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Special Issue "Earth Observations for Addressing Global Challenges"

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: 31 December 2017

Special Issue Editors

Guest Editor
Prof. & Academician Yuei-An Liou

Hydrology Remote Sensing Laboratory, Center for Space and Remote Sensing Research, National Central University, Tao-Yuan, Taiwan
Website | E-Mail
Interests: microwave and optical remote sensing; atmospheric science; GPS meteorology and methodology; cryosphere
Guest Editor
Dr. Jean-Pierre Barriot

Full Professor of Geophysics and Head of the Geodesy Observatory of Tahiti, University of French Polynesia, Punaauia, French Polynesia
Website | E-Mail
Interests: areas of research in geophysics and astronomy: gravimetry, radiosciences, atmosphere, hydrology
Guest Editor
Dr. Chung-Ru Ho

Department of Marine Environmental Informatics, National Taiwan Ocean University, Keelung, Taiwan
Website | E-Mail
Interests: remote sensing; physical oceanography; global change; satellite oceanography
Guest Editor
Dr. Yuriy Kuleshov

Professor and Academician, Australian Bureau of Meteorology, 700 Collins Street, Docklands 3008, Melbourne, Victoria, Australia
Website | E-Mail
Interests: climatology of severe weather phenomena (tropical cyclones, thunderstorms and lightning); climate prediction; satellite remote sensing for climate monitoring
Guest Editor
Dr. Chyi-Tyi Lee

Supervisor, Taiwan Group on Earth Observations; Institute of Applied Geology, National Central University, Taoyuan, 32001, Taiwan
Website | E-Mail
Phone: +886-3-4253334
Interests: engineering geology; earthquake geology; geostatistics; GIS

Special Issue Information

Dear Colleagues,

As climate changes have been of great concern worldwide for years, addressing these global climate challenges is the most significant task for humanity; thus, Group on Earth Observations (GEO) has launched initiatives across multiple societal benefit areas (agriculture, biodiversity, climate, disasters, ecosystems, energy, health, water, and weather), such as the Global Forest Observations Initiative, the GEO Carbon and GHG Initiative, the GEO Biodiversity Observation Network, the GEO Blue Planet, and so on. Related topics have been addressed and deliberated throughout the world. This Special Issue aims to bring up discussion concerning innovative techniques/approaches based on remote sensing data, and to nurture the knowledge on the acquisition of Earth observations and its applications in the contemporary practice of sustainable development. Research scientists and other subject matter experts are encouraged to submit challenging papers that describe advances in the related topics:

  • Disasters
  • Health
  • Energy
  • Climate
  • Water
  • Weather
  • Ecosystems
  • Agriculture/Forestry/Fishery
  • Biodiversity
  • Industry and Policy

Dr. Yuei-An Liou
Dr. Chyi-Tyi Lee
Dr. Yuriy Kuleshov
Dr. Jean-Pierre Barriot
Dr. Chung-Ru Ho
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 papers will be 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 monthly 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 1600 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.

Published Papers (3 papers)

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Research

Open AccessArticle Self-Learning Based Land-Cover Classification Using Sequential Class Patterns from Past Land-Cover Maps
Remote Sens. 2017, 9(9), 921; doi:10.3390/rs9090921
Received: 11 July 2017 / Revised: 25 August 2017 / Accepted: 1 September 2017 / Published: 2 September 2017
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Abstract
To improve the accuracy of classification with a small amount of training data, this paper presents a self-learning approach that defines class labels from sequential patterns using a series of past land-cover maps. By stacking past land-cover maps, unique sequence rule information from
[...] Read more.
To improve the accuracy of classification with a small amount of training data, this paper presents a self-learning approach that defines class labels from sequential patterns using a series of past land-cover maps. By stacking past land-cover maps, unique sequence rule information from sequential change patterns of land-covers is first generated, and a rule-based class label image is then prepared for a given time. After the most informative pixels with high uncertainty are selected from the initial classification, rule-based class labels are assigned to the selected pixels. These newly labeled pixels are added to training data, which then undergo an iterative classification process until a stopping criterion is reached. Time-series MODIS NDVI data sets and cropland data layers (CDLs) from the past five years are used for the classification of various crop types in Kansas. From the experiment results, it is found that once the rule-based labels are derived from past CDLs, the labeled informative pixels could be properly defined without analyst intervention. Regardless of different combinations of past CDLs, adding these labeled informative pixels to training data increased classification accuracy and the maximum improvement of 8.34 percentage points in overall accuracy was achieved when using three CDLs, compared to the initial classification result using a small amount of training data. Using more than three consecutive CDLs showed slightly better classification accuracy than when using two CDLs (minimum and maximum increases were 1.56 and 2.82 percentage points, respectively). From a practical viewpoint, using three or four CDLs was the best choice for this study area. Based on these experiment results, the presented approach could be applied effectively to areas with insufficient training data but access to past land-cover maps. However, further consideration should be given to select the optimal number of past land-cover maps and reduce the impact of errors of rule-based labels. Full article
(This article belongs to the Special Issue Earth Observations for Addressing Global Challenges)
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Open AccessArticle Diurnal Cycle in Atmospheric Water over Switzerland
Remote Sens. 2017, 9(9), 909; doi:10.3390/rs9090909
Received: 21 June 2017 / Revised: 23 August 2017 / Accepted: 30 August 2017 / Published: 31 August 2017
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Abstract
The TROpospheric WAter RAdiometer (TROWARA) is a ground-based microwave radiometer with an additional infrared channel observing atmospheric water parameters in Bern, Switzerland. TROWARA measures with nearly all-weather capability during day- and nighttime with a high temporal resolution (about 10 s). Using the almost
[...] Read more.
The TROpospheric WAter RAdiometer (TROWARA) is a ground-based microwave radiometer with an additional infrared channel observing atmospheric water parameters in Bern, Switzerland. TROWARA measures with nearly all-weather capability during day- and nighttime with a high temporal resolution (about 10 s). Using the almost complete data set from 2004 to 2016, we derive and discuss the diurnal cycles in cloud fraction (CF), integrated liquid water (ILW) and integrated water vapour (IWV) for different seasons and the annual mean. The amplitude of the mean diurnal cycle in IWV is 0.41 kg/m 2 . The sub-daily minimum of IWV is at 10:00 LT while the maximum of IWV occurs at 19:00 LT. The relative amplitudes of the diurnal cycle in ILW are up to 25% in October, November and January, which is possibly related to a breaking up of the cloud layer at 10:00 LT. The minimum of ILW occurs at 12:00 LT, which is due to cloud solar absorption. In case of cloud fraction of liquid water clouds, maximal values of +10% are reached at 07:00 LT and then a decrease starts towards the minimum of −10%, which is reached at 16:00 LT in autumn. This breakup of cloud layers in the late morning and early afternoon hours seems to be typical for the weather in Bern in autumn. Finally, the diurnal cycle in rain fraction is analysed, which shows an increase of a few percent in the late afternoon hours during summer. Full article
(This article belongs to the Special Issue Earth Observations for Addressing Global Challenges)
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Open AccessArticle Quantifying Snow Albedo Radiative Forcing and Its Feedback during 2003–2016
Remote Sens. 2017, 9(9), 883; doi:10.3390/rs9090883
Received: 26 June 2017 / Revised: 10 August 2017 / Accepted: 22 August 2017 / Published: 25 August 2017
PDF Full-text (1858 KB) | HTML Full-text | XML Full-text
Abstract
Snow albedo feedback is one of the most crucial feedback processes that control equilibrium climate sensitivity, which is a central parameter for better prediction of future climate change. However, persistent large discrepancies and uncertainties are found in snow albedo feedback estimations. Remotely sensed
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Snow albedo feedback is one of the most crucial feedback processes that control equilibrium climate sensitivity, which is a central parameter for better prediction of future climate change. However, persistent large discrepancies and uncertainties are found in snow albedo feedback estimations. Remotely sensed snow cover products, atmospheric reanalysis data and radiative kernel data are used in this study to quantify snow albedo radiative forcing and its feedback on both hemispheric and global scales during 2003–2016. The strongest snow albedo radiative forcing is located north of 30°N, apart from Antarctica. In general, it has large monthly variation and peaks in spring. Snow albedo feedback is estimated to be 0.18 ± 0.08 W∙m−2∙°C−1 and 0.04 ± 0.02 W∙m−2∙°C−1 on hemispheric and global scales, respectively. Compared to previous studies, this paper focuses specifically on quantifying snow albedo feedback and demonstrates three improvements: (1) used high spatial and temporal resolution satellite-based snow cover data to determine the areas of snow albedo radiative forcing and feedback; (2) provided detailed information for model parameterization by using the results from (1), together with accurate description of snow cover change and constrained snow albedo and snow-free albedo data; and (3) effectively reduced the uncertainty of snow albedo feedback and increased its confidence level through the block bootstrap test. Our results of snow albedo feedback agreed well with other partially observation-based studies and indicate that the 25 Coupled Model Intercomparison Project Phase 5 (CMIP5) models might have overestimated the snow albedo feedback, largely due to the overestimation of surface albedo change between snow-covered and snow-free surface in these models. Full article
(This article belongs to the Special Issue Earth Observations for Addressing Global Challenges)
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