2021
DOI: 10.1080/20964471.2020.1837529
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Coupling remote sensing and crop growth model to estimate national wheat yield in Ethiopia

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 timeseries LAI data into the WOrld FOod STudies (WOFOST) model. To improve the estimation of crop yield in the region, t… Show more

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Cited by 10 publications
(8 citation statements)
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“…(2) Integration of remotely sensed data and crop growth models where crop simulation models can be coupled with satellite measurements. In Ethiopia, Beyene et al [24] found that integrating MODIS-LAI into WOFOST (i.e., World Food Studies; a simulation model of crop production model [25]) was useful for estimating wheat yields.…”
Section: Remote Sensingmentioning
confidence: 99%
“…(2) Integration of remotely sensed data and crop growth models where crop simulation models can be coupled with satellite measurements. In Ethiopia, Beyene et al [24] found that integrating MODIS-LAI into WOFOST (i.e., World Food Studies; a simulation model of crop production model [25]) was useful for estimating wheat yields.…”
Section: Remote Sensingmentioning
confidence: 99%
“…Similarly, it was unclear what type of fields were used, as 94% of studies did not indicate whether the data assimilation approach was applied under commercial or small-scale farming systems. Nevertheless, approximately 5% of the studies indicated that they were based on commercial farms [38,43], while less than 1% were based on small-scale systems [44]. Thus, this review shows that most data assimilation studies were based on cropping models under potential conditions.…”
Section: Types Of Agricultural Cropping Systemsmentioning
confidence: 95%
“…France [35,36], Germany [37,38], and Italy [39,40] each contributed 5%. In contrast, South America (3%) and Africa (<1%) showed a lack of data assimilation research, with studies conducted only in Brazil [41,42], Uruguay [7,43], and Ethiopia [44]. The geographic distribution of the identified data as-similation research seems to reflect regional differences in the progress of and access to agricultural technologies and innovation status.…”
Section: Geographical Distributionmentioning
confidence: 96%
“…Field phenotyping using high-throughput techniques has been introduced in recent times. Remote sensing and GIS based methods has been used as crop yield predictors in wheat and maize ( Beyene et al., 2022 ; Debalke and Abebe, 2022 ) as well as physical land suitability analysis for major cereal crops ( Debesa et al., 2020 ). In essence, Ethiopia has the potential to accelerate its phenotyping capabilities.…”
Section: Overview Of the Status Of Field Phenotyping In Africamentioning
confidence: 99%