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

Tri Setiyono,Lorenzo Busetto,Manuel Campos-Taberner,Mirco Boschetti,Emma Quicho,Francesco Holecz,Nasreen Khan,Luca Gatti,Francisco García-Haro,Francesco Collivignarelli

doi:10.3390/rs10020293

Abstract

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.

Highlights

Smallholder farmers, representing 85% of the world’s farms [1], face numerous risks to their agricultural production from pest and disease outbreaks, extreme weather events, and market shocks that threaten their household food and income security [2,3]
The provision of crop yield data using a biophysical modeling approach is desirable due to the unbiased and replicable nature [11,12] and it can be carried out by exploiting remote-sensing data [13,14,15,16,17,18], using rainfall data in a statistical framework [19], using a crop growth model [20], or by integrating remote-sensing data and a crop growth model [21,22]. The latter approach is more promising than the empirical approach of translating remotely sensed vegetation indices directly into crop yield and production values [23,24,25]. This is because the integration approach exploits the synergies between: (i) remote-sensing technology strength in capturing spatial and temporal variation related to agro-practices and seasonal crop development and vegetation status; and (ii) process-based crop growth model strength in reliably simulating yield by capturing biophysical growth drivers once key parameters are properly assigned [22]
The final leaf area index (LAI) estimates for the Red River Delta from MODIS obtained by smoothing with a logistic function in this study have a comparable range of root mean square error (RMSE) (0.11–0.57) as previous studies involving higher resolution reflectance data from Sentinel-2 and the same inverted PROSAIL Radiative Transfer Model in Spain (0.56), Italy (0.82), and Greece (0.77) [41]

Summary

Introduction

Smallholder farmers, representing 85% of the world’s farms [1], face numerous risks to their agricultural production from pest and disease outbreaks, extreme weather events, and market shocks that threaten their household food and income security [2,3]. The provision of crop yield data using a biophysical modeling approach is desirable due to the unbiased and replicable nature [11,12] and it can be carried out by exploiting remote-sensing data [13,14,15,16,17,18], using rainfall data in a statistical framework [19], using a crop growth model [20], or by integrating remote-sensing data and a crop growth model [21,22] The latter approach is more promising than the empirical approach of translating remotely sensed vegetation indices directly into crop yield and production values [23,24,25]. This is because the integration approach exploits the synergies between: (i) remote-sensing technology strength in capturing spatial and temporal variation related to agro-practices (e.g., crop establishment dates) and seasonal crop development (i.e., phenology) and vegetation status (e.g., leaf area index); and (ii) process-based crop growth model strength in reliably simulating yield by capturing biophysical growth drivers (microclimate, water, and nutrient) once key parameters are properly assigned [22]

Objectives

Results

Discussion

Conclusion