Delineation of Rice Yield and Production in Combination of Crop Model and Remote Sensing


  • Minh, Vo Quang Land Resources Department, Environment and Natural Resources College, Cantho University, CanTho, 900000, Vietnam
  • Hien, Tran Thi Natural Resources and Environment Department, BenTre, 930000, VietNam



The study was to simulate rice yield in various places and outline a rice yield map for the study area using GIS, remote sensing, and a rice model. As a case study, the data were collected on the climate, soil characteristics, and rice cropping status in the AnGiang province (Southern of the Mekong River Delta in Viet Nam). The AquaCrop model was used to predict rice yield. The MODIS image delineated the rice cropping status based on spatial and temporal NDVI values. The results of the yield simulation are then put together with information about where the rice was planted, the weather, and the properties of the soil to make a map of the yield distribution. Finally, the outcomes are verified and contrasted with the statistical findings in the last step. The rice yield was predicted and compared with actual 1 and 6 percent rice yields. The anticipated rice yield map was established for the Winter-Spring cropping season 2012-2013 and the Summer-Autumn and Autumn–Winter cropping seasons 2013. Rice production and yield distribution can be divided into two major areas. The alluvial soil area produces significantly more rice than the LongXuyen quadrangle area because of the difference in soil and weather conditions. Rice yield simulation and delineation combining remote sensing and crop models is a good approach for yield prediction and better agricultural management strategy development in a country or region. The accuracy of the results depends on the quality of the input data, such as soil weather and remote sensing.

Keywords: GIS, MODIS, cropping season, rice yield


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How to Cite

Vo Quang, M., and H. Tran Thi. “Delineation of Rice Yield and Production in Combination of Crop Model and Remote Sensing”. Emirates Journal of Food and Agriculture, vol. 35, no. 12, Nov. 2023, doi:10.9755/ejfa.2023.3172.



Research Article