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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 2018

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
Prof. 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 1800 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 (6 papers)

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Research

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Open AccessArticle Temporal and Spatial Characteristics of EVI and Its Response to Climatic Factors in Recent 16 years Based on Grey Relational Analysis in Inner Mongolia Autonomous Region, China
Remote Sens. 2018, 10(6), 961; https://doi.org/10.3390/rs10060961
Received: 16 May 2018 / Revised: 7 June 2018 / Accepted: 13 June 2018 / Published: 15 June 2018
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Abstract
The Inner Mongolia Autonomous Region (IMAR) is a major source of rivers, catchment areas, and ecological barriers in the northeast of China, related to the nation’s ecological security and improvement of the ecological environment. Therefore, studying the response of vegetation to climate change
[...] Read more.
The Inner Mongolia Autonomous Region (IMAR) is a major source of rivers, catchment areas, and ecological barriers in the northeast of China, related to the nation’s ecological security and improvement of the ecological environment. Therefore, studying the response of vegetation to climate change has become an important part of current global change research. Since existing studies lack detailed descriptions of the response of vegetation to different climatic factors using the method of grey correlation analysis based on pixel, the temporal and spatial patterns and trends of enhanced vegetation index (EVI) are analyzed in the growing season in IMAR from 2000 to 2015 based on moderate resolution imaging spectroradiometer (MODIS) EVI data. Combined with the data of air temperature, relative humidity, and precipitation in the study area, the grey relational analysis (GRA) method is used to study the time lag of EVI to climate change, and the study area is finally zoned into different parts according to the driving climatic factors for EVI on the basis of lag analysis. The driving zones quantitatively show the characteristics of temporal and spatial differences in response to different climatic factors for EVI. The results show that: (1) The value of EVI generally features in spatial distribution, increasing from the west to the east and the south to the north. The rate of change is 0.22/10°E from the west to the east, 0.28/10°N from the south to the north; (2) During 2000–2015, the EVI in IMAR showed a slightly upward trend with a growth rate of 0.021/10a. Among them, the areas with slight and significant improvement accounted for 21.1% and 7.5% of the total area respectively, ones with slight and significant degradation being 24.6% and 4.3%; (3) The time lag analysis of climatic factors for EVI indicates that vegetation growth in the study area lags behind air temperature by 1–2 months, relative humidity by 1–2 months, and precipitation by one month respectively; (4) During the growing season, the EVI of precipitation driving zone (21.8%) in IMAR is much larger than that in the air temperature driving zone (8%) and the relative humidity driving zone (11.6%). The growth of vegetation in IMAR generally has the closest relationship with precipitation. The growth of vegetation does not depend on the change of a single climatic factor. Instead, it is the result of the combined action of multiple climatic factors and human activities. Full article
(This article belongs to the Special Issue Earth Observations for Addressing Global Challenges)
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Open AccessArticle Comparison of the Spatial Characteristics of Four Remotely Sensed Leaf Area Index Products over China: Direct Validation and Relative Uncertainties
Remote Sens. 2018, 10(1), 148; https://doi.org/10.3390/rs10010148
Received: 21 November 2017 / Revised: 18 December 2017 / Accepted: 16 January 2018 / Published: 22 January 2018
Cited by 1 | PDF Full-text (11882 KB) | HTML Full-text | XML Full-text
Abstract
Leaf area index (LAI) is a key input for many land surface models, ecological models, and yield prediction models. In order to make the model simulation and/or prediction more reliable and applicable, it is crucial to know the characteristics and uncertainties of remotely
[...] Read more.
Leaf area index (LAI) is a key input for many land surface models, ecological models, and yield prediction models. In order to make the model simulation and/or prediction more reliable and applicable, it is crucial to know the characteristics and uncertainties of remotely sensed LAI products before they are input into models. In this study, we conducted a comparison of four global remotely sensed LAI products—Global Land Surface Satellite (GLASS), Global LAI Product of Beijing Normal University (GLOBALBNU), Global LAI Map of Chinese Academy of Sciences (GLOBMAP), and Moderate-resolution Imaging Spectrometer (MODIS) LAI, while the former three products are newly developed by three Chinese research groups on the basis of the MODIS land reflectance product over China between 2001 and 2011. Direct validation by comparing the four products to ground LAI observations both globally and over China demonstrates that GLASS LAI shows the best performance, with R2 = 0.70 and RMSE = 0.96 globally and R2 = 0.94 and RMSE = 0.61 over China; MODIS performs worst (R2 = 0.55, RMSE = 1.23 globally and R2 = 0.03, RMSE = 2.12 over China), and GLOBALBNU and GLOBMAP performs moderately. Comparison of the four products shows that they are generally consistent with each other, giving the smallest spatial correlation coefficient of 0.7 and the relative standard deviation around the order of 0.3. Compared with MODIS LAI, GLOBALBNU LAI is the most similar, followed by GLASS LAI and GLOBMAP. Large differences mainly occur in southern regions of China. LAI difference analysis indicates that evergreen needleleaf forest (ENF), woody savannas (SAV) biome types and temperate dry hot summer, temperate warm summer dry winter and temperate hot summer no dry season climate types correspond to high standard deviation, while ENF and grassland (GRA) biome types and temperate warm summer dry winter and cold dry winter warm summer climate types are responsible for the large relative standard deviation of the four products. Our results indicate that although the three newly developed products have improved the accuracy of LAI estimates, much work remains to improve the LAI products especially in ENF, SAV, and GRA regions and temperate climate zones. Findings from our study can provide guidance to communities regarding the performance of different LAI products over mainland China. Full article
(This article belongs to the Special Issue Earth Observations for Addressing Global Challenges)
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Open AccessArticle Self-Learning Based Land-Cover Classification Using Sequential Class Patterns from Past Land-Cover Maps
Remote Sens. 2017, 9(9), 921; https://doi.org/10.3390/rs9090921
Received: 11 July 2017 / Revised: 25 August 2017 / Accepted: 1 September 2017 / Published: 2 September 2017
Cited by 1 | PDF Full-text (1827 KB) | HTML Full-text | XML Full-text
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; https://doi.org/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; https://doi.org/10.3390/rs9090883
Received: 26 June 2017 / Revised: 10 August 2017 / Accepted: 22 August 2017 / Published: 25 August 2017
Cited by 3 | 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
[...] Read more.
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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Review

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Open AccessReview Gaps Analysis and Requirements Specification for the Evolution of Copernicus System for Polar Regions Monitoring: Addressing the Challenges in the Horizon 2020–2030
Remote Sens. 2018, 10(7), 1098; https://doi.org/10.3390/rs10071098
Received: 7 June 2018 / Revised: 4 July 2018 / Accepted: 6 July 2018 / Published: 10 July 2018
PDF Full-text (295 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
This work was developed as part of the European H2020 ONION (Operational Network of Individual Observation Nodes) project, aiming at identifying the technological opportunity areas to complement the Copernicus space infrastructure in the horizon 2020–2030 for polar region monitoring. The European Earth Observation
[...] Read more.
This work was developed as part of the European H2020 ONION (Operational Network of Individual Observation Nodes) project, aiming at identifying the technological opportunity areas to complement the Copernicus space infrastructure in the horizon 2020–2030 for polar region monitoring. The European Earth Observation (EO) infrastructure is assessed through of comprehensive end-user need and data gap analysis. This review was based on the top 10 use cases, identifying 20 measurements with gaps and 13 potential EO technologies to cover the identified gaps. It was found that the top priority is the observation of polar regions to support sustainable and safe commercial activities and the preservation of the environment. Additionally, an analysis of the technological limitations based on measurement requirements was performed. Finally, this analysis was used for the basis of the architecture design of a potential polar mission. Full article
(This article belongs to the Special Issue Earth Observations for Addressing Global Challenges)
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