Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1029/2018WR024162 |
Estimation of Surface Soil Moisture With Downscaled Land Surface Temperatures Using a Data Fusion Approach for Heterogeneous Agricultural Land | |
Bai, Liangliang; Long, Di; Yan, La | |
2019-02-01 | |
发表期刊 | WATER RESOURCES RESEARCH
![]() |
ISSN | 0043-1397 |
EISSN | 1944-7973 |
出版年 | 2019 |
卷号 | 55期号:2页码:1105-1128 |
文章类型 | Article |
语种 | 英语 |
国家 | Peoples R China |
英文摘要 | Field-scale surface soil moisture (SSM, 0-10cm), which is closely linked with land surface temperature (LST), is particularly important to agricultural water resource management. Active and passive microwave remote sensing-based SSM retrievals on the order of kilometer squared resolutions are difficult to apply to heterogeneous agricultural land surfaces that may need SSM data at a resolution of 30m. In this study, the High-resolution Urban Thermal Sharpener and Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model were applied to downscale optical and thermal remote sensing data simultaneously by blending Landsat and MODIS red-near infrared-LST data, with the ultimate goal to generate field-scale SSM values from the trapezoidal approach. To evaluate the performance of the downscaled LSTE (based on the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model method) and SSM, an irrigation district (Area 1) in Inner Mongolia and an irrigation district in the North China Plain (Area 2) with varying spatial heterogeneity were selected as the testbeds. Results indicated that the downscaled LSTE was highly consistent with synchronous Landsat LSTH and in situ LST measurements in Area 1, with the root-mean-square error ranging from 0.73 to 2.75K. Compared with the MODIS SSM, the average root-mean-square error of the downscaled SSM improved from 0.048 to 0.038cm(3)/cm(3) for both areas. The downscaled LSTE and SSM developed in this study enhance the spatiotemporal resolutions of the SSM estimates, maximizing the potential of remotely sensed information for agricultural water resource management. Plain Language Summary Field-scale (30 m) surface soil moisture (SSM), closely linked with land surface temperature (LST), is particularly important for agricultural water resource management, such as for assessment of agricultural droughts, optimization of irrigation schedules and improvement of water use efficiency, particularly in the heterogeneous agricultural land. Here, the High resolution Urban Thermal Sharpener (HUTS) and Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) were jointly adopted to downscale optical and thermal remote sensing data simultaneously by blending Landsat and MODIS Red-Near infrared-LST data, with the ultimate goal to generate field-scale SSM values from the downscaled Red-Near infrared-LST remote sensing data using the theoretical trapezoidal approach. The field-scale LST and SSM developed in this study maximize the potential of remotely sensed information, improve both the spatial and temporal resolutions of SSM, and provide more valuable information on heterogeneous land surfaces for agricultural water resource management. |
英文关键词 | land surface temperature surface soil moisture data fusion Landsat MODIS heterogeneous agricultural lands |
领域 | 资源环境 |
收录类别 | SCI-E |
WOS记录号 | WOS:000461858900013 |
WOS关键词 | AIR-TEMPERATURE ; WINTER-WHEAT ; WATER-USE ; RESOLUTION ; TIME ; EVAPOTRANSPIRATION ; IRRIGATION ; DISAGGREGATION ; REFLECTANCE ; SPACE |
WOS类目 | Environmental Sciences ; Limnology ; Water Resources |
WOS研究方向 | Environmental Sciences & Ecology ; Marine & Freshwater Biology ; Water Resources |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/181284 |
专题 | 资源环境科学 |
作者单位 | Tsinghua Univ, Dept Hydraul Engn, Beijing, Peoples R China |
推荐引用方式 GB/T 7714 | Bai, Liangliang,Long, Di,Yan, La. Estimation of Surface Soil Moisture With Downscaled Land Surface Temperatures Using a Data Fusion Approach for Heterogeneous Agricultural Land[J]. WATER RESOURCES RESEARCH,2019,55(2):1105-1128. |
APA | Bai, Liangliang,Long, Di,&Yan, La.(2019).Estimation of Surface Soil Moisture With Downscaled Land Surface Temperatures Using a Data Fusion Approach for Heterogeneous Agricultural Land.WATER RESOURCES RESEARCH,55(2),1105-1128. |
MLA | Bai, Liangliang,et al."Estimation of Surface Soil Moisture With Downscaled Land Surface Temperatures Using a Data Fusion Approach for Heterogeneous Agricultural Land".WATER RESOURCES RESEARCH 55.2(2019):1105-1128. |
条目包含的文件 | 条目无相关文件。 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论