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Open AccessArticle

Spatial Rice Yield Estimation Based on MODIS and Sentinel-1 SAR Data and ORYZA Crop Growth Model

International Rice Research Institute, DAPO Box 7777, Metro Manila 1301, Philippines
Sarmap, Cascine di Barico 10, Purasca 6989, Switzerland
Department of Earth Physics and Thermodynamics, Faculty of Physics, Universitat de València, València 46100, Spain
Institute for Electromagnetic Sensing of the Environment, Italian National Research Council, Via Bassini 15, Milan 20133, Italy
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(2), 293;
Received: 7 December 2017 / Revised: 17 January 2018 / Accepted: 23 January 2018 / Published: 14 February 2018
Crop insurance is a viable solution to reduce the vulnerability of smallholder farmers to risks from pest and disease outbreaks, extreme weather events, and market shocks that threaten their household food and income security. In developing and emerging countries, the implementation of area yield-based insurance, the form of crop insurance preferred by clients and industry, is constrained by the limited availability of detailed historical yield records. Remote-sensing technology can help to fill this gap by providing an unbiased and replicable source of the needed data. This study is dedicated to demonstrating and validating the methodology of remote sensing and crop growth model-based rice yield estimation with the intention of historical yield data generation for application in crop insurance. The developed system combines MODIS and SAR-based remote-sensing data to generate spatially explicit inputs for rice using a crop growth model. MODIS reflectance data were used to generate multitemporal LAI maps using the inverted Radiative Transfer Model (RTM). SAR data were used to generate rice area maps using MAPScape-RICE to mask LAI map products for further processing, including smoothing with logistic function and running yield simulation using the ORYZA crop growth model facilitated by the Rice Yield Estimation System (Rice-YES). Results from this study indicate that the approach of assimilating MODIS and SAR data into a crop growth model can generate well-adjusted yield estimates that adequately describe spatial yield distribution in the study area while reliably replicating official yield data with root mean square error, RMSE, of 0.30 and 0.46 t ha−1 (normalized root mean square error, NRMSE of 5% and 8%) for the 2016 spring and summer seasons, respectively, in the Red River Delta of Vietnam, as evaluated at district level aggregation. The information from remote-sensing technology was also useful for identifying geographic locations with peculiarity in the timing of rice establishment, leaf growth, and yield level, and thus contributing to the spatial targeting of further investigation and interventions needed to reduce yield gaps. View Full-Text
Keywords: rice yield; leaf area index; LAI; remote sensing; MODIS; synthetic aperture radar; SAR; Sentinel-1; ORYZA crop growth model rice yield; leaf area index; LAI; remote sensing; MODIS; synthetic aperture radar; SAR; Sentinel-1; ORYZA crop growth model
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MDPI and ACS Style

Setiyono, T.D.; Quicho, E.D.; Gatti, L.; Campos-Taberner, M.; Busetto, L.; Collivignarelli, F.; García-Haro, F.J.; Boschetti, M.; Khan, N.I.; Holecz, F. Spatial Rice Yield Estimation Based on MODIS and Sentinel-1 SAR Data and ORYZA Crop Growth Model. Remote Sens. 2018, 10, 293.

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