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

Abstract

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.

Satellite-Based Drought Impact Assessment on Rice Yield in Thailand with SIMRIW−RS

“…This app collects inputs from farmers (crowdsourcing) and displays simulation outputs as well as precautions for some scenarios. To illustrate the concept, Figure 15 shows a related project called "RiceSAP"-a mobile app for rice crop monitoring and management for farmers [21]. The app acts as a user interface to the cloud platform.…”

“…To standardize these satellite products derived in Sections 2.3.1-2.3.3 for ease of implementation, they are resampled to a 1-km resolution, time-synchronized, and put into archive to be used as inputs for both models. Related work [21] shows reasonably good correlations between the satellite rainfall and LST products with rainfall and average air temperature from TMD weather stations nationwide. Its level-1C data (called S2MSI1C) are top-of-atmosphere (TOA) reflectance and need to be processed with the Sentinel Application Platform (SNAP) and Sen2Cor plugin to generate level 2A data.…”

Remote sensing-based estimation of rice yields using various models: A critical review

“…Sensor fusion, the synergistic use of multiple sensors, is used to combine the strengths of sensors in terms of spatial resolution, revisit times, or cloud penetration capabilities (Zhang 2010;Van Tricht et al 2018). For example, STARFM synthesizes a dataset with Landsat's 30-m resolution at MODIS's higher revisit times (Gao et al 2015).…”