Estimation of high-resolution terrestrial evapotranspiration from Landsat data using a simple Taylor skill fusion method
Estimation of high-resolution terrestrial evapotranspiration (ET) from Landsat data is important in many climatic, hydrologic, and agricultural applications, as it can help bridging the gap between existing coarse-resolution ET products and point-based field measurements. However, there is large unc...
| Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
|---|---|
| Formato: | Artículo |
| Lenguaje: | Inglés |
| Publicado: |
2017
|
| Materias: | |
| Acceso en línea: | http://hdl.handle.net/20.500.12123/1551 https://www.sciencedirect.com/science/article/pii/S0022169417305395 https://doi.org/10.1016/j.jhydrol.2017.08.013 |
| _version_ | 1855482905495601152 |
|---|---|
| author | Yunjun, Yao Shunlin, Liang Yuhu, Zhang Jiquan, Chen Xianglan, Li Kun, Jia Xiaotong, Zhang Fisher, Joshua B. Xuanyu, Wang Lilin, Zhang Jia, Xu Changliang, Shao Posse Beaulieu, Gabriela Yingnian, Li Magliulo, Vincenzo Varlagin, Andrej Moors, Eddy J. Boike, Julia Macfarlane, Craig Kato, Tomomichi Buchmann, Nina Billesbach, D.P. Beringer, Jason Wolf, Sebastian Papuga, Shirley A. Wohlfahrt, Georg Montagnani, Leonardo Ardö, Jonas Paul-Limoges, Eugénie Emmel, Carmen Hörtnagl, Lukas Sachs, Torsten Gruening, Carsten Gioli, Beniamino López-Ballesteros, Ana Steinbrecher, Rainer Gielen, Bert |
| author_browse | Ardö, Jonas Beringer, Jason Billesbach, D.P. Boike, Julia Buchmann, Nina Changliang, Shao Emmel, Carmen Fisher, Joshua B. Gielen, Bert Gioli, Beniamino Gruening, Carsten Hörtnagl, Lukas Jia, Xu Jiquan, Chen Kato, Tomomichi Kun, Jia Lilin, Zhang López-Ballesteros, Ana Macfarlane, Craig Magliulo, Vincenzo Montagnani, Leonardo Moors, Eddy J. Papuga, Shirley A. Paul-Limoges, Eugénie Posse Beaulieu, Gabriela Sachs, Torsten Shunlin, Liang Steinbrecher, Rainer Varlagin, Andrej Wohlfahrt, Georg Wolf, Sebastian Xianglan, Li Xiaotong, Zhang Xuanyu, Wang Yingnian, Li Yuhu, Zhang Yunjun, Yao |
| author_facet | Yunjun, Yao Shunlin, Liang Yuhu, Zhang Jiquan, Chen Xianglan, Li Kun, Jia Xiaotong, Zhang Fisher, Joshua B. Xuanyu, Wang Lilin, Zhang Jia, Xu Changliang, Shao Posse Beaulieu, Gabriela Yingnian, Li Magliulo, Vincenzo Varlagin, Andrej Moors, Eddy J. Boike, Julia Macfarlane, Craig Kato, Tomomichi Buchmann, Nina Billesbach, D.P. Beringer, Jason Wolf, Sebastian Papuga, Shirley A. Wohlfahrt, Georg Montagnani, Leonardo Ardö, Jonas Paul-Limoges, Eugénie Emmel, Carmen Hörtnagl, Lukas Sachs, Torsten Gruening, Carsten Gioli, Beniamino López-Ballesteros, Ana Steinbrecher, Rainer Gielen, Bert |
| author_sort | Yunjun, Yao |
| collection | INTA Digital |
| description | Estimation of high-resolution terrestrial evapotranspiration (ET) from Landsat data is important in many climatic, hydrologic, and agricultural applications, as it can help bridging the gap between existing coarse-resolution ET products and point-based field measurements. However, there is large uncertainty among existing ET products from Landsat that limit their application. This study presents a simple Taylor skill fusion (STS) method that merges five Landsat-based ET products and directly measured ET from eddy covariance (EC) to improve the global estimation of terrestrial ET. The STS method uses a weighted average of the individual ET products and weights are determined by their Taylor skill scores (S). The validation with site-scale measurements at 206 EC flux towers showed large differences and uncertainties among the five ET products. The merged ET product exhibited the best performance with a decrease in the averaged root-mean-square error (RMSE) by 2–5 W/m2 when compared to the individual products. To evaluate the reliability of the STS method at the regional scale, the weights of the STS method for these five ET products were determined using EC ground-measurements. An example of regional ET mapping demonstrates that the STS-merged ET can effectively integrate the individual Landsat ET products. Our proposed method provides an improved high-resolution ET product for identifying agricultural crop water consumption and providing a diagnostic assessment for global land surface models. |
| format | Artículo |
| id | INTA1551 |
| institution | Instituto Nacional de Tecnología Agropecuaria (INTA -Argentina) |
| language | Inglés |
| publishDate | 2017 |
| publishDateRange | 2017 |
| publishDateSort | 2017 |
| record_format | dspace |
| spelling | INTA15512019-03-21T18:13:57Z Estimation of high-resolution terrestrial evapotranspiration from Landsat data using a simple Taylor skill fusion method Yunjun, Yao Shunlin, Liang Yuhu, Zhang Jiquan, Chen Xianglan, Li Kun, Jia Xiaotong, Zhang Fisher, Joshua B. Xuanyu, Wang Lilin, Zhang Jia, Xu Changliang, Shao Posse Beaulieu, Gabriela Yingnian, Li Magliulo, Vincenzo Varlagin, Andrej Moors, Eddy J. Boike, Julia Macfarlane, Craig Kato, Tomomichi Buchmann, Nina Billesbach, D.P. Beringer, Jason Wolf, Sebastian Papuga, Shirley A. Wohlfahrt, Georg Montagnani, Leonardo Ardö, Jonas Paul-Limoges, Eugénie Emmel, Carmen Hörtnagl, Lukas Sachs, Torsten Gruening, Carsten Gioli, Beniamino López-Ballesteros, Ana Steinbrecher, Rainer Gielen, Bert Evapotranspiración Landsat Imágenes por Satélites Datos Atmosféricos Evapotranspiration Satellite Imagery Atmospheric Data Estimation of high-resolution terrestrial evapotranspiration (ET) from Landsat data is important in many climatic, hydrologic, and agricultural applications, as it can help bridging the gap between existing coarse-resolution ET products and point-based field measurements. However, there is large uncertainty among existing ET products from Landsat that limit their application. This study presents a simple Taylor skill fusion (STS) method that merges five Landsat-based ET products and directly measured ET from eddy covariance (EC) to improve the global estimation of terrestrial ET. The STS method uses a weighted average of the individual ET products and weights are determined by their Taylor skill scores (S). The validation with site-scale measurements at 206 EC flux towers showed large differences and uncertainties among the five ET products. The merged ET product exhibited the best performance with a decrease in the averaged root-mean-square error (RMSE) by 2–5 W/m2 when compared to the individual products. To evaluate the reliability of the STS method at the regional scale, the weights of the STS method for these five ET products were determined using EC ground-measurements. An example of regional ET mapping demonstrates that the STS-merged ET can effectively integrate the individual Landsat ET products. Our proposed method provides an improved high-resolution ET product for identifying agricultural crop water consumption and providing a diagnostic assessment for global land surface models. Inst. de Clima y Agua Fil: Yunjun, Yao. Beijing Normal University. Faculty of Geographical Science. State Key Laboratory of Remote Sensing Science; China Fil: Shunlin, Liang. Beijing Normal University. Faculty of Geographical Science. State Key Laboratory of Remote Sensing Science; China Fil: Xianglan, Li. Beijing Normal University. College of Global Change and Earth System Science; China Fil: Yuhu, Zhang. Capital Normal University. College of Resource Environment and Tourism; China Fil: Jiquan, Chen. Michigan State University. CGCEO/Geography; Estados Unidos Fil: Kun, Jia. Beijing Normal University. Faculty of Geographical Science. State Key Laboratory of Remote Sensing Science; China Fil: Xiaotong, Zhang. Beijing Normal University. Faculty of Geographical Science. State Key Laboratory of Remote Sensing Science; China Fil: Fisher, Joshua B. California Institute of Technology. Jet Propulsion Laboratory; Estados Unidos Fil: Xuanyu, Wang. Beijing Normal University. Faculty of Geographical Science. State Key Laboratory of Remote Sensing Science; China Fil: Lilin, Zhang. Beijing Normal University. Faculty of Geographical Science. State Key Laboratory of Remote Sensing Science; China Fil: Jia, Xu. Beijing Normal University. Faculty of Geographical Science. State Key Laboratory of Remote Sensing Science; China Fil: Changliang, Shao. Michigan State University. CGCEO/Geography; Estados Unidos Fil: Posse Beaulieu, Gabriela. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; Argentina Fil: Yingnian, Li. Chinese Academy of Sciences. Northwest Institute of Plateau Biology; China Fil: Magliulo, Vincenzo. Consiglio Nazionale delle Ricerche. Institute of Mediterranean Forest and Agricultural Systems; Italia Fil: Varlagin, Andrej. Russian Academy of Sciences. A.N. Severtsov Institute of Ecology and Evolution; Rusia Fil: Moors, Eddy J. Wageningen University and Research, Wageningen Environmental Research; Holanda Fil: Boike, Julia. Alfred Wegener Institute for Polar and Marine Research; Alemania Fil: Macfarlane, Craig. Commonwealth Scientific and Industrial Research Organisation (CSIRO) Land and Water; Australia Fil: Kato, Tomomichi. Hokkaido University. Research Faculty of Agriculture; Japón Fil: Buchmann, Nina. ETH Zurich. Department of Environmental Systems Science; Suiza Fil: Billesbach, D.P. University of Nebraska. Department of Biological Systems Engineering and School of Natural Resources; Estados Unidos Fil: Beringer, Jason. University of Western Australia. School of Agriculture and Environment; Australia Fil: Wolf, Sebastian. ETH Zurich. Department of Environmental Systems Science; Suiza Fil: Papuga, Shirley A. University of Arizona. School of Natural Resources and the Environment; Estados Unidos Fil: Wohlfahrt, Georg. University of Innsbruck. Institute of Ecology; Austria Fil: Montagnani, Leonardo. Free University of Bolzano. Faculty of Science and Technology; Italia Fil: Ardö, Jonas. Lund University. Physical Geography and Ecosystem Science; Suecia Fil: Paul-Limoges, Eugénie. ETH Zurich. Department of Environmental Systems Science; Suiza Fil: Emmel, Carmen. ETH Zurich. Department of Environmental Systems Science; Suiza Fil: Hörtnagl, Lukas. ETH Zurich. Department of Environmental Systems Science; Suiza Fil: Sachs, Torsten. GFZ German Research Centre for Geosciences, Section Remote Sensing; Alemania Fil: Gruening, Carsten. European Commission, Joint Research Centre; Italia Fil: Gioli, Beniamino. National Research Council. Institute of Biometeorology; Italia Fil: López-Ballesteros, Ana. University of Granada. Faculty of Sciences. Department of Ecology; España Fil: Steinbrecher, Rainer. Karlsruhe Institute of Technology (KIT), Institute of Meteorology and Climate Research (IMK-IFU); Alemania Fil: Gielen, Bert. University of Antwerp. Department of Biology. Centre of Excellence PLECO; Bélgica 2017-10-20T14:13:49Z 2017-10-20T14:13:49Z 2017-10 info:ar-repo/semantics/artículo info:eu-repo/semantics/article info:eu-repo/semantics/acceptedVersion http://hdl.handle.net/20.500.12123/1551 https://www.sciencedirect.com/science/article/pii/S0022169417305395 0022-1694 https://doi.org/10.1016/j.jhydrol.2017.08.013 eng info:eu-repo/semantics/restrictedAccess application/pdf Journal of hydrology 553 : 508-526. (October 2017) |
| spellingShingle | Evapotranspiración Landsat Imágenes por Satélites Datos Atmosféricos Evapotranspiration Satellite Imagery Atmospheric Data Yunjun, Yao Shunlin, Liang Yuhu, Zhang Jiquan, Chen Xianglan, Li Kun, Jia Xiaotong, Zhang Fisher, Joshua B. Xuanyu, Wang Lilin, Zhang Jia, Xu Changliang, Shao Posse Beaulieu, Gabriela Yingnian, Li Magliulo, Vincenzo Varlagin, Andrej Moors, Eddy J. Boike, Julia Macfarlane, Craig Kato, Tomomichi Buchmann, Nina Billesbach, D.P. Beringer, Jason Wolf, Sebastian Papuga, Shirley A. Wohlfahrt, Georg Montagnani, Leonardo Ardö, Jonas Paul-Limoges, Eugénie Emmel, Carmen Hörtnagl, Lukas Sachs, Torsten Gruening, Carsten Gioli, Beniamino López-Ballesteros, Ana Steinbrecher, Rainer Gielen, Bert Estimation of high-resolution terrestrial evapotranspiration from Landsat data using a simple Taylor skill fusion method |
| title | Estimation of high-resolution terrestrial evapotranspiration from Landsat data using a simple Taylor skill fusion method |
| title_full | Estimation of high-resolution terrestrial evapotranspiration from Landsat data using a simple Taylor skill fusion method |
| title_fullStr | Estimation of high-resolution terrestrial evapotranspiration from Landsat data using a simple Taylor skill fusion method |
| title_full_unstemmed | Estimation of high-resolution terrestrial evapotranspiration from Landsat data using a simple Taylor skill fusion method |
| title_short | Estimation of high-resolution terrestrial evapotranspiration from Landsat data using a simple Taylor skill fusion method |
| title_sort | estimation of high resolution terrestrial evapotranspiration from landsat data using a simple taylor skill fusion method |
| topic | Evapotranspiración Landsat Imágenes por Satélites Datos Atmosféricos Evapotranspiration Satellite Imagery Atmospheric Data |
| url | http://hdl.handle.net/20.500.12123/1551 https://www.sciencedirect.com/science/article/pii/S0022169417305395 https://doi.org/10.1016/j.jhydrol.2017.08.013 |
| work_keys_str_mv | AT yunjunyao estimationofhighresolutionterrestrialevapotranspirationfromlandsatdatausingasimpletaylorskillfusionmethod AT shunlinliang estimationofhighresolutionterrestrialevapotranspirationfromlandsatdatausingasimpletaylorskillfusionmethod AT yuhuzhang estimationofhighresolutionterrestrialevapotranspirationfromlandsatdatausingasimpletaylorskillfusionmethod AT jiquanchen estimationofhighresolutionterrestrialevapotranspirationfromlandsatdatausingasimpletaylorskillfusionmethod AT xianglanli estimationofhighresolutionterrestrialevapotranspirationfromlandsatdatausingasimpletaylorskillfusionmethod AT kunjia estimationofhighresolutionterrestrialevapotranspirationfromlandsatdatausingasimpletaylorskillfusionmethod AT xiaotongzhang estimationofhighresolutionterrestrialevapotranspirationfromlandsatdatausingasimpletaylorskillfusionmethod AT fisherjoshuab estimationofhighresolutionterrestrialevapotranspirationfromlandsatdatausingasimpletaylorskillfusionmethod AT xuanyuwang estimationofhighresolutionterrestrialevapotranspirationfromlandsatdatausingasimpletaylorskillfusionmethod AT lilinzhang estimationofhighresolutionterrestrialevapotranspirationfromlandsatdatausingasimpletaylorskillfusionmethod AT jiaxu estimationofhighresolutionterrestrialevapotranspirationfromlandsatdatausingasimpletaylorskillfusionmethod AT changliangshao estimationofhighresolutionterrestrialevapotranspirationfromlandsatdatausingasimpletaylorskillfusionmethod AT possebeaulieugabriela estimationofhighresolutionterrestrialevapotranspirationfromlandsatdatausingasimpletaylorskillfusionmethod AT yingnianli estimationofhighresolutionterrestrialevapotranspirationfromlandsatdatausingasimpletaylorskillfusionmethod AT magliulovincenzo estimationofhighresolutionterrestrialevapotranspirationfromlandsatdatausingasimpletaylorskillfusionmethod AT varlaginandrej estimationofhighresolutionterrestrialevapotranspirationfromlandsatdatausingasimpletaylorskillfusionmethod AT moorseddyj estimationofhighresolutionterrestrialevapotranspirationfromlandsatdatausingasimpletaylorskillfusionmethod AT boikejulia estimationofhighresolutionterrestrialevapotranspirationfromlandsatdatausingasimpletaylorskillfusionmethod AT macfarlanecraig estimationofhighresolutionterrestrialevapotranspirationfromlandsatdatausingasimpletaylorskillfusionmethod AT katotomomichi estimationofhighresolutionterrestrialevapotranspirationfromlandsatdatausingasimpletaylorskillfusionmethod AT buchmannnina estimationofhighresolutionterrestrialevapotranspirationfromlandsatdatausingasimpletaylorskillfusionmethod AT billesbachdp estimationofhighresolutionterrestrialevapotranspirationfromlandsatdatausingasimpletaylorskillfusionmethod AT beringerjason estimationofhighresolutionterrestrialevapotranspirationfromlandsatdatausingasimpletaylorskillfusionmethod AT wolfsebastian estimationofhighresolutionterrestrialevapotranspirationfromlandsatdatausingasimpletaylorskillfusionmethod AT papugashirleya estimationofhighresolutionterrestrialevapotranspirationfromlandsatdatausingasimpletaylorskillfusionmethod AT wohlfahrtgeorg estimationofhighresolutionterrestrialevapotranspirationfromlandsatdatausingasimpletaylorskillfusionmethod AT montagnanileonardo estimationofhighresolutionterrestrialevapotranspirationfromlandsatdatausingasimpletaylorskillfusionmethod AT ardojonas estimationofhighresolutionterrestrialevapotranspirationfromlandsatdatausingasimpletaylorskillfusionmethod AT paullimogeseugenie estimationofhighresolutionterrestrialevapotranspirationfromlandsatdatausingasimpletaylorskillfusionmethod AT emmelcarmen estimationofhighresolutionterrestrialevapotranspirationfromlandsatdatausingasimpletaylorskillfusionmethod AT hortnagllukas estimationofhighresolutionterrestrialevapotranspirationfromlandsatdatausingasimpletaylorskillfusionmethod AT sachstorsten estimationofhighresolutionterrestrialevapotranspirationfromlandsatdatausingasimpletaylorskillfusionmethod AT grueningcarsten estimationofhighresolutionterrestrialevapotranspirationfromlandsatdatausingasimpletaylorskillfusionmethod AT giolibeniamino estimationofhighresolutionterrestrialevapotranspirationfromlandsatdatausingasimpletaylorskillfusionmethod AT lopezballesterosana estimationofhighresolutionterrestrialevapotranspirationfromlandsatdatausingasimpletaylorskillfusionmethod AT steinbrecherrainer estimationofhighresolutionterrestrialevapotranspirationfromlandsatdatausingasimpletaylorskillfusionmethod AT gielenbert estimationofhighresolutionterrestrialevapotranspirationfromlandsatdatausingasimpletaylorskillfusionmethod |