Estimating Wheat Grain Yield Using Sentinel-2 Imagery and Exploring Topographic Features and Rainfall Effects on Wheat Performance in Navarre, Spain
Aranjuelo Michelena, Iker
Araus Ortega, José Luis
Kefauver, Shawn C.
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first_page settings Open AccessArticle Estimating Wheat Grain Yield Using Sentinel-2 Imagery and Exploring Topographic Features and Rainfall Effects on Wheat Performance in Navarre, Spain by Joel Segarra 1,2, Jon González-Torralba 3, Íker Aranjuelo 4 [OrcID] , Jose Luis Araus 1,2 [OrcID] and Shawn C. Kefauver 1,2,* [OrcID] 1 Integrative Crop Ecophysiology Group, Plant Physiology Section, Faculty of Biology, University of Barcelona, 08028 Barcelona, Spain 2 AGROTECNIO (Center for Research in Agrotechnology), 25198 Lleida, Spain 3 Grupo AN, Campo Tajonar, 31192 Tajonar, Spain 4 Instituto de Agrobiotecnología (IdAB), CSIC-Gobierno de Navarra, 31192 Pamplona, Spain * Author to whom correspondence should be addressed. Remote Sens. 2020, 12(14), 2278; https://doi.org/10.3390/rs12142278 Received: 4 June 2020 / Revised: 10 July 2020 / Accepted: 13 July 2020 / Published: 15 July 2020 (This article belongs to the Special Issue Remote Sensing of Plant-Climate Interactions) Download PDF Browse Figures Review Reports Abstract Reliable methods for estimating wheat grain yield before harvest could help improve farm management and, if applied on a regional level, also help identify spatial factors that influence yield. Regional grain yield can be estimated using conventional methods, but the typical process is complex and labor-intensive. Here we describe the development of a streamlined approach using publicly accessible agricultural data, field-level yield, and remote sensing data from Sentinel-2 satellite to estimate regional wheat grain yield. We validated our method on wheat croplands in Navarre in northern Spain, which features heterogeneous topography and rainfall. First, this study developed stepwise multilinear equations to estimate grain yield based on various vegetation indices, which were measured at various phenological stages in order to determine the optimal timings. Second, the most suitable model was used to estimate grain yield in wheat parcels mapped from Sentinel-2 satellite images. We used a supervised pixel-based random forest classification and the estimates were compared to government-published post-harvest yield statistics. When tested, the model achieved an R2 of 0.83 in predicting grain yield at field level. The wheat parcels were mapped with an accuracy close to 86% for both overall accuracy and compared to official statistics. Third, the validated model was used to explore potential relationships of the mapped per-parcel grain yield estimation with topographic features and rainfall by using geographically weighted regressions. Topographic features and rainfall together accounted for an average for 11 to 20% of the observed spatial variation in grain yield in Navarre. These results highlight the ability of our method for estimating wheat grain yield before harvest and determining spatial factors that influence yield at the regional scale.
Is part ofRemote Sensing, 2020, vol. 12, núm. 14, p. 2278
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Except where otherwise noted, this item's license is described as cc-by (c) Segarra, Joel et al., 2020
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