Coupling remote sensing and crop growth model to estimate national wheat yield in Ethiopia. Issue 1 (2nd January 2022)

• Record Type: Journal Article
• Authors: Beyene, Awetahegn Niguse; Zeng, Hongwei; Wu, Bingfang; Zhu, Liang; Gebremicael, Tesfay Gebretsadkan; Zhang, Miao; Bezabh, Temesgen
• Is Part Of: Big earth data. Volume 6:Issue 1(2022)
• Page Start: 18
• Page End: 35
• Publication Date: 2022-01-02
• Subjects: Wheat yield -- WOFOST model -- Ensemble Kalman Filter -- remote sensing; Earth sciences -- Periodicals; Earth sciences -- Research -- Periodicals; Geographic information systems Periodicals; 550
• DOI: 10.1080/20964471.2020.1837529
• Languages: English
• ISSNs: 2096-4471

Abstract

ABSTRACT: Estimation of crop yield at a regional level is essential for making agricultural planning and addressing food security issues in Ethiopia. Remote sensing observations, particularly the leaf area index (LAI), have a strong relationship with crop yield. This study has proposed an approach to estimate wheat yield at field level and regional scale in Ethiopia by assimilating the retrieved MODIS time-series LAI data into the WOrld FOod STudies (WOFOST) model. To improve the estimation of crop yield in the region, the Ensemble Kalman Filter (EnKF) was used to incorporate the LAI into the WOFOST model. The estimation accuracy of wheat crop yield was validated using field-measured yields collected during the 2018 growing season. Our findings indicated that wheat yield was more precisely estimated by WOFOST (at water-limited mode) with EnKF algorithm ( R 2 = 0.80 and RMSE = 413 kg ha −1 ) compared to that of without assimilating remotely sensed LAI ( R 2 = 0.58, RMSE = 592 kg ha −1 ). These results demonstrated that assimilating MODIS-LAI into WOFOST has high potential and practicality to give a reference for wheat yield estimation. The findings from this study can provide information to policy, decision-makers, and other similar sectors to implement an appropriate and timely yield estimation measure.