Performance of orbital data and indirect models of evapotranspiration estimation in the agreste region of Pernambuco, Brazil

Ana Patrícia Gomes Silva; Estephania Silva Jovino; Juliana Farias Santos de Moraes; Rebecca Borja Gonçalves Gomes de  Menezes; Ubiratan Joaquim da Silva Junior; Sylvana Melo dos Santos; Leidjane Maria Maciel de Oliveira

doi:10.5327/Z2176-94782724

Authors

Ana Patrícia Gomes Silva Universidade Federal de Pernambuco (UFPE) - Brazil https://orcid.org/0009-0000-0855-9798
Estephania Silva Jovino Universidade Federal de Pernambuco (UFPE) - Brazil https://orcid.org/0000-0002-6694-3533
Juliana Farias Santos de Moraes Universidade Federal de Pernambuco (UFPE) - Brazil https://orcid.org/0000-0002-3241-844X
Rebecca Borja Gonçalves Gomes de Menezes Universidade Federal de Pernambuco (UFPE) - Brazil https://orcid.org/0000-0002-7886-6168
Ubiratan Joaquim da Silva Junior Universidade Federal de Pernambuco (UFPE) - Brazil https://orcid.org/0000-0001-7995-6416
Sylvana Melo dos Santos Universidade Federal de Pernambuco (UFPE) - Brazil https://orcid.org/0000-0003-3106-5301
Leidjane Maria Maciel de Oliveira Universidade Federal de Pernambuco (UFPE) - Brazil https://orcid.org/0000-0003-1251-6998

DOI:

https://doi.org/10.5327/Z2176-94782724

Keywords:

Penman–Monteith; Hargreaves–Samani; Jensen–Haise; validation; remote sensing; ODS.

Abstract

Accurate estimates of reference evapotranspiration (ET₀) are essential for water management and agriculture, especially in data-scarce regions such as the Brazilian semi-arid. In this context, the aim of this study was to evaluate the performance of indirect empirical methods for determining ET₀, Hargreaves–Samani, solar radiation, and Jensen–Haise, together with the MOD16A2 remote sensing product and the BR-DWGD gridded dataset. The methodology consisted of an analysis based on observed data from the municipalities of Garanhuns, Surubim, and Caruaru, in the Agreste region of Pernambuco, using the Penman–Monteith FAO-56 model as the standard reference. The results indicated that the solar radiation method achieved the best performance across all three municipalities, being classified as “excellent” (c > 0.85) in every case. The Jensen–Haise and Hargreaves–Samani models also performed well, with classifications ranging from “very good” to “excellent”. In contrast, the MOD16A2 product showed limitations, with greater variability and lower accuracy among the evaluated sites. The BR-DWGD dataset, in turn, demonstrated strong performance, with low error margins and a high correlation with observed data. These results demonstrate that radiation- and temperature-based models are suitable for estimating ET₀ in the Agreste region, while also highlighting the need to improve remote sensing ET al algorithms in climatically heterogeneous environments.

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References

Agência Pernambucana de Águas e Clima (APAC), 2023. Atlas climatológico do Estado de Pernambuco: normais climatológicas 1991-2020 (Accessed May 19, 2025) at:. https://www.apac.pe.gov.br/images/webAtlas-Climatologico-do-Estado-de-Pernambuco-APAC.pdf.

Alam, M.M.; Akter, M.Y.; Islam, A.R.M.T.; Mallick, J.; Kabir, Z.; Chu, R.; Senapathi, V., 2024. A review of recent advances and future prospects in calculation of reference evapotranspiration in Bangladesh using soft computing models. Journal of Environmental Management, v. 351, 119714. https://doi.org/10.1016/j.jenvman.2023.119714.

Allen, R.G.; Pereira, L.S.; Raes, D.; Smith, M., 1998. Crop evapotranspiration: guidelines for computing crop water requirements. FAO Irrigation and Drainage Paper 56. FAO, Rome.

Alvalá, R.C.S.; Cunha, A.P.M.A.; Brito, S.S.B.B; Seluchi, M.E.; Marengo, J.A.; Moraes, O.L.L.; Carvalho, M.A., 2017. Drought monitoring in the Brazilian Semiarid region. Annals of the Brazilian Academy of Sciences, v. 91 (Suppl. 1). https://doi.org/10.1590/0001-3765201720170209.

Andrade, A.R.S.; Godoy Neto, A.H.; Cruz, A.F.S.; Andrade, E.K.P.; Santos, V.F.; Silva, T.N.P., 2018. Geoestatística aplicada à variabilidade espacial e padrões nas séries temporais da precipitação no Agreste pernambucano. Journal of Environmental Analysis and Progress, v. 3 (1), 126-145. https://doi.org/10.24221/jeap.3.1.2018.1668.126-145.

Andrade, J.M.; Ribeiro Neto, A.; Bezerra, U.A.; Moraes, A.C.C.; Montenegro, S.M.G.L., 2022. A comprehensive assessment of precipitation products: temporal and spatial analyses over terrestrial biomes in Northeastern Brazil. Remote Sensing Applications-Society And Environment, v. 28, 100842. https://doi.org/10.1016/j.rsase.2022.100842.

Antunes, S.C.R.; Ribeiro, C.B.M.; Lima, R.N.S.; Getirana, A., 2024. Efeitos de mudanças no uso e cobertura do solo na evapotranspiração real a partir da criação de um produto de sensoriamento remoto na bacia do Xingu. Revista Brasileira de Ciências Ambientais (RBCIAMB), v. 59, e1658. https://doi.org/10.5327/Z2176-94781658.

Barbosa, H.A.; Buriti, C.O.; Kumar, T.V.L., 2024. Deep Learning for Flash Drought Detection: A Case Study in Northeastern Brazil. Atmosphere, v. 15, 761. https://doi.org/10.3390/atmos15070761.

Bu, J.; Gan, G.; Chen, J.; Su, Y.; Yuan, M.; Gao, Y.; Domingo, F.; López-Ballesteros, A.; Migliavacca, M.; El-Madany, T. S., 2024. Dryland evapotranspiration from remote sensing solar-induced chlorophyll fluorescence: constraining an optimal stomatal model within a two-source energy balance model. Remote Sensing of Environment, v. 303, 113999. https://doi.org/10.1016/j.rse.2024.113999.

Cabral Júnior, J.B.; Silva, C.M.S.; Almeida, H.A., 2017. Comparação mensal, sazonal e anual de métodos de estimativas da evapotranspiração de referência para Juazeiro-BA e Petrolina-PE. Revista de Geociências do Nordeste, v. 3 (2), 23,42. https://doi.org/10.21680/2447-3359.2017v3n2ID12448.

Camargo, A.P.; Sentelhas, P.C., 1997. Avaliação do desempenho de diferentes métodos de estimativa da evapotranspiração potencial no estado de São Paulo, Brasil. Revista Brasileira de Agrometeorologia, Santa Maria, v. 05, 89-97.

Claudino, C.M.A.; Bertrand, G.F.; Nóbrega, R.L.B.; Almeida, C.N.; Gusmão, A.C.V.; Montenegro, S.M.G.L.; Silva, B.B.; Patriota, E.G.; Lemos, F.C.; Coutinho, J.V., 2025. ESTIMET: enhanced and spatial-temporal improvement of modis evapotranspiration algorithm for all sky conditions in tropical biomes. Remote Sensing of Environment, v. 325, 114771. https://doi.org/10.1016/j.rse.2025.114771.

Cunha, P.C.R.; Nascimento, J.L.; Silveira, P.M.; Alves Jr., J., 2013. Eficiência de métodos para o cálculo de coeficientes do tanque classe A na estimativa da evapotranspiração de referência. Pesquisa Agropecuária Tropical, Goiânia, v. 43 (2), 114-122. https://doi.org/10.1590/S1983-40632013000200005.

Dalcol, D.S.; Boiaski, N.T.; Pinheiro, D.K., 2024. Influência do aquecimento estratosférico repentino sobre as chuvas no Brasil nos anos de 1988, 2002 e 2019. Ciência e Natura, v. 46 (esp. 2), e87951. https://doi.org/10.5902/2179460X87951.

Ersi, C.; Sudu, B.; Song, Z.; Bao, Y.; Wei, S.; Zhang, J.; Tong, Z.; Liu, X.; Le, W.; Rina, S., 2024. The potential of NIRvP in estimating evapotranspiration. Remote Sensing of Environment, v. 315, 114405. https://doi.org/10.1016/j.rse.2024.114405.

Freitas, C.H.; Coelho, R.D.; Costa, J.O.; Sentelhas, P.C., 2024. Smart coffee: machine learning techniques for estimating Arabica coffee yield. AgriEngineering, v. 6 (4), 4925-4942. https://doi.org/10.3390/agriengineering6040281

Gharehbaghi, A.; Kaya, B., 2022. Calibration and evaluation of six popular evapotranspiration formula based on the Penman-Monteith model for continental climate in Turkey. Physics and Chemistry of the Earth, Parts A/B/C, v. 127, 103190. https://doi.org/10.1016/j.pce.2022.103190.

Instituto Brasileiro de Geografia e Estatística (IBGE), 2023 (Accessed June 07, 2025) at:. httpS://www.ibge.gov.br/estadosat.

Instituto Nacional de Meteorologia (INMET), 2023. Estado do Clima no Brasil em 2022. INMET, Brasília (Accessed November 05, 2025) at:. https://portal.inmet.gov.br/uploads/notastecnicas/Estado-do-clima-no-Brasil-em-2022-OFICIAL.pdf.

Islam, S.; Alam, A.K.M.R., 2021. Performance evaluation of FAO Penman-Monteith and best alternative models for estimating reference evapotranspiration in Bangladesh. Heliyon, v. 7 (7), e07487. https://doi.org/10.1016/j.heliyon.2021.e07487.

Jaafar, H.H.; Sujud, L.H., 2024. High-resolution satellite imagery reveals a recent accelerating rate of increase in land evapotranspiration. Remote Sensing of Environment, v. 315, 114489. https://doi.org/10.1016/j.rse.2024.114489.

Liu, W.; Ma, S.; Xi, H.; Liang, L.; Feng, K.; Tsunekawa, A., 2025. The evaluation of the suitability of potential evapotranspiration models for drought monitoring based on observed pan evaporation and potential evapotranspiration from eddy covariance. Journal of Hydrology, v. 660 (Part B), 133434. https://doi.org/10.1016/j.jhydrol.2025.133434.

Lopes Sobrinho, O.P.; Castro Júnior, W.L.; Cipriano, D.A.; Cantanhede, E.K.P., 2024. Métodos empíricos de estimativa da evapotranspiração de referência para Codó, Maranhão, Brasil. Revista Brasileira de Climatologia, v. 34 (20), 1-12. https://doi.org/10.55761/abclima.v34i20.17275.

Marengo, J.A.; Alves, L.M.; Alvalá, R.C.S.; Cunha, A.P.; Brito, S.; Moraes, O.L.L., 2018. Climatic characteristics of the 2010-2016 drought in the semiarid Northeast Brazil region. Annals of the Brazilian Academy of Sciences, v. 90 (2 Suppl. 1), 1973-1985. https://doi.org/10.1590/0001-3765201720170206.

Medeiros, R.M.; Holanda, R.M.; França, M.V.; Saboya, L.M.F.; Rolim Neto, F.C.; Araújo, W.R., 2021. Espacialização pelo modelo da krigagem nas variabilidades pluvial, evapotranspiração e evaporação no estado do Pernambuco – Brasil. RECIMA21 - Revista Científica Multidisciplinar, v. 2, n. 8. https://doi.org/10.47820/recima21.v2i8.573.

Monteiro, A.F.M.; Martins, F.B.; Torres, R.R.; de Almeida, V.H.M.; Abreu, M.C.; Mattos, E.V., 2021. Intercomparison and uncertainty assessment of methods for estimating evapotranspiration using a high-resolution gridded weather dataset over Brazil. Theoretical Applied Climatology, v. 146, 583-597. https://doi.org/10.1007/s00704-021-03747-1.

Moraes, R.G.S.; Lima, E.F.; Oliveira, P.L.S.; Damascena, J.F.; Silva, C.M., 2023. Métodos de estimativa da evapotranspiração de referência no período seco e chuvoso em Imperatriz, MA. Revista Brasileira de Climatologia, v. 33 (19), 169-188. https://doi.org/10.55761/abclima.v33i19.16162.

Moriasi, D.N.; Arnold, J.G.; Van Liew, M.W.; Bingner, R.L.; Harmel, R.D.; Veith, T.L., 2007. Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Transactions of the ASABE, v. 50 (3), 885-900. https://doi.org/10.13031/2013.23153.

Mostafa, R.R.; Kisi, O.; Adnan, R.M.; Sadeghifar, T.; Kuriqi, A., 2023. Modeling potential evapotranspiration by improved machine learning methods using limited climatic data. Water, v. 15 (3), 486. https://doi.org/10.3390/w15030486.

Mousinho, F.H.G.; Lima, J.M.S.; Pereira, M.M.A.; Pessoa, J.O.; Santos, S.M.; Oliveira, L.M.M.; Paiva, A.L.R., 2024. Caracterização pluviométrica dos últimos 50 anos em Caruaru – PE, com análise de tendências, máximas diárias, curvas IDF e distribuição Gumbel. Revista Brasileira de Geografia Física, v. 17 (2), 958-973. https://doi.org/10.26848/rbgf.v17.2.p958-973.

Oliveira, E.D.; Ferreira, T.R.; Azevedo, C.D.S.; Leitão, M.M.V.B.R.; Melo, M.L., 2025. Assessment of employing different cloud cover data sources to model the Brazilian solar energy potentiality. Revista Brasileira de Ciências Ambientais (RBCIAMB), v. 60, e2451. https://doi.org/10.5327/Z2176-94782451.

Ongaratto, J.M.; Bortolin, T.A., 2021. Comparação entre métodos de estimativa de evapotranspiração de referência no município de São José dos Ausentes (RS), Brasil. Engenharia Sanitária e Ambiental, v. 26 (5), 979-987. https://doi.org/10.1590/S1413-415220190196.

Ouaadi, N.; Jarlan, L.; Villard, L.; Chakir, A.; Khabba, S.; Fanise, P.; Kasbani, M.; Rafi, Z.; Dantec, V.L.; Ezzahar, J., 2024. Temporal decorrelation of C-band radar data over wheat in a semi-arid area using sub-daily tower-based observations. Remote Sensing of Environment, v. 304, 114059. https://doi.org/10.1016/j.rse.2024.114059.

Pedreira Junior, A.L.; Biudes, M.S.; Machado, N.G.; Vourlitis, G.L.; Geli, H.M.E.; Santos, L.O.F.; Querino, C.A.S.; Ivo, I.O.; Lotufo Neto, N., 2021. Assessment of Remote Sensing and Re-Analysis Estimates of Regional Precipitation over Mato Grosso, Brazil. Water, v. 13, 333. https://doi.org/10.3390/w13030333.

Perleberg, B.R.; Hosser, S.; Pinto, L.B.; Lindemann, D.S., 2025. Análise comparativa de dados meteorológicos de Rio Brilhante MS: estação INMET, banco de dados BR-DWGD e reanálise ERA5. Ciência e Natura, v. 47 (esp. 3), e84142. https://doi.org/10.5902/2179460X84142.

Rasera, J.B.; Silva, R.F.D.; Piedade, S.; Mourão Filho, F.D.A.A.; Delbem, A.C.B.; Saraiva, A.M.; Sentelhas, P.C.; Marques, P.A.A., 2023. Do Gridded Weather Datasets Provide High-Quality Data for Agroclimatic Research in Citrus Production in Brazil? AgriEngineering, v. 5 (2), 924-940. https://doi.org/10.3390/agriengineering5020057.

Rocha Júnior, R.L.; Silva, F.D.S.; Costa, R.L.; Gomes, H.B.; Gomes, H.B.; Silva, M.C.L.; Pinto, D.D.C.; Herdies, D.L.; Cabral Júnior, J.B.; Pita Díaz, O., 2020. Mudança de longo prazo e regionalização da evapotranspiração de referência no Nordeste brasileiro. Revista Brasileira de Meteorologia, v. 35 ( Especial), 891-902. https://doi.org/10.1590/0102-77863550126.

Shirmohammadi-Aliakbarkhani, Z.; Saberali, S.F., 2020. Evaluating of eight evapotranspiration estimation methods in arid regions of Iran. Agricultural Water Management, v. 239, 106243. https://doi.org/10.1016/j.agwat.2020.106243.

Silva, G.H.; Dias, S.H.B.; Ferreira, L.B.; Santos, J.É.O.; Cunha, F.F., 2018. Performance of diferente methods for reference evapotranspiration estimation in Jaíba, Brazil. Revista Brasileira de Engenharia Agrícola e Ambiental, v. 22 (2), 83-89. https:/doi.org/10.1590/1807-1929/agriambi.v22n2p83-89.

Srivastava, A.; Sahoo, B.; Raghuwanshi, N.S.; Singh, R., 2017. Evaluation of variable-infiltration capacity model evapotranspiration estimates in a river basin with tropical monsoon-type climatology. Journal of Irrigation and Drainage Engineering, v. 143 (8). https://doi.org/10.1061/(ASCE)IR.1943-4774.0001199.

Su, Q.; Singh, V.P.; Karthikeyan, R., 2022. Improved reference evapotranspiration methods for regional irrigation water demand estimation. Agricultural Water Management, v. 274, 107979. https://doi.org/10.1016/j.agwat.2022.107979.

Tang, R.; Peng, Z.; Liu, M.; Li, Z-L.; Jiang, Y.; Hu, Y.; Huang, L.; Wang, Y.; Wang, J.; Jia, L.; Zheng, C.; Zhang, Y.; Zhang, K.; Yao, Y.; Chen, X.; Xiong, Y.; Zeng, Z.; Fisher, J.B., 2024. Spatial-temporal patterns of land surface evapotranspiration from global products. Remote Sensing of Environment, v. 304, 114066. https://doi.org/10.1016/j.rse.2024.114066.

Tito, T.M.; Delgado, R.C.,; Carvalho, D.C.; Teodoro, P.E.; Almeida, C.T.; Silva Junior, C.A.; Siciliano Silva Júnior, L.A., 2020. Assessment of evapotranspiration estimates based on surface and satellite data and its relationship with El Niño–Southern Oscillation in the Rio de Janeiro State. Environmental Monitoring and Assessment, v. 192 (7), 1-15. https://doi.org/10.1007/s10661-020-08421-z.

Turcato, F.O.; Minuzzi, R.B., 2024. Avaliação de métodos empíricos na estimativa da evapotranspiração de referência no estado do Rio Grande do Sul. Revista Brasileira de Meteorologia, v. 39 (1), 137-151. https://doi.org/10.1590/0102-77863910046.

Xavier, A.C.; Scanlon, B.R.; King, C.W.; Alves, A.I., 2022. New improved Brazilian daily weather gridded data (1961–2020). International Journal of Climatology, v. 42 (16), 8390-8404. https://doi.org/10.1002/joc.7731.

Yang, Y., 2025. Estimating actual evapotranspiration across China by improving the PML algorithm with a shortwave infrared-based surface water stress constraint. Remote Sensing of Environment, v. 318, 114544. https://doi.org/10.1016/j.rse.2024.114544.

Yu, Z.; Chen, J.; Chen, J.; Zhan, W.; Wang, C.; Ma, W.; Yao, X.; Zhou, S.; Zhu, K.; Sun, R., 2024. Enhanced observations from an optimized soil-canopy-photosynthesis and energy flux model revealed evapotranspiration-shading cooling dynamics of urban vegetation during extreme heat. Remote Sensing of Environment, v. 305, 114098. https://doi.org/10.1016/j.rse.2024.114098.