Zhang, K.; Jiang, T., and Huang, J., 2019. Spatial–temporal variation in sea surface temperature from Landsat time series data using annual temperature cycle. In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T. (eds.), Advances in Remote Sensing and Geoscience Information Systems of Coastal Environments. Journal of Coastal Research, Special Issue No. 90, pp. 58-65. Coconut Creek (Florida), ISSN 0749-0208.

Sea surface temperature (SST) plays an important role in aquatic ecosystems and the biogeochemical cycle. Multi-temporal remote-sensing observations may be desirable alternatives to traditional in situ sensors for SST measurement. However, the frequently used low-to-moderate-resolution remote sensors usually cannot identify subtle SST variations in coastal areas due to the pixel radiance contamination caused by shoreline influences. For alleviating this problem, the SST of Jiaozhou Bay (JZB) between 1986 and 2017 was estimated by means of Landsat thermal infrared data and the single-channel retrieval algorithm. The retrieved results were validated by field-measured water temperature and bootstrap method. Then, the estimated SST was divided into five-year intervals and calculated the SST climatology for each period. With use of the annual temperature cycle fitting model, the time series data not only demonstrated the spatial–temporal variation of the water temperature of JZB but also effectively compensated for the lack of remote sensing data due to adverse weather in summer. The estimation results showed that the SST of JZB increased gently during the past 30 years, especially in coastal areas. The increase of SST near artificial facilities was evident, indicating the influence of urbanization and industrialization in coastal area. A correlation analysis of SST and meteorological factors revealed that air temperature influenced SST variation, especially in the central bay, whereas wind speed and precipitation had nearly no influence.