基于多光谱影像的阿拉尔垦区棉田土壤盐分反演

doi:10.13866/j.azr.2024.05.16

Abstract

Abstract:

Given the difficulties in the field measurement of soil salinization in Xinjiang and the difficulty in quickly and broadly evaluating the potential hazards of soil salinization, this study considers cotton fields in the Aral Reclamation Area of Xinjiang as the research object, and uses multispectral remote sensing image data from Sentinel-2 SR and Landsat-9 OLI to construct a high-dimensional data set by comprehensively integrating 20 spectral indices and combining spectral indices. The optimal feature subset is screened using the method of exhaustive feature combination and cross-validation, and the inversion accuracy of soil salinization is compared for four machine learning algorithms (i.e., XGBoost, random forest, deep neural network, and K-nearest neighbor) under different feature combinations. Simultaneously, the difference in accuracy between Sentinel-2 SR and Landsat-9 OLI remote sensing images in soil salinization inversion is analyzed. The results show that: (1) The model constructed based on XGBoost algorithm can achieve high-precision prediction of cotton field salinization, with R² higher than 0.74, MSE lower than 0.04, and MAPE lower than 0.13. (2) Under the condition of feature combination 1, Sentinel-2 SR (S3+GBNDVI) and Landsat-9 OLI (SI+NDVI) remote sensing images achieved the highest prediction accuracy using XGBoost algorithm. (3) Sentinel-2 SR image data in cotton field salinization prediction (R²=0.73-0.88) is better than that of Landsat-9 OLI image data. This study realizes the precise monitoring of soil salinization in cotton fields in the Aral Reclamation Area of Xinjiang, which should provide an effective and timely technical reference for soil salinization control and prevention in cotton fields in reclamation areas.

Key words: multispectral remote sensing images, spectral indices, soil salinization, machine learning algorithms, Aral Reclamation Area

HONG Guojun, XIE Junbo, ZHANG Ling, FAN Zhenqi, YU Caili, FU Xianbing, LI Xu. Monitoring soil salinization of cotton fields in the Aral Reclamation Area using multispectral imaging[J].Arid Zone Research, 2024, 41(5): 894-904.

Figures/Tables 11

Fig. 1

Fig. 2

Tab. 1

Expression of the spectral index"

光谱指数	表达式	参考文献
SI	$b 2 × b 4$	[15]
SI₁	$b 3 × b 4$	[16]
SI₂	$[(b 2) 2 + (b 3) 2 + (b 8) 2] 2$	[17]
SI₃	$(b 2) 2 + (b 3) 2$	[18]
S₁	$b 2 / b 4$	[19]
S₂	$(b 2 - b 4) / (b 2 + b 4)$	[20]
S₃	$(b 3 × b 4) / b 2$	[19-20]
S₅	$(b 2 × b 4) / b 3$	[19-20]
CRSI	$[(b 8 × b 4) - (b 4 × b 3)] / [(b 8 × b 4) + (b 4 × b 3)]$	[19-20]
MSAVI	$(2 × b 8 - 1) - [(2 × b 8 + 1) 2 - 8 × (b 8 - b 4)] / 2$	[21-22]
NDVI	$(b 8 - b 4) / (b 8 + b 4)$	[23⇓⇓⇓⇓⇓⇓-30]
RVI	$b 8 / b 4$	[26]
DVI	$b 8 - b 4$	[27]
GNDVI	$(b 8 - b 3) / (b 8 + b 3)$	[27⇓-29]
EVI	$(1.5 × b 8 - 1.5 × b 4) / (b 8 + 6 × b 4 - 7.5 × b 2 + 1)$	[30]
OSAVI	$(b 8 - b 4) / (b 8 + b 4 + 0.16)$	[31]
SAVI	$(b 8 - b 4) × 1.5 / (b 8 + b 4 + 0.5)$	[31-32]
NDWI	$(b 8 - b 11) / (b 8 + b 11)$	[33]
GBNDVI	$(b 8 - b 3 - b 2) / (b 8 + b 3 + b 2)$	[34]
GRNDVI	$(b 8 - b 3 - b 4) / (b 8 + b 3 + b 4)$	[27]

Tab. 1

Tab. 2

Tab. 3

Tab. 4

Fig. 3

Fig. 4

Fig. 5

Fig. 6

Fig. 7

References 36

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遥感影像	特征选择	相关性（R²）	组合
Sentinel-2 SR	S3, GBNDVI	0.93	组合1
	S3, GBNDVI, MSAVI, SI1	0.91	组合2
	S3, EVI, GBNDVI, MSAVI	0.90	组合3
Landsat-9 OLI	SI, NDVI	0.87	组合1
	SI, SI3, NDVI	0.85	组合2
	SI, NDVI, GRNDVI	0.84	组合3

数据源	模型	机器学习模型评价指标	组合1	组合2	组合3
Sentinel-2 SR	KNN	R²	0.80	0.78	0.78
		MSE	0.04	0.04	0.04
		MAPE	0.15	0.16	0.16
	RF	R²	0.90	0.87	0.87
		MSE	0.02	0.02	0.03
		MAPE	0.10	0.10	0.11
	DNN	R²	0.85	0.84	0.82
		MSE	0.03	0.03	0.03
		MAPE	0.12	0.14	0.16
	XGB	R²	0.92	0.90	0.88
		MSE	0.02	0.02	0.02
		MAPE	0.09	0.09	0.010

数据源	模型	机器学习模型评价指标	组合1	组合2	组合3
Landsat-9 OLI	KNN	R²	0.77	0.76	0.72
		MSE	0.04	0.04	0.05
		MAPE	0.15	0.15	0.16
	RF	R²	0.82	0.81	0.78
		MSE	0.03	0.04	0.04
		MAPE	0.11	0.12	0.11
	DNN	R²	0.81	0.80	0.76
		MSE	0.04	0.04	0.05
		MAPE	0.14	0.15	0.16
	XGB	R²	0.85	0.84	0.82
		MSE	0.03	0.03	0.04
		MAPE	0.10	0.11	0.11

Monitoring soil salinization of cotton fields in the Aral Reclamation Area using multispectral imaging

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