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Combining Environmental Factors and Lab VNIR Spectral Data to Predict SOM by Geospatial Techniques

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Abstract

Soil organic matter (SOM) is an important parameter related to soil nutrient and miscellaneous ecosystem services. This paper attempts to improve the performance of traditional partial least square regression (PLSR) model by considering the spatial autocorrelation and soil forming factors. Surface soil samples (n = 180) were collected from Honghu City located in the middle of Jianghan Plain, China. The visible and near infrared (VNIR) spectra and six environmental factors (elevation, land use types, roughness, relief amplitude, enhanced vegetation index, and land surface water index) were used as the auxiliary variables to construct the multiple linear regression (MLR), PLSR and geographically weighted regression (GWR) models. Results showed that: 1) the VNIR spectra can increase about 39.62% prediction accuracy than the environmental factors in predicting SOM; 2) the comprehensive variables of VNIR spectra and the environmental factors can improve about 5.78% and 44.90% relative to soil spectral models and soil environmental models, respectively; 3) the spatial model (GWR) can improve about 3.28% accuracy than MLR and PLSR. Our results suggest that the combination of spectral reflectance and the environmental variables can be used as the suitable auxiliary variables in predicting SOM, and GWR is a promising model for predicting soil properties.

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Authors and Affiliations

  1. College of Resources and Environment, Huazhong Agricultural University, Wuhan, 430070, China

    Long Guo & Haitao Zhang

  2. School of Resource and Environment, Wuhan University, Wuhan, 430070, China

    Yiyun Chen & **g Qian

Authors
  1. Long Guo
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  2. Haitao Zhang
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  3. Yiyun Chen
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  4. **g Qian
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Corresponding author

Correspondence to **g Qian.

Additional information

Foundation item: Under the auspices of the Natural Science Foundation of Hubei (No. 2018CFB372), the Fundamental Research Funds for the Central Universities (No. 2662016QD032), the Key Laboratory of Aquatic Plants and Watershed Ecology of Chinese Academy of Sciences (No. Y852721s04), the Chinese National Natural Science Foundation (No. 41371227), the National Undergraduate Innovation and Entrepreneurship Training Program (No. 201810504023, 201810504030)

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Guo, L., Zhang, H., Chen, Y. et al. Combining Environmental Factors and Lab VNIR Spectral Data to Predict SOM by Geospatial Techniques. Chin. Geogr. Sci. 29, 258–269 (2019). https://doi.org/10.1007/s11769-019-1020-8

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  • DOI: https://doi.org/10.1007/s11769-019-1020-8

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