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Surface longwave radiation (SLR) is an essential geophysical parameter of Earth’s energy balance, and its estimation based on thermal infrared (TIR) remote sensing data has been extensively studied. However, it is difficult to estimate cloudy SLR from TIR measurements. Satellite passive microwave (PMW) radiometers measure microwave radiation under the clouds and therefore can estimate SLR in all weather conditions. We constructed SLR retrieval models using brightness temperature (BT) data from an Advanced Microwave Scanning Radiometer 2 (AMSR2) based on a neural network (NN) algorithm. SLR from the European Centre for Medium-Range Weather Forecasts Reanalysis v5 (ERA5) product was used as the reference. NN-based models were able to reproduce well the spatial variability of SLR from ERA5 at the global scale. Validations indicate a reasonably good performance was found for land sites, with a bias of 1.32 W/m2, root mean squared error (RMSE) of 35.37 W/m2, and coefficient of determination (R2) of 0.89 for AMSR2 surface upward longwave radiation (SULR) data, and a bias of −2.26 W/m2, RMSE of 32.94 W/m2, and R2 of 0.82 for AMSR2 surface downward longwave radiation (SDLR) data. AMSR2 SULR and SDLR retrieval accuracies were higher for oceanic sites, with biases of −2.98 and −4.04 W/m2, RMSEs of 6.50 and 13.42 W/m2, and R2 values of 0.83 and 0.66, respectively. This study provides a solid foundation for the development of a PMW SLR retrieval model applicable at the global scale to generate long-term continuous SLR products using multi-year satellite PMW data and for future research with a higher spatiotemporal resolution.

The phase occurring before a significant earthquake is a key stage from which to identify anomalous signals. Short-term anomalies of land surface temperature (LST) are proposed to represent such signals of an impending major shock and are expressed by temporal integration anomalies (TIAs). The TIA is calculated from anomalous hot and cold LST variations relative to daily reference fields using MODIS satellite data. As an increment of earthquake magnitude, hot and cold PTIAs showed increasing trends, while cold PTIA was more sensitive than hot PTIA. In addition, an intensely hot peak TIA (PTIA) occurred first, and then a significantly cold PTIA foretold the occurrence of an impending earthquake based on nine earthquakes of moderate to strong magnitude occurring in mainland China between 2012 and 2016. This significant temporal effect on responses to earthquakes may be used as a pattern from which to recognize preseismic signals.

Rugged terrain, as a large percentage of the Earth's terrestrial surface, is frequently reported to cause directionality of land surface thermal radiation (LSTR), and seriously affects the retrieval accuracy of land surface temperature (LST) and surface longwave radiation from satellite measurements. Therefore, modeling topographic effects on surface thermal anisotropy is essential to understand surface radiative processes. The directional brightness temperature (DBT) and equivalent brightness temperature (EBT) models at the pixel scale are proposed to indicate thermal anisotropy, considering viewing geometry, topographic effects, and subpixel variations based on the thermal infrared radiative transfer equation. A simulated data set of DBT and EBT at the 1-km resolution was obtained based on LST, emissivity, and terrain data with 30-m resolution. The terrain, coupled with solar and viewing geometries and subgrid variation, significantly affects the directionality of LSTR, and results in a remarkable bias between DBT and EBT. For the nadir observation, the bias is from −0.8 to 1 K, and reaches −5 to 2 K when viewing zenith angle becomes 50°. The maximal deviation is about 9 K over the most rugged mountains, which causes ${\text{57.6}}\;{\text{W/m}}^{2}$ bias of longwave radiation based on a 300 K blackbody. Furthermore, when LST is retrieved from DBT, the uncertainty of broadband emissivity of 0.01 causes LST bias of ∼0.35 K. The models are considered to be very helpful in exploring terrain-induced thermal anisotropy, and enlightening in reducing estimation bias of remote sensing products over complex terrain.

Satellite thermal infrared remote sensing has received worldwide attention in the exploration for earthquake precursors, however, this method faces great controversy. Obtaining repeatable phenomena related to earthquakes is helpful to reduce this controversy. In this paper, a total of 15 or 17 years of Moderate-resolution Imaging Spectroradiometer (MODIS)/Aqua and MODIS/Terra satellite remote sensing land surface temperature (LST) products is selected to analyze the temperature changes before and after the Mw 7.9 earthquake in Nepal on 25 April 2015 and to explore possible thermal information associated with this earthquake. Major findings are given as follows: (1) from the time course, the temperature slowly cooled before the earthquake, reached a minimum at the time of the earthquake, and returned to normal after the earthquake. Since these changes were initiated before the earthquake, they may even have been precursors to the Nepal earthquake. (2) From the space distribution, the cooling areas correspond to the seismogenic structure during the earthquake. These cooling areas are distributed along the Himalayas and are approximately 1300 km long. The widths of the East and West sides are slightly different, with an average temperature decrease of 5.6 &deg, C. For these cooling areas, the Western section is approximately 90 km wide and 500 km long, the East side is approximately 190 km wide and 800 km long. The Western side of the cooling strips appeared before the earthquake. In short, these kinds of spatial and temporal changes are tectonically related to the earthquake and may have been caused by the tectonic activity associated with the Nepal earthquake. This process began before the earthquake and therefore might even be potentially premonitory information associated with the Nepal earthquake.

Detecting various anomalies using optical satellite data prior to strong earthquakes is key to understanding and forecasting earthquake activities because of its recognition of thermal-radiation-related phenomena in seismic preparation phases. Data from satellite observations serve as a powerful tool in monitoring earthquake preparation areas at a global scale and in a nearly real-time manner. Over the past several decades, many new different data sources have been utilized in this field, and progressive anomaly detection approaches have been developed. This paper reviews the progress and development of pre-seismic anomaly detection technology in this decade. First, precursor parameters, including parameters from the top of the atmosphere, in the atmosphere, and on the Earth's surface, are stated and discussed. Second, different anomaly detection methods, which are used to extract anomalous signals that probably indicate future seismic events, are presented. Finally, certain critical problems with the current research are highlighted, and new developing trends and perspectives for future work are discussed. The development of Earth observation satellites and anomaly detection algorithms can enrich available information sources, provide advanced tools for multilevel earthquake monitoring, and improve short- and medium-term forecasting, which play a large and growing role in pre-seismic anomaly detection research.

Shortwave radiation (0.3–3.0 μm) is a dominant component of land surface all-wave radiation budget. Reliable estimation of shortwave radiation throughout the globe is particularly important for an in-depth understanding of global changes. Unfortunately, a large number of existing space-based algorithms ignore the effects of topography by simply assuming that the surface is ideally flat. As pointed out by many studies, such neglect toward topographic effects leads to significant errors in derived radiation. This study proposes a shortwave topographic radiation model (SWTRM) by quantifying solar direct radiation shielding, occlusion of sky radiation, reflected radiation from nearby terrain, and invisibility of certain targets. To drive the SWTRM, an artificial neural network (ANN) approach was employed to generate multiple components of the shortwave radiation budget. The new model was run over the Tibetan Plateau region and used MODIS products coupled with digital elevation data (DEM). Topographically corrected shortwave fluxes were derived by coupling SWTRM and ANN outputs. The results show that: (1) the proposed SWTRM works well over mountainous regions; (2) the ANN-based shortwave model provides reasonable retrieval accuracy (RMSE

There are growing observational evidences that various geophysical anomalies precede large earthquakes. However, the reliability of these anomalies for earthquake forecasting is controversial, and therefore more consistent assessment of forecasting ability is required. A framework for investigating pre-seismic anomaly detection using essential statistical indicators before global earthquakes is proposed. Surface temperature (ST) data from the Atmospheric Infrared Sounder (AIRS) sensor were used to realize this framework. First, seismic-related ST anomalies were identified, and then the statistical characteristics of forecasting ability for three indicators (accuracy, missed detection, and false alarm) were calculated in retrospective and prospective ways. The ST anomalies displayed some aggregation effects. Negative anomalies mainly concentrated on epicenters and to the north, while positive anomalies were found on the periphery; neither were strongly dependent on earthquake magnitude. The temporal evolution of forecasting metrics was relatively stable for the period 2010–2018. Mean accuracy, missed detection, and false alarm ratios were 6.01%, 1.60%, and 92.39%, respectively. Accuracy and missed detection ratios showed some spatial correlation and both peaked in the same area (e.g., eastern Japan); however, most areas showed very high false alarm ratios. Based on our findings, the combination of AIRS ST data and the Z-score anomaly detection algorithm to predict short-term earthquakes is currently not practical; the possibility of earthquake forecasting based on satellite thermal infrared measurements remains a huge challenge. However, our results confirmed the efficiency of this framework for quantitatively evaluating earthquake forecasting ability. This approach could be applied to various geophysical parameters and anomaly detection methods.

Longwave radiation (5–100 μm) is a critical component of the Earth's radiation budget. Most of the existing satellite-based retrieval algorithms are valid only for flat surfaces without accounting for topographic effects. This causes significant errors. Meanwhile, the fixed spatial resolution of remote sensing data makes it difficult to link the satellite-derived longwave radiation to different land models running on various scales. These deficiencies result in an urgent need for topographic modeling and spatial scaling studies of longwave radiation. In this paper, a longwave topographic radiation model (LWTRM) is proposed that quantifies all possible radiation-affecting factors over rugged terrain. For driving the LWTRM, a hybrid method for simultaneously deriving multiple components of longwave radiation from MODIS data is suggested based on artificial neuron networks (ANN) and the radiative transfer simulation. Topographically corrected longwave radiation is then derived by coupling the ANN outputs and LWTRM. Based on this, a general upscaling strategy for longwave radiation is presented. The results demonstrate that: (1) both the proposed LWTRM and the upscaling strategy are rather effective and work well over rugged areas; (2) the ANN-based retrieval method can produce longwave radiation with better accuracy(RMSE

Rugged terrain, as a high percent of the Earth's terrestrial surface, can cause the directionality of the surface thermal radiation, and affect the retrieved land surface temperature (LST) and longwave radiation (SLR) from satellite measurements due to the limited instantaneous field of view and observation angles. New directional brightness temperature (DBT) and equivalent brightness temperature (EBT) models were established considering terrain effects. The biases between them were also analyzed based on a simulated scene using the Advanced Spacebome Thermal Emission and Reflection Radiometer (ASTER) LST, emissivity and topographic data. The results show that BTs at the valley and peak points are clearly anisotropic, while this directionality at the cropland point is not obvious. The DBT shows hotspot effects which is closely related to the solar position. The range of DBTs can reach up to about 9 K in the valley point and the standard deviation of this difference in all view directions is 1.05 K. Thus, it can be concluded that it is hard to meet the requirement of retrieval accuracy of LST or SLR over rugged terrain if ignoring the three-dimensional structure of mountainous region and its angular thermal radiation.

Surface upward longwave radiation (SULR) is one critical component of the surface energy balance and is closely related to the surface temperature fields. The SULR with the moderate spatial resolution and appropriate precision in the Tibetan Plateau, a large and complex cryosphere, can enhance our understanding of the land surface processes, atmospheric circulations and even global climate change. The purpose of this paper is to estimate SULR from top-of-atmosphere (TOA) radiances of Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS). A hybrid method is proposed that combines extensive radiative transfer simulation and the Artificial Neuron Network (ANN) statistical model. For the sake of ensuring the availability of the simulation database, the eligible profiles from MODIS atmospheric profiles product were screened according to five criteria. And then the analyses on the important parameters of the simulation dataset were performed to check the integrality of this dataset. Moreover, the variables' importance was analyzed, and TOA radiances of MODIS channels 29, 31, 32 and 33 (or VIIRS channels M14, M15 and M16) and sensor view zenith angle (VZA) were selected to retrieve SULR. The ANN-based retrieval model was created finally based on this simulation dataset. The evaluation showed good agreements for both MODIS and VIIRS training and testing datasets with R 2 greater than 0.98. The MODIS SULR was validated using two-year ground-measured data at seven sites in the Tibetan Plateau with R 2 of 0.886, root-mean-square error (RMSE) of 26.985 W m − 2 and mean bias error (MBE) of 10.812 W m − 2 as a whole. And the relevance was obvious from the overall trend and distribution though the discrepancy was relatively large when directly comparing SULR from VIIRS and MODIS data. The validation results showed that the ANN model is a good nonlinear model to retrieve SULR with the TOA radiances and VZA.

Detecting thermal anomalies prior to strong earthquakes is a key in understanding and forecasting earthquake activities because of its recognition of thermal radiation-related phenomena in seismic preparation phases. Data from satellite observations serve as a powerful tool in monitoring earthquake preparation areas at a global scale and in a nearly real-time manner. Over the past several decades, many new different data sources have been utilized in this field, and progressive anomaly detection approaches have been developed. This paper dedicatedly reviews the progress and development of pre-seismic thermal anomaly detection technology in this decade. First, precursor parameters, including parameters from the top of the atmosphere, in the atmosphere, and on the Earth’s surface, are discussed. Second, different anomaly detection methods, which are used to extract thermal anomalous signals that probably indicate future seismic events, are presented. Finally, certain critical problems with the current research are highlighted, and new developing trends and perspectives for future work are discussed. The development of Earth observation satellites and anomaly detection algorithms can enrich available information sources, provide advanced tools for multilevel earthquake monitoring and improve short- and medium-term forecasting, which should play a large and growing role in pre-seismic thermal anomaly research.

Shortwave (SW) radiative flux (usually within 0.3~3μm) is the dominant energy source of our planet, which drives the climate as well as the matter and energy cycle of the Earth system. It is an indispensable component of surface total energy balance. Considering the importance of SW radiation, during the past decades, more and more studies have conducted for estimating surface SW radiation using satellite-based data, such as MODIS, CERES, GOES etc. Although great effort has been made, most researches neglect the topographic effect and mainly focus on the retrieval of SW radiation over ideal horizontal surfaces for both instantaneous and time-integrated radiation. For this point, we propose a topographic SW radiation model based on the existing studies. Based on this, the SW radiative flux components are derived from MODIS data by fully accounting for the surface topographic effect. The results show that the errors induced in the retrieved daily SW radiation can reach up to 400W/m 2 at 1km scale. For instantaneous radiation, the uncertainties of derived SW radiation can reach up to 300W/m 2 even at 5km scale due to topographic effect. The findings of this paper prove the importance of topographic modeling of surface radiation over rugged terrain.