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  Mid‐season county‐level corn yield forecast for US Corn Belt integrating satellite imagery and weather variables

Schwalbert, R., Amado, T., Nieto, L., Corassa, G., Rice, C., Peralta, N., Schauberger, B., Gornott, C., Ciampitti, I. (2020): Mid‐season county‐level corn yield forecast for US Corn Belt integrating satellite imagery and weather variables. - Crop Science, 60, 2, 739-750.
https://doi.org/10.1002/csc2.20053

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Schwalbert, R.1, Autor
Amado, T.1, Autor
Nieto, L.1, Autor
Corassa, G.1, Autor
Rice, C.1, Autor
Peralta, N.1, Autor
Schauberger, Bernhard2, Autor              
Gornott, Christoph2, Autor              
Ciampitti, I.1, Autor
Affiliations:
1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

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 Zusammenfassung: Yield estimations are of great interest to support interventions from governmental policies and to increase global food security. This study presents a novel model to perform in‐season corn yield predictions at the US‐county level, providing robust results under different weather and yield levels. The objectives of this study were to: (i) evaluate the performance of a random forest classification to identify corn fields using Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and weather variables (temperature, precipitation, and vapor pressure deficit, VPD); (ii) evaluate the contribution of weather variables when forecasting corn yield via remote sensing data, and perform a sensitivity analysis to explore the model performance in different dates; and (iii) develop a model pipeline for performing in‐season corn yield predictions at county‐scale. Main outcomes from this study were: (i) high accuracy (87% on average) for corn field classification achieved in late August, (ii) corn yield forecasts with a mean absolute error (MAE) of 0.89 Mg ha−1, (iii) weather variables (VPD and temperature) highly influenced the model performance, and (iv) model performance decreased when predictions were performed early in the season (mid‐July), with MAE increasing from 0.87–1.36 Mg ha−1 when forecast timing changed from day of year 232–192. This research portrays the benefits of integrating statistical techniques and remote sensing to field survey data in order to perform more reliable in‐season corn yield forecasts.

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 Datum: 2020
 Publikationsstatus: Final veröffentlicht
 Seiten: -
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: Expertenbegutachtung
 Identifikatoren: DOI: 10.1002/csc2.20053
PIKDOMAIN: RD2 - Climate Resilience
eDoc: 8587
Research topic keyword: Food & Agriculture
Research topic keyword: Climate impacts
Research topic keyword: Land use
Model / method: Machine Learning
Regional keyword: North America
Organisational keyword: RD2 - Climate Resilience
 Art des Abschluß: -

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Titel: Crop Science
Genre der Quelle: Zeitschrift, SCI, Scopus
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Ort, Verlag, Ausgabe: -
Seiten: - Band / Heft: 60 (2) Artikelnummer: - Start- / Endseite: 739 - 750 Identifikator: Anderer: Wiley
Anderer: Crop Science Society of America
Anderer: 1435-0653
ISSN: 0011-183X
CoNE: https://publications.pik-potsdam.de/cone/journals/resource/crop-science