Obtaining the daily actual evapotranspiration through remote sensing techniques application in Brazilian Semiarid
DOI:
https://doi.org/10.29150/2237-2202.2021.249016Schlagworte:
Evapotranspiration, semiarid, SEBAL, S-SEBI, SSEB.Abstract
Large volumes of water are released to the atmosphere through evaporation from soil and transpiration from vegetation, constituting evapotranspiration (ET). Estimating the water consumption in vegetated areas is important for the management and rational use of this resource. For this study were processed orbital images which correspond to Quixeré-CE, with interest at the Frutacor Farm, where there is predominance the banana crop. The main objective of this study was to assess the accuracy and applicability of S-SEBI and SSEB algorithms with regard to SEBAL to estimate the actual daily evapotranspiration ETa) of a semi-arid region of Northeast Brazil, containing areas of banana orchard, native vegetation (caatinga) and bare soil. S-SEBI. The SSEB and SSEB algorithms showed strong correlation (r > 0.93) with statistical significance of 5%. The S-SEBI exhibited errors less than 12% in the orchard and caatinga and SSEB exhibited greater errors at 22%, though for the bare soil, both models showed large discrepancies when compared with SEBAL, with errors greater than 36%. Therefore, among the two algorithms compared with SEBAL, S-SEBI had a better performance in ETa estimation with lower deviations.
Literaturhinweise
AGUIAR, R.G.; MUSIS, C.R.; AGUIAR, L.J.G.; ESPINOSA, M.M. 2018. Energy balance closure in the Southwest Amazon forest site—a statistical approach. Theoretical and Applied Climatology. 1, 1-11.
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. Rome-Italy.
ALLEN, R.; TASUMI, M.; TREZZA, R. 2002. SEBAL - Surface Energy Balance Algorithms for Land – Advanced Training and User’s Manual – Idaho Implementation, version 1.0.
ALLEN, R. G.; TASUMI, M.; TREZZA, R. 2007. Satellite-Based Energy Balance for Mapping Evapotranspiration with Internalized Calibration (METRIC) - Model. Journal of Irrigation and Drainage Engineering. 133, 380-394.
BASIT, A.; KHALIL, R. Z.; HAQUE, S. 2018. Application of simplified surface energy balance index (S-SEBI) for crop evapotranspiration using Landsat 8. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. XLII-1, 33-37.
BASTIAANSSEN, W. G. M. Regionalization of surface flux densities and moisture indicators in composite terrain: A remote sensing approach under clear skies IN Mediterranean climate. PhD. Dis., CIP Data Koninklijke Biblioteheek, Den Haag, the Netherlands. 272 p.; 1995.
BASTIAANSSEN, W. G. M.; MENENTI, M.; FEDDES, R. A.; HOLTSLAG, A. A. M. A remote sensing surface energy balance algorithm for land (SEBAL) 1. 1998a. Formulation. Journal of Hydrology. 212–213, 198–212.
BASTIAANSSEN, W. G. M.; PELGRUM, H.; WANG, J.; MA, Y.; MORENO, J. F.; ROENRINK, G. J.; VAN DER WAL, T. 1998b. A remote sensing surface energy balance algorithm for land (SEBAL) 2. Validation. Journal of Hydrology. 212–213, 213-229.
BASTIAANSSEN, W.G.M. SEBAL-based sensible and latent heat fluxes in the irrigated Gediz Basin, Turkey. Journal of Hydrology, v. 229, n. 1-2, p. 87-100, 2000.
BEZERRA, B. G.; BEZERRA, J. R. C.; SILVA, B. B.; SANTOS, C. A. C. 2015a. Surface energy exchange and evapotranspiration from cotton crops under full irrigation conditions in the Rio Grande do Norte State, Brazilian Semi-Arid. Bragantia. 74, 120-128.
BEZERRA, B.G.; SILVA, B. B.; SANTOS, A. A. C.; BEZERRA, J. R. C. 2015b. Actual Evapotranspiration Estimation Using Remote Sensing: Comparison of SEBAL and SSEB Approaches. Advances in Remote Sensing. 04, 234-247.
BLAIN, G. C.; KAYANO, M. T.; CAMARGO, M. B. P.; LULU, J. 2009. Variabilidade amostral das séries mensais de precipitação pluvial em duas regiões do Brasil: Pelotas-RS e Campinas-SP. Revista Brasileira de Meteorologia. 24.
CAI, X. L.; SHARMA, B. R. 2010. Integrating remote sensing, census and weather data for an assessment of rice yield, water consumption and water productivity in the Indo-Gangetic river basin. Agricultural Water Management. 97, 309–316.
CAI, X. L.; SHARMA, B. R. 2009. Remote sensing and census based assessment and scope for improvement of rice and wheat water productivity in the Indo-GangeticBasin. Science in China Series E: Technological Sciences. 52, 3300-3308.
CALCAGNO, G. MENDICINO, G. MONACELLI, G. SENATORE, A. VERSACE, P. 2007. Distributed Estimation of Actual Evapotranspiration through Remote Sensing Techniques. Water science and technology library. 62, 25-148.
CASTRO, C. A. C. Interações Trópicos-Extratrópicos na escala de tempo intrasazonal durante o verão austral e seus efeitos na América do Sul. Dissertação de Mestrado. Instituto Nacional de Pesquisas Espaciais, São Paulo, 2002.
DANODIA, A.; PATEL, N. R.; CHOL, C. W.; NIKAM, B. R.; SEHGAL, V. K. 2019. Application of S-SEBI model for crop evapotranspiration using Landsat-8 data overparts of North India. Geocarto International. 34, 114–131.
EMBRAPA – Empresa Brasileira de Pesquisa Agropecuária. (2020). Coeficientes de cultura para a bananeira para dois métodos de determinação de evapotranspiração de referência. Disponível em https://ainfo.cnptia.embrapa.br/digital/bitstream/CPATSA/30014/1/OPB275.pdf.
HUANG, G.; PAES, A. T. 2009. Posso usar o teste t de Student quando preciso comparar três ou mais grupos?. Einstein: Educação Continuada em Shuxmaúde. 7, 63-64.
HUXMAN T. E.; WILCOX, B. P.; SCOTT, R. L.; SNYDER, K.; HULTINE, K.; SMALL, E.; BRESHEARS, D.; POCKMAN, W.; JACKSON, R. B. 2005. Ecohydrological implications of woody plant encroachment. Ecology. 86, 308-319.
JIA, D.; KAISHAN, S.; ZONGMING, W.; BAI, Z.; DIANWEI, L. 2013. Evapotranspiration Estimation Based on MODIS Products and SurfaceEnergy Balance Algorithms for Land (SEBAL) Model in Sanjiang Plain,Northeast China. Science. 23.
JIN, X.; ZHU, X.; XUE, Y. 2019. Satellite-based analysis of regional evapotranspiration trends in a semi-arid area. International Journal of Remote Sensing. 40, 3267–3288.
KAYANO, M. T.; KOUSKY, V. E. 1996. Tropical circulation variability with emphasis on interannual and intraseasonal time scales. Revista Brasileira de Meteorologia. 11.
KAZMIER, L. J. Estatística aplicada à economia e administração. São Paulo: McGraw-Hill, 1982. (Coleção Schaum).
KNIPPER, K.; HOGUE, T.; SCOTT, R.; FRANZ, K. 2017. Evapotranspiration Estimates Derived Using Multi-Platform Remote Sensing in a Semiarid Region. Remote Sensing. 9, 1-22.
KOUSKY, V. E.; KAYANO, M. T. 1994. Principal modes of outgoing longwave radiation and 250-mb circulation for the South American sector. Journal of Climate. 7.
LIU, Z.; WANG, L.; WANG, S. 2014. Comparison of Different GPP Models in China Using MODIS Image and China FLUX Data. Remote Sensing. 6, 10215-10231.
MENDONÇA, J. C.; SOUSA, E. F.; ANDRÉ, R. G. B.; SILVA, B. B.; FERREIRA, N. J. 2012. Estimativa do fluxo do calor sensível utilizando o algoritmo SEBAL e imagens MODIS para a região norte fluminense, RJ. Revista Brasileira de Meteorologia. 27, 85 - 94.
MENEZES, H.E.A.; BRITO, J. I. B.; SILVA, L. L.; MENEZES, Hamstrong E. A. Comparação entre dados de precipitação da reanálise do NCEP/NCAR e da precipitação observada para a Zona de Convergência Secundária do Atlântico Sul.. In: I Workshop Internacional sobre Mudanças Climáticas e Biodiversidade, 2012, Recife. Mudanças Climáticas e Biodiversidade. Recife: ITEP, 2012.
NAJMADDIN, P. M.; WHELAN, M. J.; BALZTER, H. 2017. Estimating Daily Reference Evapotranspiration in a Semi-Arid Region Using Remote Sensing Data. Remote Sensing. 9, 1-20.
PLATONOV, A.; THENKABAIL, P. S.; BIRADAR, C. M.; CAI, X.; GUMMA, M.; DHEERAVATH, V.; COHEN, Y.; ALCHANATIS, V.; GOLDSHLAGER, N.; BEN-DOR, E.; VITHANAGE, J.; MANTHRITHILAKE, H.; KENDJABAEV, S.; ISAEV, S. 2008. Water Productivity Mapping (WPM) Using Landsat ETM+Data for the Irrigated Croplands of the Syrdarya River Basin inCentral Asia. Sensors. 8, 8156-8180.
ROERINK, G. J.; SU, Z.; MENENTI, M. 2000. A Simple Remote Sensing Algorithm to Estimate the Surface Energy Balance. Physics and Chemistry of the Earth (B). 25, 147-157.
RUHOFF, A. L.; PAZ, A. R.; COLLISCHONN, W.; ARAGAO, L. E. O. C.; ROCHA, H. R.; MALHI, Y. S. 2012. A MODIS-Based energy balance to estimate evapotranspiration for clear-sky days in Brazilian tropical savannas. Remote Sensing. 4, 703-725.
SAMPAIO, M. S.; ALVES, M. C.; CARVALHO, L. G.; SANCHES, L. Uso de Sistema de Informação Geográfica para comparar a classificação climática de Koppen-Geiger e de Thornthwaite. In: Anais XV Simpósio Brasileiro de Sensoriamento Remoto - SBSR, 2011, Curitiba, INPE p. 8858.
SANTOS, C. A. C. Estimativa da Evapotranspiração Real Diária Através de Análises Micrometeorológicas e de Sensoriamento Remoto. 2009. 143 f. Tese (Doutorado em Meteorologia) – Centro de Tecnologia e Recursos Naturais, Universidade Federal de Campina Grande, Campina Grande. 2009.
SANTOS, C. A. C.; SILVA, B. B. 2008. Estimativa da Evapotranspiração da Bananeira em Região Semiárida Através do Algoritmo S-SEBI. Revista Brasileira de Agrometeorologia. 16, 9-20.
SANTOS, C. A. C.; BEZERRA, B. G.; SILVA, B. B.; RAO, T. V. R. 2010. Assessment of daily actual evapotranspiration with SEBAL and S-SEBI algorithms in cotton crop. Revista Brasileira de Meteorologia. 25, 383-392.
SANTOS, C. A. C. 2011. Análise das necessidades hídricas da vegetação tamarisk através da razão de bowen e do modelo SEBAL. Revista Brasileira de Meteorologia. 26, 85-94.
SENAY, G. B.; BUDDE, M.; VERDIN, J. P.; MELESSE, A. M. 2007. A Coupled Remote Sensing and Simplified Surface Energy Balance Approach to Estimate Actual Evapotranspiration from Irrigated Fields. Sensors. 7, 979-1000.
SENAY, G. B.; BUDDE, M. E.; VERDIN, J. P. 2011a. Enhancing the Simplified Surface Energy Balance (SSEB) approach for estimating landscape ET: Validation with the METRIC model. Agricultural Water Management. 98, 606–618.
SENAY, G. B.; LEAKE, S.; NAGLER, P. L.; ARTAN, G.;DICKINSON, J.; CORDOVA, J. T.; GLENN, E. P. 2011b. Estimating basin scale evapotranspiration (ET) by water balance and remote sensing methods. Hydrological Processes. 25, 4037–4049.
SILVA, D. F.; SOUSA, A. B. 2013. Detecção de Tendências Climáticas no Estado de Alagoas. Revista Brasileira de Geografia Física. 06, 442-455.
SILVA, B. B.; MERCANTE, E.; BOAS, M. A. V.; WRUBLACK, S. C.; OLDON, L. V. 2018. Satellite-based ET estimation using Landsat 8 imsampaioages and SEBAL model. Revista Ciência Agronômica. 49, 221-227.
SOBRINO, J. A.; GÓMEZ, M.; JIMÉNEZMUÑOZ, J. C.; OLIOSO, A.; CHEHBOUNI, G. 2005. A simple algorithm to estimate evapotranspiration from DAIS data: Application to the DAISEX Campaigns. Journal of Hydrology. 315, 117-125.
SU, Z. 2002. The Surface Energy Balance System (SEBS) for estimation of turbulent heat fluxes. Hydrology and Earth System Sciences. 6, 85–99.
SUN, M.; DONG, Q.; JIAO, M.; ZHAO, X.; GAO, X.; WU, P.; WANG, A. 2018. Estimation of Actual Evapotranspiration in a Semiarid Region Based on GRACE Gravity Satellite Data — A Case Study in Loess Plateau. Remote Sensing. 10, 1 - 15.
TEXAS ET NETWORK. 2020. Grower's Guide: Using PET for Determining Crop Water Requirements and Irrigation Scheduling. https://texaset.tamu.edu/Resources/Documents/Grower's%20Guide.pdf.
TREZZA, R. Evapotranspiration using a satellite-based Surface energy balance with standardized ground control. 247f. Thesis (Doctor of Philosophy in Biological and Agricultural Engineering). Utah State University. Logan, Utah, 2002.
WANG, K.; LI, Z.; CRIBB, M. 2006. Estimation of evaporative fraction from a combination of day and night land surface temperatures and IVDN: A new method to determine the Priestley–Taylor parameter. Remote Sensing of Environment. 102, 293–305.
WELIGEPOLAGE, K. Estimation of spatial and temporaldistribution of evapotranspiration by satellite remote sensing – A case study in Hupselse Beek, The Netherlands. 2005, 114 p, Dissertation (Master of Science), International Institute for Geo-information Science and Earth Observation, Waganingen, Netherlands, 2005.
YANG, X.; REN, L.; JIAO, D.; YONG, B.; JIANG, S.; SONG, S. 2013. Estimation of daily actual evapotranspiration from ETM+ and MODIS data of the headwaters of the west Liaohe basin in the semiarid regions of china. Journal of hydrologic engineering. 18, 1530-1538.
YEBRA, M.; DIJK, A. V.; LEUNING, R.; HUETE, A.; GUERSCHMAN, J. P. 2013. Evaluation of optical remote sensing to estimate actual evapotranspiration and canopy conductance. Remote Sensing of Environment. 129, 250–261.
ZAHIRA, S.; ABDERRAHMAME, H.; MEDERBAL, K.; FREDERIC, D. 2009. Mapping Latent Heat Flux in the Western Forest Covered Regions of Algeria Using Remote Sensing Data and a Spatialized Model, Remote Sensing. 1, 795-817.
ZHANG, X.; BERHANE, T.; SEIELSTAD, G. 2008. Comparison of Landsat and MODIS estimates of heat hluxes: effect of surface heterogeneity. In: International Geoscience and Remote Sensing Symposium, 3, 2008, Boston. Anai ... Boston: IEEE. 759-762.
