All-Weather Land Surface Temperature Estimation from the Multiple Source Fusion

Remote sensing-derived land surface temperature (LST) has emerged as a vital data source for regional, and even global, energy and hydrology research. Unfortunately, significant cloud contamination severely limits the usability of these mass remote-sensed LST products. Moreover, the intricate topography and heterogeneous surface conditions pose challenges for recent reconstruction methods in estimating LST over cloud-covered areas. Consequently, an estimation method has been employed, integrating multiple sources of LST data and considering topographical factors, aiming to obtain all-weather LST measurements over rugged, mountainous, and complex terrains frequently affected by cloudy and misty conditions—a region where obtaining accurate LST data is particularly formidable. In this chapter, reanalysis data generated by a land surface model has been fused with remotely-sensed daily LST products.