期刊
AGRONOMY-BASEL
卷 11, 期 8, 页码 -出版社
MDPI
DOI: 10.3390/agronomy11081486
关键词
durum wheat; yield modelling; Sentinel-2; NDVI; EVI; NDWI; NMDI
资金
- Barilla Hellas S.A.
This study developed a modelling approach for wheat yield estimation/prediction using Sentinel-2 data, with a two-step process of establishing vegetation indices as plant signals and water/soil signals, resulting in a model that performed well in predicting yield.
In this study, a modelling approach for the estimation/prediction of wheat yield based on Sentinel-2 data is presented. Model development was accomplished through a two-step process: firstly, the capacity of Sentinel-2 vegetation indices (VIs) to follow plant ecophysiological parameters was established through measurements in a pilot field and secondly, the results of the first step were extended/evaluated in 31 fields, during two growing periods, to increase the applicability range and robustness of the models. Modelling results were examined against yield data collected by a combine harvester equipped with a yield-monitoring system. Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) were examined as plant signals and combined with Normalized Difference Water Index (NDWI) and/or Normalized Multiband Drought Index (NMDI) during the growth period or before sowing, as water and soil signals, respectively. The best performing model involved the EVI integral for the 20 April-31 May period as a plant signal and NMDI on 29 April and before sowing as water and soil signals, respectively (R-2 = 0.629, RMSE = 538). However, model versions with a single date and maximum seasonal VIs values as a plant signal, performed almost equally well. Since the maximum seasonal VIs values occurred during the last ten days of April, these model versions are suitable for yield prediction.
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