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Estimating Soil Salinity with Different Levels of Vegetation Cover by Using Hyperspectral and Non-Negative Matrix Factorization Algorithm

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  • Jianfei Cao

    (College of Geography and Environment, Shandong Normal University, Jinan 250014, China
    Zhongke Shandong Dongying Institute of Geographic Sciences, Dongying 257000, China)

  • Han Yang

    (College of Geography and Environment, Shandong Normal University, Jinan 250014, China)

  • Jianshu Lv

    (College of Geography and Environment, Shandong Normal University, Jinan 250014, China)

  • Quanyuan Wu

    (College of Geography and Environment, Shandong Normal University, Jinan 250014, China)

  • Baolei Zhang

    (College of Geography and Environment, Shandong Normal University, Jinan 250014, China)

Abstract

Hyperspectral technology has proven to be an effective method for monitoring soil salt content (SSC). However, hyperspectral estimation capabilities are limited when the soil surface is partially vegetated. This work aimed to (1) quantify the influences of different fraction vegetation coverage (FVC) on SSC estimation by hyperspectra and (2) explore the potential for a non-negative matrix factorization algorithm (NMF) to reduce the influence of various FVCs. Nine levels of mixed hyperspectra were measured from simulated mixed scenes, which were performed by strictly controlling SSC and FVC in the laboratory. NMF was implemented to extract soil spectral signals from mixed hyperspectra. The NMF-extracted soil spectra were used to estimate SSC using partial least squares regression. Results indicate that SSC could be estimated based on the original mixed spectra within a 25.76% FVC (R 2 cv = 0.68, RMSE cv = 5.18 g·kg −1 , RPD = 1.43). Compared with the mixed spectra, NMF extraction of soil spectrum improved the estimation accuracy. The NMF-extracted soil spectra from FVC below 63.55% of the mixed spectra provided acceptable estimation accuracies for SSC with the lowest results of determination of the estimation R 2 cv = 0.69, RMSE cv = 4.15 g·kg −1 , and RPD = 1.8. Additionally, we proposed a strategy for the model performance investigation that combines spearman correlation analysis and model variable importance projection analysis. The NMF-extracted soil spectra retained the sensitive wavelengths that were significantly correlated with SSC and participated in the operation as important variables of the model.

Suggested Citation

  • Jianfei Cao & Han Yang & Jianshu Lv & Quanyuan Wu & Baolei Zhang, 2023. "Estimating Soil Salinity with Different Levels of Vegetation Cover by Using Hyperspectral and Non-Negative Matrix Factorization Algorithm," IJERPH, MDPI, vol. 20(4), pages 1-15, February.
    • Handle: RePEc:gam:jijerp:v:20:y:2023:i:4:p:2853-:d:1059270
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    References listed on IDEAS

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    1. Zhu, Wei & Yang, Jingsong & Yao, Rongjiang & Xie, Wenping & Wang, Xiangping & Liu, Yuqian, 2022. "Soil water-salt control and yield improvement under the effect of compound control in saline soil of the Yellow River Delta, China," Agricultural Water Management, Elsevier, vol. 263(C).
    2. Daniel D. Lee & H. Sebastian Seung, 1999. "Learning the parts of objects by non-negative matrix factorization," Nature, Nature, vol. 401(6755), pages 788-791, October.
    3. Berry, Michael W. & Browne, Murray & Langville, Amy N. & Pauca, V. Paul & Plemmons, Robert J., 2007. "Algorithms and applications for approximate nonnegative matrix factorization," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 155-173, September.
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