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Remote Sensing of Water Cycle Science in the Cryosphere
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Remote Sensing of Water Cycle Science in the Cryosphere

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Biogeosciences Remote Sensing".

Deadline for manuscript submissions: closed (31 July 2021) | Viewed by 12196

Special Issue Editors

Dr. Kazuyoshi Suzuki

E-Mail Website
Guest Editor
Institute of Arctic Climate and Environment Research (IACE), Research Institute for Global Change (RIGC), Japan Agency for Marine-Earth Science and Technology (JAMSTEC), Yokohama 236-0001, Japan
Interests: hydro-climate; cryosphere; hydrometeorology; ecohydrology; snow and ice; data assimilation
Prof. Dr. Steven R. Fassnacht

E-Mail Website
Guest Editor
Watershed Science Program in the Department of Ecosystem Science and Sustainability, Cooperative Institute for Research in the Atmosphere, Colorado State University, 1231 Libby Coy Way, Fort Collins, CO 80523-1476, USA
Interests: snow hydrology; spatio-temporal variability; hydrological modeling
Special Issues, Collections and Topics in MDPI journals
Dr. Juan Ignacio López Moreno

E-Mail Website
Guest Editor
Instituto Pirenaico de Ecología, Campus de Aula Dei, Avda. Montañana, 50059 Zaragoza, Spain
Interests: snow hydrology; climatic change; water resources management; recent evolution of the Pyrenean Glaciers
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Because of the effects of climate change due to global warming in recent years, the remote sensing of global change has become more important in snow and ice science, such as the melting of glacier ice sheets and permafrost, the decrease of sea ice, and the change in soil water content. Regions of the cryosphere, including the poles, that are currently unmonitored are expanding, therefore increasing the importance of satellite observations for such regions.

With the increasing availability of satellite data in recent years, this Special Issue focuses on observations using remote sensing techniques, such as microwaves and optical sensors by satellites and aircrafts, unmanned aerial vehicle (UAV) and other remote sensing methods, focusing on observations of snow, ice and water circulation fluctuations from the region to the global scale, field observations, numerical experiments, data assimilation, and data. We welcome papers that use openness widely and exchange information about the latest research results and future plans. Eventually, this issue will be set up to deepen the cross-sectional connection between research methods related to snow, ice and water cycle fluctuations, and the possibility of proposing new research products using satellite data will be discussed.

Example Topics
Combining multi-sensor data to evaluate cryospheric seasonality
Comparing snowpack and sea ice-derived remote sensing products
Assimilating new remote sensing products into modelling
Cross-resolution data fusion for cryospheric properties

Dr. Kazuyoshi Suzuki
Prof. Steven R. Fassnacht
Dr. Juan Ignacio López Moreno
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 hydrology
  • Permafrost
  • Lake ice
  • Sea ice
  • Glaciers and ice sheet
  • Climate change
  • Global warming

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Published Papers (3 papers)

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Research

21 pages, 3587 KiB  
Article
Spatial Downscaling of MODIS Snow Cover Observations Using Sentinel-2 Snow Products
by Jesús Revuelto, Esteban Alonso-González, Simon Gascoin, Guillermo Rodríguez-López and Juan Ignacio López-Moreno
Remote Sens. 2021, 13(22), 4513; https://doi.org/10.3390/rs13224513 - 10 Nov 2021
Cited by 14 | Viewed by 3749
Abstract
Understanding those processes in which snow dynamics has a significant influence requires long-term and high spatio-temporal resolution observations. While new optical space-borne sensors overcome many previous snow cover monitoring limitations, their short temporal length limits their application in climatological studies. This work describes [...] Read more.
Understanding those processes in which snow dynamics has a significant influence requires long-term and high spatio-temporal resolution observations. While new optical space-borne sensors overcome many previous snow cover monitoring limitations, their short temporal length limits their application in climatological studies. This work describes and evaluates a probabilistic spatial downscaling of MODIS snow cover observations in mountain areas. The approach takes advantage of the already available high spatial resolution Sentinel-2 snow observations to obtain a snow probability occurrence, which is then used to determine the snow-covered areas inside partially snow-covered MODIS pixels. The methodology is supported by one main hypothesis: the snow distribution is strongly controlled by the topographic characteristics and this control has a high interannual persistence. Two approaches are proposed to increase the 500 m resolution MODIS snow cover observations to the 20 m grid resolution of Sentinel-2. The first of these computes the probability inside partially snow-covered MODIS pixels by determining the snow occurrence frequency for the 20 m Sentinel-2 pixels when clear-sky conditions occurred for both platforms. The second approach determines the snow probability occurrence for each Sentinel-2 pixel by computing the number of days in which snow was observed on each grid cell and then dividing it by the total number of clear-sky days per grid cell. The methodology was evaluated in three mountain areas in the Iberian Peninsula from 2015 to 2021. The 20 m resolution snow cover maps derived from the two probabilistic methods provide better results than those obtained with MODIS images downscaled to 20 m with a nearest-neighbor method in the three test sites, but the first provides superior performance. The evaluation showed that mean kappa values were at least 10% better for the two probabilistic methods, improving the scores in one of these sites by 25%. In addition, as the Sentinel-2 dataset becomes longer in time, the probabilistic approaches will become more robust, especially in areas where frequent cloud cover resulted in lower accuracy estimates. Full article
(This article belongs to the Special Issue Remote Sensing of Water Cycle Science in the Cryosphere)
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26 pages, 7733 KiB  
Article
Effect of Permafrost Thawing on Discharge of the Kolyma River, Northeastern Siberia
by Kazuyoshi Suzuki, Hotaek Park, Olga Makarieva, Hironari Kanamori, Masahiro Hori, Koji Matsuo, Shinji Matsumura, Nataliia Nesterova and Tetsuya Hiyama
Remote Sens. 2021, 13(21), 4389; https://doi.org/10.3390/rs13214389 - 31 Oct 2021
Cited by 13 | Viewed by 3795
Abstract
With permafrost warming, the observed discharge of the Kolyma River in northeastern Siberia decreased between 1930s and 2000; however, the underlying mechanism is not well understood. To understand the hydrological changes in the Kolyma River, it is important to analyze the long-term hydrometeorological [...] Read more.
With permafrost warming, the observed discharge of the Kolyma River in northeastern Siberia decreased between 1930s and 2000; however, the underlying mechanism is not well understood. To understand the hydrological changes in the Kolyma River, it is important to analyze the long-term hydrometeorological features, along with the changes in the active layer thickness. A coupled hydrological and biogeochemical model was used to analyze the hydrological changes due to permafrost warming during 1979–2012, and the simulated results were validated with satellite-based products and in situ observational records. The increase in the active layer thickness by permafrost warming suppressed the summer discharge contrary to the increased summer precipitation. This suggests that the increased terrestrial water storage anomaly (TWSA) contributed to increased evapotranspiration, which likely reduced soil water stress to plants. As soil freeze–thaw processes in permafrost areas serve as factors of climate memory, we identified a two-year lag between precipitation and evapotranspiration via TWSA. The present results will expand our understanding of future Arctic changes and can be applied to Arctic adaptation measures. Full article
(This article belongs to the Special Issue Remote Sensing of Water Cycle Science in the Cryosphere)
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19 pages, 14166 KiB  
Article
Dynamic Mapping of Subarctic Surface Water by Fusion of Microwave and Optical Satellite Data Using Conditional Adversarial Networks
by Hiroki Mizuochi, Yoshihiro Iijima, Hirohiko Nagano, Ayumi Kotani and Tetsuya Hiyama
Remote Sens. 2021, 13(2), 175; https://doi.org/10.3390/rs13020175 - 6 Jan 2021
Cited by 7 | Viewed by 3540
Abstract
Surface water monitoring with fine spatiotemporal resolution in the subarctic is important for understanding the impact of climate change upon hydrological cycles in the region. This study provides dynamic water mapping with daily frequency and a moderate (500 m) resolution over a heterogeneous [...] Read more.
Surface water monitoring with fine spatiotemporal resolution in the subarctic is important for understanding the impact of climate change upon hydrological cycles in the region. This study provides dynamic water mapping with daily frequency and a moderate (500 m) resolution over a heterogeneous thermokarst landscape in eastern Siberia. A combination of random forest and conditional generative adversarial networks (pix2pix) machine learning (ML) methods were applied to data fusion between the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Advanced Microwave Scanning Radiometer 2, with the addition of ancillary hydrometeorological information. The results show that our algorithm successfully filled in observational gaps in the MODIS data caused by cloud interference, thereby improving MODIS data availability from 30.3% to almost 100%. The water fraction estimated by our algorithm was consistent with that derived from the reference MODIS data (relative mean bias: −2.43%; relative root mean squared error: 14.7%), and effectively rendered the seasonality and heterogeneous distribution of the Lena River and the thermokarst lakes. Practical knowledge of the application of ML to surface water monitoring also resulted from the preliminary experiments involving the random forest method, including timing of the water-index thresholding and selection of the input features for ML training. Full article
(This article belongs to the Special Issue Remote Sensing of Water Cycle Science in the Cryosphere)
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