Journal
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
Volume 52, Issue 5, Pages 2960-2976Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TGRS.2013.2268161
Keywords
Landsat 5; Libyan Desert; Sonoran Desert; time series analysis; vicarious calibration
Categories
Funding
- National Oceanic and Atmospheric Administration
- National Aeronautics and Space Administration [NNX09AN36G]
- Korea Institute of Marine Science & Technology Promotion (KIMST) [201301782] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
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The monitoring of top-of-atmosphere (TOA) reflectance time series provides useful information regarding the long-term degradation of satellite sensors. For a precise assessment of sensor degradation, the TOA reflectance time series is usually corrected for surface and atmospheric anisotropy by using bidirectional reflectance models so that the angular effects do not compromise the trend estimates. However, the models sometimes fail to correct the angular effects, particularly for spectral bands that exhibit a large seasonal oscillation due to atmospheric variability. This paper investigates the use of time series algorithms to identify both the angular effects and the atmospheric variability simultaneously in the time domain using their periodical patterns within the time series. Two nonstationary time series algorithms were tested with the Landsat 5 Thematic Mapper time series data acquired over two pseudoinvariant desert sites, the Sonoran and Libyan Deserts, to compute a precise long-term trend of the time series by removing the seasonal variability. The trending results of the time series algorithms were compared to those of the original TOA reflectance time series and those normalized by a widely used bidirectional-reflectance-distribution-function model. The time series results showed an effective removal of seasonal oscillation, caused by angular and atmospheric effects, producing trending results that have a higher statistical significance than other approaches.
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