Inversion of soil total iron content using random forest model based on multi-spectral transformation and principle compoment analysis

doi:10.3969/j.issn.1004-1524.20240733

Acta Agriculturae Zhejiangensis ›› 2025, Vol. 37 ›› Issue (7): 1521-1532.DOI: 10.3969/j.issn.1004-1524.20240733

• Environmental Science • Previous Articles Next Articles

Inversion of soil total iron content using random forest model based on multi-spectral transformation and principle compoment analysis

JIANG Zhenlan¹(), CHEN Fuxun¹, LUO Shuangfei¹, LUO Yeqin¹, SHA Jinming²^,^*()

1. College of Geography and Oceanography, Minjiang University, Fuzhou 350108, China
2. School of Geographical Science, Fujian Normal University, Fuzhou 350108, China

Received:2024-08-12 Online:2025-07-25 Published:2025-08-20

Abstract

Abstract:

Typical hyperspectral inversion models for soil total iron content use single spectral variables as input, which neglect the complementarity among spectral variables. Additionally, the redundancy of spectral bands affects the prediction accuracy and generalization ability of models. In the present study, a random forest (RF) model optimized by integrating spectral variables and principal component analysis (PCA) was proposed, and the soil total iron content in Fuzhou City of China was selected as the study object. By incorporating the original reflectance and its 13 mathematical transformations, a combined spectral variable set was constructed. For variable optimization, PCA was employed in conjunction with various variable selection methods, including multiple linear regression (MLR), competitive adaptive reweighted sampling (CARS), genetic algorithm (GA), successive projections algorithm (SPA), and uninformative variable elimination (UVE). Based on the optimized variable set, RF inversion models were established to predict soil total iron content. The results indicated that all the constructed models exhibited excellent predictive capability in the validation set, with determination coefficient (R²) values higher than 0.8 and relative percent difference (RPD) values exceeding 2.8. Among these model, the CARS-PCA-RF, GA-PCA-RF and MLR-PCA-RF models demonstrated strong predictive abilities, with RPD values in the validation set exceeding 3. Notably, the CARS-PCA-RF model performed the best, with an RPD value of 3.292 in the validation set, highlighting the advantages and potential of the variable selection method which combines PCA and CARS in the hyperspectral prediction of soil total iron content. This study proposed a method for predicting soil total iron content based on multiple spectral transformations and PCA-optimized input variables. This approach improved the accuracy and stability of soil total iron content prediction, providing a new solution for the hyperspectral prediction of regional soil total iron content.

Key words: soil total iron content, spectral transformation, random forest, principal component analysis, hyperspectral prediction

CLC Number:

S127
S153.6

JIANG Zhenlan, CHEN Fuxun, LUO Shuangfei, LUO Yeqin, SHA Jinming. Inversion of soil total iron content using random forest model based on multi-spectral transformation and principle compoment analysis[J]. Acta Agriculturae Zhejiangensis, 2025, 37(7): 1521-1532.

Figures/Tables 8

References 34

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样本集 Data set	样本数 Sample size	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/%
总集Whole set	132	5.54	106.35	24.14	14.62	60.56
建模集Modeling set	88	7.89	106.35	24.53	15.45	62.98
验证集Validation set	44	5.54	66.36	23.35	12.91	55.29

样本集 Data set	样本数 Sample size	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/%
总集Whole set	132	5.54	106.35	24.14	14.62	60.56
建模集Modeling set	88	7.89	106.35	24.53	15.45	62.98
验证集Validation set	44	5.54	66.36	23.35	12.91	55.29

光谱 Spectra	特征波长 Characteristic wavelengths/nm	相关系数 Correlation coefficient
SR	350~359	-0.523^**
RT	380~389	0.651^**
RTFD	530~539	-0.716^**
RTSD	450~459	0.703^**
AT	350~359	0.589^**
ATFD	540~549	-0.596^**
ATSD	740~749	0.592^**
CR	490~499	-0.606^**
FD	1 150~1 159	0.453^**
SD	860~869	0.486^**
SNV	1 440~1 449	0.477^**
MSC	1 440~1 449	0.480^**
DT	1 430~1 439	0.438^**
	2 150~2 159	-0.438^**
WT	350~359	-0.523^**

光谱 Spectra	特征波长 Characteristic wavelengths/nm	相关系数 Correlation coefficient
SR	350~359	-0.523^**
RT	380~389	0.651^**
RTFD	530~539	-0.716^**
RTSD	450~459	0.703^**
AT	350~359	0.589^**
ATFD	540~549	-0.596^**
ATSD	740~749	0.592^**
CR	490~499	-0.606^**
FD	1 150~1 159	0.453^**
SD	860~869	0.486^**
SNV	1 440~1 449	0.477^**
MSC	1 440~1 449	0.480^**
DT	1 430~1 439	0.438^**
	2 150~2 159	-0.438^**
WT	350~359	-0.523^**

变量选择方法 Variable selection method	变量数量 Number of variables	输入变量 Input variables	在建模集上的效果 Performance on the modeling set		在验证集上的效果 Performance on the validation set
变量选择方法 Variable selection method	变量数量 Number of variables	输入变量 Input variables	R²	RMSE/ (g·kg^-1)	R²	RMSE/ (g·kg^-1)	RPD
CSV	15	SR_350~359, FD_1 150~1 159, SD_860~869, AT_350~359, ATFD_540~549, ATSD_740~749, RT_380~389, RTFD_530~539, RTSD_450~459, CR_490~499, SNV_1 440~1 449, MSC_1 440~1 449, DT_1 430~1 439, DT_2 150~2 159, WT_350~359	0.918	4.463	0.444	9.744	1.325
MLR	6	SR_350~359, ATFD_540~549, ATSD_740~749, RTSD_450~459, MSC_1 440~1 449, DT_1 430~1 439	0.915	4.531	0.526	8.998	1.435
CARS	12	SR_350~359, SD_860~869, AT_350~359, ATFD_540~549, ATSD_740~749, RT_380~389, CR_490~499, SNV_1 440~1 449, MSC_1 440~1 449, DT_1 430~1 439, DT_2 150~2 159, WT_350~359	0.917	4.468	0.443	9.750	1.324
GA	7	SR_350~359, ATFD_540~549, ATSD_740~749, RTSD_450~459, MSC_1 440~1 449, DT_1 430~1 439, WT_350~359	0.919	4.419	0.539	8.871	1.455
SPA	5	FD_1150~1159, ATSD_740~749, RTSD_450~459, SNV_1 440~1 449, DT_2 150~2 159	0.908	4.719	0.369	10.377	1.244
UVE	10	SR_350~359, ATFD_540~549, ATSD_740~749, RT_370~389, RTSD_450~459, CR_490~499, SNV_1 440~1 449, MSC_1 440~1 449, DT_1 430~1 439, DT_2 150~2 159	0.919	4.425	0.440	9.780	1.320

Inversion of soil total iron content using random forest model based on multi-spectral transformation and principle compoment analysis

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变量选择方法 Variable selection method	主成分个数 Number of principal components	累积贡献率 Cumulative contribution rate/%	在建模集上的效果 Performance on the modeling set		在验证集上的效果 Performance on the validation set
变量选择方法 Variable selection method	主成分个数 Number of principal components	累积贡献率 Cumulative contribution rate/%	R²	RMSE/(g·kg^-1)	R²	RMSE/(g·kg^-1)	RPD
CSV-PCA	6	95.83	0.908	4.713	0.888	4.375	2.951
MLR-PCA	4	95.92	0.906	4.778	0.893	4.277	3.019
CARS-PCA	5	96.14	0.936	3.925	0.910	3.922	3.292
GA-PCA	4	96.36	0.912	4.614	0.896	4.219	3.060
SPA-PCA	4	95.77	0.929	4.141	0.888	4.382	2.946
UVE-PCA	5	96.16	0.907	4.742	0.876	4.609	2.801

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