Adjustment From Temperature Annual Dynamics for Reconstructing Land Surface Temperature Based on Downscaled Microwave Observations

Abstract

Land surface temperature (LST) is crucial to wide varieties of environmental issues, whereas its low tolerance to cloud contamination greatly challenges its applications. Passive microwave (PMW) measurements are employed to retrieve LST due to its great capability of penetrating clouds. Despite of great efforts from previous studies, their further applications are limited by coarse resolution of PMW, uncertainty from cloudy coverage, and less attention on large-scale applications. To address these problems, this paper proposes a method combining adjustment from annual temperature circle to reconstruct land surface temperature based on downscaled microwave observations over mainland China. In order to conduct a comprehensive and solid accuracy evaluation, the proposed method is validated according to three strategies namely comparing with MODIS LST, ground surface temperature (GST), and in-situ observations from the Heihe Watershed Allied Telemetry Experimental Research (HiWATER) respectively. In the strategy 1, mean RMSE of predicted LST is 2.39 K and 1.33 K at day and night. According to the strategy 2, mean RMSE of MODIS, predicted and merged LST is 4.97 K, 5.07 K, and 4.93 K at daytime, and 2.32 K, 4.57 K and 3.39 K at nighttime. In the strategy 3, they are 3.75 K, 3.90 K and 2.87 K at day, and 2.80 K, 3.61 K and 2.79 K at night. Furthermore, adjustments from annual temperature cycle are discussed. The proposed method is promising in future research for reconstructing LST with high spatiotemporal resolution at large scales thanks to its simple process, high accuracy, and seamless reconstruction. Index Terms-land surface temperature; passive microwave; annual temperature cycle; downscaling

Combining Spatiotemporally Global and Local Interpolations Improves Modeling of Annual Land Surface Temperature Cycles

“…Microwave data can be used to directly obtain the surface thermal status under overcast conditions and, therefore, could be useful for LST reconstruction. In situ measurements under overcast conditions, such as the LST or other remote sensing data that are directly related to LST (e.g., radiation), could also be helpful to minimize the overestimation [10,[13][14][15]41,[69][70][71][72][73][74][75]. The performance of the ATC_GL is expected to improve once these auxiliary data are integrated.…”

Comparative Analysis of Variations and Patterns between Surface Urban Heat Island Intensity and Frequency across 305 Chinese Cities

“…Taking advantage of remote sensing, SUHI intensity (SUHII) has been widely investigated. Despite considerable applications of satellite-based TIR data, TIR measurements are largely limited by their low tolerance to cloud cover [37][38][39][40][41], resulting in over half of the missing data [42]. Since there is a hard availability of daily seamless LST directly from the TIR products, the spatial-temporal variations of SUHI frequency (SUHIF) and its controls remain largely unknown.…”

“…To compensate for missing data in the TIR products, several methods were proposed to reconstruct seamless LST. These methods can generally be grouped into two broad classes, namely spatial-temporal interpolation [37,[41][42][43][44] and model simulation [38][39][40]45]. The spatial-temporal interpolation refers to two major ways, including gap-filling based on spatial neighboring or temporal adjacent pixels [37], and correlation between LST and other data sources.…”