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Remote Sensing for Climate Change

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

Deadline for manuscript submissions: closed (30 November 2022) | Viewed by 52300

Printed Edition Available!
A printed edition of this Special Issue is available here.

Special Issue Editor

Dr. Xander Wang

E-Mail Website
Guest Editor
School of Climate Change and Adaptation, University of Prince Edward Island, Charlottetown, PEI C1A 4P3, Canada
Interests: regional climate modeling; climate downscaling; hydrological modeling and flooding risk analysis; energy systems modeling under climate change; climate change impact assessment and adaptation studies; GIS; spatial modeling and analysis; big data analysis and visualization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Observations from weather stations have been widely used to study climate change over a long period of time. However, due to the scarcity of point-based weather observations, our understanding of the Earth’s changing climate is very limited. This impedes the advancement in our knowledge of the Earth’s climate system and our capability to develop well-suited climate models to simulate future climate change, which further results in considerable uncertainties associated with future climate projections. How to quantify and minimize these uncertainties is thus becoming one of the most challenging issues to be addressed for climate change impact assessment and adaptation studies. Remote sensing offers a new way of observing the Earth’s climate system with continuous and high-resolution spatial coverage through satellite-based, aircraft-based, or drone-based sensor technologies. This can significantly improve our understanding of climate change and its potential impacts at global, regional, and local scales. The data collected with remote sensing technologies can also be used to validate our climate models, improve our knowledge of the physical and dynamical processes of the climate system, and help us to project future climate change and its impacts with minimized uncertainties.

This Special Issue focuses on the latest research advances in remote sensing technologies and their applications for observing, understanding, modeling, visualizing, and communicating climate change and the potential impacts on agriculture, water, air quality, energy, land use/cover, flood, drought, wildfire, urban infrastructure, ecosystem, human health, glaciers, permafrost, ice sheet, sea level rise, etc. Submissions in the form of research articles, reviews, perspectives, and case studies are all welcome.

Dr. Xander Wang
Guest Editor

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 observation and monitoring
  • Climate modeling and validation
  • Climate downscaling and projection
  • Climate change impact assessment
  • Climate change mitigation and adaptation
  • Climate uncertainty
  • Visualization and communication of climate risks

Published Papers (17 papers)

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Editorial

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4 pages, 189 KiB  
Editorial
Remote Sensing Applications to Climate Change
by Xander Wang
Remote Sens. 2023, 15(3), 747; https://doi.org/10.3390/rs15030747 - 28 Jan 2023
Cited by 3 | Viewed by 4267
Abstract
Climate change research remains a challenging task, as it requires vast quantities of long-term data to investigate the past, present, and future scenarios of Earth’s climate system and other biophysical systems at global to local scales [...] Full article
(This article belongs to the Special Issue Remote Sensing for Climate Change)

Research

Jump to: Editorial

17 pages, 4001 KiB  
Article
Long-Term Tibetan Alpine Vegetation Responses to Elevation-Dependent Changes in Temperature and Precipitation in an Altered Regional Climate: A Case Study for the Three Rivers Headwaters Region, China
by Keyi Wang, Yang Zhou, Jingcheng Han, Chen Chen and Tiejian Li
Remote Sens. 2023, 15(2), 496; https://doi.org/10.3390/rs15020496 - 13 Jan 2023
Cited by 3 | Viewed by 1653
Abstract
Recent studies offer more evidence that the rate of warming is amplified with elevation, indicating thereby that high-elevation ecosystems tend to be exposed to more accelerated changes in temperature than ecosystems at lower elevations. The phenomenon of elevation-dependent warming (EDW), as one of [...] Read more.
Recent studies offer more evidence that the rate of warming is amplified with elevation, indicating thereby that high-elevation ecosystems tend to be exposed to more accelerated changes in temperature than ecosystems at lower elevations. The phenomenon of elevation-dependent warming (EDW), as one of the regional climate-change impacts, has been observed across the Tibetan Plateau. Studies have often found large-scale greening trends, but the drivers of vegetation dynamics are still not fully understood in this region, such that the local implications of vegetation change have been infrequently discussed. This study was designed to quantify and characterize the seasonal changes in vegetation across the Three Rivers Headwaters Region (TRHR), where the land cradles the headwaters of the Yangtze, the Yellow, and the Lancang (Mekong). By mapping the normalized difference vegetation index (NDVI) over the growing season from 1982 to 2015, we were able to evaluate seasonal changes in vegetation cover over time. The results show a slightly increased tendency in green vegetation cover, which could possibly be attributed to sustained warming in this region over the past three decades, whereas a decline in the green-up rate with elevation was found, indicating an inconsistent trend of vegetation greening with EDW. The cause of the green-up rate decline at high elevations could be linked to the reduced soil water availability induced by the fast increase in warming rates associated with EDW. The findings of this study have important implications for devising adaptation strategies for alpine ecosystems in a changing climate. Full article
(This article belongs to the Special Issue Remote Sensing for Climate Change)
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15 pages, 17460 KiB  
Article
Tracking Deforestation, Drought, and Fire Occurrence in Kutai National Park, Indonesia
by Ryan Guild, Xiuquan Wang and Anne E. Russon
Remote Sens. 2022, 14(22), 5630; https://doi.org/10.3390/rs14225630 - 8 Nov 2022
Cited by 2 | Viewed by 2479
Abstract
The dry lowland and mangrove forests of Kutai National Park (KNP) in Indonesia provide invaluable ecosystem services to local human populations (>200,000 in number), serve as immense carbon sinks to recapture anthropogenic emissions, and safeguard habitats for thousands of wildlife species including the [...] Read more.
The dry lowland and mangrove forests of Kutai National Park (KNP) in Indonesia provide invaluable ecosystem services to local human populations (>200,000 in number), serve as immense carbon sinks to recapture anthropogenic emissions, and safeguard habitats for thousands of wildlife species including the critically endangered Northeast Bornean orangutan (Pongo pygmaeus morio). With recent reports of ongoing illegal logging and large-scale wildfires within this National Park, we sought to leverage the extensive catalogue and processing power of Google Earth Engine to track the rates and influences of forest loss within KNP over various time periods since 1997. We present estimates of forest loss from the Hansen Global Forest Change v1.9 dataset (2000–2021) which detected a loss of 15% (272 km2) of forest cover within KNP since 2000, half of which (137 km2) coincided with the El Niño-induced wildfires of 2015–2016. Using the MCD64A1 C6.1 MODIS dataset, we found significant spatial overlap between burned area and forest loss detections during the 2015–2016 period but identified considerable omissions in the burned area dataset over smallholder farms within KNP. We discuss the implications of deforestation in areas of primary orangutan habitat and how patterns of forest loss have influenced drought and fire dynamics within KNP. Finally, we compare time-series estimates of precipitation, the ENSO index, burned area, and forest loss to demonstrate that fire risk within KNP depends largely—but not exclusively—on drought severity, and that rates of non-fire (gradual) and fire-related (extreme) forest loss threaten the remaining forests of this National Park. Full article
(This article belongs to the Special Issue Remote Sensing for Climate Change)
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15 pages, 6995 KiB  
Article
Weakened Impacts of the East Asia-Pacific Teleconnection on the Interannual Variability of Summertime Precipitation over South China since the Mid-2000s
by Wei Lu, Yimin Zhu, Zhong Zhong, Yijia Hu and Yao Ha
Remote Sens. 2022, 14(20), 5098; https://doi.org/10.3390/rs14205098 - 12 Oct 2022
Cited by 1 | Viewed by 1496
Abstract
The current study concentrates on the interdecadal shift in the interannual variability of summertime precipitation (IVSP) over South China (SC). Possible causes for the interdecadal shift are explored. The IVSP on a decadal time scale presents a significant weakening after the mid-2000s. The [...] Read more.
The current study concentrates on the interdecadal shift in the interannual variability of summertime precipitation (IVSP) over South China (SC). Possible causes for the interdecadal shift are explored. The IVSP on a decadal time scale presents a significant weakening after the mid-2000s. The results show that the variances of the interannual precipitation variability over the SC region between 1993 and 2004 (hereafter S1) and 2005 and 2020 (hereafter S2) are 1.40 mm d−1 and 0.58 mm d−1, respectively. The variance of the IVSP has decreased by 58.6% since the mid-2000s. The current study reveals that the reduction in the IVSP over SC after the mid-2000s is prominently attributed to the weakened impact of the East Asia-Pacific (EAP) teleconnection. Before the mid-2000s, the interannual variation of the east-west movement of the western Pacific subtropical high was more significant. The warming over the tropical central-eastern Pacific (CEP) and cooling over the western Pacific (WP) suppress the Walker cell in the tropical Pacific and induce anomalous Hadley cell with its descending branch over the WP in the wet years. The anomalies of SST and atmospheric circulation show opposite phases in the dry years. This SSTA pattern enhances the northward propagation of the EAP teleconnection through a Rossby-wave-type response, which triggers an ascending/descending branch with active/suppressed convection over the northwestern Pacific in the wet/dry years. Therefore, the cooling WP and El Niño in its developing phase provide an ideal condition for more precipitation over SC. However, the above ocean–atmosphere interactions changed after the mid-2000s. The significant SST changes in the tropical CEP and the WP weaken the EAP teleconnection and atmospheric circulation anomalies over SC, leading to a significant interdecadal reduction in the IVSP over SC after the mid-2000s. Full article
(This article belongs to the Special Issue Remote Sensing for Climate Change)
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18 pages, 40043 KiB  
Article
Comparison of Land Use Land Cover Classifiers Using Different Satellite Imagery and Machine Learning Techniques
by Sana Basheer, Xiuquan Wang, Aitazaz A. Farooque, Rana Ali Nawaz, Kai Liu, Toyin Adekanmbi and Suqi Liu
Remote Sens. 2022, 14(19), 4978; https://doi.org/10.3390/rs14194978 - 6 Oct 2022
Cited by 55 | Viewed by 7603
Abstract
Accurate land use land cover (LULC) classification is vital for the sustainable management of natural resources and to learn how the landscape is changing due to climate. For accurate and efficient LULC classification, high-quality datasets and robust classification methods are required. With the [...] Read more.
Accurate land use land cover (LULC) classification is vital for the sustainable management of natural resources and to learn how the landscape is changing due to climate. For accurate and efficient LULC classification, high-quality datasets and robust classification methods are required. With the increasing availability of satellite data, geospatial analysis tools, and classification methods, it is essential to systematically assess the performance of different combinations of satellite data and classification methods to help select the best approach for LULC classification. Therefore, this study aims to evaluate the LULC classification performance of two commonly used platforms (i.e., ArcGIS Pro and Google Earth Engine) with different satellite datasets (i.e., Landsat, Sentinel, and Planet) through a case study for the city of Charlottetown in Canada. Specifically, three classifiers in ArcGIS Pro, including support vector machine (SVM), maximum likelihood (ML), and random forest/random tree (RF/RT), are utilized to develop LULC maps over the period of 2017–2021. Whereas four classifiers in Google Earth Engine, including SVM, RF/RT, minimum distance (MD), and classification and regression tree (CART), are used to develop LULC maps for the same period. To identify the most efficient and accurate classifier, the overall accuracy and kappa coefficient for each classifier is calculated throughout the study period for all combinations of satellite data, classification platforms, and methods. Change detection is then conducted using the best classifier to quantify the LULC changes over the study period. Results show that the SVM classifier in both ArcGIS Pro and Google Earth Engine presents the best performance compared to other classifiers. In particular, the SVM in ArcGIS Pro shows an overall accuracy of 89% with Landsat, 91% with Sentinel, and 94% with Planet. Similarly, in Google Earth Engine, the SVM shows an accuracy of 87% with Landsat 8 and 92% with Sentinel 2. Furthermore, change detection results show that 13.80% and 14.10% of forest areas have been turned into bare land and urban class, respectively, and 3.90% of the land has been converted into the urban area from 2017 to 2021, suggesting the intensive urbanization. The results of this study will provide the scientific basis for selecting the remote sensing classifier and satellite imagery to develop accurate LULC maps. Full article
(This article belongs to the Special Issue Remote Sensing for Climate Change)
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20 pages, 3644 KiB  
Article
Climate Sensitivity of the Arid Scrublands on the Tibetan Plateau Mediated by Plant Nutrient Traits and Soil Nutrient Availability
by Ben Chen, Hui Chen, Meng Li, Sebastian Fiedler, Mihai Ciprian Mărgărint, Arkadiusz Nowak, Karsten Wesche, Britta Tietjen and Jianshuang Wu
Remote Sens. 2022, 14(18), 4601; https://doi.org/10.3390/rs14184601 - 15 Sep 2022
Cited by 5 | Viewed by 2101
Abstract
Climate models predict the further intensification of global warming in the future. Drylands, as one of the most fragile ecosystems, are vulnerable to changes in temperature, precipitation, and drought extremes. However, it is still unclear how plant traits interact with soil properties to [...] Read more.
Climate models predict the further intensification of global warming in the future. Drylands, as one of the most fragile ecosystems, are vulnerable to changes in temperature, precipitation, and drought extremes. However, it is still unclear how plant traits interact with soil properties to regulate drylands’ responses to seasonal and interannual climate change. The vegetation sensitivity index (VSI) of desert scrubs in the Qaidam Basin (NE Tibetan Plateau) was assessed by summarizing the relative contributions of temperature (SGST), precipitation (SGSP), and drought (temperature vegetation dryness index, STVDI) to the dynamics of the normalized difference vegetation index (NDVI) during plant growing months yearly from 2000 to 2015. Nutrient contents, including carbon, nitrogen, phosphorus, and potassium in topsoils and leaves of plants, were measured for seven types of desert scrub communities at 22 sites in the summer of 2016. Multiple linear and structural equation models were used to reveal how leaf and soil nutrient regimes affect desert scrubs’ sensitivity to climate variability. The results showed that total soil nitrogen (STN) and leaf carbon content (LC), respectively, explained 25.9% and 17.0% of the VSI variance across different scrub communities. Structural equation modeling (SEM) revealed that STN and total soil potassium (STK) mediated desert scrub’s VSI indirectly via SGST (with standardized path strength of −0.35 and +0.32, respectively) while LC indirectly via SGST and SGSP (with standardized path strength of −0.31 and −0.19, respectively). Neither soil nor leave nutrient contents alone could explain the VSI variance across different sites, except for the indirect influences of STN and STK via STVDI (−0.18 and 0.16, respectively). Overall, this study disentangled the relative importance of plant nutrient traits and soil nutrient availability in mediating the climatic sensitivity of desert scrubs in the Tibetan Plateau. Integrating soil nutrient availability with plant functional traits together is recommended to better understand the mechanisms behind dryland dynamics under global climate change. Full article
(This article belongs to the Special Issue Remote Sensing for Climate Change)
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17 pages, 7505 KiB  
Article
Satellite Fog Detection at Dawn and Dusk Based on the Deep Learning Algorithm under Terrain-Restriction
by Yinze Ran, Huiyun Ma, Zengwei Liu, Xiaojing Wu, Yanan Li and Huihui Feng
Remote Sens. 2022, 14(17), 4328; https://doi.org/10.3390/rs14174328 - 1 Sep 2022
Cited by 4 | Viewed by 2513
Abstract
Fog generally forms at dawn and dusk, which exerts serious impacts on public traffic and human health. Terrain strongly affects fog formation, which provides a useful clue for fog detection from satellite observation. With the aid of the advanced Himawari-8 imager data (H8/AHI), [...] Read more.
Fog generally forms at dawn and dusk, which exerts serious impacts on public traffic and human health. Terrain strongly affects fog formation, which provides a useful clue for fog detection from satellite observation. With the aid of the advanced Himawari-8 imager data (H8/AHI), this study develops a deep learning algorithm for fog detection at dawn and dusk under terrain-restriction and enhanced channel domain attention mechanism (DDF-Net). The DDF-Net is based on the traditional U-Net model, with the digital elevation model (DEM) data acting as the auxiliary information to separate fog from the low stratus. Furthermore, the squeeze-and-excitation networks (SE-Net) is integrated to optimize the information extraction for eliminating the influence of solar zenith angles (SZA) on the spectral characteristics over a large region. Results show acceptable accuracy of the DDF-Net. The overall probability of detection (POD) is 84.0% at dawn and 83.7% at dusk. In addition, the terrain-restriction strategy improves the results at the edges of foggy regions and reduces the false alarm rate (FAR) for low stratus. The accuracy is expected to be improved when training at a season or month scale, rather than at a longer temporal scale. Results of our study help to improve the accuracy of fog detection, which could further support the relevant traffic planning or healthy travel. Full article
(This article belongs to the Special Issue Remote Sensing for Climate Change)
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22 pages, 17761 KiB  
Article
Projection of Future Extreme Precipitation in China Based on the CMIP6 from a Machine Learning Perspective
by Yilin Yan, Hao Wang, Guoping Li, Jin Xia, Fei Ge, Qiangyu Zeng, Xinyue Ren and Linyin Tan
Remote Sens. 2022, 14(16), 4033; https://doi.org/10.3390/rs14164033 - 18 Aug 2022
Cited by 12 | Viewed by 2631
Abstract
In recent years, China has suffered from frequent extreme precipitation events, and predicting their future trends has become an essential part of the current research on this issue. Because of the inevitable uncertainties associated with individual models for climate prediction, this study uses [...] Read more.
In recent years, China has suffered from frequent extreme precipitation events, and predicting their future trends has become an essential part of the current research on this issue. Because of the inevitable uncertainties associated with individual models for climate prediction, this study uses a machine learning approach to integrate and fit multiple models. The results show that the use of several evaluation metrics provides better results than the traditional ensemble median method. The correlation coefficients with the actual observations were found to improve from about 0.8 to 0.9, while the correlation coefficients of the precipitation amount (PRCPTOT), very heavy precipitation days (R20mm), and extreme precipitation intensity (SDII95) reached 0.95. Based on this, the precipitation simulations of moderate forced scenario for sharing socio-economic path (SSP2-4.5) from 27 coupled models in the Coupled Model Intercomparison Project Phase 6 (CMIP6) were used to explore potential changes in future extreme precipitation events in China and to calculate the distribution and trends of the PRCPTOT, extreme precipitation amount (R95pTOT), maximum consecutive 5-day precipitation (Rx5day), precipitation intensity (SDII), SDII95, and R20mm for the early 21st century (2023–2050), mid-21st century (2051–2075), and late 21st century (2076–2100), respectively. The results showed that the most significant increase in extreme precipitation indices is expected to occur by the end of the century, with the R95pTOT, Rx5day, and SDII95 increasing by 13.73%, 9.43%, and 9.34%, respectively, from the base period. The remaining three precipitation indexes, the PRCPTOT, SDII, and R20mm, also showed increases of 8.77%, 6.84%, and 4.02%, respectively. Additionally, there were apparent differences in the spatial variation of extreme precipitation. There were significant increasing trends of extreme precipitation indexes in central China and northeast China in the three periods, among which the total annual precipitation showed an increasing trend in central and northern China and a decreasing trend in western and south China. An increasing trend of annual precipitation intensity was found to be mainly concentrated in central China and south China, and the annual precipitation frequency showed a larger increasing trend at the beginning of this century. The annual precipitation frequency showed an increasing trend in the early part of this century. In general, all the indices showed an overall increasing trend in the future period, with the PRCPTOT, Rx5day, and SDII95 showing the most significant overall increasing trends. Full article
(This article belongs to the Special Issue Remote Sensing for Climate Change)
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18 pages, 7270 KiB  
Article
Temperature Contributes More than Precipitation to Runoff in the High Mountains of Northwest China
by Mengtian Fan, Jianhua Xu, Yaning Chen, Meihui Fan, Wenzheng Yu and Weihong Li
Remote Sens. 2022, 14(16), 4015; https://doi.org/10.3390/rs14164015 - 18 Aug 2022
Cited by 5 | Viewed by 1434
Abstract
In alpine areas in Northwest China, such as the Tianshan Mountains, the lack of climate data (because of scarce meteorological stations) makes it difficult to assess the impact of climate change on runoff. The main contribution of this study was to develop an [...] Read more.
In alpine areas in Northwest China, such as the Tianshan Mountains, the lack of climate data (because of scarce meteorological stations) makes it difficult to assess the impact of climate change on runoff. The main contribution of this study was to develop an integrated method to assess the impact of climate change on runoff in data-scarce high mountains. Based on reanalysis products, this study firstly downscaled climate data using machine learning algorithms, then developed a Batch Gradient Descent Linear Regression to calculate the contributions of temperature and precipitation to runoff. Applying this method to six mountainous basins originating from the Tianshan Mountains, we found that climate changes in high mountains are more significant than in lowlands. In high mountains, the runoff changes are mainly affected by temperature, whereas in lowlands, precipitation contributes more than temperature to runoff. The contributions of precipitation and temperature to runoff changes were 20% and 80%, respectively, in the Kumarik River. The insights gained in this study can guide other studies on climate and hydrology in high mountain basins. Full article
(This article belongs to the Special Issue Remote Sensing for Climate Change)
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19 pages, 8062 KiB  
Article
Quantifying the Trends and Variations in the Frost-Free Period and the Number of Frost Days across China under Climate Change Using ERA5-Land Reanalysis Dataset
by Hongyuan Li, Guohua Liu, Chuntan Han, Yong Yang and Rensheng Chen
Remote Sens. 2022, 14(10), 2400; https://doi.org/10.3390/rs14102400 - 17 May 2022
Cited by 13 | Viewed by 2429
Abstract
Understanding the spatio-temporal variations in the frost-free period (FFP) and the number of frost days (FD) is beneficial to reduce the harmful effects of climate change on agricultural production and enhancing agricultural adaptation. However, the spatio-temporal variations in FFP and FD and their [...] Read more.
Understanding the spatio-temporal variations in the frost-free period (FFP) and the number of frost days (FD) is beneficial to reduce the harmful effects of climate change on agricultural production and enhancing agricultural adaptation. However, the spatio-temporal variations in FFP and FD and their response to climate change remain unclear across China. To investigate the impact of climate change on FFP and FD, the trends and variations in FFP and FD across China from 1950 to 2020 were quantified using ERA5-Land, a reanalysis dataset with high spatial and temporal resolution. The results showed that ERA5-Land has good applicability in quantifying the trends and variations in FFP and FD across China under climate change. The spatial distribution of multi-year average FFP and FD across China showed significant latitudinal zonality and altitude dependence, i.e., FFP decreased with increasing latitude and altitude, while FD increased with increasing latitude and altitude. As a result of climate warming across China, the FFP showed an increasing trend with an increase rate of 1.25 d/10a and the maximum increasing rate of FFP in the individual region was 6.2 d/10a, while the FD showed a decreasing trend with a decrease rate of 1.41 d/10a and the maximum decreasing rate of FD in the individual region was −6.7 d/10a. Among the five major climate zones in China, the subtropical monsoon climate zone (SUMZ) with the greatest increasing rate of 1.73 d/10a in FFP, while the temperate monsoon climate zone (TEMZ) with the greatest decreasing rate of −1.72 d/10a in FD. In addition, the coefficient of variation (Cv) of FFP showed greater variability at higher altitudes, while the Cv of FD showed greater variability at lower latitudes in southern China. Without considering the adaptation to temperature of crops, a general increase in FFP and a general decrease in FD were both beneficial to agricultural production in terms of FFP and FD promoting a longer growing period and reducing frost damage on crops. This study provides a comprehensive understanding of the trends and variations in FFP and FD under climate change, which is of great scientific significance for the adjustment of the agricultural production layout to adapt to climate change in China. Full article
(This article belongs to the Special Issue Remote Sensing for Climate Change)
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19 pages, 6186 KiB  
Article
Flood Management, Characterization and Vulnerability Analysis Using an Integrated RS-GIS and 2D Hydrodynamic Modelling Approach: The Case of Deg Nullah, Pakistan
by Ijaz Ahmad, Xiuquan Wang, Muhammad Waseem, Muhammad Zaman, Farhan Aziz, Rana Zain Nabi Khan and Muhammad Ashraf
Remote Sens. 2022, 14(9), 2138; https://doi.org/10.3390/rs14092138 - 29 Apr 2022
Cited by 10 | Viewed by 3690
Abstract
One-dimensional (1D) hydraulic models have been extensively used to conduct flood simulations for investigating flood depth and extent maps. However, the 1D models cannot simulate many other flood characteristics, such as flood velocity, duration, arrival time and recession time when the flow is [...] Read more.
One-dimensional (1D) hydraulic models have been extensively used to conduct flood simulations for investigating flood depth and extent maps. However, the 1D models cannot simulate many other flood characteristics, such as flood velocity, duration, arrival time and recession time when the flow is not restricted within the channel. These flood characteristics cannot be disregarded as they play an important role in developing flood mitigation and evacuation strategies. This study formulates a two-dimensional (2D) hydrodynamic model combined with remote sensing (RS) and geographic information system (GIS) approach to generate additional flood characteristic maps that cannot be produced with 1D models. The model was applied to a transboundary river of Deg Nullah in Pakistan to simulate an extreme flood event experience in 2014. The flood extent images from the moderate resolution imaging spectroradiometer (MODIS) and observed flood extents were used to evaluate the model performance. Moreover, an entropy distance-based approach was proposed to facilitate the integrated multivariate flood vulnerability classification. The simulated 2D flood modeling results showed a good agreement with the flood extents registered by MODIS and the observed ones. The northwest parts of Deg Nullah near Seowal, Dullam Kahalwan and Zafarwal were the most vulnerable areas due to high flood depths and prolonged flooding duration. Whereas high flood velocities, short flood arrival time, prolonged flood duration and recession times were observed in the upper reach of Deg Nullah thereby making it the most susceptible, critical and vulnerable region to flooding events. Full article
(This article belongs to the Special Issue Remote Sensing for Climate Change)
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20 pages, 10736 KiB  
Article
Resolution-Sensitive Added Value Analysis of CORDEX-CORE RegCM4-7 Past Seasonal Precipitation Simulations over Africa Using Satellite-Based Observational Products
by Gnim Tchalim Gnitou, Guirong Tan, Yan Hongming, Isaac Kwesi Nooni and Kenny Thiam Choy Lim Kam Sian
Remote Sens. 2022, 14(9), 2102; https://doi.org/10.3390/rs14092102 - 27 Apr 2022
Cited by 3 | Viewed by 1561
Abstract
This study adopts a two-way approach to CORDEX-CORE RegCM4-7 seasonal precipitation simulations’ Added Value (AV) analysis over Africa, which aims to quantify potential improvements introduced by the downscaling approach at high and low resolution, using satellite-based observational products. The results show that RegCM4-7 [...] Read more.
This study adopts a two-way approach to CORDEX-CORE RegCM4-7 seasonal precipitation simulations’ Added Value (AV) analysis over Africa, which aims to quantify potential improvements introduced by the downscaling approach at high and low resolution, using satellite-based observational products. The results show that RegCM4-7 does add value to its driving Global Climate Models (GCMs) with a positive Added Value Coverage (AVC) ranging between 20 and 60% at high resolution, depending on the season and the boundary conditions. At low resolution, the results indicate an increase in the positive AVC by up to 20% compared to the high-resolution results, with an up to 8% decrease for instances where an increase is not observed. Typical climate zones such as West Africa, Central Africa, and Southern East Africa, where improvements by Regional Climate Models (RCMs) are expected due to strong dependence on mesoscale and fine-scale features, show positive AVC greater than 20%, regardless of the season and the driving GCM. These findings provide more evidence for confirming the hypothesis that the RCMs AV is influenced by their internal physics rather than being the product of a mere disaggregation of large-scale features provided by GCMs. Although the results show some dependencies to the driving GCMs relating to their equilibrium climate sensitivity nature, the findings at low resolutions similar to the native GCM resolutions make the influence of internal physics more important. The findings also feature the CORDEX-CORE RegCM4-7 precipitation simulations’ potential in bridging the quality and resolution gap between coarse GCMs and high-resolution remote sensing datasets. Even if further post-processing activities, such as bias correction, may still be needed to remove persistent biases at high resolution, using upscaled RCMs as an alternative to GCMs for large-scale precipitation studies over Africa can be insightful if the AV and other performance statistics are satisfactory for the intended application. Full article
(This article belongs to the Special Issue Remote Sensing for Climate Change)
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16 pages, 9266 KiB  
Article
Dynamical Downscaling of Temperature Variations over the Canadian Prairie Provinces under Climate Change
by Xiong Zhou, Guohe Huang, Yongping Li, Qianguo Lin, Denghua Yan and Xiaojia He
Remote Sens. 2021, 13(21), 4350; https://doi.org/10.3390/rs13214350 - 29 Oct 2021
Cited by 14 | Viewed by 2459
Abstract
In this study, variations of daily mean, maximum, and minimum temperature (expressed as Tmean, Tmax, and Tmin) over the Canadian Prairie Provinces were dynamically downscaled through regional climate simulations. How the regional climate would increase in response [...] Read more.
In this study, variations of daily mean, maximum, and minimum temperature (expressed as Tmean, Tmax, and Tmin) over the Canadian Prairie Provinces were dynamically downscaled through regional climate simulations. How the regional climate would increase in response to global warming was subsequently revealed. Specifically, the Regional Climatic Model (RegCM) was undertaken to downscale the boundary conditions of Geophysical Fluid Dynamics Laboratory Earth System Model Version 2M (GFDL-ESM2M) over the Prairie Provinces. Daily temperatures (i.e., Tmean, Tmax, and Tmin) were subsequently extracted from the historical and future climate simulations. Temperature variations in the two future periods (i.e., 2036 to 2065 and 2065 to 2095) are then investigated relative to the baseline period (i.e., 1985 to 2004). The spatial distributions of temperatures were analyzed to reveal the regional impacts of global warming on the provinces. The results indicated that the projected changes in the annual averages of daily temperatures would be amplified from the southwest in the Rocky Mountain area to the northeast in the prairie region. It was also suggested that the projected temperature averages would be significantly intensified under RCP8.5. The projected temperature variations could provide scientific bases for adaptation and mitigation initiatives on multiple sectors, such as agriculture and economic sectors over the Canadian Prairies. Full article
(This article belongs to the Special Issue Remote Sensing for Climate Change)
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21 pages, 5416 KiB  
Article
Long-Term Projection of Water Cycle Changes over China Using RegCM
by Chen Lu, Guohe Huang, Guoqing Wang, Jianyun Zhang, Xiuquan Wang and Tangnyu Song
Remote Sens. 2021, 13(19), 3832; https://doi.org/10.3390/rs13193832 - 25 Sep 2021
Cited by 6 | Viewed by 1880
Abstract
The global water cycle is becoming more intense in a warming climate, leading to extreme rainstorms and floods. In addition, the delicate balance of precipitation, evapotranspiration, and runoff affects the variations in soil moisture, which is of vital importance to agriculture. A systematic [...] Read more.
The global water cycle is becoming more intense in a warming climate, leading to extreme rainstorms and floods. In addition, the delicate balance of precipitation, evapotranspiration, and runoff affects the variations in soil moisture, which is of vital importance to agriculture. A systematic examination of climate change impacts on these variables may help provide scientific foundations for the design of relevant adaptation and mitigation measures. In this study, long-term variations in the water cycle over China are explored using the Regional Climate Model system (RegCM) developed by the International Centre for Theoretical Physics. Model performance is validated through comparing the simulation results with remote sensing data and gridded observations. The results show that RegCM can reasonably capture the spatial and seasonal variations in three dominant variables for the water cycle (i.e., precipitation, evapotranspiration, and runoff). Long-term projections of these three variables are developed by driving RegCM with boundary conditions of the Geophysical Fluid Dynamics Laboratory Earth System Model under the Representative Concentration Pathways (RCPs). The results show that increased annual average precipitation and evapotranspiration can be found in most parts of the domain, while a smaller part of the domain is projected with increased runoff. Statistically significant increasing trends (at a significant level of 0.05) can be detected for annual precipitation and evapotranspiration, which are 0.02 and 0.01 mm/day per decade, respectively, under RCP4.5 and are both 0.03 mm/day per decade under RCP8.5. There is no significant trend in future annual runoff anomalies. The variations in the three variables mainly occur in the wet season, in which precipitation and evapotranspiration increase and runoff decreases. The projected changes in precipitation minus evapotranspiration are larger than those in runoff, implying a possible decrease in soil moisture. Full article
(This article belongs to the Special Issue Remote Sensing for Climate Change)
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10 pages, 5971 KiB  
Communication
Glacier Velocity Changes in the Himalayas in Relation to Ice Mass Balance
by Yu Zhou, Jianlong Chen and Xiao Cheng
Remote Sens. 2021, 13(19), 3825; https://doi.org/10.3390/rs13193825 - 24 Sep 2021
Cited by 13 | Viewed by 2584
Abstract
Glacier evolution with time provides important information about climate variability. Here, we investigated glacier velocity changes in the Himalayas and analysed the patterns of glacier flow. We collected 220 scenes of Landsat-7 panchromatic images between 1999 and 2000, and Sentinel-2 panchromatic images between [...] Read more.
Glacier evolution with time provides important information about climate variability. Here, we investigated glacier velocity changes in the Himalayas and analysed the patterns of glacier flow. We collected 220 scenes of Landsat-7 panchromatic images between 1999 and 2000, and Sentinel-2 panchromatic images between 2017 and 2018, to calculate surface velocities of 36,722 glaciers during these two periods. We then derived velocity changes between 1999 and 2018 for the early winter period, based on which we performed a detailed analysis of motion of each individual glacier, and noted that the changes are spatially heterogeneous. Of all the glaciers, 32% have sped up, 24.5% have slowed down, and the rest 43.5% have remained stable. The amplitude of glacier slowdown, as a result of glacier mass loss, is significantly larger than that of speedup. At regional scales, we found that glacier surface velocity in winter has uniformly decreased in the western part of the Himalayas between 1999 and 2018, while increased in the eastern part; this contrasting difference may be associated with decadal changes in accumulation and/or melting under different climatic regimes. We also found that the overall trend of surface velocity exhibits seasonal variability: summer velocity changes are positively correlated with mass loss, i.e., velocity increases with increasing mass loss, whereas winter velocity changes show a negative correlation. Our study suggests that glacier velocity changes in the Himalayas are spatially and temporally heterogeneous, in agreement with studies that previously highlighted this trend, emphasising complex interactions between glacier dynamics and environmental forcing. Full article
(This article belongs to the Special Issue Remote Sensing for Climate Change)
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20 pages, 13677 KiB  
Article
The UVSQ-SAT/INSPIRESat-5 CubeSat Mission: First In-Orbit Measurements of the Earth’s Outgoing Radiation
by Mustapha Meftah, Thomas Boutéraon, Christophe Dufour, Alain Hauchecorne, Philippe Keckhut, Adrien Finance, Slimane Bekki, Sadok Abbaki, Emmanuel Bertran, Luc Damé, Jean-Luc Engler, Patrick Galopeau, Pierre Gilbert, Laurent Lapauw, Alain Sarkissian, André-Jean Vieau, Patrick Lacroix, Nicolas Caignard, Xavier Arrateig, Odile Hembise Fanton d’Andon, Antoine Mangin, Jean-Paul Carta, Fabrice Boust, Michel Mahé and Christophe Mercieradd Show full author list remove Hide full author list
Remote Sens. 2021, 13(8), 1449; https://doi.org/10.3390/rs13081449 - 8 Apr 2021
Cited by 13 | Viewed by 4414
Abstract
UltraViolet & infrared Sensors at high Quantum efficiency onboard a small SATellite (UVSQ-SAT) is a small satellite at the CubeSat standard, whose development began as one of the missions in the International Satellite Program in Research and Education (INSPIRE) consortium in 2017. UVSQ-SAT [...] Read more.
UltraViolet & infrared Sensors at high Quantum efficiency onboard a small SATellite (UVSQ-SAT) is a small satellite at the CubeSat standard, whose development began as one of the missions in the International Satellite Program in Research and Education (INSPIRE) consortium in 2017. UVSQ-SAT is an educational, technological and scientific pathfinder CubeSat mission dedicated to the observation of the Earth and the Sun. It was imagined, designed, produced and tested by LATMOS in collaboration with its academic and industrial partners, and the French-speaking radioamateur community. About the size of a Rubik’s Cube and weighing about 2 kg, this satellite was put in orbit in January 2021 by the SpaceX Falcon 9 launch vehicle. After briefly introducing the UVSQ-SAT mission, this paper will present the importance of measuring the Earth’s radiation budget and its energy imbalance and the scientific objectives related to its various components. Finally, the first in-orbit observations will be shown (maps of the solar radiation reflected by the Earth and of the outgoing longwave radiation at the top of the atmosphere during February 2021). UVSQ-SAT is one of the few CubeSats worldwide with a scientific goal related to climate studies. It represents a research in remote sensing technologies for Climate observation and monitoring. Full article
(This article belongs to the Special Issue Remote Sensing for Climate Change)
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14 pages, 3608 KiB  
Communication
Current Status and Variation since 1964 of the Glaciers around the Ebi Lake Basin in the Warming Climate
by Lin Wang, Changbin Bai and Jing Ming
Remote Sens. 2021, 13(3), 497; https://doi.org/10.3390/rs13030497 - 30 Jan 2021
Cited by 6 | Viewed by 2497
Abstract
This work analyzed the spatial and temporal variations of the glaciers in the Ebi Lake basin during the period 1964 to 2019, based on the 1st and 2nd Chinese Glacier Inventories (CGI) and remote sensing data; this is believed to be the first [...] Read more.
This work analyzed the spatial and temporal variations of the glaciers in the Ebi Lake basin during the period 1964 to 2019, based on the 1st and 2nd Chinese Glacier Inventories (CGI) and remote sensing data; this is believed to be the first long-term comprehensive remote sensing investigation on the glacier change in this area, and it also diagnosed the response of the glaciers to the warming climate by analyzing digital elevation modeling and meteorology. The results show that there are 988 glaciers in total in the basin, with a total area of 560 km2 and average area of 0.57 km2 for a single glacier. The area and number of the glaciers oriented north and northeast are 205 km2 (327 glaciers) and 180 km2 (265 glaciers), respectively. The glaciers are categorized into eight classes as per their area, which are less than 0.1, 0.1–0.5, 0.5–1.0, 1.0–2.0, 2.0–5.0, 5.0–10.0, 10.0–20.0, and greater than 20.0 km2, respectively. The smaller glaciers between 0.1 km2 and 10.0 km2 account for 509 km2 or 91% in total area, and, in particular, the glaciers smaller than 0.5 km2 account for 74% in the total number. The glacial area is concentrated at 3500–4000 m in altitude (512 km2 or 91.4% in total). The number of glaciers in the basin decreased by 10.5% or 116, and their area decreased by 263.29 km2 (−4.79 km2 a−1) or 32% (−0.58% a−1) from 1964 to 2019; the glaciers with an area between 2.0 km2 and 5.0 km2 decreased by the largest, −82.60 km2 or −40.67% in the total area at −1.50 km2 a−1 or −0.74% a−1), and the largest decrease in number (i.e., 126 glaciers) occurs between 0.1 km2 and 0.5 km2. The total ice storage in the basin decreased by 97.84–153.22 km3 from 1964 to 2019, equivalent to 88.06–137.90 km3 water (taking 0.9 g cm−3 as ice mass density). The temperature increase rate in the basin was +0.37 °C decade−1, while the precipitation was +13.61 mm decade−1 during the last fifty-five years. This analysis shows that the increase in precipitation in the basin was not sufficient to compensate the mass loss of glaciers caused by the warming during the same period. The increase in temperature was the dominant factor exceeding precipitation mass supply for ruling the retreat of the glaciers in the entire basin. Full article
(This article belongs to the Special Issue Remote Sensing for Climate Change)
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