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

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Abstract

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 Paraná 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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Availability of data and material (data transparency)

Data associated with this research is available with the author L.G.T.C. upon request.

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Funding

This work was supported by the National Council for Scientific and Technological Development—CNPq; Central Public-Interest Scientific Institution Basal Research Fund [Y2021GH18]; Innovation Project of Chinese Academy of Agricultural Sciences [G202120-5]; and the Talented Young Scientist Program—China Science and Technology Exchange Center [Brazil–19-004].

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Authors and Affiliations

  1. Key Laboratory of Agricultural Remote Sensing, Ministry of Agriculture/CAAS-CIAT Joint Laboratory in Advanced Technologies for Sustainable Agriculture—Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, 100081, Beijing, China

    L. G.T. Crusiol, Liang Sun, R. Chen, Z. Sun & D. Wuyun

  2. Department of Agronomy, State University of Maringá, 87020-900, Maringá, PR, Brazil

    L. G.T. Crusiol, M. R. Nanni, R. H. Furlanetto & E. Cezar

  3. Embrapa Soja (National Soybean Research Center—Brazilian Agricultural Research Corporation), 86001-970, Londrina, PR, Brazil

    R. N.R. Sibaldelli, A. L. Nepomuceno & J. R.B. Farias

  4. Integrada Cooperativa Agroindustrial, 86010-480, Londrina, PR, Brazil

    V. Felipe Junior & W. X. Furlaneti

  5. Digitalization and Informatics Division, Food and Agricultural Organization of the United Nations, Terme Caracalla, 00153, Rome, Italy

    Z. Chen

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  1. L. G.T. Crusiol
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  2. Liang Sun
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Supplementary table Spatial and spectral characteristics of Sentinel-2 spectral bands
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figure a

Average soybean spectral response at the R5 phenological stage from the Global-based models (Sentinel-2) and from Embrapa Soja (Sentinel-2-like)

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Crusiol, L.G., Sun, L., Sibaldelli, R.N. et al. Strategies for monitoring within-field soybean yield using Sentinel-2 Vis-NIR-SWIR spectral bands and machine learning regression methods. Precision Agric 23, 1093–1123 (2022). https://doi.org/10.1007/s11119-022-09876-5

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