Sensoriamento remoto orbital na estimativa de evapotranspiração em sistema de pivô central
| Ano de defesa: | 2022 |
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| Autor(a) principal: | |
| Orientador(a): | |
| Banca de defesa: | , , , , |
| Tipo de documento: | Tese |
| Tipo de acesso: | Acesso aberto |
| Idioma: | por |
| Instituição de defesa: |
Universidade Estadual do Oeste do Paraná
Cascavel |
| Programa de Pós-Graduação: |
Programa de Pós-Graduação em Engenharia Agrícola
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| Departamento: |
Centro de Ciências Exatas e Tecnológicas
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| País: |
Brasil
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| Palavras-chave em Português: | |
| Área do conhecimento CNPq: | |
| Link de acesso: | https://tede.unioeste.br/handle/tede/6543 |
Resumo: | Water, an essential element for life, is used in various human activities. However, due to the amount applied in agriculture and the increase in irrigated areas, the sustainable use of this resource has become a global concern. In this sense, the estimation of evapotranspiration (ET) is essential for properly managing water resources, and remote sensing (SR), with its constant evolution has become an important tool for the accurate estimation of this parameter. Therefore, the objective of this research is to determine the real evapotranspiration (ETr) using three algorithms: Surface Energy Balance Algorithm for Land (SEBAL), Mapping Evapotranspiration at High Resolution ith Internalized Calibration (METRIC) and Simple Algorithm for Evapotranspiration Retrieving (SAFER) in irrigated agricultural systems with center pivot in cotton culture for decision making in crop management. The thesis was divided into three articles: i) ARTICLE 1 - Systematic literature study (SLS) whose focus was to identify and group research developed worldwide focused on water balance with application of SR in monitoring ET in agriculture. Among the observed results, 220 studies were selected, and 32 ET estimation models based on SR were identified. In addition, the SEBAL model was the algorithm with the greatest application, becoming an established and validated model in different parts of the world and climatic conditions; ii) ARTICLE 2 - Orbital remote sensing in the estimation of evapotranspiration in a cotton cropping system irrigated by center pivot: This study was carried out at the commercial farm Busato I, located in the western region of the state of Bahia, Brazil. The property’s main crop is cotton, produced under central pivot. The cotton sowing, for the three years evaluated (2018, 2019 and 2020), always took place in the first week of January and the harvest between the end of June and the beginning of July. Images from the ETM+ sensors aboard the Landsat 7 satellite and the Landsat 8 OLI sensor were used. The ETr was estimated using the SEBAL, SAFER and METRIC algorithms. Therefore, the greatest correlation was observed in the comparison between ETSEBAL (mm d-1) vs ETMETRIC (mm d-1) with R² = 0.8 and RMSE values 0.84, BEM = -0.83, MAE = 0 .83 on pivot P4 in 2019. However, for the same comparisons, there was low correlation between models in 2020 with metrics greater than 1 mm (RMSE = 1.7, MBE = -1.7, MAE = 1.7 and R² = 0.5) and finally iii) ARTICLE 3 - Orbital SR in the management of irrigated areas with central pivot system, in which two central pivots of the Busato I farm were selected, the start date of each cotton stage was selected from the ET data. Afterwards, the data were applied in AgDataBox-Map for mining and perform the interpolation to generate the irrigation management zones (ZMIs). The design of ZMIs with ETr data is a viable alternative from a technical and operational point of view, for smart irrigation and cost reduction. The METRIC method presented the greatest ease in obtaining the data used in estimating the ETr for generating the ZMIs. The groupings carried out had selection of ZMIs of two, three, and four zones, in which there was an average cost reduction of R$ 9.55 per millimeter applied over the three agricultural years. Classes C1, C2, and C3 had lower ETr and concentrated on average 60% of the entire irrigated area, which required less water consumption than the crop demand. |