Abstract
Land surface temperature (LST) is one of the most valuable variables for applications relating to hydrological processes, drought monitoring and climate change. LST from satellite data provides consistent estimates over large scales but is only available for cloud-free pixels, greatly limiting applications over frequently cloud-covered regions. With this study, we propose a method for estimating all-weather 1 km LST by combining passive microwave and thermal infrared data. The product is based on clear-sky LST retrieved from Moderate-resolution Imaging Spectroradiometer (MODIS) thermal infrared measurements complemented by LST estimated from the Advanced Microwave Scanning Radiometer Version 2 (AMSR2) brightness temperature to fill gaps caused by clouds. Terrain, vegetation conditions, and AMSR2 multiband information were selected as the auxiliary variables. The random forest algorithm was used to establish the non-linear relationship between the auxiliary variables and LST over the Tibetan Plateau. To assess the error of this method, we performed a validation experiment using clear-sky MODIS LST and in situ measurements. The estimated all-weather LST approximated MODIS LST with an acceptable error, with a coefficient of correlation (r) between 0.87 and 0.99 and a root mean square error (RMSE) between 2.24 K and 5.35 K during the day. At night-time, r was between 0.89 and 0.99 and the RMSE was between 1.02 K and 3.39 K. The error between the estimated LST and in situ LST was also found to be acceptable, with the RMSE for cloudy pixels between 5.15 K and 6.99 K. This method reveals a significant potential to derive all-weather 1 km LST using AMSR2 and MODIS data at a regional and global scale, which will be explored in the future.
Highlights
Land surface temperature (LST) is an important environmental variable that controls land surface energy exchanges and water balance [1,2,3,4]
This study aims at evaluating the performance of a random forest (RF) regression method for constructing a nonlinear relationship between TIR LST and other surface variables, blending passive microwave (PMW) and TIR observations to estimate all-weather 1 km LST
We found that when the number of estimators is 131 v-pol brightness temperature was used to constrain the range of LST, and the 10.65 GHz and the max depth is 39, the RF yields the smallest root mean square error (RMSE)
Summary
Land surface temperature (LST) is an important environmental variable that controls land surface energy exchanges and water balance [1,2,3,4]. LST data sets are essential for a wide range of applications in urban heat islands [5,6,7,8], drought monitoring [9,10,11], climate change [12,13], hydrological processes [14,15,16] and crop yield estimation [17,18]. The development of remote sensing technologies made it possible to consistently estimate LST at regional to global scales at high temporal and spatial resolutions. Thermal infrared (TIR) sensors are widely used to retrieve satellite-based LST. There are several TIR LST retrieval algorithms, such as the single-window algorithm, the splitwindow algorithm and the multi-channel algorithm [19,20,21,22]. TIR-derived LST has a relatively low error (approximately 0.3–2 K) and moderate spatial resolution
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