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Prediction of Soil Nutrients Based on Topographic Factors and Remote Sensing Index in a Coal Mining Area, China

Author

Listed:
  • Hongfen Zhu

    (College of Resource and Environment, Shanxi Agricultural University, Shanxi 030801, China
    National Experimental Teaching Demonstration Center for Agricultural Resources and Environment, Shanxi Agricultural University, Shanxi 030801, China)

  • Ruipeng Sun

    (College of Resource and Environment, Shanxi Agricultural University, Shanxi 030801, China)

  • Zhanjun Xu

    (College of Resource and Environment, Shanxi Agricultural University, Shanxi 030801, China
    National Experimental Teaching Demonstration Center for Agricultural Resources and Environment, Shanxi Agricultural University, Shanxi 030801, China)

  • Chunjuan Lv

    (College of Resource and Environment, Shanxi Agricultural University, Shanxi 030801, China
    National Experimental Teaching Demonstration Center for Agricultural Resources and Environment, Shanxi Agricultural University, Shanxi 030801, China)

  • Rutian Bi

    (College of Resource and Environment, Shanxi Agricultural University, Shanxi 030801, China
    National Experimental Teaching Demonstration Center for Agricultural Resources and Environment, Shanxi Agricultural University, Shanxi 030801, China)

Abstract

(1) Background: Coal mining operations caused severe land subsidence and altered the distributions of soil nutrients that influenced by multiple environmental factors at different scales. However, the prediction performances for soil nutrients based on their scale-specific relationships with influencing factors remains undefined in the coal mining area. The objective of this study was to establish prediction models of soil nutrients based on their scale-specific relationships with influencing factors in a coal mining area. (2) Methods: Soil samples were collected based on a 1 × 1 km regular grid, and contents of soil organic matter, soil available nitrogen, soil available phosphorus, and soil available potassium were measured. The scale components of soil nutrients and the influencing factors collected from remote sensing and topographic factors were decomposed by two-dimensional empirical mode decomposition (2D-EMD), and the predictions for soil nutrients were established using the methods of multiple linear stepwise regression or partial least squares regression based on original samples (MLSR Ori or PLSR Ori ), partial least squares regression based on bi-dimensional intrinsic mode function (PLSR BIMF ), and the combined method of 2D-EMD, PLSR, and MLSR (2D-EMD PM ). (3) Results: The correlation types and correlation coefficients between soil nutrients and influencing factors were scale-dependent. The variances of soil nutrients at smaller scale were stochastic and non-significantly correlated with influencing factors, while their variances at the larger scales were stable. The prediction performances in the coal mining area were better than those in the non-coal mining area, and 2D-EMD PM had the most stable performance. (4) Conclusions: The scale-dependent predictions can be used for soil nutrients in the coal mining areas.

Suggested Citation

  • Hongfen Zhu & Ruipeng Sun & Zhanjun Xu & Chunjuan Lv & Rutian Bi, 2020. "Prediction of Soil Nutrients Based on Topographic Factors and Remote Sensing Index in a Coal Mining Area, China," Sustainability, MDPI, vol. 12(4), pages 1-16, February.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:4:p:1626-:d:323518
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    References listed on IDEAS

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    1. Manfred M. Fischer & Jinfeng Wang, 2011. "Spatial Data Analysis," SpringerBriefs in Regional Science, Springer, number 978-3-642-21720-3, April.
    2. Zhanjun Xu & Yuan Zhang & Jason Yang & Fenwu Liu & Rutian Bi & Hongfen Zhu & Chunjuan Lv & Jian Yu, 2019. "Effect of Underground Coal Mining on the Regional Soil Organic Carbon Pool in Farmland in a Mining Subsidence Area," Sustainability, MDPI, vol. 11(18), pages 1-19, September.
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