STEEP: A remotely-sensed energy balance model for evapotranspiration estimation in seasonally dry tropical forests: A remotely-sensed energy balance model for evapotranspiration estimation in seasonally dry tropical forests

Ulisses A. Bezerra; John Cunha; Fernanda Valente; Rodolfo L.B. Nóbrega; João M. Andrade; Magna S.B. Moura; Anne Verhoef; Aldrin M. Perez-Marin; Carlos O. Galvão

doi:10.1016/j.agrformet.2023.109408

STEEP: A remotely-sensed energy balance model for evapotranspiration estimation in seasonally dry tropical forests: A remotely-sensed energy balance model for evapotranspiration estimation in seasonally dry tropical forests

Ulisses A. Bezerra^*, John Cunha, Fernanda Valente, Rodolfo L.B. Nóbrega, João M. Andrade, Magna S.B. Moura, Anne Verhoef, Aldrin M. Perez-Marin, Carlos O. Galvão

^*Corresponding author for this work

Research output: Contribution to journal › Article (Academic Journal) › peer-review

3 Citations (Scopus)

Abstract

Improvement of evapotranspiration (ET) estimates using remote sensing (RS) products based on multispectral and thermal sensors has been a breakthrough in hydrological research. In large-scale applications, methods that use the approach of RS-based surface energy balance (SEB) models often rely on oversimplifications. The use of these models for Seasonally Dry Tropical Forests (SDTF) has been challenging due to incompatibilities between the assumptions underlying those models and the specificities of this environment, such as the highly contrasting phenological phases or ET being mainly controlled by soil–water availability. We developed a RS-based SEB model from a one-source bulk transfer equation, called Seasonal Tropical Ecosystem Energy Partitioning (STEEP). Our model uses the plant area index to represent the woody structure of the plants in calculating the moment roughness length. We included the parameter kB⁻¹ and its correction using RS soil moisture in the calculation of the aerodynamic resistance for heat transfer. Besides, λET caused by remaining water availability in endmembers pixels was quantified using the Priestley-Taylor equation. We implemented the algorithm on Google Earth Engine, using freely available data. To evaluate our model, we used eddy covariance data from four sites in the Caatinga, the largest SDTF in South America, in the Brazilian semiarid region. Our results show that STEEP increased the accuracy of ET estimates without requiring any additional climatological information. This improvement is more pronounced during the dry season, which, in general, ET for these SDTF is overestimated by traditional SEB models, such as the Surface Energy Balance Algorithms for Land (SEBAL). The STEEP model had similar or superior behavior and performance statistics relative to global ET products (MOD16 and PMLv2). This work contributes to an improved understanding of the drivers and modulators of the energy and water balances at local and regional scales in SDTF.

Original language	English
Article number	109408
Journal	Agricultural and Forest Meteorology
Volume	333
Early online date	17 Mar 2023
DOIs	https://doi.org/10.1016/j.agrformet.2023.109408
Publication status	Published - 15 Apr 2023

Bibliographical note

Funding Information:
The Coordenação de Aperfeiçoamento de Pessoal de Nível Superior-Brazil (CAPES)-Finance Code 001, provided scholarships to the first and fifth authors. This work was funded by the Brazilian National Council for Scientific and Technological Development (CNPq) , grant 409341/2021–5 , by the Paraiba Scientific Foundation (FAPESQ) , under grants 010/2021 and 403/2021 , and by São Paulo Scientific Foundation (FAPESP) , grant 2015/24461–2 . CEF is a research unit funded by Fundação para a Ciência e a Tecnologia I.P. (FCT), Portugal ( UIDB/00239/2020 ). MSBM, AV and RLBN acknowledge support by the Newton/NERC/FAPESP Nordeste project ( NE/N012526/1 ICL 652 and NE/N012488/1 UoR ). RLBN acknowledges support from the European Research Council under the European Union's Horizon 2020 research and innovation program (grant agreement No: 787203 REALM ). MSBM thanks to Fundação de Amparo à Ciência e Tecnologia do Estado de Pernambuco (FACEPE) for funding this through the Project FACEPE APQ 0062–1.07/15 (Caatinga-FLUX). MSBM and AMPM acknowledge to the National Observatory of Water and Carbon Dynamics in the Caatinga Biome ( INCT: NOWCBCB ) supported by FACEPE (grant: APQ-0498–3.07/17 ONDACBC ), CNPq (grant: 465764/2014–2 ), and CAPES (grants: 88887.136369/2017–00 ).

Publisher Copyright:
© 2023 Elsevier B.V.

Keywords

Aerodynamic resistance for heat transfer
Caatinga
Google Earth Engine
Sensible heat flux
Surface energy balance

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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Bezerra, U. A., Cunha, J., Valente, F., Nóbrega, R. L. B., Andrade, J. M., Moura, M. S. B., Verhoef, A., Perez-Marin, A. M., & Galvão, C. O. (2023). STEEP: A remotely-sensed energy balance model for evapotranspiration estimation in seasonally dry tropical forests: A remotely-sensed energy balance model for evapotranspiration estimation in seasonally dry tropical forests. Agricultural and Forest Meteorology, 333, Article 109408. https://doi.org/10.1016/j.agrformet.2023.109408

@article{b913fcfee28746b08502ccaff9d41409,

title = "STEEP: A remotely-sensed energy balance model for evapotranspiration estimation in seasonally dry tropical forests: A remotely-sensed energy balance model for evapotranspiration estimation in seasonally dry tropical forests",

abstract = "Improvement of evapotranspiration (ET) estimates using remote sensing (RS) products based on multispectral and thermal sensors has been a breakthrough in hydrological research. In large-scale applications, methods that use the approach of RS-based surface energy balance (SEB) models often rely on oversimplifications. The use of these models for Seasonally Dry Tropical Forests (SDTF) has been challenging due to incompatibilities between the assumptions underlying those models and the specificities of this environment, such as the highly contrasting phenological phases or ET being mainly controlled by soil–water availability. We developed a RS-based SEB model from a one-source bulk transfer equation, called Seasonal Tropical Ecosystem Energy Partitioning (STEEP). Our model uses the plant area index to represent the woody structure of the plants in calculating the moment roughness length. We included the parameter kB−1 and its correction using RS soil moisture in the calculation of the aerodynamic resistance for heat transfer. Besides, λET caused by remaining water availability in endmembers pixels was quantified using the Priestley-Taylor equation. We implemented the algorithm on Google Earth Engine, using freely available data. To evaluate our model, we used eddy covariance data from four sites in the Caatinga, the largest SDTF in South America, in the Brazilian semiarid region. Our results show that STEEP increased the accuracy of ET estimates without requiring any additional climatological information. This improvement is more pronounced during the dry season, which, in general, ET for these SDTF is overestimated by traditional SEB models, such as the Surface Energy Balance Algorithms for Land (SEBAL). The STEEP model had similar or superior behavior and performance statistics relative to global ET products (MOD16 and PMLv2). This work contributes to an improved understanding of the drivers and modulators of the energy and water balances at local and regional scales in SDTF.",

keywords = "Aerodynamic resistance for heat transfer, Caatinga, Google Earth Engine, Sensible heat flux, Surface energy balance",

author = "Bezerra, {Ulisses A.} and John Cunha and Fernanda Valente and N{\'o}brega, {Rodolfo L.B.} and Andrade, {Jo{\~a}o M.} and Moura, {Magna S.B.} and Anne Verhoef and Perez-Marin, {Aldrin M.} and Galv{\~a}o, {Carlos O.}",

note = "Funding Information: The Coordena{\c c}{\~a}o de Aperfei{\c c}oamento de Pessoal de N{\'i}vel Superior-Brazil (CAPES)-Finance Code 001, provided scholarships to the first and fifth authors. This work was funded by the Brazilian National Council for Scientific and Technological Development (CNPq) , grant 409341/2021–5 , by the Paraiba Scientific Foundation (FAPESQ) , under grants 010/2021 and 403/2021 , and by S{\~a}o Paulo Scientific Foundation (FAPESP) , grant 2015/24461–2 . CEF is a research unit funded by Funda{\c c}{\~a}o para a Ci{\^e}ncia e a Tecnologia I.P. (FCT), Portugal ( UIDB/00239/2020 ). MSBM, AV and RLBN acknowledge support by the Newton/NERC/FAPESP Nordeste project ( NE/N012526/1 ICL 652 and NE/N012488/1 UoR ). RLBN acknowledges support from the European Research Council under the European Union's Horizon 2020 research and innovation program (grant agreement No: 787203 REALM ). MSBM thanks to Funda{\c c}{\~a}o de Amparo {\`a} Ci{\^e}ncia e Tecnologia do Estado de Pernambuco (FACEPE) for funding this through the Project FACEPE APQ 0062–1.07/15 (Caatinga-FLUX). MSBM and AMPM acknowledge to the National Observatory of Water and Carbon Dynamics in the Caatinga Biome ( INCT: NOWCBCB ) supported by FACEPE (grant: APQ-0498–3.07/17 ONDACBC ), CNPq (grant: 465764/2014–2 ), and CAPES (grants: 88887.136369/2017–00 ). Publisher Copyright: {\textcopyright} 2023 Elsevier B.V.",

year = "2023",

month = apr,

day = "15",

doi = "10.1016/j.agrformet.2023.109408",

language = "English",

volume = "333",

journal = "Agricultural and Forest Meteorology",

issn = "0168-1923",

publisher = "Amsterdam:Elsevier",

}

Bezerra, UA, Cunha, J, Valente, F, Nóbrega, RLB, Andrade, JM, Moura, MSB, Verhoef, A, Perez-Marin, AM & Galvão, CO 2023, 'STEEP: A remotely-sensed energy balance model for evapotranspiration estimation in seasonally dry tropical forests: A remotely-sensed energy balance model for evapotranspiration estimation in seasonally dry tropical forests', Agricultural and Forest Meteorology, vol. 333, 109408. https://doi.org/10.1016/j.agrformet.2023.109408

STEEP: A remotely-sensed energy balance model for evapotranspiration estimation in seasonally dry tropical forests: A remotely-sensed energy balance model for evapotranspiration estimation in seasonally dry tropical forests. / Bezerra, Ulisses A.; Cunha, John; Valente, Fernanda et al.
In: Agricultural and Forest Meteorology, Vol. 333, 109408, 15.04.2023.

Research output: Contribution to journal › Article (Academic Journal) › peer-review

TY - JOUR

T1 - STEEP: A remotely-sensed energy balance model for evapotranspiration estimation in seasonally dry tropical forests

T2 - A remotely-sensed energy balance model for evapotranspiration estimation in seasonally dry tropical forests

AU - Bezerra, Ulisses A.

AU - Cunha, John

AU - Valente, Fernanda

AU - Nóbrega, Rodolfo L.B.

AU - Andrade, João M.

AU - Moura, Magna S.B.

AU - Verhoef, Anne

AU - Perez-Marin, Aldrin M.

AU - Galvão, Carlos O.

N1 - Funding Information: The Coordenação de Aperfeiçoamento de Pessoal de Nível Superior-Brazil (CAPES)-Finance Code 001, provided scholarships to the first and fifth authors. This work was funded by the Brazilian National Council for Scientific and Technological Development (CNPq) , grant 409341/2021–5 , by the Paraiba Scientific Foundation (FAPESQ) , under grants 010/2021 and 403/2021 , and by São Paulo Scientific Foundation (FAPESP) , grant 2015/24461–2 . CEF is a research unit funded by Fundação para a Ciência e a Tecnologia I.P. (FCT), Portugal ( UIDB/00239/2020 ). MSBM, AV and RLBN acknowledge support by the Newton/NERC/FAPESP Nordeste project ( NE/N012526/1 ICL 652 and NE/N012488/1 UoR ). RLBN acknowledges support from the European Research Council under the European Union's Horizon 2020 research and innovation program (grant agreement No: 787203 REALM ). MSBM thanks to Fundação de Amparo à Ciência e Tecnologia do Estado de Pernambuco (FACEPE) for funding this through the Project FACEPE APQ 0062–1.07/15 (Caatinga-FLUX). MSBM and AMPM acknowledge to the National Observatory of Water and Carbon Dynamics in the Caatinga Biome ( INCT: NOWCBCB ) supported by FACEPE (grant: APQ-0498–3.07/17 ONDACBC ), CNPq (grant: 465764/2014–2 ), and CAPES (grants: 88887.136369/2017–00 ). Publisher Copyright: © 2023 Elsevier B.V.

PY - 2023/4/15

Y1 - 2023/4/15

N2 - Improvement of evapotranspiration (ET) estimates using remote sensing (RS) products based on multispectral and thermal sensors has been a breakthrough in hydrological research. In large-scale applications, methods that use the approach of RS-based surface energy balance (SEB) models often rely on oversimplifications. The use of these models for Seasonally Dry Tropical Forests (SDTF) has been challenging due to incompatibilities between the assumptions underlying those models and the specificities of this environment, such as the highly contrasting phenological phases or ET being mainly controlled by soil–water availability. We developed a RS-based SEB model from a one-source bulk transfer equation, called Seasonal Tropical Ecosystem Energy Partitioning (STEEP). Our model uses the plant area index to represent the woody structure of the plants in calculating the moment roughness length. We included the parameter kB−1 and its correction using RS soil moisture in the calculation of the aerodynamic resistance for heat transfer. Besides, λET caused by remaining water availability in endmembers pixels was quantified using the Priestley-Taylor equation. We implemented the algorithm on Google Earth Engine, using freely available data. To evaluate our model, we used eddy covariance data from four sites in the Caatinga, the largest SDTF in South America, in the Brazilian semiarid region. Our results show that STEEP increased the accuracy of ET estimates without requiring any additional climatological information. This improvement is more pronounced during the dry season, which, in general, ET for these SDTF is overestimated by traditional SEB models, such as the Surface Energy Balance Algorithms for Land (SEBAL). The STEEP model had similar or superior behavior and performance statistics relative to global ET products (MOD16 and PMLv2). This work contributes to an improved understanding of the drivers and modulators of the energy and water balances at local and regional scales in SDTF.

AB - Improvement of evapotranspiration (ET) estimates using remote sensing (RS) products based on multispectral and thermal sensors has been a breakthrough in hydrological research. In large-scale applications, methods that use the approach of RS-based surface energy balance (SEB) models often rely on oversimplifications. The use of these models for Seasonally Dry Tropical Forests (SDTF) has been challenging due to incompatibilities between the assumptions underlying those models and the specificities of this environment, such as the highly contrasting phenological phases or ET being mainly controlled by soil–water availability. We developed a RS-based SEB model from a one-source bulk transfer equation, called Seasonal Tropical Ecosystem Energy Partitioning (STEEP). Our model uses the plant area index to represent the woody structure of the plants in calculating the moment roughness length. We included the parameter kB−1 and its correction using RS soil moisture in the calculation of the aerodynamic resistance for heat transfer. Besides, λET caused by remaining water availability in endmembers pixels was quantified using the Priestley-Taylor equation. We implemented the algorithm on Google Earth Engine, using freely available data. To evaluate our model, we used eddy covariance data from four sites in the Caatinga, the largest SDTF in South America, in the Brazilian semiarid region. Our results show that STEEP increased the accuracy of ET estimates without requiring any additional climatological information. This improvement is more pronounced during the dry season, which, in general, ET for these SDTF is overestimated by traditional SEB models, such as the Surface Energy Balance Algorithms for Land (SEBAL). The STEEP model had similar or superior behavior and performance statistics relative to global ET products (MOD16 and PMLv2). This work contributes to an improved understanding of the drivers and modulators of the energy and water balances at local and regional scales in SDTF.

KW - Aerodynamic resistance for heat transfer

KW - Caatinga

KW - Google Earth Engine

KW - Sensible heat flux

KW - Surface energy balance

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U2 - 10.1016/j.agrformet.2023.109408

DO - 10.1016/j.agrformet.2023.109408

M3 - Article (Academic Journal)

AN - SCOPUS:85150253802

SN - 0168-1923

VL - 333

JO - Agricultural and Forest Meteorology

JF - Agricultural and Forest Meteorology

M1 - 109408

ER -

Bezerra UA, Cunha J, Valente F, Nóbrega RLB, Andrade JM, Moura MSB et al. STEEP: A remotely-sensed energy balance model for evapotranspiration estimation in seasonally dry tropical forests: A remotely-sensed energy balance model for evapotranspiration estimation in seasonally dry tropical forests. Agricultural and Forest Meteorology. 2023 Apr 15;333:109408. Epub 2023 Mar 17. doi: 10.1016/j.agrformet.2023.109408

STEEP: A remotely-sensed energy balance model for evapotranspiration estimation in seasonally dry tropical forests: A remotely-sensed energy balance model for evapotranspiration estimation in seasonally dry tropical forests

Abstract

Bibliographical note

Keywords

UN SDGs

Access to Document

Handle.net

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Cite this