Using Imaging Spectroscopy to study soil properties

被引:370
作者
Ben-Dor, E. [1 ]
Chabrillat, S. [2 ]
Dematte, J. A. M. [3 ]
Taylor, G. R. [4 ]
Hill, J. [5 ]
Whiting, M. L. [6 ]
Sommer, S. [7 ]
机构
[1] Tel Aviv Univ, Dept Geog & Human Environm, Tel Aviv, Israel
[2] Geoforschungszentrum Potsdam, Sect Remote Sensing 1 4, D-14473 Potsdam, Germany
[3] Univ Sao Paulo, ESALQ, Dept Soil Sci, Piracicaba, Brazil
[4] Univ New S Wales, Sch Biol Earth & Environm Sci, Sydney, NSW, Australia
[5] Univ Trier, Fac Geog Geosci, Remote Sensing Dept, D-54286 Trier, Germany
[6] Univ Calif Davis, Dept Land Air Water Resources, Davis, CA 95616 USA
[7] Commiss European Communities, DG Joint Res Ctr, Inst Environm & Sustainabil, Ispra, Italy
关键词
Soil reflectance; Image spectroscopy; Soil properties; Soil applications; NEAR-INFRARED ANALYSIS; SPECTRAL REFLECTANCE PROPERTIES; SANTA-MONICA MOUNTAINS; SIMULTANEOUSLY EVALUATE; QUANTITATIVE-ANALYSIS; WEATHERING SEQUENCE; ORGANIC-MATTER; FIELD; MOISTURE; WATER;
D O I
10.1016/j.rse.2008.09.019
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Imaging Spectroscopy (IS) is a promising tool for studying soil properties in large spatial domains. Going from point to image spectrometry is not only a journey from micro to macro scales, but also a long stage where problems such as dealing with data having a low signal-to-noise level, contamination of the atmosphere, large data sets, the BRDF effect and more are often encountered. In this paper we provide an up-to-date overview of some of the case studies that have used IS technology for soil science applications. Besides a brief discussion on the advantages and disadvantages of IS for studying soils, the following cases are comprehensively discussed: soil degradation (salinity, erosion, and deposition), soil mapping and classification, soil genesis and formation, soil contamination, soil water content, and soil swelling. We review these case studies and suggest that the 15 data be provided to the end-users as real reflectance and not as raw data and with better signal-to-noise ratios than presently exist. This is because converting the raw data into reflectance is a complicated stage that requires experience, knowledge, and specific infrastructures not available to many users, whereas quantitative spectral models require good quality data. These limitations serve as a barrier that impedes potential end-users, inhibiting researchers from trying this technique for their needs. The paper ends with a general call to the soil science audience to extend the utilization of the IS technique, and it provides some ideas on how to propel this technology forward to enable its widespread adoption in order to achieve a breakthrough in the field of soil science and remote sensing. (C) 2009 Elsevier Inc. All rights reserved.
引用
收藏
页码:S38 / S55
页数:18
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