title: Use of MODIS Sensor Images Combined with Reanalysis Products to Retrieve Net Radiation in Amazonia.
abstract: In the Amazon region, the estimation of radiation fluxes through remote sensing techniques is hindered by the lack of ground measurements required as input in the models, as well as the difficulty to obtain cloud-free images. Here, we assess an approach to estimate net radiation (Rn) and its components under all-sky conditions for the Amazon region through the Surface Energy Balance Algorithm for Land (SEBAL) model utilizing only remote sensing and reanalysis data. The study period comprised six years, between January 2001-December 2006, and images from MODIS sensor aboard the Terra satellite and GLDAS reanalysis products were utilized. The estimates were evaluated with flux tower measurements within the Large-Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) project. Comparison between estimates obtained by the proposed method and observations from LBA towers showed errors between 12.5% and 16.4% and 11.3% and 15.9% for instantaneous and daily Rn, respectively. Our approach was adequate to minimize the problem related to strong cloudiness over the region and allowed to map consistently the spatial distribution of net radiation components in Amazonia. We conclude that the integration of reanalysis products and satellite data, eliminating the need for surface measurements as input model, was a useful proposition for the spatialization of the radiation fluxes in the Amazon region, which may serve as input information needed by algorithms that aim to determine evapotranspiration, the most important component of the Amazon hydrological balance.
authors.affiliation: Remote Sensing Division, National Institute for Space Research, 1758 Astronautas Avenue, S&#227;o Jos&#233; dos Campos, SP 12227-010, Brazil. gdo@dsr.inpe.br.|Department of Geography and Atmospheric Science, University of Kansas, 1475 Jayhawk Boulevard, Lawrence, KS 66045, USA. gdo@dsr.inpe.br.
authors.forename: Gabriel
authors.initials: G
authors.lastname: de Oliveira
authors.affiliation: Department of Geography and Atmospheric Science, University of Kansas, 1475 Jayhawk Boulevard, Lawrence, KS 66045, USA. brunsell@ku.edu.
authors.forename: Nathaniel A
authors.initials: NA
authors.lastname: Brunsell
authors.affiliation: Remote Sensing Division, National Institute for Space Research, 1758 Astronautas Avenue, S&#227;o Jos&#233; dos Campos, SP 12227-010, Brazil. bete@dsr.inpe.br.
authors.forename: Elisabete C
authors.initials: EC
authors.lastname: Moraes
authors.affiliation: Remote Sensing Division, National Institute for Space Research, 1758 Astronautas Avenue, S&#227;o Jos&#233; dos Campos, SP 12227-010, Brazil. gabrielb@dsr.inpe.br.
authors.forename: Gabriel
authors.initials: G
authors.lastname: Bertani
authors.affiliation: Department of Soil, Water and Climate, University of Minnesota, 1991 Upper Bufford Circle, Saint Paul, MN 55108, USA. dossa013@umn.edu.
authors.forename: Thiago V
authors.initials: TV
authors.lastname: Dos Santos
authors.affiliation: Remote Sensing Division, National Institute for Space Research, 1758 Astronautas Avenue, S&#227;o Jos&#233; dos Campos, SP 12227-010, Brazil. yosio@dsr.inpe.br.
authors.forename: Yosio E
authors.initials: YE
authors.lastname: Shimabukuro
authors.affiliation: Remote Sensing Division, National Institute for Space Research, 1758 Astronautas Avenue, S&#227;o Jos&#233; dos Campos, SP 12227-010, Brazil. laragao@dsr.inpe.br.|College of Life and Environmental Sciences, University of Exeter, Rennes Drive, Exeter EX4 4RJ, UK. laragao@dsr.inpe.br.
authors.forename: Luiz E O C
authors.initials: LE
authors.lastname: Aragão
keywords: Amazon region; GLDAS data; LBA project; MODIS sensor; net radiation