RESEARCH OF SOIL NUTRIENT CONTENT INVERSION MODEL BASED ON HYPERSPECTRAL DATA

TAO Pei-feng; WANG Jian-hua; LI Zhi-zhong; ZHOU Ping; YANG Jia-jia; GAO Fan-qi

2020 Vol. 29, No. 1

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Article navigation > Geology and Resources > 2020 > 29(1): 68-75, 84

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TAO Pei-feng, WANG Jian-hua, LI Zhi-zhong, ZHOU Ping, YANG Jia-jia, GAO Fan-qi. RESEARCH OF SOIL NUTRIENT CONTENT INVERSION MODEL BASED ON HYPERSPECTRAL DATA[J]. Geology and Resources, 2020, 29(1): 68-75, 84.

Citation:

TAO Pei-feng, WANG Jian-hua, LI Zhi-zhong, ZHOU Ping, YANG Jia-jia, GAO Fan-qi. RESEARCH OF SOIL NUTRIENT CONTENT INVERSION MODEL BASED ON HYPERSPECTRAL DATA[J]. Geology and Resources, 2020, 29(1): 68-75, 84.

RESEARCH OF SOIL NUTRIENT CONTENT INVERSION MODEL BASED ON HYPERSPECTRAL DATA

1.
School of Earth Sciences and Resources, China University of Geosciences, Beijing 100083, China
2.
International Black Soils Society, Shenyang 110034, China
3.
Shenyang Center of Geological Survey, CGS, Shenyang 110034, China
4.
Institute of Remote Sensing and Digital Earth, CAS, Beijing 100094
5.
School of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China

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Corresponding author: WANG Jian-hua, wangjh@aircas.ac.cn

Received Date: 06 August 2019

Revised Date: 10 October 2019

Publish Date: 29 February 2020

Abstract

Abstract

In order to quickly test the soil nutrient contents (SOM, TN, TP and TS), the authors collect 117 soil samples at 0-20 cm depth from Chuangye Farm in Jiansanjiang reclamation area as research objects. First derivative (FD), logarithmic reciprocal (RL), first derivative of reciprocal (FDR), multivariate scattering correction (MSC) and continuum removal (CR) transformations are performed on the raw spectral reflectance (R). By analyzing the correlation between the six spectral variables and soil nutrient content, the bands that are significantly correlated at the α=0.01 level are adopted as characteristic bands, and the methods of stepwise multiple linear regression (SMLR), partial least squares regression (PLSR) and back propagation neural network (BPNN) are used respectively to establish hyperspectral prediction model of SOM, TN, TP and TS. The model is evaluated by R², RMSE and RPD. The results show that the soil nutrient content prediction models established by PLSR and BPNN are superior to that by SMLR. The PLSR and BPNN methods can well predict the organic matter and total nitrogen content, and roughly estimate the total sulfur content. Only the CR-BPNN method can roughly estimate the total phosphorus content. The models with the best prediction effect on SOM, TN, TP and TS are, respectively, MSC-PLSR, MSC-PLSR, CR-BPNN and FDR-BPNN, with the validation set determination coefficients of 0.86, 0.75, 0.56 and 0.67 respectively.
- soil nutrient /
- hyperspectral /
- stepwise multiple linear regression (SMLR) /
- partial least squares regression (PLSR) /
- back propagation neural network (BPNN) /
- inversion model

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