A Comprehensive Evaluation of Five Evapotranspiration Datasets Based on Ground and GRACE Satellite Observations: Implications for Improvement of Evapotranspiration Retrieval Algorithm
Evapotranspiration (ET) is a vital part of the hydrological cycle and the water–energy balance. To explore the characteristics of five typical remote sensing evapotranspiration datasets and provide guidance for algorithm development, we used reconstructed evapotranspiration (Recon) data based on gro...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-06-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/13/12/2414 |
| _version_ | 1797529507018571776 |
|---|---|
| author | Lijun Chao Ke Zhang Jingfeng Wang Jin Feng Mengjie Zhang |
| author_facet | Lijun Chao Ke Zhang Jingfeng Wang Jin Feng Mengjie Zhang |
| author_sort | Lijun Chao |
| collection | DOAJ |
| description | Evapotranspiration (ET) is a vital part of the hydrological cycle and the water–energy balance. To explore the characteristics of five typical remote sensing evapotranspiration datasets and provide guidance for algorithm development, we used reconstructed evapotranspiration (Recon) data based on ground and GRACE satellite observations as a benchmark and evaluated five remote sensing datasets for 592 watersheds across the continental United States. The Global Land Evaporation Amsterdam Model (GLEAM) dataset (with bias and RMSE values of 23.18 mm/year and 106.10 mm/year, respectively), process-based land surface evapotranspiration/heat flux (P-LSH) dataset (bias = 22.94 mm/year and RMSE = 114.44 mm/year) and the Penman–Monteith–Leuning (PML) algorithm generated ET dataset (bias = −17.73 mm/year and RMSE = 108.97 mm/year) showed the better performance on a yearly scale, followed by the model tree ensemble (MTE) dataset (bias = 99.45 mm/year and RMSE = 141.32 mm/year) and the moderate-resolution imaging spectroradiometer (MODIS) dataset (bias = −106.71 mm/year and RMSE = 158.90 mm/year). The P-LSH dataset outperformed the other four ET datasets on a seasonal scale, especially from March to August. Both PML and MTE showed better overall accuracy and could accurately capture the spatial variability of evapotranspiration in arid regions. The P-LSH and GLEAM products were consistent with the Recon data in middle-value section. MODIS and MTE had larger bias and RMSE values on a yearly scale, whereby the MODIS and MTE datasets tended to underestimate and overestimate ET values in all the sections, respectively. In the future, the aim should be to reduce bias in the MODIS and MTE algorithms and further improve seasonality of the ET estimation in the GLEAM algorithm, while the estimation accuracy of the P-LSH and MODIS algorithms should be improved in arid regions. Our analysis suggests that combining artificial intelligence algorithms or data-driven algorithms and physical process algorithms will further improve the accuracy of ET estimation algorithms and the quality of ET datasets, as well as enhancing their capacity to be applied in different climate regions. |
| first_indexed | 2024-03-10T10:14:50Z |
| format | Article |
| id | doaj.art-912429c5adfb440081ad0aa5084bd8e0 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T10:14:50Z |
| publishDate | 2021-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-912429c5adfb440081ad0aa5084bd8e02023-11-22T00:55:24ZengMDPI AGRemote Sensing2072-42922021-06-011312241410.3390/rs13122414A Comprehensive Evaluation of Five Evapotranspiration Datasets Based on Ground and GRACE Satellite Observations: Implications for Improvement of Evapotranspiration Retrieval AlgorithmLijun Chao0Ke Zhang1Jingfeng Wang2Jin Feng3Mengjie Zhang4State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, ChinaState Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, ChinaSchool of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USACollege of Hydrology and Water Resources and CMA-HHU Joint Laboratory for Hydro-Meteorological Studies, Hohai University, Nanjing 210098, ChinaDepartment of Earth and Environmental Engineering, Columbia University, New York, NY 10027, USAEvapotranspiration (ET) is a vital part of the hydrological cycle and the water–energy balance. To explore the characteristics of five typical remote sensing evapotranspiration datasets and provide guidance for algorithm development, we used reconstructed evapotranspiration (Recon) data based on ground and GRACE satellite observations as a benchmark and evaluated five remote sensing datasets for 592 watersheds across the continental United States. The Global Land Evaporation Amsterdam Model (GLEAM) dataset (with bias and RMSE values of 23.18 mm/year and 106.10 mm/year, respectively), process-based land surface evapotranspiration/heat flux (P-LSH) dataset (bias = 22.94 mm/year and RMSE = 114.44 mm/year) and the Penman–Monteith–Leuning (PML) algorithm generated ET dataset (bias = −17.73 mm/year and RMSE = 108.97 mm/year) showed the better performance on a yearly scale, followed by the model tree ensemble (MTE) dataset (bias = 99.45 mm/year and RMSE = 141.32 mm/year) and the moderate-resolution imaging spectroradiometer (MODIS) dataset (bias = −106.71 mm/year and RMSE = 158.90 mm/year). The P-LSH dataset outperformed the other four ET datasets on a seasonal scale, especially from March to August. Both PML and MTE showed better overall accuracy and could accurately capture the spatial variability of evapotranspiration in arid regions. The P-LSH and GLEAM products were consistent with the Recon data in middle-value section. MODIS and MTE had larger bias and RMSE values on a yearly scale, whereby the MODIS and MTE datasets tended to underestimate and overestimate ET values in all the sections, respectively. In the future, the aim should be to reduce bias in the MODIS and MTE algorithms and further improve seasonality of the ET estimation in the GLEAM algorithm, while the estimation accuracy of the P-LSH and MODIS algorithms should be improved in arid regions. Our analysis suggests that combining artificial intelligence algorithms or data-driven algorithms and physical process algorithms will further improve the accuracy of ET estimation algorithms and the quality of ET datasets, as well as enhancing their capacity to be applied in different climate regions.https://www.mdpi.com/2072-4292/13/12/2414actual evapotranspiration reconstructionwater balanceremote sensingevapotranspiration evaluation |
| spellingShingle | Lijun Chao Ke Zhang Jingfeng Wang Jin Feng Mengjie Zhang A Comprehensive Evaluation of Five Evapotranspiration Datasets Based on Ground and GRACE Satellite Observations: Implications for Improvement of Evapotranspiration Retrieval Algorithm Remote Sensing actual evapotranspiration reconstruction water balance remote sensing evapotranspiration evaluation |
| title | A Comprehensive Evaluation of Five Evapotranspiration Datasets Based on Ground and GRACE Satellite Observations: Implications for Improvement of Evapotranspiration Retrieval Algorithm |
| title_full | A Comprehensive Evaluation of Five Evapotranspiration Datasets Based on Ground and GRACE Satellite Observations: Implications for Improvement of Evapotranspiration Retrieval Algorithm |
| title_fullStr | A Comprehensive Evaluation of Five Evapotranspiration Datasets Based on Ground and GRACE Satellite Observations: Implications for Improvement of Evapotranspiration Retrieval Algorithm |
| title_full_unstemmed | A Comprehensive Evaluation of Five Evapotranspiration Datasets Based on Ground and GRACE Satellite Observations: Implications for Improvement of Evapotranspiration Retrieval Algorithm |
| title_short | A Comprehensive Evaluation of Five Evapotranspiration Datasets Based on Ground and GRACE Satellite Observations: Implications for Improvement of Evapotranspiration Retrieval Algorithm |
| title_sort | comprehensive evaluation of five evapotranspiration datasets based on ground and grace satellite observations implications for improvement of evapotranspiration retrieval algorithm |
| topic | actual evapotranspiration reconstruction water balance remote sensing evapotranspiration evaluation |
| url | https://www.mdpi.com/2072-4292/13/12/2414 |
| work_keys_str_mv | AT lijunchao acomprehensiveevaluationoffiveevapotranspirationdatasetsbasedongroundandgracesatelliteobservationsimplicationsforimprovementofevapotranspirationretrievalalgorithm AT kezhang acomprehensiveevaluationoffiveevapotranspirationdatasetsbasedongroundandgracesatelliteobservationsimplicationsforimprovementofevapotranspirationretrievalalgorithm AT jingfengwang acomprehensiveevaluationoffiveevapotranspirationdatasetsbasedongroundandgracesatelliteobservationsimplicationsforimprovementofevapotranspirationretrievalalgorithm AT jinfeng acomprehensiveevaluationoffiveevapotranspirationdatasetsbasedongroundandgracesatelliteobservationsimplicationsforimprovementofevapotranspirationretrievalalgorithm AT mengjiezhang acomprehensiveevaluationoffiveevapotranspirationdatasetsbasedongroundandgracesatelliteobservationsimplicationsforimprovementofevapotranspirationretrievalalgorithm AT lijunchao comprehensiveevaluationoffiveevapotranspirationdatasetsbasedongroundandgracesatelliteobservationsimplicationsforimprovementofevapotranspirationretrievalalgorithm AT kezhang comprehensiveevaluationoffiveevapotranspirationdatasetsbasedongroundandgracesatelliteobservationsimplicationsforimprovementofevapotranspirationretrievalalgorithm AT jingfengwang comprehensiveevaluationoffiveevapotranspirationdatasetsbasedongroundandgracesatelliteobservationsimplicationsforimprovementofevapotranspirationretrievalalgorithm AT jinfeng comprehensiveevaluationoffiveevapotranspirationdatasetsbasedongroundandgracesatelliteobservationsimplicationsforimprovementofevapotranspirationretrievalalgorithm AT mengjiezhang comprehensiveevaluationoffiveevapotranspirationdatasetsbasedongroundandgracesatelliteobservationsimplicationsforimprovementofevapotranspirationretrievalalgorithm |