Developing a Gap-Filling Algorithm Using DNN for the Ts-VI Triangle Model to Obtain Temporally Continuous Daily Actual Evapotranspiration in an Arid Area of China
Temporally continuous daily actual evapotranspiration (ET) data play a critical role in water resource management in arid areas. As a typical remotely sensed land surface temperature (LST)-based ET model, the surface temperature-vegetation index (Ts-VI) triangle model provides direct monitoring of E...
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MDPI AG
2020-04-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/12/7/1121 |
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| author | Yaokui Cui Shihao Ma Zhaoyuan Yao Xi Chen Zengliang Luo Wenjie Fan Yang Hong |
| author_facet | Yaokui Cui Shihao Ma Zhaoyuan Yao Xi Chen Zengliang Luo Wenjie Fan Yang Hong |
| author_sort | Yaokui Cui |
| collection | DOAJ |
| description | Temporally continuous daily actual evapotranspiration (ET) data play a critical role in water resource management in arid areas. As a typical remotely sensed land surface temperature (LST)-based ET model, the surface temperature-vegetation index (Ts-VI) triangle model provides direct monitoring of ET, but these estimates are temporally discontinuous due to cloud contamination. In this work, we present a gap-filling algorithm (TSVI_DNN) using a deep neural network (DNN) with the Ts-VI triangle model to obtain temporally continuous daily actual ET at regional scale. The TSVI_DNN model is evaluated against in situ measurements in an arid area of China during 2009–2011 and shows good agreement with eddy covariance (EC) observations. The temporal coverage was improved from 16.1% with the original Ts-VI tringle model to 67.1% with the TSVI_DNN model. The correlation coefficient (R), root mean square error (RMSE), bias, and mean absolute difference (MAD) are 0.9, 0.86 mm d<sup>−1</sup>, −0.16 mm d<sup>−1</sup>, and 0.65 mm d<sup>−1</sup>, respectively. When compared with the National Aeronautics and Space Administration (NASA) official MOD16 version 6 ET product, estimates of ET using TSVI_DNN are improved by approximately 49.2%. The method presented here can potentially contribute to enhanced water resource management in arid areas, especially under climate change. |
| first_indexed | 2024-03-10T20:45:10Z |
| format | Article |
| id | doaj.art-b34b1cdd9c064339bc90c6ae69fc3bac |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T20:45:10Z |
| publishDate | 2020-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-b34b1cdd9c064339bc90c6ae69fc3bac2023-11-19T20:23:30ZengMDPI AGRemote Sensing2072-42922020-04-01127112110.3390/rs12071121Developing a Gap-Filling Algorithm Using DNN for the Ts-VI Triangle Model to Obtain Temporally Continuous Daily Actual Evapotranspiration in an Arid Area of ChinaYaokui Cui0Shihao Ma1Zhaoyuan Yao2Xi Chen3Zengliang Luo4Wenjie Fan5Yang Hong6Institute of RS and GIS, School of Earth and Space Sciences, Peking University, Beijing 100871, ChinaCarey Business School, Johns Hopkins University, Washington, DC 20036, USAInstitute of RS and GIS, School of Earth and Space Sciences, Peking University, Beijing 100871, ChinaInstitute of RS and GIS, School of Earth and Space Sciences, Peking University, Beijing 100871, ChinaInstitute of RS and GIS, School of Earth and Space Sciences, Peking University, Beijing 100871, ChinaInstitute of RS and GIS, School of Earth and Space Sciences, Peking University, Beijing 100871, ChinaInstitute of RS and GIS, School of Earth and Space Sciences, Peking University, Beijing 100871, ChinaTemporally continuous daily actual evapotranspiration (ET) data play a critical role in water resource management in arid areas. As a typical remotely sensed land surface temperature (LST)-based ET model, the surface temperature-vegetation index (Ts-VI) triangle model provides direct monitoring of ET, but these estimates are temporally discontinuous due to cloud contamination. In this work, we present a gap-filling algorithm (TSVI_DNN) using a deep neural network (DNN) with the Ts-VI triangle model to obtain temporally continuous daily actual ET at regional scale. The TSVI_DNN model is evaluated against in situ measurements in an arid area of China during 2009–2011 and shows good agreement with eddy covariance (EC) observations. The temporal coverage was improved from 16.1% with the original Ts-VI tringle model to 67.1% with the TSVI_DNN model. The correlation coefficient (R), root mean square error (RMSE), bias, and mean absolute difference (MAD) are 0.9, 0.86 mm d<sup>−1</sup>, −0.16 mm d<sup>−1</sup>, and 0.65 mm d<sup>−1</sup>, respectively. When compared with the National Aeronautics and Space Administration (NASA) official MOD16 version 6 ET product, estimates of ET using TSVI_DNN are improved by approximately 49.2%. The method presented here can potentially contribute to enhanced water resource management in arid areas, especially under climate change.https://www.mdpi.com/2072-4292/12/7/1121evapotranspirationremote sensingLSTTs-VI triangle modelDNNarid area |
| spellingShingle | Yaokui Cui Shihao Ma Zhaoyuan Yao Xi Chen Zengliang Luo Wenjie Fan Yang Hong Developing a Gap-Filling Algorithm Using DNN for the Ts-VI Triangle Model to Obtain Temporally Continuous Daily Actual Evapotranspiration in an Arid Area of China Remote Sensing evapotranspiration remote sensing LST Ts-VI triangle model DNN arid area |
| title | Developing a Gap-Filling Algorithm Using DNN for the Ts-VI Triangle Model to Obtain Temporally Continuous Daily Actual Evapotranspiration in an Arid Area of China |
| title_full | Developing a Gap-Filling Algorithm Using DNN for the Ts-VI Triangle Model to Obtain Temporally Continuous Daily Actual Evapotranspiration in an Arid Area of China |
| title_fullStr | Developing a Gap-Filling Algorithm Using DNN for the Ts-VI Triangle Model to Obtain Temporally Continuous Daily Actual Evapotranspiration in an Arid Area of China |
| title_full_unstemmed | Developing a Gap-Filling Algorithm Using DNN for the Ts-VI Triangle Model to Obtain Temporally Continuous Daily Actual Evapotranspiration in an Arid Area of China |
| title_short | Developing a Gap-Filling Algorithm Using DNN for the Ts-VI Triangle Model to Obtain Temporally Continuous Daily Actual Evapotranspiration in an Arid Area of China |
| title_sort | developing a gap filling algorithm using dnn for the ts vi triangle model to obtain temporally continuous daily actual evapotranspiration in an arid area of china |
| topic | evapotranspiration remote sensing LST Ts-VI triangle model DNN arid area |
| url | https://www.mdpi.com/2072-4292/12/7/1121 |
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