Scientia Agricultura Sinica ›› 2022, Vol. 55 ›› Issue (19): 3738-3750.doi: 10.3864/j.issn.0578-1752.2022.19.005

• TILLAGE & CULTIVATION·PHYSIOLOGY & BIOCHEMISTRY·AGRICULTURE INFORMATION TECHNOLOGY • Previous Articles     Next Articles

Prediction of Soil Organic Carbon Content in Jiangxi Province by Vis-NIR Spectroscopy Based on the CARS-BPNN Model

WU Jun1(),GUO DaQian3,LI Guo2,4,GUO Xi1,2(),ZHONG Liang1,ZHU Qing1,GUO JiaXin1,YE YingCong1   

  1. 1College of Land Resources and Environment, Jiangxi Agricultural University/Key Laboratory of Poyang Lake Watershed Agricultural Resources and Ecology of Jiangxi Province, Nanchang 330045
    2Ecological Restoration and Innovation Research Institute of Jiangxi Province, Nanchang 330045
    3The National Land and Space Survey and Planning Research Institute of Jiangxi Province, Nanchang 330045
    4912 Brigade, Geological Bureau of Jiangxi Province, Nanchang 330045
  • Received:2021-12-07 Accepted:2022-03-30 Online:2022-10-01 Published:2022-10-10
  • Contact: Xi GUO E-mail:JuneWu6667@163.com;guoxi@jxau.edu.cn

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Abstract:

【Objective】 This study explored the roles of spectral variable selection and stratified calibration based on soil type in visible-near-infrared (Vis-NIR) spectroscopy for predicting soil organic carbon (SOC) content on a large spatial scale. 【Method】 A total of 490 samples were collected in Jiangxi province (Southeast China) and used for modeling with partial least squares regression (PLSR), support vector machine (SVM), random forests (RF), and back-propagation neural network (BPNN). The competitive adaptive reweighted sampling (CARS) procedure was used to select the feature bands of different soil types and total samples (i.e., sum of red soils and paddy soils). The prediction accuracy of models incorporating full bands or feature bands was evaluated for the different soil types. Further, the prediction accuracy of these models based on their global and stratification calibration was compared for the total samples. 【Result】 (1) The feature bands of red soils were 484, 683-714, and 2 219-2 227 nm, while those of paddy soils were 484, 689-702, and 2 146-2 156 nm. The CARS-BPNN model showed the best prediction performance for red soils (validation set R2 = 0.82), being 0.07 higher than that of BPNN with full bands. The CARS-RF model also had the best prediction performance for paddy soils (validation set R2 = 0.83), being 0.13 higher than that of RF with full bands. (2) Based on the stratified calibration, the best prediction performance was obtained using the CARS-BPNN model (validation set R2 = 0.82), which was 0.06 higher than that of the model based on global calibration. 【Conclusion】 The CARS-BPNN model combined with stratified calibration based on soil type could accurately predict SOC content in the study area.

Key words: soil organic carbon, competitive adaptive reweighted sampling, stratified calibration, random forest, back propagation neural network

Fig. 1

Location of the study area and distribution of sampling points"

Fig. 2

Technical process"

Table 1

Descriptive statistical characteristics of soil organic carbon content in Jiangxi Province"

土壤类型
Type of soil		 样本类型
Type of sample		 样本数量
Number of samples		 最小值
Minimum
(g·kg-1)		 最大值
Maximum
(g·kg-1)		 平均值
Mean
(g·kg-1)		 标准差
Standard deviation (g·kg-1)		 变异系数
Coefficient of variation
全部
Total		 Ⅰ 490 4.12 34.11 16.75 6.21 0.37
Ⅱ 367 4.63 34.11 16.89 6.10 0.36
Ⅲ 123 4.12 29.58 16.33 6.52 0.40
红壤
Red soil		 Ⅰ 242 4.44 28.89 16.17 5.91 0.37
Ⅱ 182 4.44 28.89 16.29 5.89 0.36
Ⅲ 60 4.44 27.20 15.83 5.99 0.38
水稻土
Paddy soil		 Ⅰ 248 4.12 34.11 17.32 6.45 0.37
Ⅱ 186 4.63 34.11 17.53 6.28 0.36
Ⅲ 62 4.12 30.92 16.71 6.95 0.42

Fig. 3

Spectral curves of red soil and paddy soil"

Fig.4

The process and results of CARS algorithm for selecting characteristic bands a: Changes in the number of waveband variables; b: Variation of RMSECV; c: Path of variable regression coefficients; d-f: Characteristic bands of red soil, paddy soil and total soil"

Table 2

Inversion accuracy of organic carbon content in different soil types"

方法
Method		 土壤类型
Type of soil		 训练集 Training set 验证集 Validation set
R2 RMSE (g·kg-1) RPD R2 RMSE (g·kg-1) RPD
PLSR R 0.80 2.61 2.25 0.74 3.03 1.96
P 0.76 3.07 2.04 0.73 3.59 1.92
SVM R 0.84 2.38 2.47 0.76 2.91 2.05
P 0.77 2.99 2.10 0.76 3.41 2.02
RF R 0.88 2.05 2.86 0.74 3.01 1.97
P 0.76 3.06 2.05 0.70 3.78 1.82
BPNN R 0.86 2.19 2.69 0.75 2.95 2.01
P 0.80 2.81 2.23 0.77 3.32 2.08
CARS-PLSR R 0.83 2.41 2.44 0.81 2.61 2.28
P 0.81 2.74 2.29 0.79 3.12 2.21
CARS-SVM R 0.83 2.45 2.40 0.78 2.79 2.13
P 0.79 2.87 2.18 0.77 3.31 2.08
CARS-RF R 0.89 1.97 2.98 0.80 2.68 2.21
P 0.85 2.46 2.55 0.83 2.85 2.42
CARS-BPNN R 0.85 2.28 2.58 0.82 2.50 2.38
P 0.82 2.69 2.33 0.81 2.99 2.31

Fig. 5

Comparison between measured and estimated values of organic carbon content in red soil and paddy soil under different models of validation set"

Table 3

Inversion accuracy of soil organic carbon content based on global and classification modeling"

模型
Model		 全局/分类
Global/Classification		 训练集 Training set 验证集 Validation set
R2 RMSE (g·kg-1) RPD R2 RMSE (g·kg-1) RPD
PLSR G 0.71 3.26 1.87 0.67 3.73 1.75
C 0.78 2.85 2.14 0.73 3.33 1.96
SVM G 0.79 2.78 2.19 0.73 3.40 1.91
C 0.80 2.70 2.26 0.76 3.17 2.05
RF G 0.84 2.45 2.49 0.61 4.08 1.59
C 0.82 2.61 2.34 0.72 3.42 1.90
BPNN G 0.79 2.78 2.19 0.61 4.05 1.60
C 0.83 2.52 2.42 0.76 3.14 2.07
CARS-PLSR G 0.80 2.74 2.22 0.76 3.20 2.03
C 0.82 2.58 2.36 0.80 2.88 2.26
CARS-SVR G 0.79 2.78 2.19 0.74 3.32 1.95
C 0.81 2.67 2.28 0.77 3.07 2.12
CARS-RF G 0.85 2.33 2.61 0.72 3.46 1.88
C 0.87 2.23 2.73 0.82 2.77 2.35
CARS-BPNN G 0.80 2.70 2.26 0.76 3.18 2.04
C 0.83 2.49 2.44 0.82 2.75 2.36

Fig. 6

Comparison between measured and estimated values of organic carbon content in global and classification models under different validation set"

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