Integrating Remote Sensing and Proximal Sensors for the Detection of Soil Moisture and Salinity Variability in Coastal Areas

doi:10.1016/S2095-3119(13)60290-7

Journal of Integrative Agriculture ›› 2013, Vol. 12 ›› Issue (4): 723-731.DOI: 10.1016/S2095-3119(13)60290-7

Integrating Remote Sensing and Proximal Sensors for the Detection of Soil Moisture and Salinity Variability in Coastal Areas

GUO Yan, SHI Zhou, ZHOU Lian-qing, JIN Xi, TIAN Yan-feng , TENG Hong-fen

Institute of Agricultural Remote Sensing and Information Technology Application, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, P.R.China

收稿日期:2012-06-16 出版日期:2013-04-01 发布日期:2013-04-07
通讯作者: Correspondence SHI Zhou, Tel/Fax: +86-571-88982831, E-mail: shizhou@zju.edu.cn
基金资助:
This study was funded by the Zhejiang Provincial Natural Science Foundation of China (R5100140), the National Natural Science Foundation of China (40871100) and the Science and Technology Project of Zhejiang Province, China (2011C13010).

Integrating Remote Sensing and Proximal Sensors for the Detection of Soil Moisture and Salinity Variability in Coastal Areas

GUO Yan, SHI Zhou, ZHOU Lian-qing, JIN Xi, TIAN Yan-feng , TENG Hong-fen

Institute of Agricultural Remote Sensing and Information Technology Application, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, P.R.China

Received:2012-06-16 Online:2013-04-01 Published:2013-04-07
Contact: Correspondence SHI Zhou, Tel/Fax: +86-571-88982831, E-mail: shizhou@zju.edu.cn
Supported by:
This study was funded by the Zhejiang Provincial Natural Science Foundation of China (R5100140), the National Natural Science Foundation of China (40871100) and the Science and Technology Project of Zhejiang Province, China (2011C13010).

摘要/Abstract

摘要： Soil moisture and salinity are two crucial coastal saline soil variables, which influence the soil quality and agricultural productivity in the reclaimed coastal region. Accurately characterizing the spatial variability of these soil parameters is critical for the rational development and utilization of tideland resources. In the present study, the spatial variability of soil moisture and salinity in the reclaimed area of Hangzhou gulf, Shangyu City, Zhejiang Province, China, was detected using the data acquired from radar image and the proximal sensor EM38. Soil moisture closely correlates radar scattering coefficient, and a simplified inversion model was built based on a backscattering coefficient extracted from multi-polarization data of ALOS/PALSAR and in situ soil moisture measured by a time domain reflectometer to detect soil moisture variations. The result indicated a higher accuracy of soil moisture inversion by the HH polarization mode than those by the HV mode. Soil salinity is reflected by soil apparent electrical conductivity (ECa). Further, ECa can be rapidly detected by EM38 equipment in situ linked with GPS for characterizing the spatial variability of soil salinity. Based on the strong spatial variability and interactions of soil moisture and salinity, a cokriging interpolation method with auxiliary variable of backscattering coefficient was adopted to map the spatial variability of ECa. When compared with a map of ECa interpolated by the ordinary kriging method, detail was revealed and the accuracy was increased by 15.3%. The results conclude that the integrating active remote sensing and proximal sensors EM38 are effective and acceptable approaches for rapidly and accurately detecting soil moisture and salinity variability in coastal areas, especially in the subtropical coastal zones of China with frequent heavy cloud cover.

关键词: remote sensing , proximal sensor , soil moisture , salinity , backscattering coefficient , soil apparent electrical conductivity (ECa)

Abstract: Soil moisture and salinity are two crucial coastal saline soil variables, which influence the soil quality and agricultural productivity in the reclaimed coastal region. Accurately characterizing the spatial variability of these soil parameters is critical for the rational development and utilization of tideland resources. In the present study, the spatial variability of soil moisture and salinity in the reclaimed area of Hangzhou gulf, Shangyu City, Zhejiang Province, China, was detected using the data acquired from radar image and the proximal sensor EM38. Soil moisture closely correlates radar scattering coefficient, and a simplified inversion model was built based on a backscattering coefficient extracted from multi-polarization data of ALOS/PALSAR and in situ soil moisture measured by a time domain reflectometer to detect soil moisture variations. The result indicated a higher accuracy of soil moisture inversion by the HH polarization mode than those by the HV mode. Soil salinity is reflected by soil apparent electrical conductivity (ECa). Further, ECa can be rapidly detected by EM38 equipment in situ linked with GPS for characterizing the spatial variability of soil salinity. Based on the strong spatial variability and interactions of soil moisture and salinity, a cokriging interpolation method with auxiliary variable of backscattering coefficient was adopted to map the spatial variability of ECa. When compared with a map of ECa interpolated by the ordinary kriging method, detail was revealed and the accuracy was increased by 15.3%. The results conclude that the integrating active remote sensing and proximal sensors EM38 are effective and acceptable approaches for rapidly and accurately detecting soil moisture and salinity variability in coastal areas, especially in the subtropical coastal zones of China with frequent heavy cloud cover.

Key words: remote sensing , proximal sensor , soil moisture , salinity , backscattering coefficient , soil apparent electrical conductivity (ECa)

GUO Yan, SHI Zhou, ZHOU Lian-qing, JIN Xi, TIAN Yan-feng , TENG Hong-fen. Integrating Remote Sensing and Proximal Sensors for the Detection of Soil Moisture and Salinity Variability in Coastal Areas[J]. Journal of Integrative Agriculture, 2013, 12(4): 723-731.

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Integrating Remote Sensing and Proximal Sensors for the Detection of Soil Moisture and Salinity Variability in Coastal Areas

Integrating Remote Sensing and Proximal Sensors for the Detection of Soil Moisture and Salinity Variability in Coastal Areas

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