College of Behavioral & Social Sciences

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    Estimation and Spatiotemporal Analysis of All-sky Land Surface Temperature from Multiple Satellite Data
    (2023) Jia, Aolin; Wang, Dongdong; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    The daily surface temperature variability, characterizing the dispersion of day-to-day temperature anomalies, is a fundamental aspect of the climate. It can be represented by the temperature standard deviation in a week. Studies reveal that daily temperature variability is a critical determinant of societal and natural outcomes, such as public health, crop yield, economic growth, etc. Although the overall warming trend is now well established in the scientific community, previous studies have shown little consensus about changes in daily temperature variability over the globe in recent decades; this is due to limited simulation accuracy and in-situ measurement distribution. Therefore, it is urgently needed to generate a reliable, global, long-term, observation-derived, daily temperature dataset in order to analyze variability changes and potential driving factors. The Advanced Very High-Resolution Radiometer (AVHRR) data provide an exceptional chance to record long-term land surface temperature (LST) over the entire globe. However, the AVHRR LST suffers from two restrictions: cloud contamination and orbital drift. Accordingly, we develop a surface energy balance (SEB)-based algorithm to recover the LST under clouds, and a two-step method to correct the artificial spurious temperature variation due to orbital drift. In the SEB method, 1) the hypothetical LST of missing pixels is first reconstructed by assimilating dispersed clear-sky retrievals into a continuous LST time-evolving model built by reanalysis data, and 2) the reconstructed LST is then corrected by superposing the cloud effect, estimated by satellite radiation products based on SEB theory. The two-step correction includes 1) calibrating the systematic bias of diurnal temperature cycles (DTCs) simulated from reanalysis data using satellite product climatology, 2) correcting the calibrated DTCs in detail by historical AVHRR LSTs during the years 1981-2021, and averaging the corrected DTCs to get daily mean LSTs. Global, 5-km, all-sky, daily mean LSTs from 1982 to 2021 are produced for the daily variability analysis. In order to mitigate the impact of orbital drift, the SEB method is examined by MODIS and VIIRS LST products. Ground validation suggests that the cloudy-sky VIIRS LST exhibits a root mean square error (RMSE) of 3.54 K, a bias of −0.36 K, and R2 of 0.94, comparable to the accuracy of clear-sky LST and the MODIS results. Thus, the algorithm is sensor independent and also works for AVHRR data. To obtain satellite-derived DTC climatology for calibrating simulated DTCs, an optimization module is created to extend the feasibility of the SEB method at night. By collecting clear-sky LSTs from geostationary satellite sensors and two MODIS sensors, global, hourly, 5 km, all-sky LSTs from 2011 to 2021 are produced. The overall RMSE of the hourly LSTs is 3.38 K, with a bias of −0.53 K based on 197 global sites. Finally, after integrating the SEB method and two-step correction method, the target AVHRR LST is recovered with an RMSE of 1.97 K over the globe and few biases. Spatiotemporal analysis of the AVHRR LST suggests that the globally averaged daily LST variability does not have a significant trend from 1982 to 2021 under the global warming background, whereas it showed diverse variation both regionally and seasonally. A significant decrease/increase is detected at high/low latitudes, which matches previous simulation conclusions. However, contrary to the simulation, it reveals significant variability increases in the mid-latitudes, such as the western US, the Mediterranean Basin, and northern China. Historical auxiliary observations indicate that the variability decrease at high-latitudes is driven by downward longwave (DLW) radiation. Arctic amplification mitigates cold temperature anomalies at high latitudes in winter. The enhanced atmospheric convection in the tropics causes the increasing variability of cloud cover and downward shortwave radiation (DSR), and the LST variability has also increased. Climate internal variability, DLW, and DSR all show considerable impact at mid-latitudes. This study proposed innovative cloud-sky LST estimation and orbital drift correction methods. The first global, all-sky, 5-km, daily mean LST product (1982 - 2021) was generated, which shows great potential for long-term energy budget and hydrological cycling analysis. Furthermore, the study fills the knowledge gap about the unknown daily temperature variability trend over the globe and provides an attribution based on historical observations, which will assist the community in understanding the mechanism of high-frequency temperature change, improving model prediction, and coordinating resources for extreme weather adaptation.
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    An Optimization Algorithm for Separating Land Surface Temperature and Emissivity from Multispectral Thermal Infrared Imagery
    (Institute of Electrical and Electronics Engineers, 2001-02) Liang, Shunlin
    Land surface temperature (LST) and emissivity are important components of land surface modeling and applications. The only practical means of obtaining LST at spatial and temporal resolutions appropriate for most modeling applications is through remote sensing. While the popular split-window method has been widely used to estimate LST, it requires known emissivity values. Multispectral thermal infrared imagery provides us with an excellent opportunity to estimate both LST and emissivity simultaneously, but the difficulty is that a single multispectral thermal measurement with bands presents equations in + 1 unknowns ( spectral emissivities and LST). In this study, we developed a general algorithm that can separate land surface emissivity and LST from any multispectral thermal imagery, such as moderate-resolution imaging spectroradiometer (MODIS) and advanced spaceborne thermal emission and reflection radiometer (ASTER). The central idea was to establish empirical constraints, and regularization methods were used to estimate both emissivity and LST through an optimization algorithm. It allows us to incorporate any prior knowledge in a formal way. The numerical experiments showed that this algorithm is very effective (more than 43.4% inversion results differed from the actual LST within 0.5 , 70.2% within 1 and 84% within 1.5 ), although improvements are still needed.