In-season forecasting of within-field grain yield from Sentinel-2 time series data
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Título: | In-season forecasting of within-field grain yield from Sentinel-2 time series data |
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Autor/es: | Amin, Eatidal | Pipia, Luca | Belda, Santiago | Perich, Gregor | Graf, Lukas Valentin | Aasen, Helge | Van Wittenberghe, Shari | Moreno, José | Verrelst, Jochem |
Centro, Departamento o Servicio: | Universidad de Alicante. Departamento de Matemática Aplicada |
Palabras clave: | Sentinel-2 | Crop yield forecasting | Machine learning | Gaussian process regression (GPR) | Time series gap-filling | Growing degree days (GDD) |
Fecha de publicación: | 26-dic-2023 |
Editor: | Elsevier |
Cita bibliográfica: | International Journal of Applied Earth Observation and Geoinformation. 2024, 126: 103636. https://doi.org/10.1016/j.jag.2023.103636 |
Resumen: | Precise knowledge of cropland productivity is relevant for farmers to enable optimizing managing practices; particularly with the perspective of anticipating crop yield ahead of harvest. The current availability of high spatiotemporal resolution Sentinel-2 satellite data offers a unique opportunity to monitor croplands over time. In this context, the recently introduced kernel NDVI (kNDVI) statistically optimizes the conventional NDVI formulation by applying a nonlinear function to the involved bands, and so maximizes the spectral information extraction. This study proposes a workflow for within-field yield forecasting from Sentinel-2 kNDVI time series analysis focusing on winter cereal croplands in Switzerland over three years, comparing with NDVI as baseline. For a temporally continuous modelling of crop yields, Gaussian Process Regression (GPR) was applied to reconstruct cloud-free time series of the complete crop growing seasons. Following, distinct machine learning regression models (GPR, Kernel Ridge Regression and Random Forest) were developed to forecast yield at any point in time throughout the cropland growing season. The integration of Growing Degree Days (GDD) information as temporal spacing reference of the time series considerably improved the accuracy and consistency of in-season yield forecasting. Training and testing within the same year demonstrated that yield can be accurately forecast approximately 2–2.5 months ahead of harvest, at crops’ anthesis (flowering) phase, with an RMSE up to 0.71 t/ha and a relative RMSE of 7.60%. Although the forecasting accuracy of the models decreased when predicting yield for the unseen years, still satisfactory results were obtained: RMSE = 0.97 t/ha, relative RMSE = 11.47%. |
Patrocinador/es: | EA was supported by the predoctoral scholarship grant number ACIF/2019/187 funded by the Generalitat Valenciana, Spain and co-funded by the European Social Fund. EA and SVW were supported by the ERC-2021-STG PHOTOFLUX project (grant agreement 101041768). SB was supported by Generalitat Valenciana, Spain SEJIGENT program (SEJIGENT/2021/001) and European Union NextGenerationEU (ZAMBRANO 21-04). GP was funded within the project “DeepField” by the Swiss Federal Office of Agriculture, Switzerland. LVG acknowledges funding of the Swiss National Science Foundation, Switzerland for the project “PhenomEn” (grant number IZCOZ0-198091). JV was funded by the ERC-2017-STG SENTIFLEX project (grant number 755617) and by Ramón y Cajal Contract (Spanish Ministry of Science, Innovation and Universities) . This research was supported by the Action CA17134 SENSECO (Optical synergies for spatiotemporal sensing of scalable ecophysiological traits) funded by COST (European Cooperation in Science and Technology, https://www.cost.eu/). |
URI: | http://hdl.handle.net/10045/139501 |
ISSN: | 1569-8432 (Print) | 1872-826X (Online) |
DOI: | 10.1016/j.jag.2023.103636 |
Idioma: | eng |
Tipo: | info:eu-repo/semantics/article |
Derechos: | © 2024 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
Revisión científica: | si |
Versión del editor: | https://doi.org/10.1016/j.jag.2023.103636 |
Aparece en las colecciones: | Personal Investigador sin Adscripción a Grupo Investigaciones financiadas por la UE |
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Amin_etal_2024_IntJApplEarthObservatGeoinfo.pdf | 3,31 MB | Adobe PDF | Abrir Vista previa | |
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