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Advanced technologies in the agricultural sector have been adopted as global trends in response to the impact of climate change on food sustainability. An ability to monitor and predict crop yields is imperative for effective agronomic decision making and better crop management. This work proposes RiceSAP, a satellite-based AquaCrop processing system for rice whose climatic input is derived from TERRA/MODIS-LST and FY-2/IR-rainfall products to provide crop monitoring and yield prediction services at regional-scale with no need for weather station. The yield prediction accuracy is significantly improved by our proposed recalibration algorithm on the simulated canopy cover (CC) using Sentinel-2 NDVI product. A developed mobile app provides an intuitive interface for collecting farm-scale inputs and providing timely feedbacks to farmers to make informed decisions. We show that RiceSAP could predict yields 2 months before harvest with a mean absolute percentage error (MAPE) of 14.8%, in the experimental field. Further experiments on randomly selected 20 plots with various soil series showed comparable results with an average MAPE of 16.7%. Thus, this work is potentially applicable countrywide; and can be beneficial to all stakeholders in the entire rice supply chain for effective adaptation to climate change.

RiceSAP: An Efficient Satellite-Based AquaCrop Platform for Rice Crop Monitoring and Yield Prediction on a Farm- to Regional-Scale