干旱区生态环境知识资源中心(Arid): A Comprehensive Evaluation of Five Evapotranspiration Datasets Based on Ground and GRACE Satellite Observations: Implications for Improvement of Evapotranspiration Retrieval Algorithm

Arid

DOI	10.3390/rs13122414
	A Comprehensive Evaluation of Five Evapotranspiration Datasets Based on Ground and GRACE Satellite Observations: Implications for Improvement of Evapotranspiration Retrieval Algorithm
	Chao, Lijun; Zhang, Ke; Wang, Jingfeng; Feng, Jin; Zhang, Mengjie
通讯作者	Zhang, K (corresponding author), Hohai Univ, State Key Lab Hydrol Water Resources & Hydraul En, Nanjing 210098, Peoples R China. ; Zhang, K (corresponding author), Hohai Univ, Yangtze Inst Conservat & Dev, Nanjing 210098, Peoples R China. ; Zhang, K (corresponding author), Hohai Univ, Coll Hydrol & Water Resources, Nanjing 210098, Peoples R China. ; Zhang, K (corresponding author), Hohai Univ, CMA HHU Joint Lab Hydrometeorol Studies, Nanjing 210098, Peoples R China.
来源期刊	REMOTE SENSING
EISSN	2072-4292
出版年	2021
卷号	13 期号:12
英文摘要	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.
英文关键词	actual evapotranspiration reconstruction water balance remote sensing evapotranspiration evaluation
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000666400100001
WOS关键词	GLOBAL TERRESTRIAL EVAPOTRANSPIRATION ; ENERGY-BALANCE ; PENMAN-MONTEITH ; WATER-STRESS ; EVAPORATION ; MODIS ; FLUX ; MODELS ; MOISTURE ; GROSS
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
来源机构	河海大学
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/351535
作者单位	[Chao, Lijun; Zhang, Ke] Hohai Univ, State Key Lab Hydrol Water Resources & Hydraul En, Nanjing 210098, Peoples R China; [Chao, Lijun; Zhang, Ke] Hohai Univ, Yangtze Inst Conservat & Dev, Nanjing 210098, Peoples R China; [Chao, Lijun] Hohai Univ, Coll Agr Sci & Engn, Nanjing 210098, Peoples R China; [Zhang, Ke; Feng, Jin] Hohai Univ, Coll Hydrol & Water Resources, Nanjing 210098, Peoples R China; [Zhang, Ke; Feng, Jin] Hohai Univ, CMA HHU Joint Lab Hydrometeorol Studies, Nanjing 210098, Peoples R China; [Wang, Jingfeng] Georgia Inst Technol, Sch Civil & Environm Engn, Atlanta, GA 30332 USA; [Zhang, Mengjie] Columbia Univ, Dept Earth & Environm Engn, New York, NY 10027 USA
推荐引用方式 GB/T 7714	Chao, Lijun,Zhang, Ke,Wang, Jingfeng,et al. A Comprehensive Evaluation of Five Evapotranspiration Datasets Based on Ground and GRACE Satellite Observations: Implications for Improvement of Evapotranspiration Retrieval Algorithm[J]. 河海大学,2021,13(12).
APA	Chao, Lijun,Zhang, Ke,Wang, Jingfeng,Feng, Jin,&Zhang, Mengjie.(2021).A Comprehensive Evaluation of Five Evapotranspiration Datasets Based on Ground and GRACE Satellite Observations: Implications for Improvement of Evapotranspiration Retrieval Algorithm.REMOTE SENSING,13(12).
MLA	Chao, Lijun,et al."A Comprehensive Evaluation of Five Evapotranspiration Datasets Based on Ground and GRACE Satellite Observations: Implications for Improvement of Evapotranspiration Retrieval Algorithm".REMOTE SENSING 13.12(2021).