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Remote Sensing for Climate Extremes and Water Resources

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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 2022) | Viewed by 13971

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Special Issue Editors

Dr. Yun Chen

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Guest Editor

CSIRO Land and Water, Canberra, ACT 2601, Australia
Interests: remote sensing; GIS; risk/disaster evaluation; vulnerability/suitability assessment; multicriteria decision making; big data and geospatial modelling
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Shahbaz Khan

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Guest Editor

Director of UNESCO Beijing and Rep to China, DPRK, Japan, Mongolia and Republic of Korea
Interests: arid ecosystems; water resources; environmental issues; climate change; soil and water management
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Dr. Tingbao Xu

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Guest Editor

Fenner School of Environment & Society, Australian National University, Canberra, ACT 2601, Australia
Interests: evolution of regional and global spatial distribution patterns of precipitation under a changing climate and its impact on water resources; climate change impact on agriculture and environment; bioclimatic assessment and mapping of regional and global biodiversity under a changing climate

Dr. Chang Huang

E-Mail Website
Guest Editor

Key Laboratory of Earth Surface System and Environmental Carrying Capacity, Northwest University, Xi’an 710127, China
Interests: remote sensing of surface water; river discharge; flood inundation; image fusion; Google Earth Engine
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Prof. Dr. Lin Zhu

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Guest Editor

College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China
Interests: application of GIS and remote sensing in land subsidence; groundwater; vegetation; hydrogeology; related ecosystems

Dr. Linyi Li

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Guest Editor

School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
Interests: remote sensing; water elements monitoring; object recognition; feature extraction; intelligent algorithm
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Climate extremes such as floods and droughts impose significant negative impacts on water resources and environment sustainability. The ongoing climate change is likely to increase the frequency and amplify the severity of these extreme climates in the near future. This will place more threats and harsher pressures on worldwide water resources and sustainability. There is a growing necessity to strengthen our capability to investigate, assess, and map the current status and future trends of climate variability in a timely manner and therefore enrich regional capacity in managing water resources and mitigating climate change globally. Recent developments in readily available remotely sensed data with improved sensing characteristics (i.e., high spatial, spectral, and radiometric resolutions), accompanying advancements in geospatial techniques and cloud computer platforms (i.e., deep learning and Google Earth Engine), have offered an efficient and effective means to meet the challenge. In line with the United Nations Sustainable Development Goal (SDG) 6 that seeks to “ensure the availability and sustainable management of water and sanitation for all” and SDG 13 that aims to “take urgent action to combat climate change and its impacts”, this Special Issue aims to provide a scientific forum for publishing peer-reviewed articles that apply state-of-the-art remote sensing approaches, methods, and techniques in incorporating cutting-edge machine learning and geospatial technologies for monitoring, assessing, and predicting water resources under a changing climate at various spatial scales. Themes considered include but are not limited to mapping and evaluating climate extremes and corresponding freshwater (underground and surface) quality and quantity. Integrating big data from multiple spatial, spectral, and thematic scales to estimate and quantify spatiotemporal changes in these areas is among our priorities.

Specifically, topics of interest may cover, without being limited to, the following fields:

Characterization of climate extremes/variability;
Water quality evaluation and pollution detection;
Estimation of water resource scarcity and security;
Assessment of surface water and groundwater change dynamics;
Hazard, disaster, and risk mapping;
Remote sensing re-analyzed climate data for serial and trend analysis;
Evaluation/validation of data for the above methods/applications;
Development of machine learning algorithms for the above remote sensing studies .

Dr. Yun Chen
Prof. Dr. Shahbaz Khan
Dr. Tingbao Xu
Dr. Chang Huang
Prof. Dr. Lin Zhu
Dr. Linyi Li
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

climate change and variability
droughts and floods
SDG and sustainability
water availability
water resource management
spatiotemporal integration
artificial intelligence
machine Leaning
big data and cloud computing

Published Papers (5 papers)

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Research

19 pages, 4420 KiB

Open AccessArticle

Vegetation Dynamics and Their Influencing Factors in China from 1998 to 2019

by Jiahui Chang, Qihang Liu, Simeng Wang and Chang Huang

Remote Sens. 2022, 14(14), 3390; https://doi.org/10.3390/rs14143390 - 14 Jul 2022

Cited by 12 | Viewed by 2223

Abstract

Vegetation is a critical component of ecosystems that is influenced by climate change and human activities. It is therefore of great importance to investigate trends in vegetation dynamics and explore how these are influenced by climate and human activities. This will help formulate effective ecological restoration policies and ensure sustainable development. As the Normalized Difference Vegetation Index (NDVI) is strongly correlated with vegetation dynamics and may be used as a proxy measure for vegetation condition, the spatiotemporal characteristics of NDVI derived from SPOT/VEGETATION NDVI data in China over the 1998–2019 period were assessed using the Mann–Kendall test and the Hurst exponent. The Pearson correlation analysis and residual analysis methods were employed to analyze the influencing factors of NDVI dynamics. Integrating the results of the Hurst exponent and the NDVI trend analysis, it was found that the majority area of China is presenting an increasing NDVI trend at present but is likely to reverse in the future. A significant positive correlation between the NDVI and temperature was observed on the southeast coast of China and the north Qinghai–Tibet Plateau. Precipitation was the dominant factor affecting vegetation dynamics as indicated by a positive correlation with the NDVI for most parts of China except for the inland area in the Northwest and the Hengduan Mountains in Southwest China. Extreme temperature and extreme precipitation have also shown varying degrees of influence on vegetation dynamics at various locations. In addition, this study revealed trends of increasing NDVI, suggesting improved vegetation condition attributable to the implementation of ecological engineering projects. This study is helpful for studying the interaction mechanisms between terrestrial ecosystems and climate and for sustaining the ecological environment. Full article

(This article belongs to the Special Issue Remote Sensing for Climate Extremes and Water Resources)

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20 pages, 3929 KiB

Open AccessArticle

Development of a Multi-Index Method Based on Landsat Reflectance Data to Map Open Water in a Complex Environment

by Catherine Ticehurst, Jin Teng and Ashmita Sengupta

Remote Sens. 2022, 14(5), 1158; https://doi.org/10.3390/rs14051158 - 26 Feb 2022

Cited by 9 | Viewed by 2734

Abstract

Mapping surface water extent is important for managing water supply for agriculture and the environment. Remote sensing technologies, such as Landsat, provide an affordable means of capturing surface water extent with reasonable spatial and temporal coverage suited to this purpose. Many methods are available for mapping surface water including the modified Normalised Difference Water Index (mNDWI), Fisher’s water index (FWI), Water Observations from Space (WOfS), and the Tasseled Cap Wetness index (TCW). While these methods can discriminate water, they have their strengths and weaknesses, and perform at their best in different environments, and with different threshold values. This study combines the strengths of these indices by developing rules that applies an index to the environment where they perform best. It compares these indices across the Murray-Darling Basin (MDB) in southeast Australia, to assess performance and compile a heuristic rule set for accurate application across the MDB. The results found that all single indices perform well with the Kappa statistic showing strong agreement, ranging from 0.78 for WOfS to 0.84 for TCW (with threshold −0.035), with improvement in the overall output when the index best suited for an environment was selected. mNDWI (using a threshold of −0.3) works well within river channels, while TCW (with threshold −0.035) is best for wetlands and flooded vegetation. FWI and mNDWI (with threshold 0.63 and 0, respectively) work well for remaining areas. Selecting the appropriate index for an environment increases the overall Kappa statistic to 0.88 with a water pixel accuracy of 90.5% and a dry pixel accuracy of 94.8%. An independent assessment illustrates the benefit of using the multi-index approach, making it suitable for regional-scale multi-temporal analysis. Full article

(This article belongs to the Special Issue Remote Sensing for Climate Extremes and Water Resources)

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21 pages, 8824 KiB

Open AccessArticle

Attributing the Evapotranspiration Trend in the Upper and Middle Reaches of Yellow River Basin Using Global Evapotranspiration Products

by Zhihui Wang, Zepeng Cui, Tian He, Qiuhong Tang, Peiqing Xiao, Pan Zhang and Lingling Wang

Remote Sens. 2022, 14(1), 175; https://doi.org/10.3390/rs14010175 - 31 Dec 2021

Cited by 10 | Viewed by 2454

Abstract

Climate variation and underlying surface dynamics have caused a significant change in the trend of evapotranspiration (ET) in the Yellow River Basin (YRB) over the last two decades. Combined with the measured rainfall, runoff and gravity recovery and climate experiment (GRACE) product, five global ET products were firstly merged using a linear weighting method. Linear slope, “two-step” multiple regression, partial differential, and residual methods were then employed to explore the quantitative impacts of precipitation (PCPN), temperature (Temp), sunshine duration (SD), vapor pressure deficit (VPD), wind speed (WS), leaf area index (LAI), and the residual factors (e.g., microtopography changes, irrigation, etc.) on the ET trend in the YRB. The results show that: (1) The ET estimates were improved by merging five global ET products using the linear weighting method. The sensitivities of climatic factors and LAI on the ET trend can be separately calculated using proposed “two-step” statistical regression method; (2) the overall ET trend in the entire study area during 2000–2018 was 3.82 mm/yr, and the highest ET trend was observed in the Toudaoguai-Longmen subregion. ET trend was dominantly driven by vegetation greening, with an impact of 2.47 mm/yr and a relative impact rate of 51.16%. The results indicated that the relative impact rate of the residual factors (e.g., microtopography, irrigation, etc.) on the ET trend is up to 28.17%. The PCPN and VPD had increasing roles on the ET trend, with impacts of 0.45 mm/yr and 0.05 mm/yr, respectively, whereas the Temp, SD, and WS had decreasing impacts of –0.19 mm/yr, –0.15 mm/yr, and –0.17 mm/yr, respectively. (3) The spatial pattern of impact of specific influencing factor on the ET trend was determined by the spatial pattern of change trend slope of this factor and sensitivity of ET to this factor. ET trends of the source area and the Qingtongxia–Toudaoguai were dominated by the climatic factors, while the residual factors dominated the ET trend in the Tangnaihai–Qingtongxia area. The vegetation restoration was the dominant factor causing the increase in the ET in the middle reaches of the YRB, and the impact rates of the LAI were ranked as follows: Yanhe Rive > Wudinghe River > Fenhe River > Jinghe River > Beiluohe River > Qinhe River > Kuyehe River > Yiluohe River. Full article

(This article belongs to the Special Issue Remote Sensing for Climate Extremes and Water Resources)

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22 pages, 11660 KiB

Open AccessArticle

Modelling Dynamic Hydrological Connectivity in the Zoigê Area (China) Based on Multi-Temporal Surface Water Observation

by Chao Gao, Chang Huang, Jianbang Wang and Zhi Li

Remote Sens. 2022, 14(1), 145; https://doi.org/10.3390/rs14010145 - 29 Dec 2021

Cited by 6 | Viewed by 1700

Abstract

The sustainability of wetlands is threatened by the past and present land use practices. Hydrological connectivity is one of the most important aspects to consider for wetland rehabilitation planning purposes. Circuit theory and connectivity indices can be used to model and assess hydrological connectivity. The aim of this study was to assess spatiotemporal variation in the hydrological connectivity of the Zoigê area from 2000–2019 using both methods. The study area contains a Ramsar wetland of international importance, namely the Sichuan Ruoergai Wetland National Nature Reserve. We used a global surface water observation product as the major input for both methods, and then analyzed the temporal and spatial characteristics, in terms of important components and patches. We found that the overall connectivity has increased slightly in the last 20 years, while the probability of connection between patches of surface water has increased significantly. Important components and patches represent steppingstone habitat for the dispersal of organisms in the landscape. The main determinants of hydrological connectivity are mostly human oriented, predominantly a decrease in large livestock population size and population increase. Full article

(This article belongs to the Special Issue Remote Sensing for Climate Extremes and Water Resources)

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25 pages, 6051 KiB

Open AccessArticle

Impact of Climate Change on the Hydrological Regime of the Yarkant River Basin, China: An Assessment Using Three SSP Scenarios of CMIP6 GCMs

by Yanyun Xiang, Yi Wang, Yaning Chen and Qifei Zhang

Remote Sens. 2022, 14(1), 115; https://doi.org/10.3390/rs14010115 - 28 Dec 2021

Cited by 22 | Viewed by 3963

Abstract

Quantification of the impacts of climate change on streamflow and other hydrological parameters is of high importance and remains a challenge in arid areas. This study applied a modified distributed hydrological model (HEC-HMS) to the Yarkant River basin, China to assess hydrological changes under future climate change scenarios. Climate change was assessed based on six CMIP6 general circulation models (GCMs), three shared socio-economic pathways (SSP126, SSP245, SSP370), and several bias correction methods, whereas hydrological regime changes were assessed over two timeframes, referred to as the near future (2021–2049) and the far future (2071–2099). Results demonstrate that the DM (distribution mapping) and LOCI (local intensity scaling) bias correction methods most closely fit the projections of temperature and precipitation, respectively. The climate projections predicted a rise in temperature of 1.72–1.79 °C under the three SSP scenarios for the near future, and 3.76–6.22 °C under the three SSPs for the far future. Precipitation increased by 10.79–12% in the near future, and by 14.82–29.07% during the far future. It is very likely that streamflow will increase during both the near future (10.62–19.2%) and far future (36.69–70.4%) under all three scenarios. The increase in direct flow will be greater than baseflow. Summer and winter streamflow will increase the most, while the increase in streamflow was projected to reach a maximum during June and July over the near future. Over the far future, runoff reached a peak in May and June. The timing of peak streamflow will change from August to July in comparison to historical records. Both high- and low-flow magnitudes during March, April, and May (MAM) as well as June, July, and August (JJA) will increase by varying degrees, whereas the frequency of low flows will decrease during both MAM and JJA. High flow frequency in JJA was projected to decrease. Overall, our results reveal that the hydrological regime of the Yarkant River is likely to change and will be characterized by larger seasonal uncertainty and more frequent extreme events due to significant warming over the two periods. These changes should be seriously considered during policy development. Full article

(This article belongs to the Special Issue Remote Sensing for Climate Extremes and Water Resources)

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