Temporally regular and spatially continuous monitoring of surface urban heat island (SUHI) has been extremely difficult until the advent of spaceborne land surface temperature (LST) products. The higher errors of these LST products compared with in-situ measurements, nevertheless, have resulted in a comparatively inaccuracy and may distort the interpretation of SUHI. Although reports have shown that LST quality matters to the SUHI interpretation, a systematic investigation on how the SUHI indicators are responsive to the LST quality across cities within dissimilar bioclimates remains rare. With regard to this issue, our study chose eighty-six major cities across the mainland China and analyzed the SUHI intensity (SUHII) discrepancies (referred to as ΔSUHII) between using and not using quality control (QC) flags from Moderate Resolution Imaging Spectroradiometer data. Our major findings include: (1) the SUHII can be significantly impacted by the MODIS QC flags, and the associated seasonal ΔSUHIIs generally account for 25.5&thinsp;% (29.6&thinsp;%) of the total intensity in the day (night). (2) The ΔSUHIIs differ season-by-season and significant discrepancies also appear among northern and southern cities, with northern ones often possessing a higher annual mean ΔSUHII. (3) The internal ΔSUHIIs within an individual city are also heterogeneous, with the variations exceeding 5.0&thinsp;K (3.0&thinsp;K) in northern (southern) cities. (4) The ΔSUHII is significantly negatively related to the SUHII and cloud cover percentage mostly in transitional seasons. Our findings highlight that one needs to be very careful when using the LST-product-based SUHII to interpret the SUHI.
Lai, J., Zhan, W., & Huang, F. (2017). Does quality control matter? A revisit of surface urban heat island intensity estimated by satellite-derived land surface temperature products. In International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives (Vol. 42, pp. 515–522). International Society for Photogrammetry and Remote Sensing. https://doi.org/10.5194/isprs-archives-XLII-2-W7-515-2017