Multimodel ensemble estimation of Landsat-like global terrestrial latent heat flux using a generalized deep CNN-LSTM integration algorithm
•CNN-LSTM-ILE outperforms all the LE products used in integration method.•CNN-LSTM-ILE that combines information from LE products, EC and topography.•The spatial and temporal information of the forcing inputs were integrated. Accurate estimates of high-spatial-resolution global terrestrial latent he...
Gespeichert in:
| Veröffentlicht in: | Agricultural and forest meteorology Jg. 349; S. 109962 |
|---|---|
| Hauptverfasser: | , , , , , , , , , , , , , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Elsevier B.V
15.04.2024
|
| Schlagworte: | |
| ISSN: | 0168-1923, 1873-2240 |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Abstract | •CNN-LSTM-ILE outperforms all the LE products used in integration method.•CNN-LSTM-ILE that combines information from LE products, EC and topography.•The spatial and temporal information of the forcing inputs were integrated.
Accurate estimates of high-spatial-resolution global terrestrial latent heat flux (LE) from Landsat data are crucial for many basic and applied research. Yet current Landsat-derived LE products were developed using single algorithm with large uncertainties and discrepancies. Here we proposed a convolutional neural network-long short-term memory (CNN-LSTM)-based integrated LE (CNN-LSTM-ILE) framework that integrates five Landsat-derived physical LE algorithms, topography-related variables (elevation, slope and aspect) and eddy covariance (EC) observations to estimate 30-m global terrestrial LE. CNN-LSTM-ILE not only conserves good performance of LE estimation from pure deep learning (DL) algorithm, but partially inherits physical mechanism of the individual physical algorithms for improving the generalization of the integration algorithms for extreme cases. CNN-LSTM is an algorithm that combines two deep learning structures (CNN and LSTM) to effectively utilize the spatial and temporal information contained in the forcing inputs. The data were collected from 190 globally distributed EC observations from 2000 to 2015 and were provided by FLUXNET. The cross-validation results indicated that the CNN-LSTM integration algorithm improved the LE estimates by reducing the root mean square error (RMSE) of 5–8 W/m2 and increasing Kling-Gupta efficiency (KGE) of 0.05–0.16 when compared with the individual LE algorithms and the results of three other machine learning integration algorithms (multiple linear regression, MLR; random forest, RF; and deep neural networks, DNN). The CNN-LSTM integration algorithm had highest KGE (0.81) and R2 (0.66) compared to ground-measured and was applied to generate the Landsat-like regional and global terrestrial LE. An innovation of our strategy is that the CNN-LSTM-ILE model integrates pixel proximity effects and daily LE variations to enhance the accuracy of 16-day LE estimations. This approach can produce a more reliable Landsat-like global terrestrial LE product to improve the representativeness of heterogeneous regions for monitoring hydrological variables. |
|---|---|
| AbstractList | Accurate estimates of high-spatial-resolution global terrestrial latent heat flux (LE) from Landsat data are crucial for many basic and applied research. Yet current Landsat-derived LE products were developed using single algorithm with large uncertainties and discrepancies. Here we proposed a convolutional neural network-long short-term memory (CNN-LSTM)-based integrated LE (CNN-LSTM-ILE) framework that integrates five Landsat-derived physical LE algorithms, topography-related variables (elevation, slope and aspect) and eddy covariance (EC) observations to estimate 30-m global terrestrial LE. CNN-LSTM-ILE not only conserves good performance of LE estimation from pure deep learning (DL) algorithm, but partially inherits physical mechanism of the individual physical algorithms for improving the generalization of the integration algorithms for extreme cases. CNN-LSTM is an algorithm that combines two deep learning structures (CNN and LSTM) to effectively utilize the spatial and temporal information contained in the forcing inputs. The data were collected from 190 globally distributed EC observations from 2000 to 2015 and were provided by FLUXNET. The cross-validation results indicated that the CNN-LSTM integration algorithm improved the LE estimates by reducing the root mean square error (RMSE) of 5–8 W/m² and increasing Kling-Gupta efficiency (KGE) of 0.05–0.16 when compared with the individual LE algorithms and the results of three other machine learning integration algorithms (multiple linear regression, MLR; random forest, RF; and deep neural networks, DNN). The CNN-LSTM integration algorithm had highest KGE (0.81) and R² (0.66) compared to ground-measured and was applied to generate the Landsat-like regional and global terrestrial LE. An innovation of our strategy is that the CNN-LSTM-ILE model integrates pixel proximity effects and daily LE variations to enhance the accuracy of 16-day LE estimations. This approach can produce a more reliable Landsat-like global terrestrial LE product to improve the representativeness of heterogeneous regions for monitoring hydrological variables. •CNN-LSTM-ILE outperforms all the LE products used in integration method.•CNN-LSTM-ILE that combines information from LE products, EC and topography.•The spatial and temporal information of the forcing inputs were integrated. Accurate estimates of high-spatial-resolution global terrestrial latent heat flux (LE) from Landsat data are crucial for many basic and applied research. Yet current Landsat-derived LE products were developed using single algorithm with large uncertainties and discrepancies. Here we proposed a convolutional neural network-long short-term memory (CNN-LSTM)-based integrated LE (CNN-LSTM-ILE) framework that integrates five Landsat-derived physical LE algorithms, topography-related variables (elevation, slope and aspect) and eddy covariance (EC) observations to estimate 30-m global terrestrial LE. CNN-LSTM-ILE not only conserves good performance of LE estimation from pure deep learning (DL) algorithm, but partially inherits physical mechanism of the individual physical algorithms for improving the generalization of the integration algorithms for extreme cases. CNN-LSTM is an algorithm that combines two deep learning structures (CNN and LSTM) to effectively utilize the spatial and temporal information contained in the forcing inputs. The data were collected from 190 globally distributed EC observations from 2000 to 2015 and were provided by FLUXNET. The cross-validation results indicated that the CNN-LSTM integration algorithm improved the LE estimates by reducing the root mean square error (RMSE) of 5–8 W/m2 and increasing Kling-Gupta efficiency (KGE) of 0.05–0.16 when compared with the individual LE algorithms and the results of three other machine learning integration algorithms (multiple linear regression, MLR; random forest, RF; and deep neural networks, DNN). The CNN-LSTM integration algorithm had highest KGE (0.81) and R2 (0.66) compared to ground-measured and was applied to generate the Landsat-like regional and global terrestrial LE. An innovation of our strategy is that the CNN-LSTM-ILE model integrates pixel proximity effects and daily LE variations to enhance the accuracy of 16-day LE estimations. This approach can produce a more reliable Landsat-like global terrestrial LE product to improve the representativeness of heterogeneous regions for monitoring hydrological variables. |
| ArticleNumber | 109962 |
| Author | Guo, Xiaozheng Zhang, Xiaotong Shang, Ke Jia, Kun Fisher, Joshua B. Xie, Zijing Zhang, Lilin Yao, Yunjun Shao, Changliang Yang, Junming Ning, Jing Liang, Shunlin Chen, Jiquan Yu, Ruiyang Tang, Qingxin Liu, Lu |
| Author_xml | – sequence: 1 givenname: Xiaozheng surname: Guo fullname: Guo, Xiaozheng organization: State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China – sequence: 2 givenname: Yunjun surname: Yao fullname: Yao, Yunjun email: boyyunjun@163.com organization: State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China – sequence: 3 givenname: Qingxin surname: Tang fullname: Tang, Qingxin organization: School of Geography and Environment, Liaocheng University, Liaocheng 252000, China – sequence: 4 givenname: Shunlin surname: Liang fullname: Liang, Shunlin organization: Department of Geographical Sciences, University of Hong Kong, Hongkong 999077, China – sequence: 5 givenname: Changliang surname: Shao fullname: Shao, Changliang organization: State Key Laboratory of Efficient Utilization of Arid and Semi-arid Arable Land in Northern China, National Hulunber Grassland Ecosystem Observation and Research Station, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China – sequence: 6 givenname: Joshua B. orcidid: 0000-0003-4734-9085 surname: Fisher fullname: Fisher, Joshua B. organization: Schmid College of Science and Technology, Chapman University, University Drive, Orange, CA 92866, USA – sequence: 7 givenname: Jiquan orcidid: 0000-0003-0761-9458 surname: Chen fullname: Chen, Jiquan organization: Department of Geography, Environment, and Spatial Sciences, Michigan State University, East Lansing, MI 48823, USA – sequence: 8 givenname: Kun surname: Jia fullname: Jia, Kun organization: State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China – sequence: 9 givenname: Xiaotong surname: Zhang fullname: Zhang, Xiaotong organization: State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China – sequence: 10 givenname: Ke orcidid: 0000-0001-7564-6509 surname: Shang fullname: Shang, Ke organization: School of Space Information, Space Engineering University, Beijing 101416, China – sequence: 11 givenname: Junming surname: Yang fullname: Yang, Junming organization: State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China – sequence: 12 givenname: Ruiyang orcidid: 0000-0001-5000-0779 surname: Yu fullname: Yu, Ruiyang organization: State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China – sequence: 13 givenname: Zijing surname: Xie fullname: Xie, Zijing organization: State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China – sequence: 14 givenname: Lu surname: Liu fullname: Liu, Lu organization: State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China – sequence: 15 givenname: Jing surname: Ning fullname: Ning, Jing organization: State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China – sequence: 16 givenname: Lilin orcidid: 0000-0003-3350-4566 surname: Zhang fullname: Zhang, Lilin organization: Faculty of Geo-Information and Earth Observation (ITC), University of Twente, AE Enschede 7500, the Netherlands |
| BookMark | eNqNkMFu1DAQhi3USmy3PAM-csnWTryJc-BQrYAibcuh7dly7HHqxbEX26mAR-Cp8RLEgQucZjT6_5n5vwt05oMHhF5TsqGEtleHjRyjCXGCvKlJzcq079v6BVpR3jVVXTNyhlZFySva181LdJHSgRBad12_Qj9uZ5ftFDQ4DD7BNDjAkMpIZhs8DgbvpddJ5srZz4BHFwbpcIYYiyra0juZwWf8BDJj4-aveE7Wj1jiETxE6ex30FgDHPHu7q7a3z_cYuszjHG5IN0Yos1P0yU6N9IlePW7rtHj-3cPu5tq_-nDx931vlIN47mCU2pe3u8Nb007MGKI1oSyppGt4pwrZgappCKqY5q1BGBQxPCBwQBcD80avVn2HmP4MpcUYrJJgXPSQ5iTaOi22bZkW-itUbdIVQwpRTDiGAuZ-E1QIk5_iIP4Q1-c6IuFfnG-_cupbP4VOEdp3X_4rxc_FBLPFqJIyoJXoG0ElYUO9p87fgKtqayx |
| CitedBy_id | crossref_primary_10_1007_s13131_024_2392_x crossref_primary_10_1155_etep_2442893 crossref_primary_10_1016_j_ast_2025_110283 crossref_primary_10_3390_f15081472 crossref_primary_10_1016_j_jhydrol_2025_133824 |
| Cites_doi | 10.1016/j.agrformet.2017.01.009 10.1016/j.rse.2021.112440 10.1029/2012JG002027 10.1002/qj.49711146910 10.1016/j.rse.2013.08.045 10.1175/JCLI-D-16-0758.1 10.1016/j.agrformet.2021.108582 10.1016/j.agrformet.2012.11.016 10.3390/s19183929 10.1002/2016GL072235 10.1016/S0168-1923(00)00123-4 10.1016/j.ecoinf.2021.101325 10.1002/2016WR020175 10.1007/s10712-008-9037-z 10.3390/rs12172763 10.1016/j.jhydrol.2015.06.059 10.1109/5.726791 10.1016/j.agrformet.2016.04.008 10.1016/j.agrformet.2010.01.015 10.1016/j.rse.2022.112901 10.5194/hess-26-1579-2022 10.1002/hyp.8391 10.1016/j.jhydrol.2009.08.003 10.5194/bg-12-433-2015 10.1038/s41586-019-1559-7 10.1029/2010JG001566 10.1016/j.isprsjprs.2019.06.008 10.3390/s90503801 10.1016/S0034-4257(02)00096-2 10.1002/2016JD026370 10.1016/S0022-1694(98)00254-6 10.1016/j.rse.2013.05.029 10.1016/j.rse.2021.112600 10.1111/j.1365-2486.2005.001002.x 10.1109/TPAMI.2016.2577031 10.1371/journal.pone.0160150 10.1016/j.rse.2020.111716 10.3390/rs11202333 10.1016/j.agrformet.2012.11.019 10.1016/j.agwat.2010.12.015 10.1162/neco.1997.9.8.1735 10.1016/j.rse.2011.02.019 10.1016/j.rse.2021.112750 10.1038/323533a0 10.1016/S0034-4257(96)00215-5 10.3390/rs14112651 10.1029/2019WR026058 10.1016/j.rse.2007.04.015 10.1016/S0022-1694(98)00253-4 10.1029/2011JD017037 10.1071/BT07151 10.1038/nature14539 10.1016/j.rse.2006.03.014 10.1016/j.biosystemseng.2017.09.015 10.1016/j.rse.2020.112189 10.1016/j.rse.2009.10.012 10.1890/06-0922.1 10.1016/j.jhydrol.2020.124664 10.1002/2017WR022240 10.1109/TGRS.2020.3020125 10.5194/piahs-364-398-2014 10.1016/S0168-1923(02)00109-0 10.1016/j.rse.2008.07.009 10.1016/j.neunet.2005.06.042 10.1023/A:1018991015119 10.1016/S0034-4257(01)00273-5 10.1175/JCLI-D-11-00015.1 10.1016/0168-1923(95)02265-Y 10.1016/j.rse.2019.02.015 10.1016/0893-6080(91)90009-T 10.1016/j.jher.2017.10.006 10.1038/s41591-018-0316-z 10.1109/JSTARS.2010.2048556 10.5194/essd-13-447-2021 10.1016/j.agrformet.2008.06.013 10.1061/(ASCE)0733-9437(2007)133:4(380) 10.1016/j.agwat.2019.105875 10.1016/j.agrformet.2013.11.008 10.1016/j.jhydrol.2017.08.013 10.1016/j.buildenv.2018.10.024 10.3390/rs12040687 10.1029/2007GL030014 10.1016/j.rse.2010.01.022 10.1016/j.agrformet.2009.05.016 10.1016/j.rse.2015.05.013 10.1016/j.neucom.2015.09.116 10.1016/j.isprsjprs.2017.03.022 10.5589/m03-004 10.1016/j.rse.2020.111692 10.1016/j.agrformet.2013.09.003 10.3390/atmos10070373 10.1029/2011RG000373 10.1002/jgrd.50259 10.1016/j.agrformet.2018.05.010 10.1029/2019GL085291 10.5194/hess-6-85-2002 10.1016/j.isprsjprs.2022.01.005 10.1023/A:1010933404324 10.1016/j.isprsjprs.2017.02.006 10.1016/j.envsoft.2004.04.009 10.1016/j.rse.2007.06.025 10.1016/j.rse.2004.12.011 10.5194/bg-10-4055-2013 10.1002/2013JD020864 |
| ContentType | Journal Article |
| Copyright | 2024 Elsevier B.V. |
| Copyright_xml | – notice: 2024 Elsevier B.V. |
| DBID | AAYXX CITATION 7S9 L.6 |
| DOI | 10.1016/j.agrformet.2024.109962 |
| DatabaseName | CrossRef AGRICOLA AGRICOLA - Academic |
| DatabaseTitle | CrossRef AGRICOLA AGRICOLA - Academic |
| DatabaseTitleList | AGRICOLA |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Meteorology & Climatology Agriculture |
| EISSN | 1873-2240 |
| ExternalDocumentID | 10_1016_j_agrformet_2024_109962 S0168192324000777 |
| GroupedDBID | --K --M .~1 0R~ 1B1 1RT 1~. 1~5 23M 4.4 457 4G. 5GY 5VS 7-5 71M 8P~ 9JM 9JN AABNK AABVA AACTN AAEDT AAEDW AAIAV AAIKJ AAKOC AALRI AAOAW AAQFI AATLK AAXUO ABGRD ABJNI ABLJU ABMAC ABQEM ABQYD ABYKQ ACDAQ ACGFS ACIUM ACLVX ACRLP ACSBN ADBBV ADEZE ADQTV AEBSH AEKER AENEX AEQOU AFKWA AFTJW AFXIZ AGHFR AGUBO AGYEJ AHHHB AIEXJ AIKHN AITUG AJOXV AKRWK ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ ATOGT AXJTR BKOJK BLXMC CBWCG CS3 EBS EFJIC EO8 EO9 EP2 EP3 FDB FIRID FNPLU FYGXN G-Q GBLVA IHE IMUCA J1W KOM M41 MO0 N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. Q38 RIG ROL RPZ SAB SDF SDG SDP SES SEW SPC SPCBC SSA SSE SSZ T5K WH7 Y6R ZMT ~02 ~G- ~KM 9DU AAHBH AALCJ AAQXK AATTM AAXKI AAYWO AAYXX ABEFU ABFNM ABUFD ABWVN ABXDB ACLOT ACRPL ACVFH ADCNI ADMUD ADNMO AEIPS AEUPX AFJKZ AFPUW AGQPQ AIGII AIIUN AKBMS AKYEP ANKPU APXCP ASPBG AVWKF AZFZN CITATION EFKBS EFLBG EJD FEDTE FGOYB G-2 HLV HMA HVGLF HZ~ LW9 LY3 R2- SEP WUQ ~HD 7S9 L.6 |
| ID | FETCH-LOGICAL-c348t-e101681279f86f6b40f0dd01433a6c888c4fbacac0c74d460eebc0f8b4ebe8db3 |
| ISICitedReferencesCount | 4 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001209198300001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0168-1923 |
| IngestDate | Thu Oct 02 11:41:45 EDT 2025 Sat Nov 29 07:26:20 EST 2025 Tue Nov 18 21:55:51 EST 2025 Sat Mar 30 16:18:28 EDT 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Integration algorithm High-spatial-resolution products CNN-LSTM Latent heat flux Landsat |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c348t-e101681279f86f6b40f0dd01433a6c888c4fbacac0c74d460eebc0f8b4ebe8db3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| ORCID | 0000-0001-7564-6509 0000-0003-4734-9085 0000-0003-0761-9458 0000-0003-3350-4566 0000-0001-5000-0779 |
| PQID | 3153560587 |
| PQPubID | 24069 |
| ParticipantIDs | proquest_miscellaneous_3153560587 crossref_primary_10_1016_j_agrformet_2024_109962 crossref_citationtrail_10_1016_j_agrformet_2024_109962 elsevier_sciencedirect_doi_10_1016_j_agrformet_2024_109962 |
| PublicationCentury | 2000 |
| PublicationDate | 2024-04-15 |
| PublicationDateYYYYMMDD | 2024-04-15 |
| PublicationDate_xml | – month: 04 year: 2024 text: 2024-04-15 day: 15 |
| PublicationDecade | 2020 |
| PublicationTitle | Agricultural and forest meteorology |
| PublicationYear | 2024 |
| Publisher | Elsevier B.V |
| Publisher_xml | – name: Elsevier B.V |
| References | Shen, Jiang, Li, Cheng, Zeng, Zhang (bib0091) 2020; 240 Yuan, Liu, Yu (bib0125) 2010; 114 Foken (bib0034) 2008; 18 Widlowski, Pinty, Lavergne, Verstraete, Gobron (bib0109) 2006; 103 Liu, Xu, Song, Zhao, Ge, Xu, Ma, Zhu, Jia, Zhang (bib0068) 2016; 230 Shuttleworth, Wallace (bib0094) 1985; 111 Huete, Didan, Miura, Rodriguez, Gao, Ferreira (bib0047) 2002; 83 Kandasamy, Baret, Verger, Neveux, Weiss (bib0055) 2013; 10 McCabe, Aragon, Houborg, Mascaro (bib0074) 2017; 53 Mu, Heinsch, Zhao, Running (bib0077) 2007; 111 Gupta, Kling, Yilmaz, Martinez (bib0042) 2009; 377 Kraft, Jung, Körner, Koirala, Reichstein (bib0061) 2021; 26 Ren, He, Girshick, Sun (bib0084) 2017; 39 Hornik (bib0046) 1991; 4 Anderson, Norman, Kustas, Houborg, Starks, Agam (bib0006) 2008; 112 Talsma, Good, Jimenez, Martens, Fisher, Miralles, McCabe, Purdy (bib0098) 2018; 260-261 Twine, Kustas, Norman, Cook, Houser, Meyers, Prueger, Starks, Wesely (bib0101) 2000; 103 Cascone, Coma, Gagliano, Pérez (bib0017) 2019; 147 Mu, Zhao, Running (bib0078) 2011; 115 Masolele, De Sy, Herold, Marcos, Verbesselt, Gieseke, Mullissa, Martius (bib0072) 2021; 264 Chang, Luo (bib0018) 2019; 11 Zhao, Gentine, Reichstein, Zhang, Zhou, Wen, Lin, Li, Qiu (bib0129) 2019; 46 Allen, Pereira, Howell, Jensen (bib0002) 2011; 98 Jin, Li, Liang, Ma, Xie, Liu, He (bib0050) 2022; 6 Amazirh, Er-Raki, Chehbouni, Rivalland, Diarra, Khabba, Ezzahar, Merlin (bib0004) 2017; 164 Jung, Reichstein, Margolis, Cescatti, Richardson, Arain, Arneth, Bernhofer, Bonal, Chen, Gianelle, Gobron, Kiely, Kutsch, Lasslop, Law, Lindroth, Merbold, Montagnani, Moors, Papale, Sottocornola, Vaccari, Williams (bib0052) 2011; 116 Fisher, Lee, Purdy, Halverson, Dohlen, Cawse-Nicholson (bib0031) 2020; 56 Shang, Yao, Liang, Zhang, Fisher, Chen, Liu, Xu, Zhang, Jia, Zhang, Yang, Bei, Guo, Yu, Xie, Zhang (bib0088) 2021; 308-309 Yuan, Shen, Li, Li, Li, Jiang, Xu, Tan, Yang, Wang, Gao, Zhang (bib0124) 2020; 241 Bhattarai, Shaw, Quackenbush, Im, Niraula (bib0013) 2016; 49 Fisher, Tu, Baldocchi (bib0033) 2008; 112 Hochreiter, Schmidhuber (bib0045) 1997; 9 McVicar, Jupp (bib0076) 2002; 79 Wang, Dickinson, Wild, Liang (bib0105) 2010; 115 Liang, Feng, Fisher, Li, Li, Liu, Ma, Miyata, Mu, Sun, Tang, Wang, Wen, Xue, Yu, Zha, Zhang, Zhang, Zhao, Zhao, Yuan (bib0019) 2014; 140 Ershadi, Mccabe, Evans, Chaney, Wood (bib0027) 2014; 187 Anderson, Yang, Xue, Knipper, Yang, Gao, Hain, Kustas, Cawse-Nicholson, Hulley, Fisher, Alfieri, Meyers, Prueger, Baldocchi, Rey-Sanchez (bib0007) 2021; 252 Bastiaanssen, Menenti, Feddes, Holtslag (bib0011) 1998; 212 Mallick, Jarvis, Wohlfahrt, Kiely, Hirano, Miyata, Yamamoto, Hoffmann (bib0073) 2015; 12 Talsma, Good, Miralles, Fisher, Martens, Jiménez, Purdy (bib0097) 2018; 10 Shi, Chen, Li, Wang (bib0093) 2020; 28 Yao, Liang, Yu, Chen, Liu, Lin, Fisher, McVicar, Cheng, Jia, Zhang, Xie, Jiang, Sun (bib0122) 2017; 122 Yao, Liang, Li, Zhang, Chen, Jia, Zhang, Fisher, Wang, Zhang, Xu, Shao, Posse, Li, Magliulo, Varlagin, Moors, Boike, Macfarlane, Kato, Buchmann, Billesbach, Beringer, Wolf, Papuga, Wohlfahrt, Montagnani, Ardo, Paul-Limoges, Emmel, Hortnagl, Sachs, Gruening, Gioli, Lopez-Ballesteros, Steinbrecher, Gielen (bib0120) 2017; 553 Rumelhart, Hinton, Williams (bib0087) 1986; 323 Yamaç, Todorovic (bib0113) 2020; 228 Zhang, Liang, Zhu, Ma, He (bib0127) 2022; 185 Burchard-Levine, Nieto, Riaño, Migliavacca, El-Madany, Guzinski, Carrara, Martín (bib0016) 2021; 260 Oishi, Oren, Stoy (bib0080) 2008; 148 Esteva, Robicquet, Ramsundar, Kuleshov, DePristo, Chou, Cui, Corrado, Thrun, Dean (bib0028) 2019; 25 Li, Tang, Wan, Bi, Zhou, Tang, Yan, Zhang (bib0065) 2009; 9 Tang, Li, Tang (bib0099) 2010; 114 Yang, Shang (bib0114) 2013; 118 Glenn, Doody, Guerschman, Huete, King, McVicar, Van Dijk, Van Niel, Yebra, Zhang (bib0036) 2011; 25 Ham, Kim, Luo (bib0043) 2019; 573 Norman, Kustas, Humes (bib0079) 1995; 77 Sun, Di, Sun, Shen, Lai (bib0096) 2019 Guo, Liu, Oerlemans, Lao, Wu, Lew (bib0041) 2016; 187 Bai, Zhang, Bhattarai, Mallick, Liu, Tang, Im, Guo, Zhang (bib0009) 2021; 298 Khaldi, Khaldi, Hamimed (bib0058) 2014; 364 Wei, Wang, Jasechko, Lee, Yoshimura (bib0108) 2017 Baldocchi (bib0010) 2008; 56 Kalma, McVicar, McCabe (bib0053) 2008; 29 Kool, Agam, Lazarovitch, Heitman, Sauer, Ben-Gal (bib0059) 2014; 184 Anderson, Norman, Diak, Kustas, Mecikalski (bib0005) 1997; 60 Breiman (bib0015) 2001; 45 Allen, Tasumi, Trezza (bib0003) 2007; 133 Jiang, Han, Liang, Liang, Yin, Peng, He, Ma (bib0049) 2023; 23 Perez-Priego, El-Madany, Migliavacca, Kowalski, Jung, Carrara, Kolle, Martin, Pacheco-Labrador, Moreno, Reichstein (bib0082) 2017; 236 Jia, Liu, Xu, Chen, Zhu (bib0048) 2012; 117 Wulder, Loveland, Roy, Crawford, Masek, Woodcock, Allen, Anderson, Belward, Cohen, Dwyer, Erb, Gao, Griffiths, Helder, Hermosillo, Hipple, Hostert, Hughes, Huntington, Johnson, Kennedy, Kilic, Li, Lymburner, McCorkel, Pahlevan, Scambos, Schaaf, Schott, Sheng, Storey, Vermote, Vogelmann, White, Wynne, Zhu (bib0112) 2019; 225 Zamani Joharestani, Cao, Ni, Bashir, Talebiesfandarani (bib0126) 2019; 10 Song, Wang, He, Wang, Liang (bib0095) 2022; 6 Gelaro, McCarty, Suarez, Todling, Molod, Takacs, Randles, Darmenov, Bosilovich, Reichle, Wargan, Coy, Cullather, Draper, Akella, Buchard, Conaty, da Silva, Gu, Kim, Koster, Lucchesi, Merkova, Nielsen, Partyka, Pawson, Putman, Rienecker, Schubert, Sienkiewicz, Zhao (bib0035) 2017; 30 Guo, Yao, Zhang, Lin, Jiang, Jia, Zhang, Xie, Zhang, Shang, Yang, Bei (bib0040) 2020; 12 Yao, Liang, Fisher, Zhang, Cheng, Chen, Jia, Zhang, Bei, Shang, Guo, Yang (bib0117) 2021; 59 Allen, Pereira, Raes, Smith, Allen, Pereira, Martin (bib0001) 1998; 56 Kormann, Meixner (bib0060) 2001; 99 Fisher, Melton, Middleton, Hain, Anderson, Allen, McCabe, Hook, Baldocchi, Townsend, Kilic, Tu, Miralles, Perret, Lagouarde, Waliser, Purdy, French, Schimel, Famiglietti, Stephens, Wood (bib0030) 2017; 53 Demarty, Chevallier, Friend, Viovy, Piao, Ciais (bib0024) 2007; 34 Lecun, Bottou, Bengio, Haffner (bib0064) 1998; 86 Liang, Wang, Zhang, Wild (bib0066) 2010; 3 Wilson, Goldstein, Falge, Aubinet, Baldocchi, Berbigier, Bernhofer, Ceulemans, Dolman, Field, Grelle, Ibrom, Law, Kowalski, Meyers, Moncrieff, Monson, Oechel, Tenhunen, Valentini, Verma (bib0110) 2002; 113 Elnashar, Wang, Wu, Zhu, Zeng (bib0026) 2021; 13 LeCun, Bengio, Hinton (bib0063) 2015; 521 Rienecker, Suarez, Gelaro, Todling, Bacmeister, Liu, Bosilovich, Schubert, Takacs, Kim, Bloom, Chen, Collins, Conaty, Da Silva, Gu, Joiner, Koster, Lucchesi, Molod, Owens, Pawson, Pegion, Redder, Reichle, Robertson, Ruddick, Sienkiewicz, Woollen (bib0085) 2011; 24 Zhao, Heinsch, Nemani (bib0128) 2005; 95 Feng, Li, Yao, Liang, Chen, Zhao, Jia, Pinter, McCaughey (bib0032) 2016; 11 Kessomkiat, Franssen, Graf, Vereecken (bib0057) 2013; 171 Yebra, Dennison, Chuvieco, Riaño, Zylstra, Hunt, Danson, Qi, Jurdao (bib0123) 2013; 136 Yao, Liang, Cheng, Liu, Fisher, Zhang, Jia, Zhao, Qin, Zhao, Han, Zhou, Zhou, Li, Zhao (bib0116) 2013; 171-172 Wagle, Bhattarai, Gowda, Kakani (bib0103) 2017; 128 Wang, Dickinson (bib0104) 2012; 50 Graves, Schmidhuber (bib0038) 2005; 18 Fisher, DeBiase, Qi, Xu, Goldstein (bib0029) 2005; 20 Mahrt (bib0071) 2010; 150 Yao, Liang, Li, Hong, Fisher, Zhang, Chen, Cheng, Zhao, Zhang, Jiang, Sun, Jia, Wang, Chen, Mu, Feng (bib0119) 2014; 119 Bastiaanssen, Pelgrum, Wang, Ma, Moreno, Roerink, van der Wal (bib0012) 1998; 212 Ke, Im, Park, Gong (bib0056) 2017; 126 Shirmard, Farahbakhsh, Müller, Chandra (bib0092) 2022; 268 Lin, Huang, Zheng, Zhang, Yuan (bib0067) 2022; 14 Goulden, Anderson, Bales, Kelly, Meadows, Winston (bib0037) 2012; 117 Bai, Bhattarai, Mallick, Zhang, Hu, Zhang (bib0008) 2022; 271 Tsagkatakis, Aidini, Fotiadou, Giannopoulos, Tsakalides (bib0100) 2019; 19 Jin, Li, Xu, Xiao, Jiang, Xue (bib0051) 2019; 154 Boulila, Ghandorh, Khan, Ahmed, Ahmad (bib0014) 2021; 64 Yao, Liang, Li, Chen, Wang, Jia, Cheng, Jiang, Fisher, Mu, Grunwald, Bemhofer, Roupsard (bib0118) 2015; 169 Kustas, Anderson (bib0062) 2009; 149 Shang, Yao, Li, Yang, Jia, Zhang, Chen, Bei, Guo (bib0089) 2020; 12 Reichstein, Falge, Baldocchi, Papale, Aubinet, Berbigier, Bernhofer, Buchmann, Gilmanov, Granier, Grünwald, Havránková, Ilvesniemi, Janous, Knohl, Laurila, Lohila, Loustau, Matteucci, Meyers, Miglietta, Ourcival, Pumpanen, Rambal, Rotenberg, Sanz, Tenhunen, Seufert, Vaccari, Vesala, Yakir, Valentini (bib0083) 2005; 11 Penman (bib0081) 1948; 193 Chen, Yuan, Xia, Fisher, Dong, Zhang, Liang, Ye, Cai, Feng (bib0021) 2015; 528 Wu, Yang, Liu, Wang (bib0111) 2020; 584 Eklundh, Hall, Eriksson, Ardo, Pilesjo (bib0025) 2003; 29 Su (bib131) 2002; 6 Kormann (10.1016/j.agrformet.2024.109962_bib0060) 2001; 99 Bastiaanssen (10.1016/j.agrformet.2024.109962_bib0012) 1998; 212 Talsma (10.1016/j.agrformet.2024.109962_bib0098) 2018; 260-261 Bhattarai (10.1016/j.agrformet.2024.109962_bib0013) 2016; 49 Kessomkiat (10.1016/j.agrformet.2024.109962_bib0057) 2013; 171 Talsma (10.1016/j.agrformet.2024.109962_bib0097) 2018; 10 Eklundh (10.1016/j.agrformet.2024.109962_bib0025) 2003; 29 Kool (10.1016/j.agrformet.2024.109962_bib0059) 2014; 184 Mallick (10.1016/j.agrformet.2024.109962_bib0073) 2015; 12 Guo (10.1016/j.agrformet.2024.109962_bib0041) 2016; 187 Cascone (10.1016/j.agrformet.2024.109962_bib0017) 2019; 147 Jin (10.1016/j.agrformet.2024.109962_bib0050) 2022; 6 Norman (10.1016/j.agrformet.2024.109962_bib0079) 1995; 77 Allen (10.1016/j.agrformet.2024.109962_bib0001) 1998; 56 Jia (10.1016/j.agrformet.2024.109962_bib0048) 2012; 117 Yao (10.1016/j.agrformet.2024.109962_bib0122) 2017; 122 Ham (10.1016/j.agrformet.2024.109962_bib0043) 2019; 573 Yao (10.1016/j.agrformet.2024.109962_bib0120) 2017; 553 Graves (10.1016/j.agrformet.2024.109962_bib0038) 2005; 18 Chang (10.1016/j.agrformet.2024.109962_bib0018) 2019; 11 Zhao (10.1016/j.agrformet.2024.109962_bib0129) 2019; 46 Mu (10.1016/j.agrformet.2024.109962_bib0077) 2007; 111 Su (10.1016/j.agrformet.2024.109962_bib131) 2002; 6 Anderson (10.1016/j.agrformet.2024.109962_bib0006) 2008; 112 Sun (10.1016/j.agrformet.2024.109962_bib0096) 2019 Feng (10.1016/j.agrformet.2024.109962_bib0032) 2016; 11 Li (10.1016/j.agrformet.2024.109962_bib0065) 2009; 9 Fisher (10.1016/j.agrformet.2024.109962_bib0030) 2017; 53 Liu (10.1016/j.agrformet.2024.109962_bib0068) 2016; 230 Zhao (10.1016/j.agrformet.2024.109962_bib0128) 2005; 95 Allen (10.1016/j.agrformet.2024.109962_bib0003) 2007; 133 Glenn (10.1016/j.agrformet.2024.109962_bib0036) 2011; 25 Widlowski (10.1016/j.agrformet.2024.109962_bib0109) 2006; 103 McVicar (10.1016/j.agrformet.2024.109962_bib0076) 2002; 79 Mu (10.1016/j.agrformet.2024.109962_bib0078) 2011; 115 Shirmard (10.1016/j.agrformet.2024.109962_bib0092) 2022; 268 Yuan (10.1016/j.agrformet.2024.109962_bib0125) 2010; 114 Wang (10.1016/j.agrformet.2024.109962_bib0104) 2012; 50 Kraft (10.1016/j.agrformet.2024.109962_bib0061) 2021; 26 Wu (10.1016/j.agrformet.2024.109962_bib0111) 2020; 584 Fisher (10.1016/j.agrformet.2024.109962_bib0029) 2005; 20 Tsagkatakis (10.1016/j.agrformet.2024.109962_bib0100) 2019; 19 Baldocchi (10.1016/j.agrformet.2024.109962_bib0010) 2008; 56 Foken (10.1016/j.agrformet.2024.109962_bib0034) 2008; 18 Khaldi (10.1016/j.agrformet.2024.109962_bib0058) 2014; 364 Burchard-Levine (10.1016/j.agrformet.2024.109962_bib0016) 2021; 260 Kandasamy (10.1016/j.agrformet.2024.109962_bib0055) 2013; 10 Tang (10.1016/j.agrformet.2024.109962_bib0099) 2010; 114 Bastiaanssen (10.1016/j.agrformet.2024.109962_bib0011) 1998; 212 Breiman (10.1016/j.agrformet.2024.109962_bib0015) 2001; 45 Lin (10.1016/j.agrformet.2024.109962_bib0067) 2022; 14 Wilson (10.1016/j.agrformet.2024.109962_bib0110) 2002; 113 Demarty (10.1016/j.agrformet.2024.109962_bib0024) 2007; 34 Shi (10.1016/j.agrformet.2024.109962_bib0093) 2020; 28 Perez-Priego (10.1016/j.agrformet.2024.109962_bib0082) 2017; 236 Zhang (10.1016/j.agrformet.2024.109962_bib0127) 2022; 185 Anderson (10.1016/j.agrformet.2024.109962_bib0005) 1997; 60 Jin (10.1016/j.agrformet.2024.109962_bib0051) 2019; 154 Anderson (10.1016/j.agrformet.2024.109962_bib0007) 2021; 252 Reichstein (10.1016/j.agrformet.2024.109962_bib0083) 2005; 11 Rienecker (10.1016/j.agrformet.2024.109962_bib0085) 2011; 24 Song (10.1016/j.agrformet.2024.109962_bib0095) 2022; 6 Yuan (10.1016/j.agrformet.2024.109962_bib0124) 2020; 241 Bai (10.1016/j.agrformet.2024.109962_bib0008) 2022; 271 Chen (10.1016/j.agrformet.2024.109962_bib0021) 2015; 528 Fisher (10.1016/j.agrformet.2024.109962_bib0031) 2020; 56 Bai (10.1016/j.agrformet.2024.109962_bib0009) 2021; 298 Boulila (10.1016/j.agrformet.2024.109962_bib0014) 2021; 64 Zamani Joharestani (10.1016/j.agrformet.2024.109962_bib0126) 2019; 10 Lecun (10.1016/j.agrformet.2024.109962_bib0064) 1998; 86 Yao (10.1016/j.agrformet.2024.109962_bib0116) 2013; 171-172 Goulden (10.1016/j.agrformet.2024.109962_bib0037) 2012; 117 Shang (10.1016/j.agrformet.2024.109962_bib0088) 2021; 308-309 Gelaro (10.1016/j.agrformet.2024.109962_bib0035) 2017; 30 Oishi (10.1016/j.agrformet.2024.109962_bib0080) 2008; 148 Jiang (10.1016/j.agrformet.2024.109962_bib0049) 2023; 23 Yamaç (10.1016/j.agrformet.2024.109962_bib0113) 2020; 228 Guo (10.1016/j.agrformet.2024.109962_bib0040) 2020; 12 Wei (10.1016/j.agrformet.2024.109962_bib0108) 2017 Ershadi (10.1016/j.agrformet.2024.109962_bib0027) 2014; 187 Allen (10.1016/j.agrformet.2024.109962_bib0002) 2011; 98 Ke (10.1016/j.agrformet.2024.109962_bib0056) 2017; 126 Jung (10.1016/j.agrformet.2024.109962_bib0052) 2011; 116 LeCun (10.1016/j.agrformet.2024.109962_bib0063) 2015; 521 Wulder (10.1016/j.agrformet.2024.109962_bib0112) 2019; 225 Amazirh (10.1016/j.agrformet.2024.109962_bib0004) 2017; 164 Wagle (10.1016/j.agrformet.2024.109962_bib0103) 2017; 128 Ren (10.1016/j.agrformet.2024.109962_bib0084) 2017; 39 Yebra (10.1016/j.agrformet.2024.109962_bib0123) 2013; 136 Elnashar (10.1016/j.agrformet.2024.109962_bib0026) 2021; 13 Liang (10.1016/j.agrformet.2024.109962_bib0066) 2010; 3 Penman (10.1016/j.agrformet.2024.109962_bib0081) 1948; 193 Huete (10.1016/j.agrformet.2024.109962_bib0047) 2002; 83 Wang (10.1016/j.agrformet.2024.109962_bib0105) 2010; 115 Yao (10.1016/j.agrformet.2024.109962_bib0118) 2015; 169 Gupta (10.1016/j.agrformet.2024.109962_bib0042) 2009; 377 Fisher (10.1016/j.agrformet.2024.109962_bib0033) 2008; 112 Masolele (10.1016/j.agrformet.2024.109962_bib0072) 2021; 264 Kustas (10.1016/j.agrformet.2024.109962_bib0062) 2009; 149 Shen (10.1016/j.agrformet.2024.109962_bib0091) 2020; 240 Twine (10.1016/j.agrformet.2024.109962_bib0101) 2000; 103 Shang (10.1016/j.agrformet.2024.109962_bib0089) 2020; 12 Yao (10.1016/j.agrformet.2024.109962_bib0119) 2014; 119 Hochreiter (10.1016/j.agrformet.2024.109962_bib0045) 1997; 9 Liang (10.1016/j.agrformet.2024.109962_bib0019) 2014; 140 Mahrt (10.1016/j.agrformet.2024.109962_bib0071) 2010; 150 Rumelhart (10.1016/j.agrformet.2024.109962_bib0087) 1986; 323 Yao (10.1016/j.agrformet.2024.109962_bib0117) 2021; 59 Shuttleworth (10.1016/j.agrformet.2024.109962_bib0094) 1985; 111 Yang (10.1016/j.agrformet.2024.109962_bib0114) 2013; 118 Kalma (10.1016/j.agrformet.2024.109962_bib0053) 2008; 29 McCabe (10.1016/j.agrformet.2024.109962_bib0074) 2017; 53 Esteva (10.1016/j.agrformet.2024.109962_bib0028) 2019; 25 Hornik (10.1016/j.agrformet.2024.109962_bib0046) 1991; 4 |
| References_xml | – volume: 49 start-page: 75 year: 2016 end-page: 86 ident: bib0013 article-title: Evaluating five remote sensing based single-source surface energy balance models for estimating daily evapotranspiration in a humid subtropical climate publication-title: Int. J. Appl. Earth Obs. Geoinf. – volume: 6 start-page: 85 year: 2002 end-page: 100 ident: bib131 article-title: The Surface Energy Balance System (SEBS) for estimation of turbulent heatfluxes publication-title: Hydrol. Earth Syst. Sci. – volume: 11 year: 2019 ident: bib0018 article-title: Bidirectional convolutional LSTM neural network for remote sensing image super-resolution publication-title: Remote Sens. – volume: 114 start-page: 540 year: 2010 end-page: 551 ident: bib0099 article-title: An application of the Ts–VI triangle method with enhanced edges determination for evapotranspiration estimation from MODIS data in arid and semi-arid regions: implementation and validation publication-title: Remote Sens. Environ. – volume: 60 start-page: 195 year: 1997 end-page: 216 ident: bib0005 article-title: A two-source time-integrated model for estimating surface fluxes using thermal infrared remote sensing publication-title: Remote Sens. Environ. – volume: 126 start-page: 79 year: 2017 end-page: 93 ident: bib0056 article-title: Spatiotemporal downscaling approaches for monitoring 8-day 30m actual evapotranspiration publication-title: ISPRS J. Photogramm. Remote Sens. – volume: 133 start-page: 380 year: 2007 end-page: 394 ident: bib0003 article-title: Satellite-based energy balance for mapping evapotranspiration with internalized calibration (METRIC) - model publication-title: J. Irrig. Drain. Eng. – volume: 18 start-page: 602 year: 2005 end-page: 610 ident: bib0038 article-title: Framewise phoneme classification with bidirectional LSTM and other neural network architectures publication-title: Neural Networks – volume: 95 start-page: 164 year: 2005 end-page: 176 ident: bib0128 article-title: Improvements of the MODIS terrestrial gross and net primary production global data set publication-title: Remote Sens. Environ. – volume: 115 start-page: 1781 year: 2011 end-page: 1800 ident: bib0078 article-title: Improvements to a MODIS global terrestrial evapotranspiration algorithm publication-title: Remote Sens. Environ. – volume: 149 start-page: 2071 year: 2009 end-page: 2081 ident: bib0062 article-title: Advances in thermal infrared remote sensing for land surface modeling publication-title: Agric. For. Meteorol. – volume: 236 start-page: 87 year: 2017 end-page: 99 ident: bib0082 article-title: Evaluation of eddy covariance latent heat fluxes with independent lysimeter and sapflow estimates in a Mediterranean savannah ecosystem publication-title: Agric. For. Meteorol. – volume: 103 start-page: 279 year: 2000 end-page: 300 ident: bib0101 article-title: Correcting eddy-covariance flux underestimates over a grassland publication-title: Agric. For. Meteorol. – volume: 112 start-page: 901 year: 2008 end-page: 919 ident: bib0033 article-title: Global estimates of the land-atmosphere water flux based on monthly AVHRR and ISLSCP-II data, validated at 16 FLUXNET sites publication-title: Remote Sens. Environ. – volume: 45 start-page: 5 year: 2001 end-page: 32 ident: bib0015 article-title: Random forests publication-title: Mach. Learn. – volume: 29 start-page: 349 year: 2003 end-page: 362 ident: bib0025 article-title: Investigating the use of landsat thematic mapper data for estimation of forest leaf area index in southern Sweden publication-title: Can. J. Remote Sens. – volume: 271 year: 2022 ident: bib0008 article-title: Thermally derived evapotranspiration from the surface temperature initiated closure (STIC) model improves cropland GPP estimates under dry conditions publication-title: Remote Sens. Environ. – volume: 11 year: 2016 ident: bib0032 article-title: An empirical orthogonal function-based algorithm for estimating terrestrial latent heat flux from eddy covariance, meteorological and satellite observations publication-title: PLoS One – volume: 184 start-page: 56 year: 2014 end-page: 70 ident: bib0059 article-title: A review of approaches for evapotranspiration partitioning publication-title: Agric. For. Meteorol. – year: 2017 ident: bib0108 article-title: Revisiting the contribution of transpiration to global terrestrial evapotranspiration publication-title: Geophys. Res. Lett. – volume: 30 start-page: 5419 year: 2017 end-page: 5454 ident: bib0035 article-title: The modern-era retrospective analysis for research and applications, version 2 (MERRA-2) publication-title: J. Clim. – volume: 50 year: 2012 ident: bib0104 article-title: A review of global terrestrial evapotranspiration: observation, modeling, climatology, and climatic variability publication-title: Rev. Geophys. – volume: 6 year: 2022 ident: bib0095 article-title: Estimation and validation of 30m fractional vegetation cover over China through integrated use of Landsat 8 and Gaofen 2 data publication-title: Sci. Remote Sens. – volume: 39 start-page: 1137 year: 2017 end-page: 1149 ident: bib0084 article-title: Faster R-CNN: towards real-time object detection with region proposal networks publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – volume: 59 start-page: 4105 year: 2021 end-page: 4119 ident: bib0117 article-title: A novel NIR-red spectral domain evapotranspiration model from the Chinese GF-1 satellite: application to the Huailai agricultural region of China publication-title: IEEE Trans. Geosci. Remote Sens. – volume: 212 start-page: 198 year: 1998 end-page: 212 ident: bib0011 article-title: A remote sensing surface energy balance algorithm for land (SEBAL) - 1. Formulation publication-title: J. Hydrol. – volume: 86 start-page: 2278 year: 1998 end-page: 2324 ident: bib0064 article-title: Gradient-based learning applied to document recognition publication-title: Proc. IEEE – volume: 11 start-page: 1424 year: 2005 end-page: 1439 ident: bib0083 article-title: On the separation of net ecosystem exchange into assimilation and ecosystem respiration: review and improved algorithm publication-title: Glob. Change Biol. – volume: 140 start-page: 279 year: 2014 end-page: 293 ident: bib0019 article-title: Comparison of satellite-based evapotranspiration models over terrestrial ecosystems in China publication-title: Remote Sens. Environ. – volume: 573 start-page: 568 year: 2019 end-page: 572 ident: bib0043 article-title: Deep learning for multi-year ENSO forecasts publication-title: Nature – volume: 6 year: 2022 ident: bib0050 article-title: Generating high spatial resolution GLASS FAPAR product from Landsat images publication-title: Sci. Remote Sens. – volume: 264 year: 2021 ident: bib0072 article-title: Spatial and temporal deep learning methods for deriving land-use following deforestation: a pan-tropical case study using Landsat time series publication-title: Remote Sens. Environ. – volume: 117 year: 2012 ident: bib0048 article-title: Validation of remotely sensed evapotranspiration over the Hai River Basin, China publication-title: J. Geophys. Res. Atmos. – start-page: 19 year: 2019 ident: bib0096 article-title: County-level soybean yield prediction using deep CNN-LSTM model publication-title: Sensors – volume: 79 start-page: 199 year: 2002 end-page: 212 ident: bib0076 article-title: Using covariates to spatially interpolate moisture availability in the Murray–Darling Basin: a novel use of remotely sensed data publication-title: Remote Sens. Environ. – volume: 98 start-page: 899 year: 2011 end-page: 920 ident: bib0002 article-title: Evapotranspiration information reporting: I. Factors governing measurement accuracy publication-title: Agric. Water Manag. – volume: 171 start-page: 203 year: 2013 end-page: 219 ident: bib0057 article-title: Estimating random errors of eddy covariance data: an extended two-tower approach publication-title: Agric. For. Meteorol. – volume: 12 year: 2020 ident: bib0040 article-title: Discrepancies in the simulated global terrestrial latent heat flux from GLASS and MERRA-2 surface net radiation products publication-title: Remote Sens. – volume: 364 start-page: 398 year: 2014 end-page: 403 ident: bib0058 article-title: Using the priestley-taylor expression for estimating actual evapotranspiration from satellite landsat ETM + data publication-title: Proc. IAHS – volume: 99 start-page: 207 year: 2001 end-page: 224 ident: bib0060 article-title: An analytical footprint model for non-neutral stratification publication-title: Bound. Layer Meteorol. – volume: 10 start-page: 4055 year: 2013 end-page: 4071 ident: bib0055 article-title: A comparison of methods for smoothing and gap filling time series of remote sensing observations - application to MODIS LAI products publication-title: Biogeosciences – volume: 83 start-page: 195 year: 2002 end-page: 213 ident: bib0047 article-title: Overview of the radiometric and biophysical performance of the MODIS vegetation indices publication-title: Remote Sens. Environ. – volume: 14 start-page: 2651 year: 2022 ident: bib0067 article-title: An open data approach for estimating vegetation gross primary production at fine spatial resolution publication-title: Remote Sens. – volume: 24 start-page: 3624 year: 2011 end-page: 3648 ident: bib0085 article-title: MERRA: nASA's modern-era retrospective analysis for research and applications publication-title: J. Clim. – volume: 3 start-page: 225 year: 2010 end-page: 240 ident: bib0066 article-title: Review on estimation of land surface radiation and energy budgets from ground measurement, remote sensing and model simulations publication-title: IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. – volume: 53 year: 2017 ident: bib0074 article-title: CubeSats in hydrology: ultrahigh-resolution insights into vegetation dynamics and terrestrial evaporation publication-title: Water Resour. Res. – volume: 252 year: 2021 ident: bib0007 article-title: Interoperability of ECOSTRESS and Landsat for mapping evapotranspiration time series at sub-field scales publication-title: Remote Sens. Environ. – volume: 19 start-page: 3929 year: 2019 ident: bib0100 article-title: Survey of deep-learning approaches for remote sensing observation enhancement publication-title: Sensors – volume: 187 start-page: 27 year: 2016 end-page: 48 ident: bib0041 article-title: Deep learning for visual understanding: a review publication-title: Neurocomputing. – volume: 25 start-page: 24 year: 2019 end-page: 29 ident: bib0028 article-title: A guide to deep learning in healthcare publication-title: Nat. Med. – volume: 115 year: 2010 ident: bib0105 article-title: Evidence for decadal variation in global terrestrial evapotranspiration between 1982 and 2002: 1. Model development publication-title: J. Geophys. Res. Atmos. – volume: 377 start-page: 80 year: 2009 end-page: 91 ident: bib0042 article-title: Decomposition of the mean squared error and NSE performance criteria: implications for improving hydrological modelling publication-title: J. Hydrol. – volume: 323 start-page: 533 year: 1986 end-page: 536 ident: bib0087 article-title: Learning representations by back-propagating errors publication-title: Nature – volume: 10 year: 2019 ident: bib0126 article-title: PM2.5 prediction based on random forest, XGBoost, and deep learning using multisource remote sensing data publication-title: Atmosphere – volume: 128 start-page: 192 year: 2017 end-page: 203 ident: bib0103 article-title: Performance of five surface energy balance models for estimating daily evapotranspiration in high biomass sorghum publication-title: ISPRS J. Photogramm. Remote Sens. – volume: 64 year: 2021 ident: bib0014 article-title: A novel CNN-LSTM-based approach to predict urban expansion publication-title: Ecol. Inform. – volume: 230 start-page: 97 year: 2016 end-page: 113 ident: bib0068 article-title: Upscaling evapotranspiration measurements from multi-site to the satellite pixel scale over heterogeneous land surfaces publication-title: Agric. For. Meteorol. – volume: 553 start-page: 508 year: 2017 end-page: 526 ident: bib0120 article-title: Estimation of high-resolution terrestrial evapotranspiration from Landsat data using a simple Taylor skill fusion method publication-title: J. Hydrol. – volume: 308-309 year: 2021 ident: bib0088 article-title: DNN-MET: a deep neural networks method to integrate satellite-derived evapotranspiration products, eddy covariance observations and ancillary information publication-title: Agric. For. Meteorol. – volume: 9 start-page: 3801 year: 2009 end-page: 3853 ident: bib0065 article-title: A review of current methodologies for regional evapotranspiration estimation from remotely sensed data publication-title: Sensors – volume: 77 start-page: 263 year: 1995 end-page: 293 ident: bib0079 article-title: Source approach for estimating soil and vegetation energy fluxes in observations of directional radiometric surface-temperature publication-title: Agric. For. Meteorol. – volume: 9 start-page: 1735 year: 1997 end-page: 1780 ident: bib0045 article-title: Long short-term memory publication-title: Neural Comput. – volume: 212 start-page: 213 year: 1998 end-page: 229 ident: bib0012 article-title: A remote sensing surface energy balance algorithm for land (SEBAL) - 2. Validation publication-title: J. Hydrol. – volume: 112 start-page: 4227 year: 2008 end-page: 4241 ident: bib0006 article-title: A thermal-based remote sensing technique for routine mapping of land-surface carbon, water and energy fluxes from field to regional scales publication-title: Remote Sens. Environ. – volume: 103 start-page: 379 year: 2006 end-page: 397 ident: bib0109 article-title: Horizontal radiation transport in 3-D forest canopies at multiple spatial resolutions: simulated impact on canopy absorption publication-title: Remote Sens. Environ. – volume: 56 start-page: 1 year: 2008 end-page: 26 ident: bib0010 article-title: Breathing of the terrestrial biosphere: lessons learned from a global network of carbon dioxide flux measurement systems publication-title: Aust. J. Bot. – volume: 268 year: 2022 ident: bib0092 article-title: A review of machine learning in processing remote sensing data for mineral exploration publication-title: Remote Sens. Environ. – volume: 25 start-page: 4103 year: 2011 end-page: 4116 ident: bib0036 article-title: Actual evapotranspiration estimation by ground and remote sensing methods: the Australian experience publication-title: Hydrol. Process. – volume: 225 start-page: 127 year: 2019 end-page: 147 ident: bib0112 article-title: Current status of Landsat program, science, and applications publication-title: Remote Sens. Environ. – volume: 13 start-page: 447 year: 2021 end-page: 480 ident: bib0026 article-title: Synthesis of global actual evapotranspiration from 1982 to 2019 publication-title: Earth Syst. Sci. Data – volume: 46 start-page: 14496 year: 2019 end-page: 14507 ident: bib0129 article-title: Physics-constrained machine learning of evapotranspiration publication-title: Geophys. Res. Lett. – volume: 260-261 start-page: 131 year: 2018 end-page: 143 ident: bib0098 article-title: Partitioning of evapotranspiration in remote sensing-based models publication-title: Agric. For. Meteorol. – volume: 164 start-page: 68 year: 2017 end-page: 84 ident: bib0004 article-title: Modified Penman–Monteith equation for monitoring evapotranspiration of wheat crop: relationship between the surface resistance and remotely sensed stress index publication-title: Biosyst. Eng. – volume: 111 start-page: 519 year: 2007 end-page: 536 ident: bib0077 article-title: Development of a global evapotranspiration algorithm based on MODIS and global meteorology data publication-title: Remote Sens. Environ. – volume: 113 start-page: 223 year: 2002 end-page: 243 ident: bib0110 article-title: Energy balance closure at FLUXNET sites publication-title: Agric. For. Meteorol. – volume: 28 start-page: 1 year: 2020 end-page: 14 ident: bib0093 article-title: A new method for estimation of spatially distributed rainfall through merging satellite observations, raingauge records, and terrain digital elevation model data publication-title: J. Hydro Environ. Res. – volume: 116 year: 2011 ident: bib0052 article-title: Global patterns of land-atmosphere fluxes of carbon dioxide, latent heat, and sensible heat derived from eddy covariance, satellite, and meteorological observations publication-title: J. Geophys. Res. Biogeosciences – volume: 584 year: 2020 ident: bib0111 article-title: A spatiotemporal deep fusion model for merging satellite and gauge precipitation in China publication-title: J. Hydrol. – volume: 118 start-page: 2284 year: 2013 end-page: 2300 ident: bib0114 article-title: A hybrid dual-source scheme and trapezoid framework–based evapotranspiration model (HTEM) using satellite images: algorithm and model test publication-title: J. Geophys. Res. Atmos. – volume: 154 start-page: 176 year: 2019 end-page: 188 ident: bib0051 article-title: Evaluation of topographic effects on multiscale leaf area index estimation using remotely sensed observations from multiple sensors publication-title: ISPRS J. Photogramm. Remote Sens. – volume: 29 start-page: 421 year: 2008 end-page: 469 ident: bib0053 article-title: Estimating land surface evaporation: a review of methods using remotely sensed surface temperature data publication-title: Surv. Geophys. – volume: 122 start-page: 5211 year: 2017 end-page: 5236 ident: bib0122 article-title: A simple temperature domain two-source model for estimating agricultural field surface energy fluxes from Landsat images publication-title: J. Geophys. Res. Atmos. – volume: 148 start-page: 1719 year: 2008 end-page: 1732 ident: bib0080 article-title: Estimating components of forest evapotranspiration: a footprint approach for scaling sap flux measurements publication-title: Agric. For. Meteorol. – volume: 187 start-page: 46 year: 2014 end-page: 61 ident: bib0027 article-title: Multi-site evaluation of terrestrial evaporation models using FLUXNET data publication-title: Agric. For. Meteorol. – volume: 228 year: 2020 ident: bib0113 article-title: Estimation of daily potato crop evapotranspiration using three different machine learning algorithms and four scenarios of available meteorological data publication-title: Agric. Water Manag. – volume: 193 start-page: 120 year: 1948 end-page: 145 ident: bib0081 article-title: Natural evaporation from open water, bare soil and grass publication-title: Proc. R. Soc. Lond. Ser. A Math. Phys. Sci. – volume: 260 year: 2021 ident: bib0016 article-title: The effect of pixel heterogeneity for remote sensing based retrievals of evapotranspiration in a semi-arid tree-grass ecosystem publication-title: Remote Sens. Environ. – volume: 150 start-page: 501 year: 2010 end-page: 509 ident: bib0071 article-title: Computing turbulent fluxes near the surface: needed improvements publication-title: Agric. For. Meteorol. – volume: 10 start-page: 1 year: 2018 end-page: 28 ident: bib0097 article-title: Sensitivity of evapotranspiration components in remote sensing-based models publication-title: Remote Sens. – volume: 23 year: 2023 ident: bib0049 article-title: The Hi-GLASS all-wave daily net radiation product: algorithm and product validation publication-title: Sci. Remote Sens. – volume: 56 year: 1998 ident: bib0001 article-title: Crop evapotranspiration: guidelines for computing crop water requirements, FAO irrigation and drainage paper 56 publication-title: FAO – volume: 117 year: 2012 ident: bib0037 article-title: Evapotranspiration along an elevation gradient in California's Sierra Nevada publication-title: J. Geophys. Res. Biogeosciences – volume: 136 start-page: 455 year: 2013 end-page: 468 ident: bib0123 article-title: A global review of remote sensing of live fuel moisture content for fire danger assessment: moving towards operational products publication-title: Remote Sens. Environ. – volume: 298 year: 2021 ident: bib0009 article-title: On the use of machine learning based ensemble approaches to improve evapotranspiration estimates from croplands across a wide environmental gradient publication-title: Agric. For. Meteorol. – volume: 240 year: 2020 ident: bib0091 article-title: Deep learning-based air temperature mapping by fusing remote sensing, station, simulation and socioeconomic data publication-title: Remote Sens. Environ. – volume: 53 start-page: 2618 year: 2017 end-page: 2626 ident: bib0030 article-title: The future of evapotranspiration: global requirements for ecosystem functioning, carbon and climate feedbacks, agricultural management, and water resources publication-title: Water Resour. Res. – volume: 12 start-page: 433 year: 2015 end-page: 451 ident: bib0073 article-title: Components of near-surface energy balance derived from satellite soundings - Part 1: noontime net available energy publication-title: Biogeosciences. – volume: 56 year: 2020 ident: bib0031 article-title: ECOSTRESS: nASA's next generation mission to measure evapotranspiration from the international space station publication-title: Water Resour. Res. – volume: 528 start-page: 537 year: 2015 end-page: 549 ident: bib0021 article-title: Using Bayesian model averaging to estimate terrestrial evapotranspiration in China publication-title: J. Hydrol. – volume: 521 start-page: 436 year: 2015 end-page: 444 ident: bib0063 article-title: Deep learning publication-title: Nature – volume: 12 year: 2020 ident: bib0089 article-title: Fusion of five satellite-derived products using extremely randomized trees to estimate terrestrial latent heat flux over Europe publication-title: Remote Sens. – volume: 18 start-page: 1351 year: 2008 end-page: 1367 ident: bib0034 article-title: The energy balance closure problem: an overview publication-title: Ecol. Appl. – volume: 119 start-page: 4521 year: 2014 end-page: 4545 ident: bib0119 article-title: Bayesian multimodel estimation of global terrestrial latent heat flux from eddy covariance, meteorological, and satellite observations publication-title: J. Geophys. Res. Atmos. – volume: 241 year: 2020 ident: bib0124 article-title: Deep learning in environmental remote sensing: achievements and challenges publication-title: Remote Sens. Environ. – volume: 4 start-page: 251 year: 1991 end-page: 257 ident: bib0046 article-title: Approximation capabilities of multilayer feedforward networks publication-title: Neural Netw. – volume: 185 start-page: 32 year: 2022 end-page: 47 ident: bib0127 article-title: Soil moisture content retrieval from Landsat 8 data using ensemble learning publication-title: ISPRS J. Photogramm. Remote Sens. – volume: 26 start-page: 1579 year: 2021 end-page: 1614 ident: bib0061 article-title: Towards hybrid modeling of the global hydrological cycle publication-title: Hydrol. Earth Syst. Sci. – volume: 20 start-page: 783 year: 2005 end-page: 796 ident: bib0029 article-title: Evapotranspiration models compared on a Sierra Nevada forest ecosystem publication-title: Environ. Model. Softw. – volume: 171-172 start-page: 187 year: 2013 end-page: 202 ident: bib0116 article-title: MODIS-driven estimation of terrestrial latent heat flux in China based on a modified priestley–taylor algorithm publication-title: Agric. For. Meteorol. – volume: 147 start-page: 337 year: 2019 end-page: 355 ident: bib0017 article-title: The evapotranspiration process in green roofs: a review publication-title: Build. Environ. – volume: 169 start-page: 216 year: 2015 end-page: 233 ident: bib0118 article-title: A satellite-based hybrid algorithm to determine the priestley-taylor parameter for global terrestrial latent heat flux estimation across multiple biomes publication-title: Remote Sens. Environ. – volume: 34 year: 2007 ident: bib0024 article-title: Assimilation of global MODIS leaf area index retrievals within a terrestrial biosphere model publication-title: Geophys. Res. Lett. – volume: 114 start-page: 1416 year: 2010 end-page: 1431 ident: bib0125 article-title: Global estimates of evapotranspiration and gross primary production based on MODIS and global meteorology data publication-title: Remote Sens. Environ. – volume: 111 start-page: 839 year: 1985 end-page: 855 ident: bib0094 article-title: Evaporation from sparse crops-an energy combination theory publication-title: Q. J. R. Meteorol. Soc. – volume: 236 start-page: 87 year: 2017 ident: 10.1016/j.agrformet.2024.109962_bib0082 article-title: Evaluation of eddy covariance latent heat fluxes with independent lysimeter and sapflow estimates in a Mediterranean savannah ecosystem publication-title: Agric. For. Meteorol. doi: 10.1016/j.agrformet.2017.01.009 – volume: 260 year: 2021 ident: 10.1016/j.agrformet.2024.109962_bib0016 article-title: The effect of pixel heterogeneity for remote sensing based retrievals of evapotranspiration in a semi-arid tree-grass ecosystem publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2021.112440 – volume: 117 year: 2012 ident: 10.1016/j.agrformet.2024.109962_bib0037 article-title: Evapotranspiration along an elevation gradient in California's Sierra Nevada publication-title: J. Geophys. Res. Biogeosciences doi: 10.1029/2012JG002027 – volume: 111 start-page: 839 year: 1985 ident: 10.1016/j.agrformet.2024.109962_bib0094 article-title: Evaporation from sparse crops-an energy combination theory publication-title: Q. J. R. Meteorol. Soc. doi: 10.1002/qj.49711146910 – volume: 140 start-page: 279 year: 2014 ident: 10.1016/j.agrformet.2024.109962_bib0019 article-title: Comparison of satellite-based evapotranspiration models over terrestrial ecosystems in China publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2013.08.045 – volume: 30 start-page: 5419 year: 2017 ident: 10.1016/j.agrformet.2024.109962_bib0035 article-title: The modern-era retrospective analysis for research and applications, version 2 (MERRA-2) publication-title: J. Clim. doi: 10.1175/JCLI-D-16-0758.1 – volume: 308-309 year: 2021 ident: 10.1016/j.agrformet.2024.109962_bib0088 article-title: DNN-MET: a deep neural networks method to integrate satellite-derived evapotranspiration products, eddy covariance observations and ancillary information publication-title: Agric. For. Meteorol. doi: 10.1016/j.agrformet.2021.108582 – volume: 193 start-page: 120 year: 1948 ident: 10.1016/j.agrformet.2024.109962_bib0081 article-title: Natural evaporation from open water, bare soil and grass publication-title: Proc. R. Soc. Lond. Ser. A Math. Phys. Sci. – volume: 171-172 start-page: 187 year: 2013 ident: 10.1016/j.agrformet.2024.109962_bib0116 article-title: MODIS-driven estimation of terrestrial latent heat flux in China based on a modified priestley–taylor algorithm publication-title: Agric. For. Meteorol. doi: 10.1016/j.agrformet.2012.11.016 – volume: 19 start-page: 3929 year: 2019 ident: 10.1016/j.agrformet.2024.109962_bib0100 article-title: Survey of deep-learning approaches for remote sensing observation enhancement publication-title: Sensors doi: 10.3390/s19183929 – year: 2017 ident: 10.1016/j.agrformet.2024.109962_bib0108 article-title: Revisiting the contribution of transpiration to global terrestrial evapotranspiration publication-title: Geophys. Res. Lett. doi: 10.1002/2016GL072235 – volume: 103 start-page: 279 year: 2000 ident: 10.1016/j.agrformet.2024.109962_bib0101 article-title: Correcting eddy-covariance flux underestimates over a grassland publication-title: Agric. For. Meteorol. doi: 10.1016/S0168-1923(00)00123-4 – volume: 10 start-page: 1 issue: 1601 year: 2018 ident: 10.1016/j.agrformet.2024.109962_bib0097 article-title: Sensitivity of evapotranspiration components in remote sensing-based models publication-title: Remote Sens. – volume: 64 year: 2021 ident: 10.1016/j.agrformet.2024.109962_bib0014 article-title: A novel CNN-LSTM-based approach to predict urban expansion publication-title: Ecol. Inform. doi: 10.1016/j.ecoinf.2021.101325 – volume: 53 start-page: 2618 year: 2017 ident: 10.1016/j.agrformet.2024.109962_bib0030 article-title: The future of evapotranspiration: global requirements for ecosystem functioning, carbon and climate feedbacks, agricultural management, and water resources publication-title: Water Resour. Res. doi: 10.1002/2016WR020175 – volume: 29 start-page: 421 year: 2008 ident: 10.1016/j.agrformet.2024.109962_bib0053 article-title: Estimating land surface evaporation: a review of methods using remotely sensed surface temperature data publication-title: Surv. Geophys. doi: 10.1007/s10712-008-9037-z – volume: 12 year: 2020 ident: 10.1016/j.agrformet.2024.109962_bib0040 article-title: Discrepancies in the simulated global terrestrial latent heat flux from GLASS and MERRA-2 surface net radiation products publication-title: Remote Sens. doi: 10.3390/rs12172763 – volume: 528 start-page: 537 year: 2015 ident: 10.1016/j.agrformet.2024.109962_bib0021 article-title: Using Bayesian model averaging to estimate terrestrial evapotranspiration in China publication-title: J. Hydrol. doi: 10.1016/j.jhydrol.2015.06.059 – volume: 86 start-page: 2278 year: 1998 ident: 10.1016/j.agrformet.2024.109962_bib0064 article-title: Gradient-based learning applied to document recognition publication-title: Proc. IEEE doi: 10.1109/5.726791 – volume: 115 year: 2010 ident: 10.1016/j.agrformet.2024.109962_bib0105 article-title: Evidence for decadal variation in global terrestrial evapotranspiration between 1982 and 2002: 1. Model development publication-title: J. Geophys. Res. Atmos. – volume: 230 start-page: 97 year: 2016 ident: 10.1016/j.agrformet.2024.109962_bib0068 article-title: Upscaling evapotranspiration measurements from multi-site to the satellite pixel scale over heterogeneous land surfaces publication-title: Agric. For. Meteorol. doi: 10.1016/j.agrformet.2016.04.008 – volume: 150 start-page: 501 year: 2010 ident: 10.1016/j.agrformet.2024.109962_bib0071 article-title: Computing turbulent fluxes near the surface: needed improvements publication-title: Agric. For. Meteorol. doi: 10.1016/j.agrformet.2010.01.015 – volume: 271 year: 2022 ident: 10.1016/j.agrformet.2024.109962_bib0008 article-title: Thermally derived evapotranspiration from the surface temperature initiated closure (STIC) model improves cropland GPP estimates under dry conditions publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2022.112901 – volume: 26 start-page: 1579 year: 2021 ident: 10.1016/j.agrformet.2024.109962_bib0061 article-title: Towards hybrid modeling of the global hydrological cycle publication-title: Hydrol. Earth Syst. Sci. doi: 10.5194/hess-26-1579-2022 – volume: 56 year: 1998 ident: 10.1016/j.agrformet.2024.109962_bib0001 article-title: Crop evapotranspiration: guidelines for computing crop water requirements, FAO irrigation and drainage paper 56 publication-title: FAO – volume: 25 start-page: 4103 year: 2011 ident: 10.1016/j.agrformet.2024.109962_bib0036 article-title: Actual evapotranspiration estimation by ground and remote sensing methods: the Australian experience publication-title: Hydrol. Process. doi: 10.1002/hyp.8391 – volume: 377 start-page: 80 year: 2009 ident: 10.1016/j.agrformet.2024.109962_bib0042 article-title: Decomposition of the mean squared error and NSE performance criteria: implications for improving hydrological modelling publication-title: J. Hydrol. doi: 10.1016/j.jhydrol.2009.08.003 – volume: 12 start-page: 433 issue: 2 year: 2015 ident: 10.1016/j.agrformet.2024.109962_bib0073 article-title: Components of near-surface energy balance derived from satellite soundings - Part 1: noontime net available energy publication-title: Biogeosciences. doi: 10.5194/bg-12-433-2015 – volume: 573 start-page: 568 year: 2019 ident: 10.1016/j.agrformet.2024.109962_bib0043 article-title: Deep learning for multi-year ENSO forecasts publication-title: Nature doi: 10.1038/s41586-019-1559-7 – volume: 116 year: 2011 ident: 10.1016/j.agrformet.2024.109962_bib0052 article-title: Global patterns of land-atmosphere fluxes of carbon dioxide, latent heat, and sensible heat derived from eddy covariance, satellite, and meteorological observations publication-title: J. Geophys. Res. Biogeosciences doi: 10.1029/2010JG001566 – volume: 154 start-page: 176 year: 2019 ident: 10.1016/j.agrformet.2024.109962_bib0051 article-title: Evaluation of topographic effects on multiscale leaf area index estimation using remotely sensed observations from multiple sensors publication-title: ISPRS J. Photogramm. Remote Sens. doi: 10.1016/j.isprsjprs.2019.06.008 – volume: 9 start-page: 3801 year: 2009 ident: 10.1016/j.agrformet.2024.109962_bib0065 article-title: A review of current methodologies for regional evapotranspiration estimation from remotely sensed data publication-title: Sensors doi: 10.3390/s90503801 – volume: 83 start-page: 195 year: 2002 ident: 10.1016/j.agrformet.2024.109962_bib0047 article-title: Overview of the radiometric and biophysical performance of the MODIS vegetation indices publication-title: Remote Sens. Environ. doi: 10.1016/S0034-4257(02)00096-2 – volume: 122 start-page: 5211 year: 2017 ident: 10.1016/j.agrformet.2024.109962_bib0122 article-title: A simple temperature domain two-source model for estimating agricultural field surface energy fluxes from Landsat images publication-title: J. Geophys. Res. Atmos. doi: 10.1002/2016JD026370 – volume: 212 start-page: 213 year: 1998 ident: 10.1016/j.agrformet.2024.109962_bib0012 article-title: A remote sensing surface energy balance algorithm for land (SEBAL) - 2. Validation publication-title: J. Hydrol. doi: 10.1016/S0022-1694(98)00254-6 – volume: 136 start-page: 455 year: 2013 ident: 10.1016/j.agrformet.2024.109962_bib0123 article-title: A global review of remote sensing of live fuel moisture content for fire danger assessment: moving towards operational products publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2013.05.029 – volume: 264 year: 2021 ident: 10.1016/j.agrformet.2024.109962_bib0072 article-title: Spatial and temporal deep learning methods for deriving land-use following deforestation: a pan-tropical case study using Landsat time series publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2021.112600 – volume: 11 start-page: 1424 year: 2005 ident: 10.1016/j.agrformet.2024.109962_bib0083 article-title: On the separation of net ecosystem exchange into assimilation and ecosystem respiration: review and improved algorithm publication-title: Glob. Change Biol. doi: 10.1111/j.1365-2486.2005.001002.x – volume: 39 start-page: 1137 year: 2017 ident: 10.1016/j.agrformet.2024.109962_bib0084 article-title: Faster R-CNN: towards real-time object detection with region proposal networks publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2016.2577031 – volume: 11 year: 2016 ident: 10.1016/j.agrformet.2024.109962_bib0032 article-title: An empirical orthogonal function-based algorithm for estimating terrestrial latent heat flux from eddy covariance, meteorological and satellite observations publication-title: PLoS One doi: 10.1371/journal.pone.0160150 – volume: 241 year: 2020 ident: 10.1016/j.agrformet.2024.109962_bib0124 article-title: Deep learning in environmental remote sensing: achievements and challenges publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2020.111716 – volume: 11 year: 2019 ident: 10.1016/j.agrformet.2024.109962_bib0018 article-title: Bidirectional convolutional LSTM neural network for remote sensing image super-resolution publication-title: Remote Sens. doi: 10.3390/rs11202333 – volume: 171 start-page: 203 year: 2013 ident: 10.1016/j.agrformet.2024.109962_bib0057 article-title: Estimating random errors of eddy covariance data: an extended two-tower approach publication-title: Agric. For. Meteorol. doi: 10.1016/j.agrformet.2012.11.019 – volume: 98 start-page: 899 year: 2011 ident: 10.1016/j.agrformet.2024.109962_bib0002 article-title: Evapotranspiration information reporting: I. Factors governing measurement accuracy publication-title: Agric. Water Manag. doi: 10.1016/j.agwat.2010.12.015 – volume: 9 start-page: 1735 year: 1997 ident: 10.1016/j.agrformet.2024.109962_bib0045 article-title: Long short-term memory publication-title: Neural Comput. doi: 10.1162/neco.1997.9.8.1735 – volume: 115 start-page: 1781 year: 2011 ident: 10.1016/j.agrformet.2024.109962_bib0078 article-title: Improvements to a MODIS global terrestrial evapotranspiration algorithm publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2011.02.019 – volume: 268 year: 2022 ident: 10.1016/j.agrformet.2024.109962_bib0092 article-title: A review of machine learning in processing remote sensing data for mineral exploration publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2021.112750 – volume: 323 start-page: 533 year: 1986 ident: 10.1016/j.agrformet.2024.109962_bib0087 article-title: Learning representations by back-propagating errors publication-title: Nature doi: 10.1038/323533a0 – volume: 60 start-page: 195 year: 1997 ident: 10.1016/j.agrformet.2024.109962_bib0005 article-title: A two-source time-integrated model for estimating surface fluxes using thermal infrared remote sensing publication-title: Remote Sens. Environ. doi: 10.1016/S0034-4257(96)00215-5 – volume: 14 start-page: 2651 year: 2022 ident: 10.1016/j.agrformet.2024.109962_bib0067 article-title: An open data approach for estimating vegetation gross primary production at fine spatial resolution publication-title: Remote Sens. doi: 10.3390/rs14112651 – volume: 56 year: 2020 ident: 10.1016/j.agrformet.2024.109962_bib0031 article-title: ECOSTRESS: nASA's next generation mission to measure evapotranspiration from the international space station publication-title: Water Resour. Res. doi: 10.1029/2019WR026058 – volume: 111 start-page: 519 year: 2007 ident: 10.1016/j.agrformet.2024.109962_bib0077 article-title: Development of a global evapotranspiration algorithm based on MODIS and global meteorology data publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2007.04.015 – volume: 212 start-page: 198 year: 1998 ident: 10.1016/j.agrformet.2024.109962_bib0011 article-title: A remote sensing surface energy balance algorithm for land (SEBAL) - 1. Formulation publication-title: J. Hydrol. doi: 10.1016/S0022-1694(98)00253-4 – volume: 117 year: 2012 ident: 10.1016/j.agrformet.2024.109962_bib0048 article-title: Validation of remotely sensed evapotranspiration over the Hai River Basin, China publication-title: J. Geophys. Res. Atmos. doi: 10.1029/2011JD017037 – volume: 23 year: 2023 ident: 10.1016/j.agrformet.2024.109962_bib0049 article-title: The Hi-GLASS all-wave daily net radiation product: algorithm and product validation publication-title: Sci. Remote Sens. – volume: 49 start-page: 75 year: 2016 ident: 10.1016/j.agrformet.2024.109962_bib0013 article-title: Evaluating five remote sensing based single-source surface energy balance models for estimating daily evapotranspiration in a humid subtropical climate publication-title: Int. J. Appl. Earth Obs. Geoinf. – volume: 56 start-page: 1 year: 2008 ident: 10.1016/j.agrformet.2024.109962_bib0010 article-title: Breathing of the terrestrial biosphere: lessons learned from a global network of carbon dioxide flux measurement systems publication-title: Aust. J. Bot. doi: 10.1071/BT07151 – volume: 521 start-page: 436 year: 2015 ident: 10.1016/j.agrformet.2024.109962_bib0063 article-title: Deep learning publication-title: Nature doi: 10.1038/nature14539 – volume: 103 start-page: 379 year: 2006 ident: 10.1016/j.agrformet.2024.109962_bib0109 article-title: Horizontal radiation transport in 3-D forest canopies at multiple spatial resolutions: simulated impact on canopy absorption publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2006.03.014 – start-page: 19 year: 2019 ident: 10.1016/j.agrformet.2024.109962_bib0096 article-title: County-level soybean yield prediction using deep CNN-LSTM model publication-title: Sensors – volume: 164 start-page: 68 year: 2017 ident: 10.1016/j.agrformet.2024.109962_bib0004 article-title: Modified Penman–Monteith equation for monitoring evapotranspiration of wheat crop: relationship between the surface resistance and remotely sensed stress index publication-title: Biosyst. Eng. doi: 10.1016/j.biosystemseng.2017.09.015 – volume: 6 year: 2022 ident: 10.1016/j.agrformet.2024.109962_bib0050 article-title: Generating high spatial resolution GLASS FAPAR product from Landsat images publication-title: Sci. Remote Sens. – volume: 252 year: 2021 ident: 10.1016/j.agrformet.2024.109962_bib0007 article-title: Interoperability of ECOSTRESS and Landsat for mapping evapotranspiration time series at sub-field scales publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2020.112189 – volume: 114 start-page: 540 year: 2010 ident: 10.1016/j.agrformet.2024.109962_bib0099 article-title: An application of the Ts–VI triangle method with enhanced edges determination for evapotranspiration estimation from MODIS data in arid and semi-arid regions: implementation and validation publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2009.10.012 – volume: 18 start-page: 1351 year: 2008 ident: 10.1016/j.agrformet.2024.109962_bib0034 article-title: The energy balance closure problem: an overview publication-title: Ecol. Appl. doi: 10.1890/06-0922.1 – volume: 584 year: 2020 ident: 10.1016/j.agrformet.2024.109962_bib0111 article-title: A spatiotemporal deep fusion model for merging satellite and gauge precipitation in China publication-title: J. Hydrol. doi: 10.1016/j.jhydrol.2020.124664 – volume: 53 year: 2017 ident: 10.1016/j.agrformet.2024.109962_bib0074 article-title: CubeSats in hydrology: ultrahigh-resolution insights into vegetation dynamics and terrestrial evaporation publication-title: Water Resour. Res. doi: 10.1002/2017WR022240 – volume: 59 start-page: 4105 year: 2021 ident: 10.1016/j.agrformet.2024.109962_bib0117 article-title: A novel NIR-red spectral domain evapotranspiration model from the Chinese GF-1 satellite: application to the Huailai agricultural region of China publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2020.3020125 – volume: 364 start-page: 398 year: 2014 ident: 10.1016/j.agrformet.2024.109962_bib0058 article-title: Using the priestley-taylor expression for estimating actual evapotranspiration from satellite landsat ETM + data publication-title: Proc. IAHS doi: 10.5194/piahs-364-398-2014 – volume: 113 start-page: 223 year: 2002 ident: 10.1016/j.agrformet.2024.109962_bib0110 article-title: Energy balance closure at FLUXNET sites publication-title: Agric. For. Meteorol. doi: 10.1016/S0168-1923(02)00109-0 – volume: 112 start-page: 4227 year: 2008 ident: 10.1016/j.agrformet.2024.109962_bib0006 article-title: A thermal-based remote sensing technique for routine mapping of land-surface carbon, water and energy fluxes from field to regional scales publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2008.07.009 – volume: 18 start-page: 602 year: 2005 ident: 10.1016/j.agrformet.2024.109962_bib0038 article-title: Framewise phoneme classification with bidirectional LSTM and other neural network architectures publication-title: Neural Networks doi: 10.1016/j.neunet.2005.06.042 – volume: 99 start-page: 207 year: 2001 ident: 10.1016/j.agrformet.2024.109962_bib0060 article-title: An analytical footprint model for non-neutral stratification publication-title: Bound. Layer Meteorol. doi: 10.1023/A:1018991015119 – volume: 79 start-page: 199 year: 2002 ident: 10.1016/j.agrformet.2024.109962_bib0076 article-title: Using covariates to spatially interpolate moisture availability in the Murray–Darling Basin: a novel use of remotely sensed data publication-title: Remote Sens. Environ. doi: 10.1016/S0034-4257(01)00273-5 – volume: 24 start-page: 3624 year: 2011 ident: 10.1016/j.agrformet.2024.109962_bib0085 article-title: MERRA: nASA's modern-era retrospective analysis for research and applications publication-title: J. Clim. doi: 10.1175/JCLI-D-11-00015.1 – volume: 77 start-page: 263 year: 1995 ident: 10.1016/j.agrformet.2024.109962_bib0079 article-title: Source approach for estimating soil and vegetation energy fluxes in observations of directional radiometric surface-temperature publication-title: Agric. For. Meteorol. doi: 10.1016/0168-1923(95)02265-Y – volume: 225 start-page: 127 year: 2019 ident: 10.1016/j.agrformet.2024.109962_bib0112 article-title: Current status of Landsat program, science, and applications publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2019.02.015 – volume: 4 start-page: 251 year: 1991 ident: 10.1016/j.agrformet.2024.109962_bib0046 article-title: Approximation capabilities of multilayer feedforward networks publication-title: Neural Netw. doi: 10.1016/0893-6080(91)90009-T – volume: 28 start-page: 1 year: 2020 ident: 10.1016/j.agrformet.2024.109962_bib0093 article-title: A new method for estimation of spatially distributed rainfall through merging satellite observations, raingauge records, and terrain digital elevation model data publication-title: J. Hydro Environ. Res. doi: 10.1016/j.jher.2017.10.006 – volume: 25 start-page: 24 year: 2019 ident: 10.1016/j.agrformet.2024.109962_bib0028 article-title: A guide to deep learning in healthcare publication-title: Nat. Med. doi: 10.1038/s41591-018-0316-z – volume: 3 start-page: 225 year: 2010 ident: 10.1016/j.agrformet.2024.109962_bib0066 article-title: Review on estimation of land surface radiation and energy budgets from ground measurement, remote sensing and model simulations publication-title: IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. doi: 10.1109/JSTARS.2010.2048556 – volume: 13 start-page: 447 issue: 2 year: 2021 ident: 10.1016/j.agrformet.2024.109962_bib0026 article-title: Synthesis of global actual evapotranspiration from 1982 to 2019 publication-title: Earth Syst. Sci. Data doi: 10.5194/essd-13-447-2021 – volume: 148 start-page: 1719 year: 2008 ident: 10.1016/j.agrformet.2024.109962_bib0080 article-title: Estimating components of forest evapotranspiration: a footprint approach for scaling sap flux measurements publication-title: Agric. For. Meteorol. doi: 10.1016/j.agrformet.2008.06.013 – volume: 133 start-page: 380 year: 2007 ident: 10.1016/j.agrformet.2024.109962_bib0003 article-title: Satellite-based energy balance for mapping evapotranspiration with internalized calibration (METRIC) - model publication-title: J. Irrig. Drain. Eng. doi: 10.1061/(ASCE)0733-9437(2007)133:4(380) – volume: 228 year: 2020 ident: 10.1016/j.agrformet.2024.109962_bib0113 article-title: Estimation of daily potato crop evapotranspiration using three different machine learning algorithms and four scenarios of available meteorological data publication-title: Agric. Water Manag. doi: 10.1016/j.agwat.2019.105875 – volume: 187 start-page: 46 year: 2014 ident: 10.1016/j.agrformet.2024.109962_bib0027 article-title: Multi-site evaluation of terrestrial evaporation models using FLUXNET data publication-title: Agric. For. Meteorol. doi: 10.1016/j.agrformet.2013.11.008 – volume: 553 start-page: 508 year: 2017 ident: 10.1016/j.agrformet.2024.109962_bib0120 article-title: Estimation of high-resolution terrestrial evapotranspiration from Landsat data using a simple Taylor skill fusion method publication-title: J. Hydrol. doi: 10.1016/j.jhydrol.2017.08.013 – volume: 147 start-page: 337 year: 2019 ident: 10.1016/j.agrformet.2024.109962_bib0017 article-title: The evapotranspiration process in green roofs: a review publication-title: Build. Environ. doi: 10.1016/j.buildenv.2018.10.024 – volume: 12 year: 2020 ident: 10.1016/j.agrformet.2024.109962_bib0089 article-title: Fusion of five satellite-derived products using extremely randomized trees to estimate terrestrial latent heat flux over Europe publication-title: Remote Sens. doi: 10.3390/rs12040687 – volume: 34 year: 2007 ident: 10.1016/j.agrformet.2024.109962_bib0024 article-title: Assimilation of global MODIS leaf area index retrievals within a terrestrial biosphere model publication-title: Geophys. Res. Lett. doi: 10.1029/2007GL030014 – volume: 114 start-page: 1416 issue: 7 year: 2010 ident: 10.1016/j.agrformet.2024.109962_bib0125 article-title: Global estimates of evapotranspiration and gross primary production based on MODIS and global meteorology data publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2010.01.022 – volume: 149 start-page: 2071 year: 2009 ident: 10.1016/j.agrformet.2024.109962_bib0062 article-title: Advances in thermal infrared remote sensing for land surface modeling publication-title: Agric. For. Meteorol. doi: 10.1016/j.agrformet.2009.05.016 – volume: 169 start-page: 216 year: 2015 ident: 10.1016/j.agrformet.2024.109962_bib0118 article-title: A satellite-based hybrid algorithm to determine the priestley-taylor parameter for global terrestrial latent heat flux estimation across multiple biomes publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2015.05.013 – volume: 187 start-page: 27 year: 2016 ident: 10.1016/j.agrformet.2024.109962_bib0041 article-title: Deep learning for visual understanding: a review publication-title: Neurocomputing. doi: 10.1016/j.neucom.2015.09.116 – volume: 128 start-page: 192 year: 2017 ident: 10.1016/j.agrformet.2024.109962_bib0103 article-title: Performance of five surface energy balance models for estimating daily evapotranspiration in high biomass sorghum publication-title: ISPRS J. Photogramm. Remote Sens. doi: 10.1016/j.isprsjprs.2017.03.022 – volume: 29 start-page: 349 year: 2003 ident: 10.1016/j.agrformet.2024.109962_bib0025 article-title: Investigating the use of landsat thematic mapper data for estimation of forest leaf area index in southern Sweden publication-title: Can. J. Remote Sens. doi: 10.5589/m03-004 – volume: 240 year: 2020 ident: 10.1016/j.agrformet.2024.109962_bib0091 article-title: Deep learning-based air temperature mapping by fusing remote sensing, station, simulation and socioeconomic data publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2020.111692 – volume: 184 start-page: 56 year: 2014 ident: 10.1016/j.agrformet.2024.109962_bib0059 article-title: A review of approaches for evapotranspiration partitioning publication-title: Agric. For. Meteorol. doi: 10.1016/j.agrformet.2013.09.003 – volume: 10 year: 2019 ident: 10.1016/j.agrformet.2024.109962_bib0126 article-title: PM2.5 prediction based on random forest, XGBoost, and deep learning using multisource remote sensing data publication-title: Atmosphere doi: 10.3390/atmos10070373 – volume: 50 year: 2012 ident: 10.1016/j.agrformet.2024.109962_bib0104 article-title: A review of global terrestrial evapotranspiration: observation, modeling, climatology, and climatic variability publication-title: Rev. Geophys. doi: 10.1029/2011RG000373 – volume: 118 start-page: 2284 year: 2013 ident: 10.1016/j.agrformet.2024.109962_bib0114 article-title: A hybrid dual-source scheme and trapezoid framework–based evapotranspiration model (HTEM) using satellite images: algorithm and model test publication-title: J. Geophys. Res. Atmos. doi: 10.1002/jgrd.50259 – volume: 260-261 start-page: 131 year: 2018 ident: 10.1016/j.agrformet.2024.109962_bib0098 article-title: Partitioning of evapotranspiration in remote sensing-based models publication-title: Agric. For. Meteorol. doi: 10.1016/j.agrformet.2018.05.010 – volume: 46 start-page: 14496 year: 2019 ident: 10.1016/j.agrformet.2024.109962_bib0129 article-title: Physics-constrained machine learning of evapotranspiration publication-title: Geophys. Res. Lett. doi: 10.1029/2019GL085291 – volume: 6 start-page: 85 year: 2002 ident: 10.1016/j.agrformet.2024.109962_bib131 article-title: The Surface Energy Balance System (SEBS) for estimation of turbulent heatfluxes publication-title: Hydrol. Earth Syst. Sci. doi: 10.5194/hess-6-85-2002 – volume: 185 start-page: 32 year: 2022 ident: 10.1016/j.agrformet.2024.109962_bib0127 article-title: Soil moisture content retrieval from Landsat 8 data using ensemble learning publication-title: ISPRS J. Photogramm. Remote Sens. doi: 10.1016/j.isprsjprs.2022.01.005 – volume: 45 start-page: 5 year: 2001 ident: 10.1016/j.agrformet.2024.109962_bib0015 article-title: Random forests publication-title: Mach. Learn. doi: 10.1023/A:1010933404324 – volume: 126 start-page: 79 year: 2017 ident: 10.1016/j.agrformet.2024.109962_bib0056 article-title: Spatiotemporal downscaling approaches for monitoring 8-day 30m actual evapotranspiration publication-title: ISPRS J. Photogramm. Remote Sens. doi: 10.1016/j.isprsjprs.2017.02.006 – volume: 6 year: 2022 ident: 10.1016/j.agrformet.2024.109962_bib0095 article-title: Estimation and validation of 30m fractional vegetation cover over China through integrated use of Landsat 8 and Gaofen 2 data publication-title: Sci. Remote Sens. – volume: 298 year: 2021 ident: 10.1016/j.agrformet.2024.109962_bib0009 article-title: On the use of machine learning based ensemble approaches to improve evapotranspiration estimates from croplands across a wide environmental gradient publication-title: Agric. For. Meteorol. – volume: 20 start-page: 783 issue: 6 year: 2005 ident: 10.1016/j.agrformet.2024.109962_bib0029 article-title: Evapotranspiration models compared on a Sierra Nevada forest ecosystem publication-title: Environ. Model. Softw. doi: 10.1016/j.envsoft.2004.04.009 – volume: 112 start-page: 901 year: 2008 ident: 10.1016/j.agrformet.2024.109962_bib0033 article-title: Global estimates of the land-atmosphere water flux based on monthly AVHRR and ISLSCP-II data, validated at 16 FLUXNET sites publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2007.06.025 – volume: 95 start-page: 164 issue: 2 year: 2005 ident: 10.1016/j.agrformet.2024.109962_bib0128 article-title: Improvements of the MODIS terrestrial gross and net primary production global data set publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2004.12.011 – volume: 10 start-page: 4055 year: 2013 ident: 10.1016/j.agrformet.2024.109962_bib0055 article-title: A comparison of methods for smoothing and gap filling time series of remote sensing observations - application to MODIS LAI products publication-title: Biogeosciences doi: 10.5194/bg-10-4055-2013 – volume: 119 start-page: 4521 year: 2014 ident: 10.1016/j.agrformet.2024.109962_bib0119 article-title: Bayesian multimodel estimation of global terrestrial latent heat flux from eddy covariance, meteorological, and satellite observations publication-title: J. Geophys. Res. Atmos. doi: 10.1002/2013JD020864 |
| SSID | ssj0012779 |
| Score | 2.4730008 |
| Snippet | •CNN-LSTM-ILE outperforms all the LE products used in integration method.•CNN-LSTM-ILE that combines information from LE products, EC and topography.•The... Accurate estimates of high-spatial-resolution global terrestrial latent heat flux (LE) from Landsat data are crucial for many basic and applied research. Yet... |
| SourceID | proquest crossref elsevier |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 109962 |
| SubjectTerms | algorithms applied research CNN-LSTM eddy covariance forests High-spatial-resolution products hydrology Integration algorithm Landsat Latent heat flux memory meteorology regression analysis |
| Title | Multimodel ensemble estimation of Landsat-like global terrestrial latent heat flux using a generalized deep CNN-LSTM integration algorithm |
| URI | https://dx.doi.org/10.1016/j.agrformet.2024.109962 https://www.proquest.com/docview/3153560587 |
| Volume | 349 |
| WOSCitedRecordID | wos001209198300001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVESC databaseName: Elsevier SD Freedom Collection Journals 2021 customDbUrl: eissn: 1873-2240 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0012779 issn: 0168-1923 databaseCode: AIEXJ dateStart: 19950101 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1Lj9MwELbKLgc4IFhAW14yEuJSZRU1aR7cqmp5abcCUaRyimzHaVPSpOomq2p_An-JP8eMHSftAip74BI1luy6nq_jz-N5EPIqEU4MxIBbA2a7lssDZgGrjy0PXmNfMBH3Y1Vswh-Pg-k0_NTp_DSxMJeZn-fBZhOu_quooQ2EjaGzNxB3Myg0wGcQOjxB7PD8J8GrkFpV4KYHR1S5xNAoTKWxbMjhGYb3stLK0u_SZASB5cUqHaqGRwb8My-RQ5a9JKs2vUoZFBiWW0YTVnoFLDWWctUbjcfW2ZfJeZN1Qjk3Z7NinZbz5TbxHc7WbZYPNNYDWYbvwwrWsljv2PbfVcp8O01ZcTWX9c6Kiomp9m9Vvqha9-Ha3v0ZZrhJW_-i1NjB5xXmAtk2bvSVT4wO7zT2Tg8OuaEOSTYK29FJTmuVi1d7WqH_thtow8TihM1UDIZE39m-e9L22M2_fW1fbLwVjSPcImoGinCgSA90ixz2_UEIKvVw-OF0-rG5xOr7OtWj-Q077oV_nNPfyNE1mqC4z-Q-uVcfWuhQg-0B6cj8iNxtRSqPSPe8FSR9TUcZ4k29PSQ_WkhSA0naQpIWCd2GJNWQpFuQpBqSFCFJEZJUQZIyugVJipCkBpJ0C5K0geQj8vXt6WT03qprgFjCcYPSkrhcQEL9MAm8xOOundhxjEkpHeaJIAiEm3AGOsUWvhu7ni0lF3YScBe0UxBz5zE5yItcHhPKfRhHcBhODlwJx4JQOoKHMuTxQIqAd4lnFj8SdYJ8rNOSRXsA0CV203Glc8Ts7_LGSDeqqa6msBFgd3_nlwYPEWwGeMPHcllUF5ED_GWAjg7-k5vP6Sm50_4Fn5GDcl3J5-S2uCzTi_WLGty_AHmx5hE |
| linkProvider | Elsevier |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Multimodel+ensemble+estimation+of+Landsat-like+global+terrestrial+latent+heat+flux+using+a+generalized+deep+CNN-LSTM+integration+algorithm&rft.jtitle=Agricultural+and+forest+meteorology&rft.au=Guo%2C+Xiaozheng&rft.au=Yao%2C+Yunjun&rft.au=Tang%2C+Qingxin&rft.au=Liang%2C+Shunlin&rft.date=2024-04-15&rft.issn=0168-1923&rft.volume=349&rft.spage=109962&rft_id=info:doi/10.1016%2Fj.agrformet.2024.109962&rft.externalDBID=n%2Fa&rft.externalDocID=10_1016_j_agrformet_2024_109962 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0168-1923&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0168-1923&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0168-1923&client=summon |