCM) was designed to simulate crop growth processes and yield using remote sensing data. The RSCM is based on the radiation use efficiency (RUE) model and employs a within-season calibration procedure recalibrating the daily crop leaf area index (LAI) using satellite images. And it has functions to calculate daily biomass, evapotranspiration (ET), gross primary productivity (GPP), and net primary productivity from the LAI in addition to crop yield. In previous studies, the essential crop growth parameters required in the model, such as RUE, light extinction coefficient, specific leaf area, base temperature, etc., were determined through field experiments. And its performances were validated using various remote sensing data, including proximity sensing data, drone images, and satellite images. Among them, this study presented the application results with satellite images in the RSCM. The target crop is rice (Oryza Sativa), one of the world's major crops, and the study areas range from South Korea to Northeast Asia. Satellite images and meteorological data were used differently depending on the study sites. The types of satellite images used in this study are the RapidEye, the Moderate Resolution Imaging Spectroradiometer (MODIS) of the Terra/Aqua satellite, and the Geostationary Ocean Color Imager (GOCI) and the Meteorological Imager (MI) of Communication, Ocean and the Meteorological (COMS) satellite. And gridded data for air temperature and solar radiation was acquired from the Korea Local Analysis and Prediction System (KLAPS) and the European Centre for Medium-Range Weather Forecasts (ECMFW). The primary application of the RSMC is to simulate rice yield, but some results showed crop growth factors such as biomass, LAI, GPP, and ET. In addition, the most recent study performed the early prediction of crop yield by combining deep learning with crop models. Through this study, it is possible to know the future utilization of the RSCM model in the agriculture and satellite application fields.This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (Ministry of Science and ICT) (RS-2022-00165154, "Development of Application Support System for Satellite Information Big Data").
Published Version
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