Estimation of hourly and daily evapotranspiration and soil moisture using downscaled LST over various urban surfaces
Surface moisture is important to link land surface temperature (LST) to people’s thermal comfort. In urban areas, the surface roughness from buildings and urban trees impacts wind speed, and consequently surface moisture. To find the role of surface roughness in surface moisture estimation, we devel...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2017-01-01
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| Series: | GIScience & Remote Sensing |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/15481603.2016.1258971 |
| _version_ | 1827811786425892864 |
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| author | Yitong Jiang Qihao Weng |
| author_facet | Yitong Jiang Qihao Weng |
| author_sort | Yitong Jiang |
| collection | DOAJ |
| description | Surface moisture is important to link land surface temperature (LST) to people’s thermal comfort. In urban areas, the surface roughness from buildings and urban trees impacts wind speed, and consequently surface moisture. To find the role of surface roughness in surface moisture estimation, we developed methods to estimate daily and hourly evapotranspiration (ET) and soil moisture, based on a case study of Indianapolis, Indiana, USA. In order to capture the spatial and temporal variations of LST, hourly and daily LST was produced by downscaling techniques. Given the heterogeneity in urban areas, fractions of vegetation, soil, and impervious surfaces were calculated. To describe the urban morphology, surface roughness parameters were calculated from digital elevation model (DEM), digital surface model (DSM), and Terrestrial Light Detection and Ranging (LiDAR). Two source energy balance (TSEB) model was employed to generate ET, and the temperature vegetation index (TVX) method was used to calculate soil moisture. Stable hourly soil moisture fluctuated from 15% to 20%, and daily soil moisture increased due to precipitation and decreased due to seasonal temperature change. ET over soil, vegetation, and impervious surface in the urban areas yielded different patterns in response to precipitation. The surface roughness from high-rise has bigger influence on ET in central urban areas. |
| first_indexed | 2024-03-11T23:09:33Z |
| format | Article |
| id | doaj.art-81d06f541dcc4df1a86abd0b77bcd62a |
| institution | Directory Open Access Journal |
| issn | 1548-1603 1943-7226 |
| language | English |
| last_indexed | 2024-03-11T23:09:33Z |
| publishDate | 2017-01-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | GIScience & Remote Sensing |
| spelling | doaj.art-81d06f541dcc4df1a86abd0b77bcd62a2023-09-21T12:34:13ZengTaylor & Francis GroupGIScience & Remote Sensing1548-16031943-72262017-01-015419511710.1080/15481603.2016.12589711258971Estimation of hourly and daily evapotranspiration and soil moisture using downscaled LST over various urban surfacesYitong Jiang0Qihao Weng1Indiana State UniversityIndiana State UniversitySurface moisture is important to link land surface temperature (LST) to people’s thermal comfort. In urban areas, the surface roughness from buildings and urban trees impacts wind speed, and consequently surface moisture. To find the role of surface roughness in surface moisture estimation, we developed methods to estimate daily and hourly evapotranspiration (ET) and soil moisture, based on a case study of Indianapolis, Indiana, USA. In order to capture the spatial and temporal variations of LST, hourly and daily LST was produced by downscaling techniques. Given the heterogeneity in urban areas, fractions of vegetation, soil, and impervious surfaces were calculated. To describe the urban morphology, surface roughness parameters were calculated from digital elevation model (DEM), digital surface model (DSM), and Terrestrial Light Detection and Ranging (LiDAR). Two source energy balance (TSEB) model was employed to generate ET, and the temperature vegetation index (TVX) method was used to calculate soil moisture. Stable hourly soil moisture fluctuated from 15% to 20%, and daily soil moisture increased due to precipitation and decreased due to seasonal temperature change. ET over soil, vegetation, and impervious surface in the urban areas yielded different patterns in response to precipitation. The surface roughness from high-rise has bigger influence on ET in central urban areas.http://dx.doi.org/10.1080/15481603.2016.1258971evapotranspiration (et)soil moistureurbandownscaling lstroughness lengthdisplacement height |
| spellingShingle | Yitong Jiang Qihao Weng Estimation of hourly and daily evapotranspiration and soil moisture using downscaled LST over various urban surfaces GIScience & Remote Sensing evapotranspiration (et) soil moisture urban downscaling lst roughness length displacement height |
| title | Estimation of hourly and daily evapotranspiration and soil moisture using downscaled LST over various urban surfaces |
| title_full | Estimation of hourly and daily evapotranspiration and soil moisture using downscaled LST over various urban surfaces |
| title_fullStr | Estimation of hourly and daily evapotranspiration and soil moisture using downscaled LST over various urban surfaces |
| title_full_unstemmed | Estimation of hourly and daily evapotranspiration and soil moisture using downscaled LST over various urban surfaces |
| title_short | Estimation of hourly and daily evapotranspiration and soil moisture using downscaled LST over various urban surfaces |
| title_sort | estimation of hourly and daily evapotranspiration and soil moisture using downscaled lst over various urban surfaces |
| topic | evapotranspiration (et) soil moisture urban downscaling lst roughness length displacement height |
| url | http://dx.doi.org/10.1080/15481603.2016.1258971 |
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