基于Landsat-8影像的干旱区土壤水分含量反演研究

杨丽萍; 侯成磊; 赵美玲; 白宇兴; 苏志强

doi:10.19336/j.cnki.trtb.2020033101

基于Landsat-8影像的干旱区土壤水分含量反演研究

doi: 10.19336/j.cnki.trtb.2020033101

1.
长安大学地质工程与测绘学院，陕西西安 710054
2.
长安大学地球科学与资源学院，陕西西安 710054

基金项目: 国家自然科学基金项目（41371220）及陕西省科技厅基金项目（2020ZDLSF06-07）资助

详细信息

作者简介:
杨丽萍（1968−），女，陕西耀县人，博士，副教授，主要从事环境遥感及3S应用研究。E-mail: zylpyang@chd.edu.cn

中图分类号: S152
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- 被引次数: 0
出版历程
- 收稿日期: 2020-03-31
- 修回日期: 2020-11-06
- 刊出日期: 2021-03-05

Study on Soil Moisture Content Inversion in an Arid Area Based on Landsat-8 Imagery

1.
School of Geological Engineering and Geomatics, Chang’an University, Xi’an 710054, China
2.
School of Earth Science and Resources, Chang’an University, Xi’an 710054, China

摘要

摘要: 土壤水分含量的精确监测对区域生态环境保护与可持续发展具有重要意义。本文以内蒙西部额济纳旗东南的居延泽地区为研究区，基于多期Landsat-8遥感影像和野外实测不同深度的土壤水分含量数据，构建了温度植被干旱指数（TVDI）、垂直干旱指数（PDI）、归一化干旱监测指数（NPDI）和土壤湿度监测指数（SMMI）等四种干旱指数模型，探讨了上述模型在居延泽地区土壤水分含量反演中的精度与适用性，选取精度较优的TVDI模型反演了研究区2015年至2017年的土壤水分含量，并使用随机森林分类法将研究区分为沙地、盐碱地、裸地、植被和滩涂五种地类，探讨了不同地类的土壤水分含量差异。结果表明四种干旱指数均与土壤水分含量实测值呈负相关；从拟合精度看，四种干旱指数均与表层土壤水分含量具有最高的拟合精度，且随着土层深度的增加，拟合精度逐渐变劣。其中TVDI综合表现最优，尤其在表层，R²可达到0.76；研究区不同地类的土壤水分含量存在差异，呈现出从沙地、盐碱地、裸地、植被到滩涂依次升高的规律。
- 土壤水分含量 /
- 干旱指数 /
- Landsat-8 /
- 干旱区
Abstract: Accurate estimation of soil moisture content is of great significance for eco-environmental conservation and sustainable development in arid areas. In Juyanze, the southeast of Ejina banner, the western Inner Mongolia, 4 drought index models, including Temperature Vegetation Drought Index (TVDI), Perpendicular Drought Index (PDI), Normalized PDI (NPDI), and Soil Moisture Monitoring Index (SMMI), were established based on 3 Landsat-8 images and 150 in-situ soil moisture samples from different soil depths. Model accuracy and applicability were compared and verified, and the optimal model of TVDI was used to retrieve soil moisture content from 2015 to 2017. Five land use types, namely sandy land, saline-alkali land, bare land, vegetation and tidal flats were classified based on random forest classification and their differences in soil moisture content were analyzed. The results showed that all the 4 drought indices were negatively correlated with the measured soil moisture content. The highest fitting accuracy was observed between the 4 drought indices and the surface soil moisture. Meanwhile, the fitting accuracy decreased with the increase of soil depth. The accuracy of TVDI model was higher than that of the other models, particularly in the surface soil, with a R² of 0.76. Discrepancies of the average soil moisture content were observed in different land use types, and generally the soil moisture content was the lowest in the sandy land, followed by saline-alkali land, bare land, and vegetation, the highest in the tidal flats.
- Soil moisture content /
- Drought index /
- Landsat-8 /
- Arid area

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图 1 研究区地理位置

Figure 1. Location of the study area

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图 2 采样点分布图

Figure 2. Map of sampling sites

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图 3 TVDI、PDI、NPDI和SMMI与不同土层土壤实测水分含量之间的关系

Figure 3. Relationship between TVDI, PDI, NPDI, SMMI and measured soil moisture content at different depths

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图 4 TVDI模型土壤水分含量反演结果

Figure 4. Retrieved soil moisture content using TVDI model

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表 1 土壤体积含水量分类统计

Table 1. Statistics of soil moisture content

地类Land type	2015年8月28日August 28, 2015		2016年7月29日July 29, 2016		2017年9月2日September 2, 2017
地类Land type	像元比例（%）Pixel ratio	平均土壤水分含量（%）Average soil moisture content	像元比例（%）Pixel ratio	平均土壤水分含量（%）Average soil moisture content	像元比例（%）Pixel ratio	平均土壤水分含量（%）Average soil moisture content
裸地	12.73	4.65	12.33	5.07	12.47	5.57
沙地	63.59	2.13	68.59	2.19	65.69	2.88
盐碱地	18.36	4.10	15.63	4.37	12.53	4.15
滩涂	1.21	31.76	0.52	21.11	2.32	47.70
植被	1.42	9.24	0.61	15.75	2.72	18.33
水体	2.69	—	2.32	—	4.26	—

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