Characterization of field scale soil variability using remotely and proximally sensed data and response surface method
文献类型： 外文期刊
作者： Guo, Yan 1 ; Shi, Zhou 2 ; Huang, Jingyi 3 ; Zhou, Lianqing 2 ; Zhou, Yin 2 ; Wang, Laigang 1 ;
作者机构： 1.Henan Acad Agr Sci, Inst Agr Econ & Informat, Zhengzhou, Peoples R China
2.Zhejiang Univ, Coll Environm & Resource Sci, Inst Agr Remote Sensing & Informat Technol Applic, Hangzhou 310003, Zhejiang, Peoples R China
3.Univ New S Wales, Sch Biol Earth & Environm Sci, Kensington, NSW 2052, Australia
4.Zhejiang Univ, Cyrus Tang Ctr Sensor Mat & Applicat, Hangzhou 310003, Zhejiang, Peoples R China
关键词： Saline soils;EM38;Backscattering coefficient;Electrical conductivity;Response surface methodology (RSM)
期刊名称：STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT （ 影响因子：3.379； 五年影响因子：3.348 ）
ISSN： 1436-3240
年卷期： 2016 年 30 卷 3 期
页码：
收录情况： SCI
摘要： Soil salinization of the reclaimed tidelands is problematic. Therefore, there is a need to characterize the spatial variability of soil salinity associated with soil moisture and other soil properties across the reclaimed tidelands. One approach is the use of easily-acquired ancillary data as surrogates for the arduous conventional soil sampling. In a reclaimed coastal tideland in the south of Hangzhou Gulf, backscattering coefficient (sigma(0)) from remotely sensed ALOS/PALSAR radar imagery (HH polarization mode) and apparent soil electrical conductivity (ECa) from a proximally sensed EM38 were used to indicate the spatial distribution of soil moisture and salinity, respectively. After that, response surface methodology (RSM) was employed to determine an optimal set of 12 soil samples using spatially referenced sigma(0) and ECa data. Spatial distributions of three soil chemical properties [i.e. soil organic matter (SOM), available nitrogen (AN), and available potassium (AK)] were predicted using inverse distance weighted method based on the 12 samples and were then compared with the predictions generated using 42 samples obtained from a conventional grid sampling scheme. It was concluded that combination of radar imagery and EM induction data can delineate the spatial variability of two key soil properties (i.e. moisture and salinity) across the study area. Besides, RSM-based sampling using radar imagery and EM induction data was highly effective in characterizing the spatial variability of SOM, AN and AK, compared with the conventional grid sampling. This new approach may be used to assist site specific management in precision agriculture.
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