Field Data Collection Methods Strongly Affect Satellite-Based Crop Yield Estimation

dc.creatorTiedeman, K.
dc.creatorChamberlin, J.
dc.creatorKosmowski, F.
dc.creatorAyalew, H.
dc.creatorSida, T.S.
dc.creatorHijmans, R.J.
dc.date2022-05-19T00:15:12Z
dc.date2022-05-19T00:15:12Z
dc.date2022
dc.date.accessioned2023-07-17T20:09:10Z
dc.date.available2023-07-17T20:09:10Z
dc.identifierhttps://hdl.handle.net/10883/22074
dc.identifier10.3390/rs14091995
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/7513839
dc.descriptionCrop yield estimation from satellite data requires field observations to fit and evaluate predictive models. However, it is not clear how much field data collection methods matter for predictive performance. To evaluate this, we used maize yield estimates obtained with seven field methods (two farmer estimates, two point transects, and three crop cut methods) and the “true yield” measured from a full-field harvest for 196 fields in three districts in Ethiopia in 2019. We used a combination of nine vegetation indices and five temporal aggregation methods for the growing season from Sentinel-2 SR data as yield predictors in the linear regression and Random Forest models. Crop-cut-based models had the highest model fit and accuracy, similar to that of full-field-harvest-based models. When the farmer estimates were used as the training data, the prediction gain was negligible, indicating very little advantage to using remote sensing to predict yield when the training data quality is low. Our results suggest that remote sensing models to estimate crop yield should be fit with data from crop cuts or comparable high-quality measurements, which give better prediction results than low-quality training data sets, even when much larger numbers of such observations are available.
dc.languageEnglish
dc.publisherMDPI
dc.relationhttps://doi.org/10.5281/zenodo.6471977
dc.rightsCIMMYT manages Intellectual Assets as International Public Goods. The user is free to download, print, store and share this work. In case you want to translate or create any other derivative work and share or distribute such translation/derivative work, please contact CIMMYT-Knowledge-Center@cgiar.org indicating the work you want to use and the kind of use you intend; CIMMYT will contact you with the suitable license for that purpose
dc.rightsOpen Access
dc.source9
dc.source14
dc.source2072-4292
dc.sourceRemote Sensing
dc.source1995
dc.subjectAGRICULTURAL SCIENCES AND BIOTECHNOLOGY
dc.subjectYield Estimation
dc.subjectMaize Yield
dc.subjectSentinel-2
dc.subjectField Methods
dc.subjectDATA COLLECTION
dc.subjectFORECASTING
dc.subjectREMOTE SENSING
dc.subjectMAIZE
dc.subjectCROP YIELD
dc.titleField Data Collection Methods Strongly Affect Satellite-Based Crop Yield Estimation
dc.typeArticle
dc.typePublished Version
dc.coverageBasel (Switzerland)


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