Spatial and temporal probabilities of obtaining cloud‐free Landsat images over the Brazilian tropical savanna

Abstract

Remotely sensed data are the best and perhaps the only possible way for monitoring large‐scale, human‐induced land occupation and biosphere‐atmosphere processes in regions such as the Brazilian tropical savanna (Cerrado). Landsat imagery has been intensively employed for these studies because of their long‐term data coverage (>30 years), suitable spatial and temporal resolutions, and ability to discriminate different land‐use and land‐cover classes. However, cloud cover is the most obvious constraint for obtaining optical remote sensing data in tropical regions, and cloud cover analysis of remotely sensed data is a requisite step needed for any optical remote sensing studies. This study addresses the extent to which cloudiness can restrict the monitoring of the Brazilian Cerrado from Landsat‐like sensors. Percent cloud cover from more than 35 500 Landsat quick‐looks were estimated by the K‐means unsupervised classification technique. The data were examined by month, season, and El Niño Southern Oscillation event. Monthly observations of any part of the biome are highly unlikely during the wet season (October–March), but very possible during the dry season, especially in July and August. Research involving seasonality is feasible in some parts of the Cerrado at the temporal satellite sampling frequency of Landsat sensors. There are several limitations at the northern limit of the Cerrado, especially in the transitional area with the Amazon. During the 1997 El Niño event, the cloudiness over the Cerrado decreased to a measurable but small degree (5% less, on average). These results set the framework and limitations of future studies of land use/land cover and ecological dynamics using Landsat‐like satellite sensors.

Acknowledgments

We would like to acknowledge the following collaborators for assisting us with downloading and processing of thousands of Landsat quick‐looks: Elaine Marra, Elaine Cristina, Heleno Bezerra, Janaina Mendes, Thaise Sussane, Gisele Amaral, Miriam R. da Silva, Debora Lima, Nathalia Hott, Monica Martins, Joao Candido da Silva Jr., and Lorena Oliveira Santos. Dr. Euzebio Medrado da Silva provided the spreadsheet with beta distribution calculations and advice about statistics. The authors also acknowledge the Brazilian National Research Council (CNPq) for providing individual research grants for the first two authors of this paper. G. Asner was supported by NASA LBA grant NCC5‐675 (LC‐21). References to commercial products do not represent endorsements or recommendations for use by the authors.