土壤盐渍化遥感监测模型构建方法现状与发展趋势

doi:10.6046/zrzyyg.2021395

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土壤盐渍化作为土壤退化的主要形式之一,会对农业生产和生态环境产生极大的危害。遥感手段能快速、宏观、及时地获取土壤光谱特征,通过构建遥感监测模型,可以实现大范围的土壤盐渍化监测和评估,开展土壤盐渍化遥感监测模型方法归纳讨论,提高土壤盐渍化遥感监测精度,在盐渍土监测和治理中具有重要意义。通过梳理近期国内外土壤盐渍化遥感研究相关文献,对土壤盐渍化遥感监测模型构建过程中因子的选取、模型的建立以及精度验证等步骤进行总结,并针对当前研究热点对研究中的局限性与发展趋势进行讨论。主要得出: ①土壤盐渍化遥感模型作为盐渍土监测和预测的重要手段,近年来该领域的研究热点在于通过新型数据源和模型的使用来提高土壤盐渍化遥感监测模型的精度; ②不同研究在遥感数据源的使用上有所差异,但建模因子均是通过光谱敏感波段、先验光谱指数及遥感衍生数据优选获得; ③用于土壤盐渍化遥感监测的模型主要包括线性回归模型以及机器学习模型,针对不同区域建立的遥感模型在模型精度和适用性上有所差异。

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	李星佑
	张飞
	王筝

关键词 ：土壤盐渍化, 遥感监测, 建模因子, 模型构建, 精度验证

Abstract：

As a major form of soil degradation, soil salinization can greatly harm agricultural production and ecological environment. Remote sensing methods can acquire soil spectral characteristics in a rapid, macroscopic, and timely manner. Based on this, remote sensing monitoring models can be built for a wide range of soil salinization monitoring and assessment. Thus, summarizing and discussing the building methods for remote sensing monitoring models of soil salinization is of great significance to improve the precision of remote sensing monitoring of soil salinization and to monitor and control salinized soil. This study reviewed the recent literature related to remote sensing studies concerning soil salinization at home and abroad. Then, it summarized the steps such as factor selection, model building, and precision verification in the building of remote sensing monitoring models of soil salinization. Focusing on the current hot research topic, this study discussed the limitations and development trends. The main conclusions are as follows. The remote sensing monitoring models of soil salinization are important means for monitoring and forecasting salinized soil. In recent years, the hot research topic in this field is to improve the model precision using new data sources and models. Differences exist in the use of remote sensing data sources among different studies, but the modeling factors are all optimized from spectral sensitive bands, prior spectral indices, and remote sensing-derived data. The remote sensing monitoring models of soil salinization mainly include the linear regression model and the machine learning model. The remote sensing models built for different regions have different precision and applicability.

Key words： soil salinization remote sensing monitoring modeling factor model building precision verification

收稿日期: 2021-11-22 出版日期: 2022-12-27

ZTFLH:

TP79

基金资助:国家自然科学基金项目“水盐胁迫下的艾比湖湿地国家级自然保护区植被高光谱诊断模型研究”(U1503302);新疆维吾尔自治区第三期天山英才计划共同资助。

通讯作者: 张飞(1980-),男,教授,主要从事干旱区资源环境遥感应用研究。Email: zhangfei3s@163.com。

作者简介: 李星佑(1996-),男,硕士,主要从事干旱区生态环境遥感应用研究。Email: lixingyou@stu.xju.edu.cn。

引用本文:

李星佑, 张飞, 王筝. 土壤盐渍化遥感监测模型构建方法现状与发展趋势[J]. 自然资源遥感, 2022, 34(4): 11-21.
LI Xingyou, ZHANG Fei, WANG Zheng. Present situation and development trend in building remote sensing monitoring models of soil salinization. Remote Sensing for Natural Resources, 2022, 34(4): 11-21.

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https://www.gtzyyg.com/CN/10.6046/zrzyyg.2021395 或 https://www.gtzyyg.com/CN/Y2022/V34/I4/11

Fig.1 CNKI和Web of Science数据库中土壤盐渍化遥感相关研究文献数量

指数类型		指数		指数公式^①	参考文献
盐分指数		盐分指数(salinity index,SI-T)		$S I - T = (R / N I R) × 100$	[44]
		盐分指数(normalized difference salinity index,NDSI)		$N D S I = (R - N I R) / (R + N I R)$	[45]
		盐分指数(salinity index1,SI1)		$S I 1 = R G$	[45]
		盐分指数(salinity index1,SI2)		$S I 2 = G 2 + R 2 + N I R 2$	[45]
		盐分指数(salinity index1,SI3)		$S I 3 = G 2 + R 2$	[45]
		盐分指数(salinity index,S1)		$S 1 = B / R$	[46]
		盐分指数(salinity index,S2)		$S 2 = (B - R) / (B + R)$	[46]
		盐分指数(salinity index,S3)		$S 3 = (G ? R) / B$	[46]
		盐分指数(salinity index,S5)		$S 5 = (B ? R) / G$	[46]
		盐分指数(salinity index,S6)		$S 6 = (R ? N I R) / G$	[46]
		盐分比指数(salinity ratio index,SAIO)		$S A I O = (R - N I R) / (G + N I R)$	[47]
		黏土指数(clay index,CLEX)		$C L E X = S W I R 1 / S W I R 2$	[47]
		石膏指数(gypsum index,GYEX)		$G Y E X = (S W I R 1 - N I R) / (S W I R 1 + N I R)$	[47]
		亮度指数(brightness index,BRI)		$B R I = G 2 + R 2$	[47]
		碳酸盐岩指数(carbonate index,CAEX)		$C A E X = R / G$	[47]
植被指数	简单比值指数(simple ratio vegetation index,SR)		$S R = N I R / R$			[48]
	冠层响应盐指数(canopy response salinity index,CRSI)		$C R S I = (N I R ? R) - (G ? R) (N I R ? R) + (G ? R)$			[49]
	归一化植被指数(normalized difference infrared index,NDVI)		$N D V I = (N I R - R) / (N I R + R)$			[45]
	增强植被指数(enhanced vegetation index,EVI)		$E V I = 2.5 (N I R - R N I R + 6 R - 7.5 B + 1)$			[50]
	差值植被指数(difference vegetation index,DVI)		$D V I = N I R - R$			[51]
	修改土壤调节植被指数(modified soil adjusted vegetation index,MSAVI)		$M S A V I = (2 N I R - 1) - (2 N I R + 1) 2 - 8 (N I R - R) 2$			[52]
	大气阻抗植被指数(atmospherically resistant vegetation index,ARVI)		$A R V I = N I R - (2 R - B) N I R + (2 R + B)$			[52]
	广义植被归一化指数(generalized difference vegetation index,GDVI)		$G D V I = (N I R 2 - R 2) / (N I R 2 + R 2)$			[53]
	双波段增强植被指数(two-band enhanced vegetation index,EVI2)		$E V I 2 = 2.5 (N I R - R) / (N I R + 2.4 R + 1)$			[54]
	扩展植被归一化指数(extended NDVI,ENDVI)		$E N D V I = N I R + S W I R 2 - R N I R + S W I R 2 + R$			[55]
	扩展植被增强指数(extented enhanced vegetation index, EEVI)		$E E V I = 2.5 (N I R + S W I R 1 - R) N I R + 2.5 (S W I R 1 + 6 N I R + R - 7.5 S W I R 1 - R) B + 1$			[55]

Tab.1 建模指数公式

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