基于遥感的土壤盐渍化风险评估及其演变规律

doi:10.13866/j.azr.2025.03.04

干旱区研究 ›› 2025, Vol. 42 ›› Issue (3): 431-444.doi: 10.13866/j.azr.2025.03.04 cstr: 32277.14.AZR.20250304

基于遥感的土壤盐渍化风险评估及其演变规律

强欣欢¹(), 高文文²(), 王博³^,⁴^,⁵, 谭剑波⁶, 赵旦⁷^,⁸, 闫世勇⁹, 隋立春¹

1.长安大学地质工程与测绘学院，陕西西安 710054
2.太原理工大学水利科学与工程学院，山西太原 030024
3.中国地质调查局西安矿产资源调查中心，陕西西安 710101
4.秦岭—黄土高原过渡带水土要素耦合与生物资源保育野外观测研究站，陕西渭南 714000
5.自然资源部自然资源要素耦合过程与效应重点实验室，北京 100055
6.长沙理工大学交通运输工程学院，湖南长沙 410001
7.中国科学院空天信息创新研究院遥感科学国家重点实验室，北京 100094
8.中国科学院大学资源与环境学院，北京 100049
9.中国矿业大学环境与测绘学院，江苏徐州 221116

收稿日期:2024-09-12 修回日期:2024-11-19 出版日期:2025-03-15 发布日期:2025-03-17
通讯作者: 高文文. E-mail: gaowenwen@tyut.edu.cn
作者简介:强欣欢（1999-），女，硕士研究生，主要从事生态遥感. E-mail: 2021126044@chd.edu.cn
基金资助:
山西省基础研究计划青年项目(202203021212273);中国地质调查局地质调查项目(DD20220882);中国地质调查局地质调查项目(KC20230013)

Remote sensing-based risk assessment of soil salinization and its change over time

QIANG Xinhuan¹(), GAO Wenwen²(), WANG Bo³^,⁴^,⁵, TAN Jianbo⁶, ZHAO Dan⁷^,⁸, YAN Shiyong⁹, SUI Lichun¹

1. School of Geological Engineering and Geomatics, Chang’an University, Xi’an 710054, Shaanxi, China
2. School of Hydro Science and Engineering, Taiyuan University of Technology, Taiyuan 030024, Shanxi, China
3. Xi’an Center of Mineral Resources Survey, China Geological Survey, Xi’an 710101, Shaanxi, China
4. Qinling-Loess Plateau Transition Zone Observation and Research Station for Coupling of Soil and Water Elements and Conservation of Biological Resources, Weinan 714000, Shaanxi, China
5. Key Laboratory of Coupling Process and Effect of Natural Resources Elements, Beijing 100055, China
6. School of Traffic and Transportation Engineering, Changsha University of Science & Technology, Changsha 410001, Hunan, China
7. State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
8. College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
9. School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, Jiangsu, China

Received:2024-09-12 Revised:2024-11-19 Published:2025-03-15 Online:2025-03-17

摘要/Abstract

摘要：

土壤盐渍化风险评估监测对于精准治理盐渍土、保障农业与生态可持续发展具有重要意义。本文以大荔县为研究对象，依据土壤盐分驱动机理和过程，结合土壤盐分表征状态，利用CRITIC赋权法构建了综合盐分指数、综合土壤指数、综合植被指数和综合地理指数，并采用AHP-熵权法组合权重法构建土壤盐渍化风险评估模型。监测发现：大荔县土壤盐渍化风险主要以轻度为主，其中2021年盐渍化风险等级较高，中度及以上盐渍化风险总面积达到近4 a最大值，占比约为50%。2020—2023年土壤盐渍化风险等级变化特征以风险升级型为主，且东部的黄河流域周边土壤盐渍化风险等级稳定性较差。耕地是治理分区内预警区和修复区的主要土地覆被类型，主要分布在大荔县东部。通过相关性验证发现土壤盐渍化风险评估结果与同期实测土壤电导率样点之间呈显著强相关关系，土壤盐渍化风险评估模型能够有效地表征大荔县土壤盐渍化时空演变特征。此外，土壤盐渍化风险加重主要受集中强降雨、气温升高、地下水位升高、地表蒸散量增加、农业生产等多因素的影响。因此，大荔县土壤盐渍化风险评估能够为精准、高效地治理盐渍土提供科学的理论依据及数据支撑，有效地促进农业生产结构调整及生态可持续发展。

关键词: 土壤盐渍化, 遥感, 大荔县, 信息演变图谱, 组合权重法

Abstract:

Assessment of the risk of soil salinization and associated monitoring are particularly significant for the precise management of saline soil and to ensure sustainable agricultural development. Taking Dali County as a focus, this study used the CRITIC weighting method to construct a comprehensive salt index, comprehensive soil index, comprehensive vegetation index, and comprehensive geographical index based on the soil salt driving mechanism and process, combined with the characterized soil salt state. It also used the analytic hierarchy process-entropy combined weight method to construct a model for assessing the risk of soil salinization. Monitoring revealed that the risk of soil salinization in Dali County is mainly mild, although the level of salinization risk in 2021 was relatively high. Meanwhile, the total area with salinization risk that is moderate or above peaked in the last four years, accounting for approximately 50% of the total. From 2020 to 2023, the changes in the level of soil salinization risk mainly involved risk escalation, and the stability of the soil salinization risk level around the Yellow River Basin in the east was relatively poor. Cultivated land is the main land cover type in the warning zone and restoration zone within the saline soil management zoning and both warning zone and restoration zone are mainly distributed in the eastern part of Dali County. Upon efforts to confirm the findings by determining the correlations between the soil salinization risk assessment results and the soil conductivity samples measured in the same period, there was a significant strong correlation between them. The model for assessing the risk of soil salinization can effectively characterize the spatiotemporal evolution of soil salinization in Dali County. In addition, the increased risk of soil salinization was shown to be mainly associated with multiple factors such as concentrated heavy rainfall, rising temperatures, rising groundwater levels, increased surface evapotranspiration, and agricultural production. Therefore, assessment of the risk of soil salinization in Dali County can provide a theoretical and scientific basis and data support for the precise and efficient management of saline soils and effectively promote the adjustment of agricultural production structure and achieve ecologically sustainable agricultural development.

Key words: soil salinization, remote sensing, Dali County, information evolution map, combined weight method

强欣欢, 高文文, 王博, 谭剑波, 赵旦, 闫世勇, 隋立春. 基于遥感的土壤盐渍化风险评估及其演变规律[J]. 干旱区研究, 2025, 42(3): 431-444.

QIANG Xinhuan, GAO Wenwen, WANG Bo, TAN Jianbo, ZHAO Dan, YAN Shiyong, SUI Lichun. Remote sensing-based risk assessment of soil salinization and its change over time[J]. Arid Zone Research, 2025, 42(3): 431-444.

图/表 15

图1

图2

表1

土壤盐渍化风险评估模型指标"

指标类型	指标名称	指标计算公式及方法	指标名称	指标计算公式及方法
土壤质地	黏土指数	$\text { CLEX }=\frac{S W I R 1}{S W I R 2}$	石膏指数	$\text { GYEX }=\frac{S W I R 1-N I R}{S W I R 2+N I R}$
土壤质地	碳化指数	$\mathrm{CAEX}=\frac{G}{B}$	亮度指数	$\mathrm{BI}=\sqrt{G^{2}+B^{2}}$
植被生长状况	比值植被指数	$\mathrm{RVI}=\frac{N I R}{R}$	差值植被指数	$\mathrm{DVI}=N I R-R$
	非线性植被指数	$\mathrm{NLI}=\frac{N I R^{2}-R^{2}}{N I R^{2}+R}$	广义差分植被指数	$\mathrm{GDVI}=\frac{N I R^{2}-R^{2}}{N I R^{2}+R^{2}}$
	增强型归一化植被指数	$\mathrm{ENDVI}=\frac{N I R+S W I R 1-R}{N I R+S W I R 2+R}$	绿色归一化差分植被指数	$\mathrm{GNDVI}=\frac{(R E 3-R)}{(R E 3+R)}$
	归一化植被指数	$\mathrm{NDVI}=\frac{(N I R-R)}{(N I R+R)}$	修改型土壤调节植被指数	$\mathrm{MSAVI} =\frac{(N I R \times 2+1)-\sqrt{(N I R \times 2+1)^{2}-(N I R-R) \times 8}}{2}$
	红外百分比植被指数	$\mathrm{IPVI}=\frac{N I R}{N I R+R}$	全球植被水分指数	$\mathrm{GVMI} =\frac{(\text { NIR }+0.1)-(S W I R 1+0.02)}{(\text { NIR }+0.1)+(S W I R 1+0.02)}$
土壤含盐量	盐分指数1	$\mathrm{SI1} =\sqrt{G \times} R$	盐分指数10	$\mathrm{SI10}=\frac{N I R \times R}{G}$
	盐分指数2	$ \mathrm{SI} 2=\sqrt{R+G}$	盐分指数11	$\mathrm{SI11} =\frac{\text { SWIR } 1-\text { SWIR2 } 2}{\text { SWIR } 1+\text { SWIR2 }}$
	盐分指数3	$\mathrm{SI} 3=\sqrt{G^{2}+R^{2}+N I R^{2}}$	盐分指数12	$\mathrm{SI12}=\frac{G \times R}{2}$
	盐分指数4	$\mathrm{SI} 4=\sqrt{G^{2}+R^{2}}$	盐分指数13	$\mathrm{SI13}=\frac{G+R+N I R}{2}$
	盐分指数5	$\mathrm{SI5}=\frac{S W I R 1}{N I R}$	盐度指数	$\mathrm{SI}-\mathrm{T}=\frac{R}{N I R} \times 100$
	盐分指数6	$\mathrm{SI} 6=\frac{B}{R}$	土壤盐碱度指数1	$\mathrm{SSSI}-1=R-N I R$
	盐分指数7	$\mathrm{SI} 7=\frac{B-R}{B+R}$	土壤盐碱度指数2	$\mathrm{SSSI}-2=\frac{R \times N I R-N I R \times N I R}{R}$
	盐分指数8	$\mathrm{SI} 8=\frac{G \times R}{B}$	归一化盐分指数	$\mathrm{NDSI}=\frac{N I R-S W I R 1}{N I R+S W I R 1}$
	盐分指数9	$\mathrm{SI9}=\frac{B \times R}{G}$	盐分比指数	$\mathrm{SAIO}=\frac{G-N I R}{B+N I R}$
	增强型土壤盐分指数	$\mathrm{ERSSI}=\frac{G^{2}}{R \times S W I R 1}$
地理环境	Slope	$S l o p e = a r c t a n e l e v a t i o n d i s t a n c e$ ，elevation为高程差，distance为水平距离，参数从DEM数据获取
	年降水量	国家级气象站点逐日降水量数据，采用克里金插值方法获取1 km空间分辨率的年累积降水数据
	HAILS	$\text { HAILS }=\frac{S_{C L E}}{S}$， $S C L E$ 为建设用地当量面积；S为区域总面积，参数从ChinaCover土地覆被数据获取

表1

表2

表3

土壤盐渍化风险演变图谱的演变模式及变化特征分区"

变化特征分区	编码	演变模式
长期稳定型	A	持续极度盐渍化风险
	B	持续重度盐渍化风险
	C	持续中度盐渍化风险
	D	持续轻度盐渍化风险
	E	持续无盐渍化风险
波动稳定型	F	重度盐渍化风险与极度盐渍化风险相互转换并维持在一个稳定状态
	G	中度盐渍化风险与重度或极度盐渍化风险相互转换并维持在一个稳定状态
	H	轻度盐渍化风险与中度、重度或极度盐渍化风险相互转换并维持在一个稳定状态
	I	无盐渍化风险与轻度、中度或重度风险盐渍化相互转换并维持在一个稳定状态
风险降级型	J	重度盐渍化风险（波动）增加，极度盐渍化风险（波动）减少
	K	中度盐渍化风险（波动）增加，重度或极度盐渍化风险（波动）减少
	L	轻度盐渍化风险（波动）增加，中度或重度盐渍化风险（波动）减少，极度盐渍化稳定
	M	轻度盐渍化风险稳定，中度盐渍化风险（波动）增加，重度盐渍化风险（波动）减少
	N	无盐渍化风险（波动）增加，轻度、中度、重度或极度盐渍化风险（波动）减少
	O	无盐渍化风险稳定，轻度盐渍化风险（波动）增加，中度或重度盐渍化风险（波动）减少
风险升级型	P	重度盐渍化风险（波动）减少，极度盐渍化风险（波动）增加
	Q	中度盐渍化风险（波动）减少，重度或极度盐渍化风险（波动）增加
	R	轻度盐渍化风险（波动）减少，中度、重度或极度盐渍化风险（波动）增加
	S	轻度盐渍化风险稳定，中度盐渍化风险（波动）减少，重度盐渍化风险（波动）增加
	T	无盐渍化风险（波动）减少，轻度、中度或重度盐渍化风险（波动）增加
	U	无盐渍化风险稳定，中度盐渍化风险（波动）减少，重度盐渍化风险（波动）增加
	V	无盐渍化风险稳定，轻度盐渍化风险（波动）减少，中度或重度盐渍化风险（波动）增加

表3

表4

图3

图4

表5

表6

图5

图6

图7

图8

图9

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转换类型	编码	含义
稳定型	a	多年均为i风险等级
增加型	b	从其他风险等级转换而来
减少型	c	转换为其他风险等级
波动稳定型	d	最后一年与初始年份风险等级相同，研究期内呈波动变化
波动增加型	e	最后一年与初始年份相比出现i风险等级，研究期内呈波动变化
波动减少型	f	最后一年与初始年份相比i风险等级消失，研究期内呈波动变化
其他型	o	多年均不为i风险等级

土壤盐渍化风险等级	2020年		2021年		2022年		2023年
土壤盐渍化风险等级	面积/km²	占比/%	面积/km²	占比/%	面积/km²	占比/%	面积/km²	占比/%
无盐渍化风险	365.825	22.876	141.668	8.859	184.744	11.552	207.160	12.954
轻度盐渍化风险	1083.651	67.763	720.603	45.062	1153.416	72.125	939.002	58.717
中度盐渍化风险	148.805	9.305	630.525	39.429	252.777	15.807	435.721	27.246
重度盐渍化风险	0.906	0.057	97.113	6.073	8.246	0.516	17.275	1.080
极度盐渍化风险	0.002	0.000	9.236	0.578	0.006	0.000	0.033	0.002
中度及以上盐渍化风险	149.713	9.362	736.874	46.080	261.029	16.323	453.029	28.328

转换类型	风险等级
	无盐渍化风险		轻度盐渍化风险		中度盐渍化风险		重度盐渍化风险		极度盐渍化风险
	面积/km²	占比/%	面积/km²	占比/%	面积/km²	占比/%	面积/km²	占比/%	面积/km²	占比/%
稳定型	37.533	7.162	369.879	25.429	23.214	2.749	0.049	0.046	0.000	0.000
增加型	84.456	16.115	215.426	14.810	174.804	20.697	10.711	9.882	0.030	0.328
减少型	216.618	41.333	208.267	14.318	43.763	5.182	0.789	0.728	0.002	0.017
波动稳定型	127.370	24.303	430.167	29.573	406.902	48.178	90.414	83.417	9.240	99.630
波动增加型	15.803	3.015	39.516	2.717	175.894	20.826	6.420	5.923	0.002	0.025
波动减少型	42.302	8.072	191.314	13.153	20.003	2.368	0.004	0.004	0.000	0.000

基于遥感的土壤盐渍化风险评估及其演变规律

Remote sensing-based risk assessment of soil salinization and its change over time

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治理分区		变化特征
治理分区		长期稳定型	波动稳定型	风险降级型	风险升级型
稳定性	演变0次	优化区	保护区	保护区	预警区
	演变1次	保护区	保护区	预警区	预警区
	演变2次	保护区	预警区	预警区	修复区
	演变3次	预警区	预警区	修复区	修复区

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