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Combining Multitemporal Sentinel-2A Spectral Imaging and Random Forest to Improve the Accuracy of Soil Organic Matter Estimates in the Plough Layer for Cultivated Land

Author

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  • Li Wang

    (Key Laboratory for Geographical Process Analysis & Simulation in Hubei Province, Central China Normal University, Wuhan 430079, China
    College of Urban & Environmental Sciences, Central China Normal University, Wuhan 430079, China)

  • Yong Zhou

    (Key Laboratory for Geographical Process Analysis & Simulation in Hubei Province, Central China Normal University, Wuhan 430079, China
    College of Urban & Environmental Sciences, Central China Normal University, Wuhan 430079, China)

Abstract

Soil organic matter (SOM) is vital for assessing the quality of arable land. A fast and reliable estimation of SOM is important to predict the soil carbon stock in cropland. In this study, we aimed to explore the potential of combining multitemporal Sentinel-2A imagery and random forest (RF) to improve the accuracy of SOM estimates in the plough layer for cultivated land at a regional scale. The field data of SOM content were utilized along with multitemporal Sentinel-2A images acquired over three years during the bare soil period to develop spectral indices. The best bands and spectral indices were selected as prediction variables by using the RF algorithm. Partial least squares (PLS), geographically weighted regression (GWR), and RF were employed to calibrate spectral indices for the SOM content, and the optimal calibration model was used for the mapping of the SOM content in arable land at a regional scale. The results showed the following. (1) The multitemporal image estimation model outperformed the single-temporal image estimation model. The estimation model that utilized the optimal bands and spectral indices as prediction variables usually had better accuracy than the models based on full spectral data. (2) For the SOM content estimates, the performance was better with RF than with PLS and GWR in almost all cases. (3) The most accurate SOM estimation in the case area was achieved by using multitemporal images from 2018 and the RF calibration model based on the optimal bands and spectral indices as prediction variables, with R 2 val (coefficient of determination of the validation data set) = 0.67, RMSE val (root mean square error of the validation dataset) = 2.05, and RPIQ val (ratio of performance to interquartile range of the validation dataset) = 3.36. (4) The estimated SOM content in the plough layer for cultivated land throughout the study area ranged from 16.17 to 36.98 g kg −1 and exhibited an increasing trend from north to south. In the current study, we developed a framework that combines multitemporal remote sensing imagery and RF for the SOM estimation, which can improve the accuracy of quantitative SOM estimations, provide a dynamic, rapid, and low-cost technique for understanding soil fertility, and offer an early warning of changes in soil quality.

Suggested Citation

  • Li Wang & Yong Zhou, 2022. "Combining Multitemporal Sentinel-2A Spectral Imaging and Random Forest to Improve the Accuracy of Soil Organic Matter Estimates in the Plough Layer for Cultivated Land," Agriculture, MDPI, vol. 13(1), pages 1-21, December.
  • Handle: RePEc:gam:jagris:v:13:y:2022:i:1:p:8-:d:1009418
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    References listed on IDEAS

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    1. Ying-Qiang Song & Lian-An Yang & Bo Li & Yue-Ming Hu & An-Le Wang & Wu Zhou & Xue-Sen Cui & Yi-Lun Liu, 2017. "Spatial Prediction of Soil Organic Matter Using a Hybrid Geostatistical Model of an Extreme Learning Machine and Ordinary Kriging," Sustainability, MDPI, vol. 9(5), pages 1-17, May.
    2. Siyu Tang & Chong Du & Tangzhe Nie, 2022. "Inversion Estimation of Soil Organic Matter in Songnen Plain Based on Multispectral Analysis," Land, MDPI, vol. 11(5), pages 1-18, April.
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    Cited by:

    1. Xayida Subi & Mamattursun Eziz & Qing Zhong, 2023. "Hyperspectral Estimation Model of Organic Matter Content in Farmland Soil in the Arid Zone," Sustainability, MDPI, vol. 15(18), pages 1-13, September.

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