Improvements of rice and wheat production models by carbon partitioning with multi-satellite imageries and meteorological reanalysis data

The sustainable crop production is intimately linked with food security as the world population growth remains stable. The recent concern about food scarcity motivates the development of the present system entitled “Remote Sensing Environmental Monitor (RSEM)” leading to the enhancement of the capability for crop yield monitoring. The system includes a photosynthetic sterility crop yield model based on precise land use and cover (LULC) classification of crop field in two eastern Asian countries. The model estimates the photosynthetic fixation of carbon dioxide (CO2) that is geared toward estimating the rice production in Japan and China with a reasonable accuracy. This study provides daily estimates of the photosynthesis rate (PSN), which is relevant to the CO2 fixation with the aid of a precise LULC classification. The computational work was carried out based on the MODIS data, NDVI data (i.e., SPOT VEGETATION), solar radiation derived from Japanese Geostationary Meteorological Satellite (GMS), and meteorological reanalysis data. The validation of such a model is based on carbon partitioning method associated with particular crop species. The ongoing thrust of improving the RSEM-based crop yield model with the carbon partitioning method has been dedicated to winter wheat using China statistical data. It will be extended to deal with similar estimation in Australian areas for model validation in the near future.