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It is of great significance to study the thermal radiation anomalies of earthquake swarms in the same area in terms of selecting abnormal characteristic determination parameters, optimizing and determining the processing model, and understanding the abnormal machine. In this paper, we investigated short-term and long-term thermal radiation anomalies induced by earthquake swarms in Iran and Pakistan between 2007 and 2016. The anomalies were extracted from infrared remote sensing black body temperature data from the China Geostationary Meteorological Satellites (FY-2C/2E/2F/2G) using the multiscale time-frequency relative power spectrum (MS T-FRPS) method. By analyzing and summarizing the thermal radiation anomalies of series earthquake groups with consistency law through a stable and reliable MS T-FRPS method, we first obtained the relationship between anomalies and ShakeMaps from USGS and proposed the anomaly regional indicator (ARI) to determine seismic anomalies and the magnitude decision factor (MDF) to determine seismic magnitude. In addition, we explored the following discussions: earthquake impact on regional thermal radiation background and the relationship between thermal anomalies and earthquake magnitude and the like. Future research directions using the MS T-FRPS method to characterize regional thermal radiation anomalies induced by strong earthquakes could help improve the accuracy of earthquake magnitude determination.

Radiative Transfer in Coupled Environmental Systems This book discusses radiative transfer in coupled media such as atmosphere-ocean systems with Lambertian as well non-Lambertian refl ecting surfaces at the lower boundary. The spectral range from the ultraviolet to the microwave region of the electromagnetic spectrum is considered, and multi-spectral as well as hyperspectral remote sensing is discussed. Solutions of the forward problem for unpolarized and polarized radiation are discussed in considerable detail, but what makes this book unique is that formulations and solutions of the inverse problem related to such coupled media are covered in a comprehensive and systematic manner. This book teaches the reader how to formulate and solve forward and inverse problems related to coupled media, and gives examples of how to solve concrete problems in environmental remote sensing of coupled atmosphere-surface systems. From the contents: * Inherent Optical Properties (IOPs) * Basic Radiative Transfer Theory * Forward Radiative Transfer Modeling * The Inverse Problem * Applications

GPS radio occultation can estimate ionospheric electron density and total electron content (TEC) with high spatial resolution, e.g., China's recent Fengyun-3C GPS radio occultation. However, high-order ionospheric delays are normally ignored. In this paper, the high-order ionospheric effects on electron density estimation from the Fengyun-3C GPS radio occultation data are estimated and investigated using the NeQuick2 ionosphere model and the IGRF12 (International Geomagnetic Reference Field, 12th generation) geomagnetic model. Results show that the high-order ionospheric delays have large effects on electron density estimation with up to 800 el cm−3, which should be corrected in high-precision ionospheric density estimation and applications. The second-order ionospheric effects are more significant, particularly at 250–300 km, while third-order ionospheric effects are much smaller. Furthermore, the high-order ionospheric effects are related to the location, the local time, the radio occultation azimuth and the solar activity. The large high-order ionospheric effects are found in the low-latitude area and in the daytime as well as during strong solar activities. The second-order ionospheric effects have a maximum positive value when the radio occultation azimuth is around 0–20°, and a maximum negative value when the radio occultation azimuth is around −180 to −160°. Moreover, the geomagnetic storm also affects the high-order ionospheric delay, which should be carefully corrected.

Clouds play an important role in Arctic amplification. This term represents the recently observed enhanced warming of the Arctic relative to the global increase of near-surface air temperature. However, there are still important knowledge gaps regarding the interplay between Arctic clouds and aerosol particles, and surface properties, as well as turbulent and radiative fluxes that inhibit accurate model simulations of clouds in the Arctic climate system. In an attempt to resolve this so-called Arctic cloud puzzle, two comprehensive and closely coordinated field studies were conducted: the Arctic Cloud Observations Using Airborne Measurements during Polar Day (ACLOUD) aircraft campaign and the Physical Feedbacks of Arctic Boundary Layer, Sea Ice, Cloud and Aerosol (PASCAL) ice breaker expedition. Both observational studies were performed in the framework of the German Arctic Amplification: Climate Relevant Atmospheric and Surface Processes, and Feedback Mechanisms (AC) project. They took place in the vicinity of Svalbard, Norway, in May and June 2017. ACLOUD and PASCAL explored four pieces of the Arctic cloud puzzle: cloud properties, aerosol impact on clouds, atmospheric radiation, and turbulent dynamical processes. The two instrumented Polar 5 and Polar 6 aircraft; the icebreaker Research Vessel (R/V) Polarstern; an ice floe camp including an instrumented tethered balloon; and the permanent ground-based measurement station at Ny-Ålesund, Svalbard, were employed to observe Arctic low- and mid-level mixed-phase clouds and to investigate related atmospheric and surface processes. The Polar 5 aircraft served as a remote sensing observatory examining the clouds from above by downward-looking sensors; the Polar 6 aircraft operated as a flying in situ measurement laboratory sampling inside and below the clouds. Most of the collocated Polar 5/6 flights were conducted either above the R/V Polarstern or over the Ny-Ålesund station, both of which monitored the clouds from below using similar but upward-looking remote sensing techniques as the Polar 5 aircraft. Several of the flights were carried out underneath collocated satellite tracks. The paper motivates the scientific objectives of the ACLOUD/PASCAL observations and describes the measured quantities, retrieved parameters, and the applied complementary instrumentation. Furthermore, it discusses selected measurement results and poses critical research questions to be answered in future papers analyzing the data from the two field campaigns.

Remote sensing (RS) techniques offer advantages over other methods for measuring soil properties, including large-scale coverage, a non-destructive nature, temporal monitoring, multispectral capabilities, and rapid data acquisition. This review highlights the different detection methods, types, parts, and applications of RS techniques in soil measurements, as well as the advantages and disadvantages of the measurements of soil properties. The choice of the methods depends on the specific requirements of the soil measurements task because it is important to consider the advantages and limitations of each method, as well as the specific context and objective of the soil measurements, to determine the most suitable RS technique. This paper follows a well-structured arrangement after investigating the existing literature to ensure a well-organized, coherent review and covers all the essential aspects related to studying the advancement of using RS in the measurements of soil properties. While several remote sensing methods are available, this review suggests spectral reflectance, which entails satellite remote sensing and other tools based on its global coverage, high spatial resolution, long-term monitoring capabilities, non-invasiveness, and cost effectiveness. Conclusively, RS has improved soil property measurements using various methods, but more research is needed for calibration, sensor fusion, artificial intelligence, validation, and machine learning applications to enhance accuracy and applicability.

The Global Land Surface Satellite (GLASS) product suite currently contains 12 products, including leaf area index, fraction of absorbed photosynthetically active radiation, fraction of green vegetation coverage, gross primary production, broadband albedo, broadband longwave emissivity, downward shortwave radiation and photosynthetically active radiation, land surface temperature, downward and upwelling thermal radiation, all-wave net radiation, and evapotranspiration. These products are generated from the Advanced Very High Resolution Radiometer and Moderate Resolution Imaging Spectroradiometer satellite data. Their unique features include long-term temporal coverage (many from 1981 to the present), high spatial resolutions of the surface radiation products (1 km and 0.05°), spatial continuities without missing pixels, and high quality and accuracy based on extensive validation using in situ measurements and intercomparisons with other existing satellite products. Moreover, the GLASS products are based on robust algorithms that have been published in peer-reviewed literature. Herein, we provide an overview of the algorithm development, product characteristics, and some preliminary applications of these products. We also describe the next steps, such as improving the existing GLASS products, generating more climate data records (CDRs), broadening product dissemination, and fostering their wider utilization. The GLASS products are freely available to the public.

Integrating Artificial Intelligence (AI) techniques with remote sensing holds great potential for revolutionizing data analysis and applications in many domains of Earth sciences. This review paper synthesizes the existing literature on AI applications in remote sensing, consolidating and analyzing AI methodologies, outcomes, and limitations. The primary objectives are to identify research gaps, assess the effectiveness of AI approaches in practice, and highlight emerging trends and challenges. We explore diverse applications of AI in remote sensing, including image classification, land cover mapping, object detection, change detection, hyperspectral and radar data analysis, and data fusion. We present an overview of the remote sensing technologies, methods employed, and relevant use cases. We further explore challenges associated with practical AI in remote sensing, such as data quality and availability, model uncertainty and interpretability, and integration with domain expertise as well as potential solutions, advancements, and future directions. We provide a comprehensive overview for researchers, practitioners, and decision makers, informing future research and applications at the exciting intersection of AI and remote sensing.