@Article{BorgesCarnSantSant:2021:ObDaAc,
               author = "Borges, Camila K. and Carneiro, Rayonil Gomes and Santos, Cleber 
                         Assis dos and Santos, Carlos A. C. dos",
          affiliation = "{Universidade Federal de Campina Grande (UFCG)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {Universidade Federal de Campina 
                         Grande (UFCG)}",
                title = "Obtaining the daily actual evapotranspiration through remote 
                         sensing techniques application in Brazilian Semiarid",
              journal = "Journal of Hyperspectral Remote Sensing",
                 year = "2021",
               volume = "11",
               number = "1",
                pages = "18--31",
             keywords = "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 Quixer{\'e}-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. SSEBI. 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.",
                 issn = "2237-2202",
             language = "en",
           targetfile = "borges_obt.pdf",
        urlaccessdate = "05 maio 2024"
}