@Article{MateusBorSilNicCat:2016:CaStAb,
               author = "Mateus, Pedro and Borma, Laura de Simone and Silva, Ricardo 
                         Dal'Agnol da and Nico, Giovanni and Catal{\~a}o, Jo{\~a}o",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {Consiglio Nazionale delle 
                         Ricerche} and {University of Lisbon}",
                title = "Assessment of two techniques to merge ground-based and TRMM 
                         rainfall measurements: a case study about Brazilian Amazon 
                         Rainforest",
              journal = "GIScience and Remote Sensing",
                 year = "2016",
               volume = "53",
               number = "6",
                pages = "689--706",
                month = "Nov.",
             keywords = "bias correction, rainfall interpolation, remote sensing, 
                         statistical data merging, TRMM.",
             abstract = "The availability of accurate rainfall data with high spatial 
                         resolution, especially in vast watersheds with low density of 
                         ground-measurements, is critical for planning and management of 
                         water resources and can increase the quality of the hydrological 
                         modeling predictions. In this study, we used two classical 
                         methods: the optimal interpolation and the successive correction 
                         method (SCM), for merging ground-measurements and satellite 
                         rainfall estimates. Cressman and Barnes schemes have been used in 
                         the SCM in order to define the error covariance matrices. The 
                         correction of bias in satellite rainfall data has been assessed by 
                         using four different algorithms: (1) the mean bias correction, (2) 
                         the regression equation, (3) the distribution transformation, and 
                         (4) the spatial transformation. The satellite rainfall data were 
                         provided by the Tropical Rainfall Measuring Mission, over the 
                         Brazilian Amazon Rainforest. Performances of the two merging data 
                         techniques are compared, qualitatively, by visual inspection and 
                         quantitatively, by a statistical analysis, collected from January 
                         1999 to December 2010. The computation of the statistical indices 
                         shows that the SCM, with the Cressman scheme, provides slightly 
                         better results.",
                  doi = "10.1080/15481603.2016.1228161",
                  url = "http://dx.doi.org/10.1080/15481603.2016.1228161",
                 issn = "1548-1603",
             language = "en",
        urlaccessdate = "27 abr. 2024"
}