Relação espectro-temporal de índices de vegetação com atributos do solo e produtividade da soja

• Autor(a) principal: Trindade, Filipe Silveira
• Data de Publicação: 2019
• Outros Autores: Alves, Marcelo de Carvalho, Noetzold, Rafael, Andrade, Igor Carvalho de, Pozza, Adélia Aziz Alexandre
• Tipo de documento: Artigo
• Idioma: por
• Título da fonte: Repositório Institucional da UFLA
• Texto Completo: http://repositorio.ufla.br/jspui/handle/1/40881
• topic: Glycine max L. Merr.; Ciclo fenológico; Sensoriamento remoto; Normalized difference vegetation index (NDVI); Enhanced vegetation index (EVI); Phenological cycle; Remote sensing

Recent researches, with the aid of technology, have shown satisfactory results aiming at the proper management of agricultural crops. Therefore, this study sought to evaluate the spectral and temporal relationships of the MODIS sensor normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) with grain yield, relief, texture and soil organic matter (SOM), during the soybean phenological cycle in Campo Verde (MT), in the 2012/2013 harvest. The EVI/NDVI of the MODIS orbital sensor products (MOD13Q1 and MYD13Q1) and the Savitzky-Golay (SG) filtering for noise correction (anomalous values) present in time series of these IVs were used. Pearson’s (r) (p ≤ 0,05) correlation was used, between the aforementioned variables with the application of SG filtering in the time series of the indices during the phenological cycle of the crop. The best phenological stages were identified to generate predictive models on soil attributes variability and productivity prediction. The coefficients of determination (R²) of EVI in the R1 stage with SOM, clay, silt and sand were, R² = 0.77; 0.75; 0.74; 0.75, respectively. With NDVI in the phenological stage R2 it was obtained R²= 0.44, with the productivity. The EVI at R1, R2 and R3 stages (beginning of the reproductive cycle) generated the best soil attributes prediction models, while the NDVI at the R2 stage resulted in the best productivity prediction. Overall, the SG filtering was a necessary tool to study, because the noise correction in the time series generated better predictive models.