Remote sensing of soil salinity: potentials and constraints

被引:734
作者
Metternicht, GI
Zinck, JA
机构
[1] Curtin Univ Technol, Dept Spatial Sci, Perth, WA 6845, Australia
[2] Int Inst Geoinformat Sci & Earth Observat, Dept Earth Syst Anal, NL-7500 AA Enschede, Netherlands
关键词
review; salinity; alkalinity; videography; remote sensing; hyperspectral; microwave; image classification; modelling; monitoring;
D O I
10.1016/S0034-4257(02)00188-8
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Soil salinity caused by natural or human-induced processes is a major environmental hazard. The global extent of primary salt-affected soils is about 955 M ha, while secondary salinization affects some 77 M ha, with 58% of these in irrigated areas. Nearly 20% of all irrigated land is salt-affected, and this proportion tends to increase in spite of considerable efforts dedicated to land reclamation. This requires careful monitoring of the soil salinity status and variation to curb degradation trends, and secure sustainable land use and management. Multitemporal optical and microwave remote sensing can significantly contribute to detecting temporal changes of salt-related surface features. Airborne geophysics and ground-based electromagnetic induction meters, combined with ground data, have shown potential for mapping depth of salinity occurrence. This paper reviews various sensors (e.g. aerial photographs, satellite- and airborne multispectral sensors, microwave sensors, video imagery, airborne geophysics, hyperspectral sensors, and electromagnetic induction meters) and approaches used for remote identification and mapping of salt-affected areas. Constraints on the use of remote sensing data for mapping salt-affected areas are shown related to the spectral behaviour of salt types, spatial distribution of salts on the terrain surface, temporal changes on salinity, interference of vegetation, and spectral confusions with other terrain surfaces. As raw remote sensing data need substantial transformation for proper feature recognition and mapping, techniques such as spectral unmixing, maximum likelihood classification, fuzzy classification, band ratioing, principal components analysis, and correlation equations are discussed. Lastly, the paper presents modelling of temporal and spatial changes of salinity using combined approaches that incorporate different data fusion and data integration techniques. (C) 2003 Elsevier Science Inc. All rights reserved.
引用
收藏
页码:1 / 20
页数:20
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