Monitoramento fenológico de canaviais usando o Google Earth Engine e TIMESAT no Vale do Submédio do São Francisco

• Autor(a) principal: MANRIQUE, Diego Rosyur Castro
• Data de Publicação: 2022
• Tipo de documento: Dissertação
• Idioma: por
• Título da fonte: Biblioteca Digital de Teses e Dissertações da UFRPE
• Texto Completo: http://www.tede2.ufrpe.br:8080/tede2/handle/tede2/9082
• topic: Cana-de-açúcar; Fenologia; Monitoramento; Índice de vegetação; Sensor MODIS; Série temporal; CIENCIAS AGRARIAS::ENGENHARIA AGRICOLA

The monitoring of the phenology of the sugarcane crop is of great importance, since the globalized market requires reliable information on the amount of raw material for the production of sugar and alcohol. The present study aimed to estimate the phenological parameters of sugarcane (beginning, middle and end of the cycle) between the 2001 and 2020 crops, using time series of MODIS images, obtained from Google Earth Engine and TIMESAT software. The study was carried out in sugarcane growing areas located in the municipality of Juazeiro, BA. The meteorological data were obtained from the virtual pages of the Meteorology Laboratory of the Federal University of São Francisco Valley and CHIRPS, while the Terra/MODIS, Landsat-5 and MapBiomas satellite images from the Google Earth Engine catalog. Precipitation data were evaluated as a function of precipitation from the CHIRPS product, obtaining a "very high" correlation (R² = 0,734); moreover, CHIRPS precipitation was related to NDVI, even in irrigated sugarcane fields, and could influence harvest dates. Furthermore, the time series of vegetation indices (NDVI, SAVI and IAF) were used to evaluate the spatial and temporal evolution of the test area in each phenological cycle. In general, the sugarcane harvest dates estimated with the time series of the MODIS NDVI sensor in the TIMESAT software compared with the actual harvest data, between the 2006 and 2012 harvests, showed an average difference of 10 days, with a performance index equal to 0,99 and a correlation coefficient of 0,99, significant at the 5% confidence level. It is concluded that the TIMESAT software was able to estimate the phenological parameters in sugarcane production areas, using MODIS images processed in Google Earth Engine during the evaluated time period (2001 to 2020).