Spatial and Temporal Analysis of Probabilities for Acquiring Cloud-free Optical Sensor Images Using MODIS Cloud Mask Products 2000-2008 in Southeast Asia

Abstract

Accessibility to cloud-free optical sensor images is essential for large-area monitoring of land and forest cover changes. In this study, the acquisition probabilities of cloud-free images were analyzed using MODIS cloud mask products from 2000 to 2008 in Southeast Asia. The daily cloud masks were summarized into monthly acquisition probabilities for cloud-free images over the period at a spatial resolution of 1km. The mean annual acquisition probability profiles were extracted averaging nine years' observation. Unsupervised clustering was conducted for zoning of the acquisition probabilities using the mean annual profiles. Annual variations in the acquisition probabilities were examined by the standard deviations calculated for each month and comparisons of the mean annual profiles of the whole period and individual years. The distributions of annual acquisition probabilities in forested areas were different in each country. These results suggested that selection of suitable methods and data allowing for the spatial and temporal differences in the acquisition probabilities is necessary for periodic large-area monitoring.

Analyzing the Probability of Acquiring Cloud-Free Imagery in China with AVHRR Cloud Mask Data

“…Therefore, as the sensor revisit interval increased, the number of days to acquire cloud-free imagery per month decreased and the decline rate decreased. Currently, many sensors observe land cover from space, which has advantages and disadvantages [21]. The number of days to acquire cloud-free imagery per month with different revisit frequencies is valuable for choosing the appropriate optical remote sensor in the study area and estimating the availability of composite images [15].…”

“…When the data come from satellite sensors with a low revisit frequency, the cloud cover data lack temporal coverage (such as Landsat) [6,13]. Furthermore, the length of time for the studies was limited by the remote sensing data, leading to a degree of failure in analyzing the changes in cloud cover at the climate scale (such as MODIS) [15,21]. Therefore, we selected the Advanced Very High Resolution Radiometer (AVHRR) with a high revisit frequency and long-term observation.…”