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This document is an editorial for a special issue of the journal Remote Sensing, which focuses on using satellite data and new methodologies to understand the preparatory phase of medium-large earthquakes. The issue includes 15 papers from authors in various countries, covering topics such as seismo-electromagnetic processes, lithospheric structure, atmospheric anomalies, ionospheric disturbances, and interactions between the lithosphere, atmosphere, and ionosphere. The editorial emphasizes the need for further research to explain the different patterns observed in earthquakes and the potential role of tectonic settings and water in these phenomena. Additionally, there is an acknowledgment section from a research paper published in the journal, expressing gratitude to the academic editors who helped evaluate the papers in the special issue. [Extracted from the article]

Earth observations from remotely sensed data have a substantial impact on natural hazard surveillance, specifically for earthquakes. The rapid emergence of diverse earthquake precursors has led to the exploration of different methodologies and datasets from various satellites to understand and address the complex nature of earthquake precursors. This study presents a novel technique to detect the ionospheric and atmospheric precursors using machine learning (ML). We examine the multiple precursors of different spatiotemporal nature from satellites in the ionosphere and atmosphere related to the Turkey earthquake on 6 February 2023 (Mw 7.8), in the form of total electron content (TEC), land surface temperature (LST), sea surface temperature (SST), air pressure (AP), relative humidity (RH), outgoing longwave radiation (OLR), and air temperature (AT). As a confutation analysis, we also statistically observe datasets of atmospheric parameters for the years 2021 and 2022 in the same epicentral region and time period as the 2023 Turkey earthquake. Moreover, the aim of this study is to find a synchronized and co-located window of possible earthquake anomalies by providing more evidence with standard deviation (STDEV) and nonlinear autoregressive network with exogenous inputs (NARX) models. It is noteworthy that both the statistical and ML methods demonstrate abnormal fluctuations as precursors within 6 to 7 days before the impending earthquake over the epicenter. Furthermore, the geomagnetic anomalies in the ionosphere are detected on the ninth day after the earthquake (Kp > 4; Dst < −70 nT; ap > 50 nT). This study indicates the relevance of using multiple earthquake precursors in a synchronized window from ML methods to support the lithosphere–atmosphere–ionosphere coupling (LAIC) phenomenon. [ABSTRACT FROM AUTHOR]