Space-time explainable modelling of regional hillslope deformation, an example from the Tibetan Plateau

IF 11.1 1区地球科学 Q1 ENVIRONMENTAL SCIENCES

Remote Sensing of Environment Pub Date : 2025-07-23 DOI:10.1016/j.rse.2025.114924

Jun He , Hakan Tanyas , Da Huang , Luigi Lombardo

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引用次数: 0

Abstract

The future of InSAR applications will undoubtedly involve data-driven solutions to predict deformation across space and time. Recent advancements in subsidence research have already integrated such approaches, primarily in flat to near-flat landscapes. However, in mountainous terrains, space-time InSAR modelling has so far focused mainly on individual slopes or small catchments. Here, we propose a modelling protocol based on a deep learning architecture capable of predicting InSAR-derived hillslope deformation. This approach is developed primarily using morphometric and meteorological variables over extensive mountainous areas (∼15,000 km²) and extended time windows (∼7 years). By aggregating the deformation signal at the Slope Unit scale while maintaining 12-day temporal intervals consistent with Sentinel-1 acquisitions, we achieve high modelling performance (PCC = 0.7). If validated in other regions, this method could represent a crucial step towards a large-scale, consistent, and highly effective scenario-based warning system for hillslope deformation.

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区域斜坡变形的时空可解释模型，以青藏高原为例

InSAR应用的未来无疑将涉及数据驱动的解决方案，以预测跨空间和时间的变形。最近沉降研究的进展已经将这些方法整合在一起，主要是在平坦到近平坦的景观中。然而，在山地地形中，时空InSAR模型迄今主要集中在单个斜坡或小集水区。在这里，我们提出了一种基于深度学习架构的建模协议，该架构能够预测insar衍生的山坡变形。该方法主要是在广阔的山区（~ 15,000 km2）和延长的时间窗口（~ 7年）中使用形态测量学和气象变量开发的。通过在坡度单位尺度上聚合变形信号，同时保持与Sentinel-1采集数据一致的12天时间间隔，我们实现了高建模性能（PCC = 0.7）。如果在其他地区得到验证，该方法可能是迈向大规模、一致和高效的基于场景的山坡变形预警系统的关键一步。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Remote Sensing of Environment 环境科学-成像科学与照相技术

CiteScore

25.10

自引率

8.90%

发文量

455

审稿时长

53 days

期刊介绍： Remote Sensing of Environment (RSE) serves the Earth observation community by disseminating results on the theory, science, applications, and technology that contribute to advancing the field of remote sensing. With a thoroughly interdisciplinary approach, RSE encompasses terrestrial, oceanic, and atmospheric sensing. The journal emphasizes biophysical and quantitative approaches to remote sensing at local to global scales, covering a diverse range of applications and techniques. RSE serves as a vital platform for the exchange of knowledge and advancements in the dynamic field of remote sensing.