ALBERT

Description: Regional haze episodes have occurred frequently in eastern China over the past decades. As a critical indicator to evaluate air quality, the mass concentration of ambient fine particulate matters smaller than 2.5 μm in aerodynamic diameter (PM2.5) is involved in many studies. To overcome the limitations of ground measurements on PM2.5 concentration, which is featured in disperse representation and coarse coverage, many statistical models were developed to depict the relationship between ground-level PM2.5 and satellite-derived aerosol optical depth (AOD). However, the current satellite-derived AOD products and statistical models on PM2.5–AOD are insufficient to investigate PM2.5 characteristics at the urban scale, in that spatial resolution is crucial to identify the relationship between PM2.5 and anthropogenic activities. This paper presents a geographically and temporally weighted regression (GTWR) model to generate ground-level PM2.5 concentrations from satellite-derived 500 m AOD. The GTWR model incorporates the SARA (simplified high resolution MODIS aerosol retrieval algorithm) AOD product with meteorological variables, including planetary boundary layer height (PBLH), relative humidity (RH), wind speed (WS), and temperature (TEMP) extracted from WRF (weather research and forecasting) assimilation to depict the spatio-temporal dynamics in the PM2.5–AOD relationship. The estimated ground-level PM2.5 concentration has 500 m resolution at the MODIS satellite’s overpass moments twice a day, which can be used for air quality monitoring and haze tracking at the urban and regional scale. To test the performance of the GTWR model, a case study was carried out in a region covering the adjacent parts of Jiangsu, Shandong, Henan, and Anhui provinces in central China. A cross validation was done to evaluate the performance of the GTWR model. Compared with OLS, GWR, and TWR models, the GTWR model obtained the highest value of coefficient of determination (R2) and the lowest values of mean absolute difference (MAD), root mean square error (RMSE), and mean absolute percentage error (MAPE).

Description: As a critical variable to characterize the biophysical processes in ecological environment, and as a key indicator in the surface energy balance, evapotranspiration and urban heat islands, Land Surface Temperature (LST) retrieved from Thermal Infra-Red (TIR) images at both high temporal and spatial resolution is in urgent need. However, due to the limitations of the existing satellite sensors, there is no earth observation which can obtain TIR at detailed spatial- and temporal-resolution simultaneously. Thus, several attempts of image fusion by blending the TIR data from high temporal resolution sensor with data from high spatial resolution sensor have been studied. This paper presents a novel data fusion method by integrating image fusion and spatio-temporal fusion techniques, for deriving LST datasets at 30 m spatial resolution from daily MODIS image and Landsat ETM+ images. The Landsat ETM+ TIR data were firstly enhanced based on extreme learning machine (ELM) algorithm using neural network regression model, from 60 m to 30 m resolution. Then, the MODIS LST and enhanced Landsat ETM+ TIR data were fused by Spatio-temporal Adaptive Data Fusion Algorithm for Temperature mapping (SADFAT) in order to derive high resolution synthetic data. The synthetic images were evaluated for both testing and simulated satellite images. The average difference (AD) and absolute average difference (AAD) are smaller than 1.7 K, where the correlation coefficient (CC) and root-mean-square error (RMSE) are 0.755 and 1.824, respectively, showing that the proposed method enhances the spatial resolution of the predicted LST images and preserves the spectral information at the same time.

Description: In the 1980's, from an analysis of satellite images, Russian scientists reported on a short-term thermal infrared radiation enhancement that occurred before some medium-to-large earthquakes in central Asia [Gorny et al. 1988]. Since then, many researchers have been studying earthquake thermal anomalies with satellite remote sensing data [Qiang et al. 1991, Tronin 1996, Tramutoli et al. 2001, Ouzounov and Freund 2004, Saraf and Choudhury 2004, Aliano et al. 2008, Blackett et al. 2011]. Recently, abnormal surface latent heat flux [Dey and Singh 2003, Cervone et al. 2005, Qin et al. 2009, Qin et al. 2011, Qin et al. 2012], outgoing long-wave radiation [Ouzounov et al. 2007] and microwave radiation [Takashi and Tadashi 2010] have also been shown to precede earthquakes. To investigate the possible physical mechanisms of such satellite thermal anomalies, some studies conducted a series of detecting experiments on rock loaded to fracturing [Wu et al. 2000, Freund 2002, Wu et al. 2002, Wu et al. 2006a, Wu et al. 2006b, Freund et al. 2007], and some hypotheses have been proposed. These have included: leaking of pore-gas, and hence the resulting greenhouse effect [Qiang et al. 1995]; activating and recombining of p-holes during rock deformation [Freund 2002]; release of latent heat due to near-surface air ionization [Pulinets et al. 2006], and stress-induced thermal effects due to friction and fluids [Wu and Liu 2009]. […] In this study, the long-term temperature data from both satellite and ground (with greater emphasis on the satellite data) have been used to determine whether there were thermal anomalies associated with this Emilia 2012 seismic sequence. In particular, the next section will be dedicated to describing both the data and the method of analysis. In Section 3, we provide the more significant results, which we discuss in Section 4, together with the main conclusions. […]

Description: A novel geostationary satellite, the H8/AHI (Himawari-8/Advanced Himawari Imager), greatly improved the scan times per day covering East Asia, and the operational products have been stably provided for a period of time. Currently, atmospheric aerosol pollution is a major concern in China. H8/AHI aerosol products with a high temporal resolution are helpful for real-time monitoring of subtle aerosol variation. However, the H8/AHI aerosol optical thickness (AOT) product has been updated three times since its launch, and the evaluation of this dataset is currently rare. In order to validate its accuracy, this study compared the H8/AHI Level-3 (L3) hourly AOT products of all versions with measurements obtained from eleven sunphotometer sites located in eastern China from 2015 to 2018. Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 AOT products from the same period were also used for inter-comparison. Although the H8/AHI AOT retrievals in version 010 show a moderate agreement with ground-based observations (correlation coefficient (R): 0.66–0.85), and the time series analysis shows that it can effectively monitor hourly variation, it suffers from an obvious underestimation of 0.3 compared to ground-based and MODIS observations. After the retrieval algorithm updated the predefined aerosol model, the overall underestimation of AHI AOTs was solved (version 010 slope: 0.43–0.62, version 030 slope: 0.75–1.02), and the AOTs in version 030 show a high agreement with observations from ten sites (R: 0.73–0.91). In addition, the surface reflectance dataset derived from the minimum reflectivity model in version 010 is inaccurate in parts of eastern China, for both “bright” and “dark” land surfaces, which leads to the overestimation of the AOT values under low aerosol loads at the Beijing and Xianghe sites. After the update of the surface dataset in version 030, this phenomenon was alleviated, resulting in no significant difference in scatterplots under different surface conditions. The AOTs of H8/AHI version 030 show a significant improvement compared to the previous two versions, but the spatial distribution of AHI is still different from MODIS AOT products due to the differences in sensors and algorithms. Therefore, although the evaluation in this study demonstrates the effectiveness of H8/AHI AOT products for aerosol monitoring at fine temporal resolutions, the performance of H8/AHI AOT products needs further study by considering more conditions.

Description: The particular process of LCA (Lithosphere-Coversphere-Atmosphere) coupling referring to local tectonic structures and coversphere conditions is very important for understanding seismic anomaly from GEOSS (Global Earth Observation System of Systems). The LCA coupling based multiple-parameters analysis should be the foundation for earthquake prewarning. Three improved criterions: deviation notable enough, time quasi-synchronism, and space geo-adjacency, plus their quantify indices are defined for earthquake anomaly recognition, and applied to thermal parameters as a DTS-T (Deviation-Time-Space-Thermal) method. A normalized reliability index is preliminarily defined considering three quantify indices for deviation-time-space criterions. As an example, the DTS-T method is applied to the Ms 7.1 Yushu earthquake of 14 April 2010 in China. Furthermore, combining with the previous analysis of six recent significant earthquakes in the world, the statistical results regarding effective parameters, the occurrence of anomaly before the main shocks and a reliability index for each earthquake are introduced. It shows that the DTS-T method is reasonable and can be applied for routine monitoring and prewarning in the tectonic seismicity region.

Description: In the research of earthquake anomaly recognition, the coupling effect of multiple geosystem spheres can be expected to reasonably interpretating the correlation between various anomalous signals before strong earthquake. Specially, the development of the Lithosphere–Atmosphere–Ionosphere (LAI) coupling model has been accepted as verified by some experimental, thermal and electromagnetic data. However, quasi-synchronous anomalies of the multiple parameters, including thermal, radon and electromagnetic data, have not been reported in a single event case for verifying the geosystem spheres coupling effect. In this paper, we firstly summarized the reported studies on the power spectrum density (PSD) in the ELF/VLF band and radon data recorded from Guza seismic station. Then, historical surface latent heat flux (SLHF) data from the NCEP/NCAR Reanalysis Project was employed for investigating anomalous change in a month before the April 14, 2010, Ms7.1 Yushu earthquake which is one of the typical intra-continental earthquakes in Tibet Plateau. The results from spatial and temporal analysis revealed that anomalous fields of PSD and SLHF data were located close to the epicenter and the ends of some active faults at Bayan Har Block and all anomalous dates converged between April 8 and 11 (6 to 3 days before the Yushu earthquake). Therefore, we suggest that the anomalies of multiple parameters before the main shock are related with the Yushu earthquake. This paper could give an ideal case study to verify the geosystem spheres coupling effect happened in a single event.