Evaluation of remote sensing-based evapotranspiration products at low-latitude eddy covariance sites

Diego Salazar-Martínez, Friso Holwerda*, Thomas R.H. Holmes, Enrico A. Yépez, Christopher R. Hain, Susana Alvarado-Barrientos, Gregorio Ángeles-Pérez, Tulio Arredondo-Moreno, Josué Delgado-Balbuena, Bernardo Figueroa-Espinoza, Jaime Garatuza-Payán, Eugenia González del Castillo, Julio C. Rodríguez, Nidia E. Rojas-Robles, Jorge M. Uuh-Sonda, Enrique R. Vivoni

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Remote sensing-based evapotranspiration (ET) products have been evaluated primarily using data from northern middle latitudes; therefore, little is known about their performance at low latitudes. To address this bias, an evaluation dataset was compiled using eddy covariance data from 40 sites between latitudes 30° S and 30° N. The flux data were obtained from the emerging network in Mexico (MexFlux) and from openly available databases of FLUXNET, AsiaFlux, and OzFlux. This unique reference dataset was then used to evaluate remote sensing-based ET products in environments that have been underrepresented in earlier studies. The evaluated products were: MODIS ET (MOD16, both the discontinued collection 5 (C5) and the latest collection (C6)), Global Land Evaporation Amsterdam Model (GLEAM) ET, and Atmosphere-Land Exchange Inverse (ALEXI) ET. Products were compared with unadjusted fluxes (ETorig) and with fluxes corrected for the lack of energy balance closure (ETebc). Three common statistical metrics were used: coefficient of determination (R2), root mean square error (RMSE), and percent bias (PBIAS). The effect of a vegetation mismatch between pixel and site on product evaluation results was investigated by examining the relationship between the statistical metrics and product-specific vegetation match indexes. Evaluation results of this study and those published in the literature were used to examine the performance of the products across latitudes. Differences between the MOD16 collection 5 and 6 datasets were generally smaller than differences with the other products. Performance and ranking of the evaluated products depended on whether ETorig or ETebc was used. When using ETorig, GLEAM generally had the highest R2, smallest PBIAS, and best RMSE values across the studied land cover types and climate zones. Neither MOD16 nor ALEXI performed consistently better than the other. When using ETebc, none of the products stood out in terms of both low bias and strong correlations. The use of ETebc instead of ETorig affected the biases more than the correlations. The product evaluation results showed no significant relationship with the degree of match between the vegetation at the pixel and site scale. The latitudinal comparison showed tendencies of lower R2 (all products) but better PBIAS and normalized RMSE values (MOD16 and GLEAM) for forests at low latitudes than for forests at northern middle latitudes. For non-forest vegetation, the products showed no clear latitudinal differences in performance.

Original languageEnglish
Article number127786
JournalJournal of Hydrology
Volume610
DOIs
StatePublished - Jul 2022

Bibliographical note

Funding Information:
This work used eddy covariance data acquired and shared by the FLUXNET community, including these networks: AmeriFlux, AfriFlux, AsiaFlux, CarboAfrica, CarboEuropeIP, CarboItaly, CarboMont, ChinaFlux, Fluxnet-Canada, GreenGrass, ICOS, KoFlux, LBA, NECC, OzFlux-TERN, TCOS-Siberia, and USCCC. The FLUXNET eddy covariance data processing and harmonization was carried out by the ICOS Ecosystem Thematic Center, AmeriFlux Management Project and Fluxdata project of FLUXNET, with the support of CDIAC, and the OzFlux, ChinaFlux and AsiaFlux offices. We are grateful to Dr. Takashi Hirano and Dr. Shih-Chieh Chang for their permission to use data from the Palangkaraya Drained Forest (PDF) and Chi-Lan Mountain (CLM) sites, respectively (AsiaFlux), and to Dr. Michael Liddell for making available data from the Cape Tribulation, Cow Bay, and Robson Creek sites (OzFlux). D.S.M. was supported by a graduate scholarship from CONACYT, Mexico (number 595847). E.A.Y. acknowledges support from CONACYT, Mexico (grant number 221014) and PROFAPI-ITSON, Mexico. N.E.R.R. was supported by a graduate scholarship from CONACYT, Mexico (number 278991). G.A.P. acknowledges support from CONACYT PN, Mexico (grant number 2017-6231) and USAID-USFS (grant number 12-IJ-11242306-033). E.G.C. acknowledges support from the Inter-American Institute for Global Change Research (grant numbers CRN2, CRN3-025) and CONACYT, Mexico (grant number 175725). J.D.B. and T.A. acknowledge support from SEMARNAT-CONACYT, Mexico (grant numbers 108000, CB 2008-01 102855, CB 2013 220788). J.D.B. was supported by a graduate scholarship from CONACYT, Mexico (number 211819). F.H. acknowledges support from CONACYT, Mexico (grant number 187646) and UNAM-PAPIIT, Mexico (grant number IB100113). S.A.B. acknowledges support from CONACYT, Mexico (grant number INFR-2016-01-269269). B.F.E and J.U.S. acknowledge support from LANRESC-CONACYT, Mexico (grant number 271544) and UNAM-PINCC-2020, Mexico. J.U.S. was supported by a graduate scholarship from CONACYT, Mexico (number 415123). J.G.P. acknowledges support from PROFAPI-ITSON, Mexico. Finally, we are grateful to the two anonymous reviewers whose comments and suggestions led to significant improvements in this work.

Funding Information:
This work used eddy covariance data acquired and shared by the FLUXNET community, including these networks: AmeriFlux, AfriFlux, AsiaFlux, CarboAfrica, CarboEuropeIP, CarboItaly, CarboMont, ChinaFlux, Fluxnet-Canada, GreenGrass, ICOS, KoFlux, LBA, NECC, OzFlux-TERN, TCOS-Siberia, and USCCC. The FLUXNET eddy covariance data processing and harmonization was carried out by the ICOS Ecosystem Thematic Center, AmeriFlux Management Project and Fluxdata project of FLUXNET, with the support of CDIAC, and the OzFlux, ChinaFlux and AsiaFlux offices. We are grateful to Dr. Takashi Hirano and Dr. Shih-Chieh Chang for their permission to use data from the Palangkaraya Drained Forest (PDF) and Chi-Lan Mountain (CLM) sites, respectively (AsiaFlux), and to Dr. Michael Liddell for making available data from the Cape Tribulation, Cow Bay, and Robson Creek sites (OzFlux). D.S.M. was supported by a graduate scholarship from CONACYT , Mexico (number 595847 ). E.A.Y. acknowledges support from CONACYT, Mexico (grant number 221014) and PROFAPI-ITSON, Mexico. N.E.R.R. was supported by a graduate scholarship from CONACYT, Mexico (number 278991). G.A.P. acknowledges support from CONACYT PN, Mexico (grant number 2017-6231) and USAID-USFS (grant number 12-IJ-11242306-033). E.G.C. acknowledges support from the Inter-American Institute for Global Change Research (grant numbers CRN2, CRN3-025) and CONACYT, Mexico (grant number 175725). J.D.B. and T.A. acknowledge support from SEMARNAT-CONACYT, Mexico (grant numbers 108000, CB 2008‐01 102855, CB 2013 220788). J.D.B. was supported by a graduate scholarship from CONACYT, Mexico (number 211819). F.H. acknowledges support from CONACYT, Mexico (grant number 187646) and UNAM-PAPIIT, Mexico (grant number IB100113). S.A.B. acknowledges support from CONACYT, Mexico (grant number INFR-2016-01-269269). B.F.E and J.U.S. acknowledge support from LANRESC-CONACYT, Mexico (grant number 271544) and UNAM-PINCC-2020, Mexico. J.U.S. was supported by a graduate scholarship from CONACYT, Mexico (number 415123). J.G.P. acknowledges support from PROFAPI-ITSON, Mexico. Finally, we are grateful to the two anonymous reviewers whose comments and suggestions led to significant improvements in this work.

Publisher Copyright:
© 2022 Elsevier B.V.

Keywords

  • ALEXI
  • GLEAM
  • MOD16
  • Subtropics
  • Tropics

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