| 引用本文: | 巩文军,郭乙霏,王文婷,等.基于混合象元分解的Landsat8与 MODIS数据融合反演土壤墒情方法研究[J].灌溉排水学报,2019,38(7):123-128. |
| ,et al.基于混合象元分解的Landsat8与 MODIS数据融合反演土壤墒情方法研究[J].灌溉排水学报,2019,38(7):123-128. |
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| 摘要: |
| 【目的】及时准确地获取灌区土壤墒情信息。【方法】以河南省焦作市广利灌区为研究对象，利用Landsat8及MODIS遥感数据分别以表观热惯量及植被供水指数法反演土壤墒情，以混合象元分解的植被和土壤的丰度作为权重因子，对2种方法反演的土壤墒情进行了融合计算。【结果】利用植被供水指数法和表观热惯量反演的土壤含水率与实测含水率相关系数分别为0.47和0.51，同时将2种方法相结合得到的反演结果精度更高，实测含水率与计算的土壤含水率相关系数达到0.73。【结论】融合方法可以更好地计算灌区非均匀覆盖区的土壤墒情。 |
| 关键词: 遥感数据； 表观热惯量； 植被供水指数； 混合像元分解； 土壤墒情反演 |
| DOI：10.13522/j.cnki.ggps.20190010 |
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| Retrieving Soil Moisture Using Spectral Mixture Analysis of Landsat8 and the Moderate Resolution Imaging Spectroradiometer |
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GONG Wenjun, GUO Yifei, WANG Wenting, HENG Weidong
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1.Guangli Irrigation District Administration Bureau of Jiaozuo, Henan Province, Jiaozuo 454550, China; 2. School of Water Conservancy & Civil Engineering, Northeast Agricultural University, Harbin 150030, China; 3. Jiaozuo Water Conservancy Survey and Design Institute, Jiaozuo 454003, China; 4. School of Water Conservancy & Environmental, Zhengzhou University, Zhengzhou 450001, China
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| Abstract: |
| 【Objective】 Soil moisture plays a critical role in many hydrological and ecological processes and understanding its spatiotemporal changes is imperative to ecosystem management and irrigation design. This paper presents and tests a method to estimate soil moisture distribution using satellite technologies. 【Method】 We took Guangli Irrigation District at Jiaozuo in Henan Province as our study area. The Landsat-8 and MODIS imageries from the area were used to retrieve soil moisture with the apparent thermal inertia and vegetation water supply index, respectively. Using the abundance of vegetation and soil moisture decomposed by the spectral mixture analysis as weighting factors, we retrieved the soil moisture distribution in the area using the two methods. 【Result】The correlation coefficient between the measured soil moisture and the retrieved using the vegetation water supply index method and the apparent thermal inertia method was 0.47 and 0.51 respectively. We found that combining the two methods could improve the correlation coefficient between the measured and retrieved soil moisture to 0.73.【Conclusion】 Our results showed that the proposed method is reliable and can adequately estimate soil moisture in irrigation district grown with a diverse of crops. |
| Key words: Remote sensing; apparent thermal inertia; vegetation water supply index; mixed pixel decomposition; soil moisture |
