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Remote Sensing and GIS for Geomorphology and Tectonic Studies
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School of Engineering and Technology, China University of Geosciences (Beijing), Beijing, China
Dr. Zuopeng Wang
School of Engineering and Technology, China University of Geosciences (Beijing), Beijing 100083, China
Geological Hazards Research Center, National Institute of Natural Hazards, Ministry of Emergency Management, Beijing, China
Prof. Dr. Shuangcheng Zhang
College of Geological Engineering and Geomatics, Chang'an University, Xi'an 710054, China
Dr. Ya Kang
School of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
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Remote Sensing and GIS for Geomorphology and Tectonic Studies

Abstract submission deadline
28 February 2027
Manuscript submission deadline
30 April 2027
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1185

Topic Information

Dear Colleagues,

Recent advances in remote sensing and geographic information systems (GISs) have profoundly transformed geomorphological and tectonic research by enabling multi-scale, high-resolution, and time-dependent observations of surface processes on Earth and other planetary bodies. Satellite-based optical imagery, InSAR, LiDAR, UAV photogrammetry, planetary mission datasets, and integrated GIS analyses now offer unprecedented capabilities to investigate landform evolution, active tectonics, surface deformation, and associated geohazards across a wide range of geological settings. Collectively, these approaches play a critical role in quantitatively assessing geomorphic responses to tectonic forcing, climate variability, and anthropogenic influences.

We are pleased to invite you to contribute to the Topic “Remote Sensing and GIS for Geomorphology and Tectonic Studies.” This Topic aims to present recent methodological advances and innovative applications of remote sensing and GIS for understanding geomorphic processes and tectonic processes on Earth and other planetary bodies. The Topic is fully aligned with the journal’s scope, emphasizing geospatial data acquisition, processing, analysis, and the interpretation for Earth and planetary science applications.

This Topic welcomes original research articles and review papers that advance theory, methodology, or applications. Suggested themes include, but are not limited to, the following: remote sensing-based geomorphic mapping and classification; tectonic landform analysis; surface deformation monitoring using InSAR; quantitative geomorphology and morphometric analysis; planetary geomorphology and surface process studies; integration of remote sensing with numerical modeling; and remote sensing applications in tectonic and geomorphological hazard assessment.

We look forward to receiving your valuable contributions.

Dr. Mingdong Zang
Dr. Zuopeng Wang
Prof. Dr. Chong Xu
Prof. Dr. Shuangcheng Zhang
Dr. Ya Kang
Topic Editors

Keywords

  • remote sensing
  • active tectonics
  • quantitative geomorphology
  • planetary geomorphology
  • surface deformation
  • geohazards
  • GIS
  • InSAR
  • LiDAR
  • UAV

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Earth
earth 		3.4 5.9 2020 21.3 Days CHF 1400 Submit
Geomatics
geomatics 		2.8 5.1 2021 22.6 Days CHF 1200 Submit
Geosciences
geosciences 		2.1 5.1 2011 23.6 Days CHF 1800 Submit
ISPRS International Journal of Geo-Information
ijgi 		2.8 7.2 2012 33.1 Days CHF 1900 Submit
Remote Sensing
remotesensing 		4.1 8.6 2009 24.3 Days CHF 2700 Submit

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Published Papers (1 paper)

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21 pages, 16281 KB  
Article
Spatially Seamless Error Characterization of ERA5, GLDAS, GLEAM, and MERRA2 ET Products Using Quadruple Collocation Analysis and Random Forest
by Wei Yue, Tingyuan Jin, Chaohui Zhong, Jiahao Chen and Kai Wu
Remote Sens. 2026, 18(8), 1239; https://doi.org/10.3390/rs18081239 - 19 Apr 2026
Viewed by 524
Abstract
Accurate estimation of global terrestrial evapotranspiration (ET) is fundamental for understanding the Earth’s water and energy cycles, yet existing multi-source ET products inevitably contain uncertainties that require spatially explicit characterization for optimal data merging or data assimilation. While Quadruple Collocation Analysis (QCA) offers [...] Read more.
Accurate estimation of global terrestrial evapotranspiration (ET) is fundamental for understanding the Earth’s water and energy cycles, yet existing multi-source ET products inevitably contain uncertainties that require spatially explicit characterization for optimal data merging or data assimilation. While Quadruple Collocation Analysis (QCA) offers a robust and reference-free approach to quantify uncertainties, its reliability in the ET discipline remains underexplored, and algorithmic non-convergence frequently results in substantial spatial data gaps. To address these limitations, this study evaluated the accuracy of the QCA method using validation errors derived from high-quality FLUXNET sites (N = 55). Moreover, we employed a Random Forest (RF) framework that is driven by 17 environmental variables to generate spatially seamless error maps for four mainstream ET products, i.e., ERA5, GLDAS, GLEAM, and MERRA2, from 2000 to 2020. Results demonstrate that QCA-based errors strongly correlated with ground-based errors as Pearson’s correlation coefficient was >0.3 for all four ET products. Furthermore, the RF model successfully reconstructed the spatial gaps in QCA errors, achieving an exceptionally low mean prediction error of approximately 0.03 mm/day. Based on these seamless maps, the global mean ET error is estimated at roughly 0.3 mm/day, with pronounced high-error clusters emerging in regions such as central Canada and northern Argentina driven by underlying land cover heterogeneity. Ultimately, this seamless gap-filling redefined the global map of product with the lowest estimated collocation error. ERA5 emerged as the superior choice across approximately 45% of the land surface (predominantly in the tropics and mid-to-high latitudes). Meanwhile, before algorithmic gap-filling, GLEAM was optimal across approximately 28% of the valid land pixels; after spatial gap-filling, it proved most effective across approximately 30% of the globe, particularly within arid deserts and glaciated regions. Our work provides useful geographic guidance for optimizing multi-source data merging and land data assimilation frameworks in future global hydrological studies. Full article
(This article belongs to the Topic Remote Sensing and GIS for Geomorphology and Tectonic Studies)
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