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Remote Sensing
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Snow Water Equivalent Retrieval Using Remote Sensing
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Snow Water Equivalent Retrieval Using Remote Sensing

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Geology, Geomorphology and Hydrology".

Deadline for manuscript submissions: 31 July 2025 | Viewed by 495

Special Issue Editors

Dr. Jianwei Yang

E-Mail Website
Guest Editor
State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Interests: microwave remote sensing of snow cover; snow forward modeling; snow depth and snow water equivalent (SWE); machine learning
Prof. Dr. Lingmei Jiang

E-Mail Website
Guest Editor
State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Interests: microwave emission/scattering modeling of the land surface; passive microwave remote sensing of snow water equivalent and soil moisture; surface freeze/thaw state
Special Issues, Collections and Topics in MDPI journals
Dr. Matias Takala

E-Mail Website
Guest Editor
Finnish Meteorological Institute, Helsinki, Finland
Interests: development of remote sensing algorithms of snow and other parameters; application of satellite data in climate change research; development of satellite services
Special Issues, Collections and Topics in MDPI journals
Dr. Yan Huang

E-Mail Website
Guest Editor
School of Geographical Sciences, East China Normal University, Shanghai 200062, China
Interests: remote sensing spatiotemporal modeling and analysis; remote sensing of cryosphere; global climate and environmental change
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Tao Che

E-Mail Website
Guest Editor
Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou 73000, China
Interests: snow cover; remote sensing; data assimilation; hydrology in cold region
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The snow water equivalent (SWE), a key metric related to water storage in snowpacks, greatly impacts agricultural production, hydropower generation, freshwater supply, and natural disaster management. In particular, microwave remote sensing is an essential tool for monitoring the seasonal SWE due to its independence from solar illumination, the penetrability of clouds, and sensitivity to dry snowpack. Also, the exploitation of more recent sensors such as LiDAR, Altimeter, and GNSS-R satellites enhances the ability of monitoring SWE on a global scale. However, significant biases and high uncertainty in SWE products pose significant challenges.

The aim of this Special Issue is to explore advancements in remote sensing techniques for monitoring seasonal SWE, covering radar (SAR, PolSAR, InSAR, and Scatterometer), microwave radiometers, LiDAR, Altimeter, GNSS-R instruments, etc.

This Special Issue invites innovative methodologies, theoretical models, experimental observations, techniques, and applications on monitoring and modeling SWE. Submissions are encouraged to cover a broad range of topics, which may include, but are not limited to, the following activities:

  • Microwave simulation of emission or backscattering from snow-covered surfaces;
  • Development and validation of algorithms for retrieving SWE;
  • Estimating high-resolution SWE by downscaling or data fusion;
  • Data assimilation methods for improving SWE retrievals;
  • Investigation of spatiotemporal variability of snow cover;
  • Studying the interactions of climate change and snowpacks;
  • Experimental campaigns in the field of microwave remote sensing.

Dr. Jianwei Yang
Prof. Dr. Lingmei Jiang
Dr. Matias Takala
Dr. Yan Huang
Prof. Dr. Tao Che
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • microwave remote sensing
  • snow water equivalent (SWE)
  • snow depth
  • SWE retrieving algorithm
  • snow forward modeling
  • field campaigns in cryosphere
  • spatiotemporal variability

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Published Papers (1 paper)

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Research

24 pages, 69644 KiB  
Article
A Texture-Enhanced Deep Learning Network for Cloud Detection of GaoFen/WFV by Integrating an Object-Oriented Dynamic Threshold Labeling Method and Texture-Feature-Enhanced Attention Module
by Bo Zhong, Xiao Tang, Xiaobo Luo, Shanlong Wu and Kai Ao
Remote Sens. 2025, 17(10), 1677; https://doi.org/10.3390/rs17101677 - 9 May 2025
Viewed by 204
Abstract
Cloud detection in satellite imagery plays a pivotal role in achieving high-accuracy retrieval of biophysical parameters and subsequent remote sensing applications. Although numerous methods have been developed and operationally deployed, their accuracy over challenging surfaces—such as snow-covered mountains, saline–alkali lands in deserts or [...] Read more.
Cloud detection in satellite imagery plays a pivotal role in achieving high-accuracy retrieval of biophysical parameters and subsequent remote sensing applications. Although numerous methods have been developed and operationally deployed, their accuracy over challenging surfaces—such as snow-covered mountains, saline–alkali lands in deserts or Gobi regions, and snow-covered surfaces—remains limited. Additionally, the efficiency of collecting training samples for prevalent deep learning-based methods heavily relies on large-scale pixel-level annotations, which are both time-consuming and labor-intensive. To address these challenges, we propose a Texture-Enhanced Network that integrates an object-oriented dynamic threshold pseudo-labeling method and a texture-feature-enhanced attention module to enhance both the efficiency of deep learning methods and detection accuracy over challenging surfaces. First, an object-oriented dynamic threshold pseudo-labeling approach is developed by leveraging object-oriented principles and adaptive thresholding techniques, enabling the efficient collection of large-scale labeled samples for challenging surfaces. Second, to exploit the spatial continuity of clouds, cross-channel correlations, and their distinctive texture features, a texture-feature-enhanced attention module is designed to improve feature discrimination for challenging positive and negative samples. Extensive experiments on a Chinese GaoFen satellite imagery dataset demonstrate that the proposed method achieves state-of-the-art performance. Full article
(This article belongs to the Special Issue Snow Water Equivalent Retrieval Using Remote Sensing)
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