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Advances in Remote Sensing of Snow Cover

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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: closed (15 May 2023) | Viewed by 8407

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Special Issue Editors

Prof. Dr. Lahoucine Hanich

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Guest Editor

Faculty of Sciences and Techniques, Cadi Ayyad University, Abdelkrim Khattabi Avenue, Marrakesh 40000, Morocco
Interests: remote sensing; hydrology; snow water; snowpack; water resources; drought; hydrogeology; climate change

Prof. Dr. Abdelghani Boudhar

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Guest Editor

Faculty of Sciences and Techniques, Sultan Moulay Slimane University, Av Med V, BP 591, Beni-Mellal 23000, Мorocco
Interests: hydrology; remote sensing applications on natural resources; water resources assessment; geospatial technologies; machine learning
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Special Issue Information

Dear Colleagues,

Seasonal snow provides water to approximately two billion people around the world. The melting of the snowpack that forms each year in mountainous areas is of great hydrological and economic importance. Mountainous regions constitute an area of water production, and this water is used in downstream plains. In mountainous areas, snow cover is a key component of the hydrological cycle because it governs how much and when water will be available downstream.

The spatiotemporal dynamics of snow cover and the spatiotemporal distribution of the water equivalent of the snowpack are relevant for the management of water resources. The precise representation of the spatial variability of snow cover is crucial to the evolution of the water equivalent. Thus, useful observations for describing the spatial variability in snow cover in mountainous regions generally requires a good spatial resolution. To monitor the evolution of snow cover over large areas, the use of remote sensing observations is needed. Remote sensors have different measurement wavelengths and employ active or passive operating principles.

The recent development of space technology has led to increasingly precise data in terms of spatial and temporal resolution, providing us with the opportunity to make better estimations of the spatiotemporal dynamics of snow cover and to assess the spatiotemporal distribution of the snow water equivalent of the snowpack.

This Special Issue aims to discuss the latest advancements in remote sensing to monitor snow cover dynamics and to therefore evaluate the snow water equivalent. Submissions of original and innovative research, especially in mountainous areas using different remote sensing techniques and ground observations that includes new approaches and algorithms is encouraged. The use of multisource data and their fusion and applications in snow hydrology and water resource quantification are also invited. Papers may cover but are not restricted to the following themes:

Snow cover fraction assessment from image processing and AI algorithms;
Long-term snow cover mapping and spatiotemporal change;
Fusion techniques for snow cover mapping;
Snow water equivalent and modelling at the local and basin scales;
Deep learning and machine learning for snow parameter retrieval;
Remotely sensed snow data and their applications;
Runoff modelling and snow cover information;
Cloud computing and snow hydrology;
Operational use of snow cover on water management;
Experimental observatory and systems for snow monitoring.

Prof. Dr. Lahoucine Hanich
Prof. Dr. Abdelghani Boudhar
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

snow cover
remote sensing
snowpack
snow water equivalent
snowmelt

Published Papers (3 papers)

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Research

28 pages, 8572 KiB

Open AccessArticle

High-Resolution Monitoring of the Snow Cover on the Moroccan Atlas through the Spatio-Temporal Fusion of Landsat and Sentinel-2 Images

by Mostafa Bousbaa, Abdelaziz Htitiou, Abdelghani Boudhar, Youssra Eljabiri, Haytam Elyoussfi, Hafsa Bouamri, Hamza Ouatiki and Abdelghani Chehbouni

Remote Sens. 2022, 14(22), 5814; https://doi.org/10.3390/rs14225814 - 17 Nov 2022

Cited by 9 | Viewed by 2949

Abstract

Mapping seasonal snow cover dynamics provides essential information to predict snowmelt during spring and early summer. Such information is vital for water supply management and regulation by national stakeholders. Recent advances in remote sensing have made it possible to reliably estimate and quantify the spatial and temporal variability of snow cover at different scales. However, because of technological constraints, there is a compromise between the temporal, spectral, and spatial resolutions of available satellites. In addition, atmospheric conditions and cloud contamination may increase the number of missing satellite observations. Therefore, data from a single satellite is insufficient to accurately capture snow dynamics, especially in semi-arid areas where snowfall is extremely variable in both time and space. Considering these limitations, the combined use of the next generation of multispectral sensor data from the Landsat-8 (L8) and Sentinel-2 (S2), with a spatial resolution ranging from 10 to 30 m, provides unprecedented opportunities to enhance snow cover mapping. Hence, the purpose of this study is to examine the effectiveness of the combined use of optical sensors through image fusion techniques for capturing snow dynamics and producing detailed and dense normalized difference snow index (NDSI) time series within a semi-arid context. Three different models include the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM), the flexible spatio-temporal data fusion model (FSDAF), and the pre-classification flexible spatio-temporal data fusion model (pre-classification FSDAF) were tested and compared to merge L8 and S2 data. The results showed that the pre-classification FSDAF model generates the most accurate precise fused NDSI images and retains spatial detail compared to the other models, with the root mean square error (RMSE = 0.12) and the correlation coefficient (R = 0.96). Our results reveal that, the pre-classification FSDAF model provides a high-resolution merged snow time series and can compensate the lack of ground-based snow cover data. Full article

(This article belongs to the Special Issue Advances in Remote Sensing of Snow Cover)

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19 pages, 4500 KiB

Open AccessArticle

Estimation of Snow Depth from AMSR2 and MODIS Data based on Deep Residual Learning Network

by De Xing, Jinliang Hou, Chunlin Huang and Weimin Zhang

Remote Sens. 2022, 14(20), 5089; https://doi.org/10.3390/rs14205089 - 12 Oct 2022

Cited by 1 | Viewed by 1642

Abstract

Advanced Microwave Scanning Radiometer 2 (AMSR2) brightness temperature (TB) observations have long been utilized for snow depth (SD) estimation. However, the traditional approaches which are based on ‘point-to-point’ predictions ignore the spatial heterogeneity within a AMSR2 pixel and are limited by the coarse spatial resolution of the AMSR2 sensor. To solve these problems, a novel deep ‘area-to-point’ SD estimation model, based on a deep residual learning network by combining convolutional neural networks (CNN) and residual blocks, was proposed. The model utilizes all channels of AMSR2 TB data along with Moderate-resolution Imaging Spectroradiometer (MODIS) normalized difference snow index (NDSI) data and auxiliary geographic information. Taking the Qinghai-Tibet Plateau (QTP) as the study area, the SD with a spatial resolution of 0.005° over the 2019–2020 snow season is estimated, and the accuracy is validated by in situ SD observations from 116 stations. The results show that: (1) the proposed SD estimation model shows desirable accuracy as the root mean square error (RMSE), mean absolute error (MAE), mean bias error (MBE), and coefficient of determination (R²) of the proposed SD estimation method are 2.000 cm, 0.656 cm, −0.013 cm, and 0.847, respectively. (2) The SD estimation error is slightly larger in medium elevation or medium slope or grassland areas, and the RMSE is 2.247 cm, 3.084 cm, and 2.213 cm, respectively. (3) The proposed SD estimation method has the most satisfactory performance in low-elevation regions, and the RMSE is only 0.523 cm. The results indicate that through considering the spatial heterogeneity of snow cover and utilizing the high spatial resolution snow information presented by the MODIS snow cover product, the proposed model has good SD estimation accuracy, which is promising for application in other study regions. Full article

(This article belongs to the Special Issue Advances in Remote Sensing of Snow Cover)

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27 pages, 6836 KiB

Open AccessArticle

Improving Mountain Snow and Land Cover Mapping Using Very-High-Resolution (VHR) Optical Satellite Images and Random Forest Machine Learning Models

by J. Michelle Hu and David Shean

Remote Sens. 2022, 14(17), 4227; https://doi.org/10.3390/rs14174227 - 27 Aug 2022

Cited by 6 | Viewed by 2705

Abstract

Very-high-resolution (VHR) optical imaging satellites can offer precise, accurate, and direct measurements of snow-covered areas (SCA) with sub-meter to meter-scale resolution in regions of complex land cover and terrain. We explore the potential of Maxar WorldView-2 and WorldView-3 in-track stereo images (WV) for land and snow cover mapping at two sites in the Western U.S. with different snow regimes, topographies, vegetation, and underlying geology. We trained random forest models using combinations of multispectral bands and normalized difference indices (i.e., NDVI) to produce land cover maps for priority feature classes (snow, shaded snow, vegetation, water, and exposed ground). We then created snow-covered area products from these maps and compared them with coarser resolution satellite fractional snow-covered area (fSCA) products from Landsat (~30 m) and MODIS (~500 m). Our models generated accurate classifications, even with limited combinations of available multispectral bands. Models trained on a single image demonstrated limited model transfer, with best results found for in-region transfers. Coarser-resolution Landsat and MODSCAG fSCA products identified many more pixels as completely snow-covered (100% fSCA) than WV fSCA. However, while MODSCAG fSCA products also identified many more completely snow-free pixels (0% fSCA) than WV fSCA, Landsat fSCA products only slightly underestimated the number of completely snow-free pixels. Overall, our results demonstrate that strategic image observations with VHR satellites such as WorldView-2 and WorldView-3 can complement the existing operational snow data products to map the evolution of seasonal snow cover. Full article

(This article belongs to the Special Issue Advances in Remote Sensing of Snow Cover)

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