The Applicability of Remote Sensing Models of Soil Salinization Based on Feature Space

Jing Liu, Li Zhang, Tong Dong, Juanle Wang, Yanmin Fan, Hongqi Wu, Qinglong Geng, Qiangjun Yang and Zhibin Zhang
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Jing Liu: College of Resources and Environment, Xinjiang Agricultural University, Urumqi 830000, China
Li Zhang: College of Resources and Environment, Xinjiang Agricultural University, Urumqi 830000, China
Tong Dong: College of Resources and Environment, Xinjiang Agricultural University, Urumqi 830000, China
Juanle Wang: State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
Yanmin Fan: College of Resources and Environment, Xinjiang Agricultural University, Urumqi 830000, China
Hongqi Wu: College of Resources and Environment, Xinjiang Agricultural University, Urumqi 830000, China
Qinglong Geng: Institute of Soil and Fertilizer and Agricultural Water Conservation, Xinjiang Academy of Agricultural Sciences, Urumqi 830000, China
Qiangjun Yang: China Geo-Engineering Corporation, Beijing 100101, China
Zhibin Zhang: College of Resources and Environment, Xinjiang Agricultural University, Urumqi 830000, China

Sustainability, 2021, vol. 13, issue 24, 1-16

Abstract: Soil salinization is a major challenge for the sustainable use of land resources. An optimal remote sensing inversion model could monitor regional soil salinity across diverse geographical areas. In this study, the feature space method was used to study the applicability of the inversion model for typical salt-affected soils in China (Yanqi Basin (arid area) and Kenli County (coastal area)), and to obtain soil salinity grade distribution maps. The salinity index (SI) surface albedo (Albedo)model was the most accurate in both arid and coastal regions with overall accuracy reaching 93.3% and 88.8%, respectively. The sensitivity factors for the inversion of salinity in both regions were the same, indicating that the SI-Albedo model is applicable for monitoring salinity in arid and coastal areas of China. We combined Landsat 8 Operational Land Imager image data and field data to obtain the distribution pattern of soil salinity using the SI-Albedo model and proposed corresponding countermeasures for soil salinity in the Yanqi Basin and Kenli County according to the degree of salinity. This study on soil salinity in arid and coastal areas of China will provide a useful reference for future research on soil salinity both in China and globally.

Keywords: salinization; feature space; remote sensing inversion; coastal areas; arid areas; applicability (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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