1. School of Earth Sciences and Resources, China University of Geosciences(Beijing), Beijing 100083, China 2. Institute of Geomechanics, Chinese Academy of Geological Sciences, Beijing 100081, China 3. Beijing Institute of Exploration Engineering, China Geological Survey, Beijing 100083, China
The soil salinization in Hetao irrigation district of Inner Mongolia has exerted severe impact on the sustainable development of local agriculture and economy. Remote sensing can be applied to achieve the real-time information of soil salinization so as to monitor salinization’s future changes. The authors used the satellite images of Landsat to extract salt index (SI) and modified soil mediation vegetation index (MSAVI) and then combined them to construct modified salinization detection index (MSDI) model so as to quantitatively analyze and monitor the soil salinization in this research. After that, the soil salinization information in the study areas obtained in 2001, 2010 and 2017 was further classified and statistically analyzed, which showed an obvious diversity of MSDI mean among various alkali soil types. The result of MSDI was validated by the precision test, field investigation and the salinity of soil samples. The validation demonstrated a strong correlation of 0.856 8 between MSDI and soil salinity, a precision test accuracy of 87.5%, and a Kappa index of 0.726. The soil salinization of this area had been mitigated according to portion changes of non-alkali soil area (from 18.5% to 30.47%) and the fragmented tendency of moderately saline land since 2001. The result indicates that MSDI based on the SI-MSAVI feature space could be applied to quantitatively extract the information of soil salinization and proves to be efficient in monitoring the development of salinization in this region.
卢晶,张绪教,叶培盛,吴杭,王涛. 基于SI-MSAVI特征空间的河套灌区盐碱化遥感监测研究[J]. 国土资源遥感, 2020, 32(1): 169-175.
Jing LU,Xujiao ZHANG,Peisheng YE,Hang WU,Tao WANG. Remote sensing monitoring of salinization in Hetao irrigation district based on SI-MSAVI feature space. Remote Sensing for Land & Resources, 2020, 32(1): 169-175.
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