Immediate remote sensing of sudden earth’s surface anomalies and its geographical significance

Abstract

Sudden earth’s surface anomalies caused by natural and anthropogenic factors pose significant threats to ecological sustainability and the safety of human life and property, highlighting the urgent need for their immediate monitoring and early warning. Satellite remote sensing is the most effective means for large-scale earth’s surface anomaly detection. However, constrained by traditional observation paradigms, satellite payload limitations, and other physical factors, current remote sensing detection faces two major challenges: “inability to observe quickly” and “inability to observe effectively”. To solve these problems, we have researched immediate remote sensing detection of sudden earth’s surface anomalies. Its core concept is to deploy the entire detection process on satellites, enabling on-orbit immediate detection of earth’s surface anomalies based on a single image through the integrated “positioning, navigation, timing, remote sensing, communication (PNTRC)” intelligent constellation and edge computing technologies. Subsequently, the detection results are transmitted directly to the subscriber mobile terminal through the BeiDou Navigation Satellite System (BDS). The immediate remote sensing of sudden earth’s surface anomalies emphasizes the continuous capture and immediate feedback of geographic processes, overcoming the longstanding reliance of traditional geography on “slow variables”. Its significance lies not only in the improvement of data acquisition efficiency but also in promoting the transformation of geography from a “descriptive science” to a “predictive science”.

Keywords

Immediate remote sensing / Earth’s surface anomalies / Geographical significance

CRediT authorship contribution statement

Qiao Wang: Writing – review & editing, Supervision, Funding acquisition, Conceptualization. Haishuo Wei: Writing – review & editing, Writing – original draft, Visualization.

Declaration of competing interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant No. 42192580).

Declaration of generative AI in scientific writing

During the preparation of this work and scientific writing, all author(s) didn’t use generative AI.

Data availability statement

Data will be made available on request.

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