Abstract
Annual crop yield fluctuation due to natural and anthropogenic factors is a major concern of the Ethiopian Government. For an immediate response to drastically changing crop yields and resulting harvest failures and to enhance the country’s food security in general, extensive area crop growth monitoring and early prediction of production are needed. In this study, we developed an early maize (Zea mays) yield forecasting model using Sentinel-2 MSI (Multispectral Instrument); the study was carried out in the Abaya district of the Oromia Regional State in Ethiopia. The model consists of the following components: (1) Sentinel-2 image-based cropland identification for different crop development stages, (2) extraction of time series Sentinel-2 vegetation indices for crop growth monitoring, and (3) a simple linear stepwise forward regression approach for yield prediction. We tested different spectral indices regarding their performance in describing the crop development and eventually predicting the expected yield. The result showed that (1) the linear Red-Edge Enhanced Vegetation Index (Red-Edge EVI), (2) the combination of the Enhanced Vegetation Index (EVI) and the Green Vegetation Index (GVI), (3) the combination of the Red-Edge EVI and Soil Adjusted Vegetation Index (SAVI), and (4) the combination of the Normalized Difference Vegetation Index (NDVI), Red-Edge EVI and SAVI offer the best predictive model about 2 months before harvesting with the highest coefficients of determination (R2) of 0.73, 0.80, 0.84, and 0.88, respectively. The correlation for the GVI was generally lowest compared to established models, and no evidence of a peak correlation for NDVI was observed. Our approach showed a high accuracy of detecting maize fields, detecting crop phenology, and early predicting of grain yield for the study year 2018. Our simple model may generate early warning information, which may support in-time decision-making regarding food supply when critical yield fluctuations are to be expected.
Zusammenfassung
Jährliche Schwankungen der Ernteerträge aufgrund natürlicher und anthropogener Faktoren sind ein Hauptanliegen der äthiopischen Regierung. Für eine sofortige Reaktion auf sich drastisch verändernde Ernteerträge und daraus resultierende Ernteausfälle sowie zur Verbesserung der Ernährungssicherheit des Landes im Allgemeinen sind eine großflächige Überwachung des Pflanzenwachstums und eine frühzeitige Vorhersage der Produktion erforderlich. In dieser Studie haben wir ein Modell zur frühzeitigen Vorhersage des Maisertrags (Zea mays) unter Verwendung des Sentinel-2 MSI (Multispectral Instrument) entwickelt; die Studie wurde im Bezirk Abaya im Regionalstaat Oromia in Äthiopien durchgeführt. Das Modell besteht aus den folgenden Komponenten: (1) Sentinel-2-Bild-basierte Ackerland-Identifikation für verschiedene Pflanzenentwicklungsstadien, (2) Extraktion von Zeitreihen von Sentinel-2-Vegetationsindizes (VIs) für die Überwachung des Pflanzenwachstums und (3) ein einfacher linearer schrittweiser Vorwärtsregressionsansatz für die Ertragsvorhersage. Wir testeten verschiedene Spektralindizes hinsichtlich ihrer Leistung bei der Beschreibung der Pflanzenentwicklung und schließlich der Vorhersage des erwarteten Ertrags. Das Ergebnis zeigte, dass: (i) der lineare Red-Edge Enhanced Vegetation Index (Red-Edge EVI), (ii) die Kombination aus dem Enhanced Vegetation Index (EVI) und dem Green Vegetation Index (GVI), (iii) die Kombination aus dem Red-Edge EVI und dem Soil Adjusted Vegetation Index (SAVI), und (iv) die Kombination aus dem Normalized Difference Vegetation Index (NDVI), Red-Edge EVI und SAVI das beste Vorhersagemodell etwa zwei Monate vor der Ernte mit den höchsten Bestimmtheitsmaßen (R2) von 0,73, 0,80, 0,84 bzw. 0,88, bieten. Die Korrelation für den GVI war im Vergleich zu den etablierten Modellen generell am niedrigsten, und für den NDVI wurde kein Hinweis auf eine Spitzenkorrelation beobachtet. Unser Ansatz zeigte eine hohe Genauigkeit bei der Erkennung von Maisfeldern, der Erkennung der Pflanzenphänologie und der frühzeitigen Vorhersage des Kornertrags für das Studienjahr 2018. Unser einfaches Modell kann Frühwarninformationen generieren, die eine rechtzeitige Entscheidungsfindung bezüglich der Nahrungsmittelversorgung unterstützen können, wenn kritische Ertragsschwankungen zu erwarten sind.
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Acknowledgements
This work was supported by Dilla University, Ethiopia, under-funds for medium and large-scale research projects. Field maize yield data and phenological stages were collected at small-scale farmlands in collaboration with local farmers. We thank ESA for the availability of Sentinel-2 MSI at Copernicus Open Access Hub. We would also like to thank the anonymous reviewers for their insightful comments.
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Bazezew, M.N., Belay, A.T., Guda, S.T. et al. Developing Maize Yield Predictive Models from Sentinel-2 MSI Derived Vegetation Indices: An Approach to an Early Warning System on Yield Fluctuation and Food Security. PFG 89, 535–548 (2021). https://doi.org/10.1007/s41064-021-00178-5
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DOI: https://doi.org/10.1007/s41064-021-00178-5