MODIS satellites provide continuous global observations on land surface temperature. It is more important in data-sparse area, such as on the Tibetan Plateau (TP) with very few meteorological stations. Images with severe data missing or poor quality pixels were often found in MODIS LST products, which mostly were caused by the influences of clouds. The traditional geo-statistic methods, including ordinary Kriging and inverse distance weighted (IDW) methods, cannot well interpolate missing-data pixels for a large area. Assuming that the changes of LST at one location would be similar with that at the locations with similar features, a novel method was proposed to interpolate the missing-data pixels by making use of other pixels with the most similar features. MODIS/Terra LST covering TP in 2005 were used as experimental data, and pixels with cloud coverage, average emissivity error greater than 0.04, and average LST error greater than 2K were identified as missing-data pixels. The images with less than 10% missing-data pixels were selected as reference images, in which the missing-data pixels were interpolated with IDW. Distances for different land surface features in images, such as DEM, slope, NDVI and LST, from the interpolating pixel to the other pixels with known LST were calculated. Similar pixels are identified as the distances less than a given threshold. Relationship of LST for those similar pixels was regressed, and was applied to estimate LSTs for the missing pixels. Compared with IDW and Kriging, the proposed method could interpolate the MODIS LST much better on the Tibetan Plateau.

Date Published: 8 November 2014
PDF: 10 pages
Proc. SPIE 9260, Land Surface Remote Sensing II, 92600Y (8 November 2014); doi: 10.1117/12.2068892

Published in SPIE Proceedings Vol. 9260:
Land Surface Remote Sensing II
Thomas J. Jackson; Jing Ming Chen; Peng Gong; Shunlin Liang, Editor(s)