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Hyperspectral Estimates of Soil Moisture Content Incorporating Harmonic Indicators and Machine Learning

Author

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  • Xueqin Jiang

    (College of Surveying and Geo-Informatics, Tongji University, Shanghai 200092, China
    School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
    These authors contributed equally to this work.)

  • Shanjun Luo

    (School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
    These authors contributed equally to this work.)

  • Qin Ye

    (College of Surveying and Geo-Informatics, Tongji University, Shanghai 200092, China)

  • Xican Li

    (School of Information Science and Engineering, Shandong Agriculture University, Tai’an 271001, China)

  • Weihua Jiao

    (Center for Agricultural and Rural Economic Research, Shandong University of Finance and Economics, Jinan 250014, China)

Abstract

Soil is one of the most significant natural resources in the world, and its health is closely related to food security, ecological security, and water security. It is the basic task of soil environmental quality assessment to monitor the temporal and spatial variation of soil properties scientifically and reasonably. Soil moisture content (SMC) is an important soil property, which plays an important role in agricultural practice, hydrological process, and ecological balance. In this paper, a hyperspectral SMC estimation method for mixed soil types was proposed combining some spectral processing technologies and principal component analysis (PCA). The original spectra were processed by wavelet packet transform (WPT), first-order differential (FOD), and harmonic decomposition (HD) successively, and then PCA dimensionality reduction was used to obtain two groups of characteristic variables: WPT-FOD-PCA (WFP) and WPT-FOD-HD-PCA (WFHP). On this basis, three regression models of principal component regression (PCR), partial least squares regression (PLSR), and back propagation (BP) neural network were applied to compare the SMC predictive ability of different parameters. Meanwhile, we also compared the results with the estimates of conventional spectral indices. The results indicate that the estimation results based on spectral indices have significant errors. Moreover, the BP models (WFP-BP and WFHP-BP) show more accurate results when the same variables are selected. For the same regression model, the choice of variables is more important. The three models based on WFHP (WFHP-PCR, WFHP-PLSR, and WFHP-BP) all show high accuracy and maintain good consistency in the prediction of high and low SMC values. The optimal model was determined to be WFHP-BP with an R 2 of 0.932 and a prediction error below 2%. This study can provide information on farm entropy before planting crops on arable land as well as a technical reference for estimating SMC from hyperspectral images (satellite and UAV, etc.).

Suggested Citation

  • Xueqin Jiang & Shanjun Luo & Qin Ye & Xican Li & Weihua Jiao, 2022. "Hyperspectral Estimates of Soil Moisture Content Incorporating Harmonic Indicators and Machine Learning," Agriculture, MDPI, vol. 12(8), pages 1-17, August.
    • Handle: RePEc:gam:jagris:v:12:y:2022:i:8:p:1188-:d:884271
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    References listed on IDEAS

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    Cited by:

    1. Jibo Yue & Chengquan Zhou & Haikuan Feng & Yanjun Yang & Ning Zhang, 2023. "Novel Applications of Optical Sensors and Machine Learning in Agricultural Monitoring," Agriculture, MDPI, vol. 13(10), pages 1-4, October.
    2. 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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