Analysis of multitemporal compositing techniques from regular and non-regular intervals using MODIS daily images in the Amazon region with a high percentage of cloud cover

• Autor(a) principal: Salgado, Cristiane Batista
• Data de Publicação: 2022
• Outros Autores: Carvalho Junior, Osmar Abílio, Santana, Nickolas Castro, Gomes, Roberto Arnaldo Trancoso, Guimarães, Renato Fontes, Silva, Cristiano Rosa
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Sociedade & natureza (Online)
• Texto Completo: https://seer.ufu.br/index.php/sociedadenatureza/article/view/65356
• topic: Time series; Remote sensing; Amazon Forest; Composite image; Time series; Remote sensing; Amazon Forest; Composite image

One challenge in the study of optical remotely sensed time series in the Amazon is the constant cloud cover. The present study evaluates different compositing techniques using regular and non-regular intervals to obtain cloud-free images over large areas. The study area was the municipality of Capixaba in the State of Acre, belonging to the Amazon region. The tests considered four compositing algorithms (maximum, minimum, mean, and median) for daily MODIS sensor data (b1 and b2, 250m). The compositing technique from regular intervals adopted the following periods: 8, 16, 24, 32, 40, and 48 days. The irregular interval composite images adopted different composition intervals for dry seasons (April to September) and rainy (October to March).  The cloud mask and viewing angle constraint allowed to obtain information without atmospheric interference and closest to nadir view. The composite images using regular intervals did not allow to overcome the high frequency of cloud cover of the region. The composite images from non-regular intervals presented a higher percentage of cloud-free pixels. The mean and median methods provided the better visual appearance of the images, corroborating with the homogeneity test. Therefore, composite images from non-regular intervals may be an appropriate alternative in places with constant cloud coverage.