New method and error analysis of lake retrieval with MetOp-A AVHRR images on the Tibetan Plateau

Land-surface water is an important factor influencing the regional environment and climate and is a key factor in the Tibetan Plateau, which is one of the most sensitive regions to global changes. Because of the high elevation, complex topography, and erratic weather of the Tibetan Plateau, direct measurement of the area of every lake is largely unfeasible. Moreover, complex natural geographic conditions increase the difficulty of image processing and information extraction with remote sensing because they enhance the uncertainty of quantitative data retrieved with satellites. Methods based on spectral features do not generate the expected results of lake area over the Tibetan Plateau due to a lack of distinction between water and other land objects, especially snow, vegetation, and low cloud cover. Therefore, a new method to extract lake area from satellite images in the Tibetan Plateau is needed. In this article, an automatic method was proposed to evaluate lake area during the wet season from 1 September to 31 October on the Tibetan Plateau with multi-day Advanced Very High Resolution Radiometer AVHRR remote-sensing images on board the Meteorological Operational satellite-A MetOp-A satellite. The method considers both spectral and textural features of lakes and does not need a cloud mask as an input. In addition, the Mixture Tuned Matched Filtering MTMF algorithm was applied to decompose the mixed pixels to better identify lakes and estimate the lake area. Based on daily lake identifications, the wet season’s lake data were composited with the maximum value composition MVC method to determine the lake area. A comparison of our work with the manually interpreted results from Landsat Thematic Mapper TM images and observational reports demonstrates the accuracy and reliability of our approach. However, certain factors, i.e. the sensor zenith angle of the polar-orbit satellite and the topography, can affect the lake area extracted from the remote-sensing images.