Estimativa do balanço de radiação com técnicas de sensoriamento remoto e dados de superfície

Detalhes bibliográficos
Ano de defesa: 2008
Autor(a) principal: GIONGO, Pedro Rogério lattes
Orientador(a): MOURA, Geber Barbosa de Albuquerque
Banca de defesa: SILVA, Bernardo Barbosa da, PANDORFI, Héliton, AZEVEDO, Marcílio de
Tipo de documento: Dissertação
Tipo de acesso: Acesso aberto
Idioma: por
Instituição de defesa: Universidade Federal Rural de Pernambuco
Programa de Pós-Graduação: Programa de Pós-Graduação em Engenharia Agrícola
Departamento: Departamento de Engenharia Agrícola
País: Brasil
Palavras-chave em Português:
Palavras-chave em Inglês:
Área do conhecimento CNPq:
Link de acesso: http://www.tede2.ufrpe.br:8080/tede2/handle/tede2/5738
Resumo: The remote sensing is a tool that has enabled major advances in studies of agrometeorology and application to areas with different types of coverage, can be used to estimate the radiation balance and its applications. Therefore this study aimed to estimate the balance of radiation to the surface, from the sensor data Thematic Mapper (TM) satellite LANDSAT 5, with the use of the algorithm SEBAL. The estimate data were compared with data from two stations in agrometeorological: one in the cerrado region, and another in sugar cane. In the study area, located in the municipality of Santa Rita do Passa Quatro - SP, Brazil. To carry out the study were obtained six orbital images from the satellite Landsat 5 TM sensors in orbit 220 in section 75, the dates of 22/02, 11/04, 29/05, 01/08, 17/08 and 21/11 all in the year 2005, the matching DJ of 53, 101, 149, 213, 229 and 325, respectively. We performed the geometric correction for images, then were generated the letters of albedo, the Normalised Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), leaf area Index (LAI), surface temperature (Ts), Long Wave Radiation of Issued and Balance of Radiation (Rn). The estimated values of Rn showed correlations r of 0,994 and 0,984 with data from the stations inthe area sugar cane and cerrado, respectively. It concludes that the proposed methodology of the algorithm SEBAL for estimation of Rn for the two areas, values achieved very consistent and satisfactory for this application.