Estimativa da produção em áreas irrigadas sob cultivo de cana-de-açúcar utilizando um modelo multiespectral

• Ano de defesa: 2013
• Autor(a) principal: Araujo, Glaucio Luciano
• Orientador(a): Mantovani, Everardo Chartuni
• Banca de defesa: Imbuzeiro, Hemlley Maria Acioli , Reis, Edvaldo Fialho dos
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Viçosa
• Programa de Pós-Graduação: Mestrado em Engenharia Agrícola
• Departamento: Construções rurais e ambiência; Energia na agricultura; Mecanização agrícola; Processamento de produ
• País: BR
• Palavras-chave em Português: Cana-de-açúcar; Irrigação; Sensoriamento remoto
• Palavras-chave em Inglês: Cane sugar; Irrigation; Remote sensing
• Área do conhecimento CNPq: CNPQ::CIENCIAS AGRARIAS::ENGENHARIA AGRICOLA
• Link de acesso: http://locus.ufv.br/handle/123456789/3652

The estimated crop yield before harvest is very important for agriculture. Reliable and accurate assessments of the harvests bring a number of benefits, allowing farmers and agencies connected to the sector, the efficient planning of their activities. Today, Brazil is the largest producer of sugarcane (Saccharum officinarum L.), and the sugar and alcohol sector shows some dependence on accurate estimates of production, both for planning and for industrialization and commercialization of the harvest. Considering this necessity, the aim of this study was estimate yield of irrigated areas cultivated with sugarcane using a multispectral model, using images of sensor Tematic Maper (TM) onboard satellite Landsat 5, comparing estimates of multispectral model with real data fields. To accomplish this goal models were coupled, the principal one a model to estimate the biomass produced by the crop, this model is dependent on a conversion factor of light energy into biomass, this factor was determined by a model that is dependent on the fraction evaporative, which in turn was estimated by the SEBAL algorithm. The multispectral model proved to be efficient to estimate the production of sugarcane in irrigated areas, demonstrating sensitivity to predict crop yield, both temporally and spatially. The spatial resolution allowed the identification of a high range of details, providing a high level of accuracy to estimates. The model compared with production values observed in the field, showed high coefficients of determination, indicating a high degree of agreement between estimated and observed. It is observed that there are no statistical differences between the estimated and observed, confirmed the effectiveness of the model to estimate the productivity of sugarcane in irrigated areas.