新疆典型盐渍土微波介电特性响应分析与建模 Response analysis and modeling of microwave dielectric properties of typical saline soil in Xinjiang期刊界 All Journals 搜尽天下杂志传播学术成果专业期刊搜索期刊信息化学术搜索

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新疆典型盐渍土微波介电特性响应分析与建模

引用本文：	赵爽,丁建丽,韩礼敬,黄帅,葛翔宇.新疆典型盐渍土微波介电特性响应分析与建模[J].干旱区地理,2022,45(5):1534-1546.

作者姓名：	赵爽丁建丽韩礼敬黄帅葛翔宇

作者单位：	1.新疆大学地理与遥感科学学院,新疆乌鲁木齐 8300462.新疆绿洲生态自治区重点实验室,新疆乌鲁木齐 8300463.智慧城市与环境建模自治区普通高校重点实验室,新疆乌鲁木齐 8300464.聊城大学地理与环境学院,山东聊城 252000

基金项目：	国家自然科学基金项目(41961059);新疆维吾尔自治区自然科学基金重点项目(2021D01D06);山东省自然科学基金(ZR2021QD112)

摘要：	土壤盐渍化对区域经济和生态可持续发展产生负面影响。微波介电常数是微波遥感探测土壤的关键因素,然而介电常数与盐分的关系仍不清晰。为分析盐分类型及含盐量对土壤介电常数的影响,在0.3~20.0 GHz频率下,测量了新疆典型的2种盐渍土类型（硫酸盐-氯化物型： $N a 2 S O 4 - N a C l$ ;氯化物-硫酸盐型： $N a C l - N a 2 S O 4$ ）的介电常数,探讨含水量、含盐量、盐分类型及质地对土壤介电特性的影响。结果表明：（1）含盐量对湿润土壤、干燥粉壤土的复介电常数实部（ $ε'$ ）和虚部（ $ε ″$ ）均产生影响。（2）对于同等级的2种湿润盐渍土在0.3 GHz频率下,整体上 $ε ″ N a 2 S O 4 - N a C l$ > $ε ″ N a C l - N a 2 S O 4$ 。（3）虚部的电模量（ $M ″$ ）与含盐量的关系更紧密,且0.3~5.0 GHz是重要的频率范围。研究结果可为复杂下垫面下土壤盐渍化的微波遥感监测提供科学支持。
关键词：	微波介电常数土壤盐渍化机器学习微波遥感
收稿时间：	2022-01-10
Response analysis and modeling of microwave dielectric properties of typical saline soil in Xinjiang

ZHAO Shuang,DING Jianli,HAN Lijing,HUANG Shuai,GE Xiangyu.Response analysis and modeling of microwave dielectric properties of typical saline soil in Xinjiang[J].Arid Land Geography,2022,45(5):1534-1546.

Authors:	ZHAO Shuang DING Jianli HAN Lijing HUANG Shuai GE Xiangyu

Institution:	1. College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830046, Xinjiang, China2. Xinjiang Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi 830046, Xinjiang, China3. Key Laboratory of Smart City and Environment Modelling of Higher Education Institute, Xinjiang University, Urumqi 830046, Xinjiang, China4. College of Geography and Environment, Liaocheng University, Liaocheng 252000, Shandong, China

Abstract:	Soil salinization negatively affects regional economic and ecologically sustainable development. Due to the unique arid climate conditions in Xinjiang, China, there are many varieties of saline soils that are widely distributed across the region. Currently, researchers have proposed many models to describe the relationship between the dielectric constant of soil samples and the soil moisture content. However, it is still difficult to establish a model that describes the soil salt dielectric properties, which can also clarify the response relationship between dielectric constant and salt contents. In order to explore the influence of soil salinity on the dielectric properties of soil, two typical saline soil types (sulfate-chloride type, $N a 2 S O 4 - N a C l$ ; chloride-sulfate type, $N a C l - N a 2 S O 4$ ) were used in this study. The dielectric constant of 240 soil samples was obtained at 500 discrete frequencies from 0.3 GHz to 20.0 GHz to analyze the effects of water content, salt content, salt type, and soil texture on the soil dielectric constant of soil (including the real part, $ε'$ ; imaginary part, $ε ″$ ; the real part of the modulus, $M'$ ; the imaginary part of the modulus, $M ″$ ). Random forest was used to develop salt estimation methods; this work permits us to evaluate the effectiveness of using the dielectric constant of soil in estimating the soil salt content. The Random forest method has been widely used in the research related to soil property prediction. The algorithms have the capacity to provide the relative importance of the variables used; this allows us to further explore the relationship between soil salt and dielectric constant. The obtained results were as follows: (1) Salt content has an effect on both the real and imaginary part of the complex dielectric constant of wet soil and dry silty loam. (2) There is a more significant difference between $ε ″$ at a frequency of 0.3 GHz for the two moist saline soils of the same class ( $ε ″ N a 2 S O 4 - N a C l$ > $ε ″ N a C l - N a 2 S O 4$ ). (3) The results of machine learning indicate that the imaginary part of the modulus ( $M ″$ ) is more closely related to the salt content and 0.3-5.0 GHz is the most important frequency range for such predictions. This research indicates that the soil salinity estimation method combined with soil dielectric characteristic analysis experiments and machine learning can effectively establish the most important dielectric constants and frequency ranges. In conclusion, the results presented here provide a scientific background for the microwave remote sensing monitoring of soil salinization on the complex underlying surface.

Keywords:	microwave dielectric constant soil salinity machine learning mircrowawe remote sensing

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