Dinâmica espectro-temporal do trigo e do feijão por meio de dados espectrais multisensor

• Autor(a) principal: Cattani, Carlos Eduardo Vizzotto
• Data de Publicação: 2018
• Tipo de documento: Dissertação
• Idioma: por
• Título da fonte: Biblioteca Digital de Teses e Dissertações do UNIOESTE
• Texto Completo: http://tede.unioeste.br/handle/tede/3763
• topic: Sensoriamento remoto; Índices de vegetação; Sensores terrestres; Remote sensing; Vegetation index; Ground sensors; CIENCIAS AGRARIAS::ENGENHARIA AGRICOLA

The temporal dynamics of agricultural crops can be translated by Vegetation Indices (IV) on multiple dates. The use of IVs in certain stages of development, or throughout their entire cycle, are related to their biophysical parameters. There are several IVs, and the most commonly used are those based on the red (RED) and near infrared (NIR) bands, although studies with IVs that used the NIR and medium infrared (SWIR) bands have shown good results in the estimation of biophysical parameters of agricultural crops. In this context, the objective of this work was to characterize the spectral-temporal profiles of different vegetation indexes (RVI, MSR, NDVI, MSAVI, NDRE, WRDVI, and NDMI) and to correlate these profiles with the biophysical parameters (IAF) and photosynthetically active intercepted radiation (RFAI). This research was carried out in commercial farming areas of wheat and beans. For the characterization of the IRs, two terrestrial remote sensors were employed: the FieldSpec4 passive resistive passive sensor (FS) and the GreenSeeker 505 Handheld (GS) active sensor. Both LAI-2200C and LI-191R sensors, respectively, were used to obtain the biophysical parameters, leaf area index (LAI), and photosynthetically active intercepted radiation (RFAI). The biophysical variable IAF for both cultures showed better results when estimated by the NDMI FS index, presenting the highest values of correlation (rs) and coefficient of agreement (dr), and the lowest errors (ME and RMSE). For wheat, the RFAI variable obtained the best fit with the NDMI index, presenting a satisfactory result according to performance index. For beans, RFAI presented a higher correlation with the MSAVI index, but showed a low degree of agreement between the adjusted and the observed data, obtaining low efficiency in the model.