| 张起鹏,田富恒,卓玛兰草,赵頔琛.基于高分遥感影像的亚高寒草甸土壤水分含量反演[J].干旱地区农业研究,2024,(3):225~235 |
| 基于高分遥感影像的亚高寒草甸土壤水分含量反演 |
| Soil moisture content inversion of subalpine meadow based on high\|resolution remote sensing image |
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| DOI：10.7606/j.issn.1000-7601.2024.03.24 |
| 中文关键词: 土壤水分含量 植被供水指数 土壤水分空间变异 青藏高原 |
| 英文关键词:soil moisture content vegetation supply water index spatial variability of soil moisture Qing\|Zang Plateau |
| 基金项目:国家自然科学基金项目（32060279） |
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| 中文摘要: |
| 通过高分卫星遥感影像计算植被供水指数来反演亚高寒草甸土壤水分含量，结合高分辨率遥感影像(GF-2)和中分辨率的遥感影像(Landsat-7)进行土壤水分反演模型建模验证，揭示高分遥感影像结合植被供水指数法在青藏高原东北缘亚高寒草甸草原上的适用性，同时分析研究区土壤水分分布及其影响因素。基于高分二号（GF-2）、Landsat-7影像数据，以甘南藏族自治州当周草原为研究区，利用植被供水指数（VSWI, vegetation supply water index）构建土壤水分反演模型得到研究区土壤水分含量反演图，通过半方差函数及主成分分析法探索研究区土壤水分空间分布及影响因素。结果表明：研究区土壤水分含量分布状态呈现出一定程度的空间变异，体现在整个研究区内以及各个地块之间，土壤水分含量主要介于0.11%~60.44%之间；土壤水分含量与坡度、海拔、坡向、NDVI、地表温度均呈正相关关系，分布主要受NDVI、坡向、坡度、海拔的影响。综上，利用植被供水指数法结合高分遥感影像监测土壤水分含量是可行的，基于GF-2遥感影像所建立的模型拟合度最优，较Landsat-7遥感影像更具优势。 |
| 英文摘要: |
| This study aims to estimate soil moisture content in the subalpine meadow of the northeastern margin of the Qing\|Zang Plateau using high\|resolution satellite remote sensing images and the vegetation supply water index (VSWI). The suitability of combining high\|resolution remote sensing images (GF-2) with the VSWI method is explored, and the distribution of soil moisture content and its influencing factors are analyzed. The study uses GF-2 and Landsat-7 satellite images to construct a soil moisture inversion model based on the VSWI. The model is applied to the grasslands of Gannan Tibetan Autonomous Prefecture to obtain a soil moisture inversion map. The spatial distribution of soil moisture and its influencing factors are analyzed using semivariogram and principal component analysis. The findings indicated a certain degree of spatial variability in soil moisture content within the study area and different positions, ranging from 0.11% to 60.44%. Soil moisture content showed a positive correlation with slope, elevation, aspect, NDVI, and surface temperature, the distribution of soil moisture content was affected by NDVI, aspect,slope, and elevation. Using the vegetation water supply index method combined with high\|resolution remote sensing image to inversion soil moisture content was feasible, and the model established based on GF-2 remote sensing image had the best fitting degree and is more advantageous than Landsat-7 remote sensing image. |
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