Strategies for monitoring within-field soybean yield using Sentinel-2 Vis-NIR-SWIR spectral bands and machine learning regression methods

文献类型： 外文期刊

第一作者： Crusiol, L. G. T.

作者： Crusiol, L. G. T.;Sun, Liang;Chen, R.;Sun, Z.;Wuyun, D.;Crusiol, L. G. T.;Nanni, M. R.;Furlanetto, R. H.;Cezar, E.;Sibaldelli, R. N. R.;Nepomuceno, A. L.;Farias, J. R. B.;Felipe Junior, V;Furlaneti, W. X.;Chen, Z.

作者机构：

关键词： Yield prediction; Yield mapping; Partial least squares regression; Support vector regression; Multispectral image; Multitemporal data

期刊名称：PRECISION AGRICULTURE （ 影响因子：5.767； 五年影响因子：5.875 ）

ISSN： 1385-2256

年卷期： 2022 年 23 卷 3 期

页码：

收录情况： SCI

摘要： Soybean crop plays an important role in world food production and food security, and agricultural production should be increased accordingly to meet the global food demand. Satellite remote sensing data is considered a promising proxy for monitoring and predicting yield. This research aimed to evaluate strategies for monitoring within-field soybean yield using Sentinel-2 visible, near-infrared and shortwave infrared (Vis/NIR/SWIR) spectral bands and partial least squares regression (PLSR) and support vector regression (SVR) methods. Soybean yield maps (over 500 ha) were recorded by a combine harvester with a yield monitor in 15 fields (3 farms) in Parana State, southern Brazil. Sentinel-2 images (spectral bands and 8 vegetation indices) across a cropping season were correlated to soybean yield. Information pooled across the cropping season presented better results compared to single images, with best performance of Vis/NIR/SWIR spectral bands under PLSR and SVR. At the grain filling stage, field-, farm- and global-based models were evaluated and presented similar trends compared to leaf-based hyperspectral reflectance collected at the Brazilian National Soybean Research Center. SVR outperformed PLSR, with a strong correlation between observed and predicted yield. For within-field soybean yield mapping, field-based SVR models (developed individually for each field) presented the highest accuracies. The results obtained demonstrate the possibility of developing within-field yield prediction models using Sentinel-2 Vis/NIR/SWIR bands through machine learning methods.

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