摘要：Land surface temperature (LST) plays a critical role in land surface processes . However, as one of the efective means for obtaining global LST observations, remote sensing observations are inherently afected by cloud cover, resulting in varying degrees of missing data in satellite-derived LST products . Here, we propose a solution . First, the data interpolating empirical orthogonal functions (DINEOF) method is used to reconstruct invalid LSTs in cloud-contaminated areas into ideal, clear-sky LSTs . Then, a cumulative distribution function (CDF) matching-based method is developed to correct the ideal, clear-sky LSTs to the real LSTs . Experimental results prove that this method can efectively reconstruct missing LST data and guarantee acceptable accuracy in most regions of the world, with RMSEs of 1–2 K and R values of 0 .820–0 .996 under ideal, clear-sky conditions and RMSEs of 4–7 K and R values of 0 .811– 0.933 under all weather conditions . Finally, a spatiotemporally continuous MODIS LST dataset at 0 .05° latitude/longitude grids is produced based on the above method .
