基于可解释机器学习模型的高寒草地土壤盐渍化反演

doi:10.12118/j.issn.1000－6060.2025.411

Abstract

Abstract:

Soil salinization in grasslands exacerbates the degradation of grassland vegetation, which affects the performance of grassland ecological functions, restricting the ecological protection and restoration of degraded grasslands. In this study, we constructed an explainable machine-learning salinity inversion model based on feature screening by ground grid sampling combined with Sentinel-2 spectral index in the salinity development area of alpine grassland at the Yellow River source of China, revealed the influencing factors of the salinity model, and monitored the spatial distribution characteristics of regional salinity. The results showed that: (1) The salinity inversion model based on stepwise regression characteristic screening combined with a random forest model resulted in the best accuracy, with a coefficient of determination of 0.64 and a root mean squared error of 0.76 g·kg^-1. (2) Shapley additive explanations analysis of the optimal model determined that among the nine feature variables, the normalized difference water index contributed most significantly to the model and showed a positive correlation with soil salinization. (3) Monitoring revealed that 47.13% of the study area exhibited mild salinization, whereas 33.19% exhibited moderate salinization. Less prevalent severe salinization and saline soils were primarily distributed along rivers and valleys. This study contributes to the exploration of interpretable salinization-monitoring models and provides a scientific basis and technical support for ecological conservation and high-quality development in the Yellow River Basin.

Key words: salinization, machine learning, SHAP, alpine grassland, the Yellow River source

YANG Mingxin, NING Xiaochun, YANG Liusheng, ZHANG Yafei, SHI Mingming, KANG Yanbin, HUANGQING Dongzhi, WANG Shouxing, ZHOU Huakun. Inversion of soil salinization in alpine grasslands based on explainable machine learning models[J].Arid Land Geography, 2026, 49(3): 549-558.

Figures/Tables 7

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References 40

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光谱指数	计算公式	参考文献
盐分指数（S1）	Blue/Re d	[27]
盐分指数（S2）	(Blue-Re d)/(Blue+Re d)	[27]
盐分指数（S3）	Green×Re d/Blue	[27]
盐分指数（S4）	$\sqrt{Blue\times NIR}$	[15]
盐分指数（S5）	Blue×Re d/Green	[27]
盐分指数（S6）	Re d×NIR/Green	[27]
盐分指数（S7）	$(Swir1-Swir\left.2\right)/(Swri1+Swri\left.2\right)$	[18]
盐分指数（S8）	(Green+Re d)/2	[18]
盐分指数（S9）	(Green+Re d+NIR)/2	[18]
盐分指数（SI）	$\sqrt{Blue+Re d}$	[27]
盐分指数（SI_T）	Re d×NIR×100	[27]
盐分指数（SI1）	$\sqrt{Green\times Re d}$	[18]
盐分指数（SI2）	$\sqrt{Gree{n}^{2}+Re {d}^{2}+NI{R}^{2}}$	[18]
盐分指数（SI3）	$\sqrt{Gree{n}^{2}+Re {d}^{2}}$	[18]
盐分指数（SI4）	Swir1/NIR	[18]
归一化盐分指数（NDSI）	(Re d-NIR)/(Re d+NIR)	[27]
归一化植被指数（NDVI）	(NIR-Re d)/(NIR+Re d)	[19]
增强型植被指数（EVI）	$2.5\times \left[(NIR-Re d)/(NIR+6\times Re d-7.5\times Blue+1)\right]$	[27]
归一化物候指数（NDPI）	$\left[NIR-(0.74\times Re d+0.26\times Swir\left.1\right)\right]/\left[NIR+(0.74\times Re d+0.26\times Swir\left.1\right)\right]$	[28]
红外植被指数（IPVI）	NIR/(NIR+Re d)	[18]
归一化水体指数（NDWI）	(NIR-Swir1)/(NIR+Swir1)	[19]
土壤调节植被指数（SAVI）	$1.5\times (NIR-Re d)/(NIR+Re d+0.5)$	[19]

盐渍化等级	分级标准/g·kg^-1	样本量	最小值/g·kg^-1	最大值/g·kg^-1	平均值/g·kg^-1	标准差/g·kg^-1	变异系数/%
非盐渍化	<1	18	0.57	0.95	0.80	0.12	15.44
轻度盐渍化	1~2	86	1.00	1.99	1.44	0.29	19.88
中度盐渍化	2~4	17	2.00	3.56	2.66	0.50	18.76
重度盐渍化	4~6	4	4.30	5.56	4.90	0.56	11.46
盐土	≥6	4	6.01	11.65	8.07	2.51	31.12
所有实测数据		129	0.57	11.65	3.57	1.45	40.61

Inversion of soil salinization in alpine grasslands based on explainable machine learning models

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