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Climate and Environmental Changes Monitored by Satellite Remote Sensing III

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

Deadline for manuscript submissions: 31 July 2024 | Viewed by 4562

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Special Issue Editors

Dr. Hainan Gong

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Guest Editor

Center for Monsoon System Research, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
Interests: climate and environmental changes; climate dynamics; atmospheric physics
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Peng Zhang

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Guest Editor

National Satellite Meteorological Centre, Chinese Meteorological Administration, Beijing 100081, China
Interests: atmospheric remote sensing; quantitative remote sensing of aerosols and dust storms; application of satellite remote sensing products
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Dr. Luca Lelli

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Guest Editor

1. Department of Physics, Institute of Environmental Physics, University Bremen, 28359 Bremen, Germany
2. Institute for Remote Sensing Methods, German Aerospace Center (DLR), Oberpfaffenhofen, Wessling, Germany
Interests: clouds; aerosols; atmospheric composition; radiative transfer; time series analysis; trend detection; climate data records; climate networks
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent decades, the global climate has experienced unprecedented changes. Extreme climate and environmental events are becoming more frequent and intense. Extreme climate and environmental events cause massive casualties and losses, high economic costs, and have far-reaching impacts on both human society and the natural environment. Therefore, knowledge of the regime of climate and environmental extremes is critical to assess the magnitude and rate of climate and environmental changes in the world. Satellite remote sensing data are very useful for monitoring the states and processes of climates and the environment at various spatiotemporal scales. Therefore, satellite remote sensing is crucial for advancing our understanding of the global climate and environmental extremes and their impacts.

This Special Issue is the third edition of “Climate and Environmental Changes Monitored by Satellite Remote Sensing” and “Climate and Environmental Changes Monitored by Satellite Remote Sensing II”, and is further devoted to advancing our understanding of climate and environmental extremes using satellite remote sensing observations and their derived products. Articles on all aspects of the analysis of climate and environmental extremes using satellite remote sensing observations are welcome, including but not limited to:

Extreme events (floods, droughts, tropical cyclones, heat waves, cold waves, dust storms, and severe haze);
Monitoring and detection;
Space–time distribution at various scales;
External forcing/drivers and internal factors/variability.

Dr. Hainan Gong
Prof. Dr. Peng Zhang
Dr. Luca Lelli
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 and environmental extremes
remote sensing
floods
droughts
tropical cyclones
heat waves
cold waves
dust storms
severe haze

Related Special Issue

Climate and Environmental Changes Monitored by Satellite Remote Sensing II in Remote Sensing (10 articles)

Published Papers (7 papers)

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Research

17 pages, 3365 KiB

Open AccessArticle

Study on the Impact of Urban Morphologies on Urban Canopy Heat Islands Based on Relocated Meteorological Stations

by Tao Shi, Yuanjian Yang and Ping Qi

Remote Sens. 2024, 16(9), 1500; https://doi.org/10.3390/rs16091500 - 24 Apr 2024

Viewed by 193

Abstract

This study addresses a crucial gap in understanding the impact of urban morphologies on the canopy urban heat islands (CUHI) effect. The selection of reference stations lacks a unified standard, and their surface air temperature (SAT) sequences are also inevitably influenced by urbanization. However, synchronous observational data from relocated meteorological stations could provide high-quality sample data for studying CUHI. Utilizing remote sensing techniques, the findings of this paper revealed that the observation environment of stations after relocation exhibited remarkable representativeness, with their observation sequences accurately reflecting the local climatic background. The differences in synchronized observation sequences could characterize the CUHI intensity (CUHII). Among the various factors, land use parameters and landscape parameters played particularly significant roles. Furthermore, the fitting performance of the random forest (RF) model for both training and testing data was significantly superior to that of the linear model and support vector regression (SVR) model. Additionally, the influence of local circulation on CUHI could not be overlooked. The mechanisms by which urban morphologies affect CUHII under different circulation backgrounds deserve further investigation. Full article

(This article belongs to the Special Issue Climate and Environmental Changes Monitored by Satellite Remote Sensing III)

20 pages, 6847 KiB

Open AccessArticle

If Some Critical Regions Achieve Carbon Neutrality, How Will the Global Atmospheric CO₂ Concentration Change?

by Jiaying Li, Xiaoye Zhang, Lifeng Guo, Junting Zhong, Deying Wang, Chongyuan Wu and Lifeng Jiang

Remote Sens. 2024, 16(9), 1486; https://doi.org/10.3390/rs16091486 - 23 Apr 2024

Viewed by 228

Abstract

Due to anthropogenic emissions, the global CO₂ concentration increases at a rate of approximately 2 ppm per year. With over 130 countries and regions committing to carbon neutrality goals and continuously reducing anthropogenic CO₂ emissions, understanding how atmospheric CO₂ concentrations will change globally and in other regions has become an intriguing question. Examining different regions’ efforts to reduce anthropogenic CO₂ emissions through atmospheric CO₂ observations is also meaningful. We used prior and posterior fluxes to drive the TM5 model. The posterior fluxes were based on the China Carbon Monitoring, Verification and Support System for Global (CCMVS-G), which assimilated the atmospheric CO₂ concentration data from ground-based observation and satellite observation. We found that the CO₂ concentration obtained using the posterior fluxes was more in line with the actual situation. Then, we presented some experiments to estimate how global and regional CO₂ concentrations would change if certain key regions and the whole world achieved net zero emissions of anthropogenic CO₂. After removing carbon fluxes from China, North America, and Europe, global CO₂ concentrations decreased by around 0.58 ppm, 0.22 ppm, and 0.10 ppm, respectively. The most significant decrease occurred in the regions where fluxes were removed, followed by other areas at the same latitude affected by westerly winds. This indicates that fossil fuel flux is the main factor affecting CO₂ concentrations, and that meteorological-driven transportation also significantly impacts CO₂ concentrations. Most importantly, using this method, it is possible to quantitatively estimate the impact of achieving carbon neutrality in one region on CO₂ concentrations in local regions as well as globally. Full article

(This article belongs to the Special Issue Climate and Environmental Changes Monitored by Satellite Remote Sensing III)

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18 pages, 7362 KiB

Open AccessArticle

Characterizing Smoke Haze Events in Australia Using a Hybrid Approach of Satellite-Based Aerosol Optical Depth and Chemical Transport Modeling

by Miles Sowden, Ivan C. Hanigan, Daniel Jamie Victor Robbins, Martin Cope, Jeremy D. Silver and Julie Noonan

Remote Sens. 2024, 16(7), 1266; https://doi.org/10.3390/rs16071266 - 03 Apr 2024

Viewed by 404

Abstract

Smoke haze events have increasingly affected Australia’s environmental quality, having demonstrable effects on air quality, climate, and public health. This study employs a hybrid methodology, merging satellite-based aerosol optical depth (AOD) data with Chemical Transport Model (CTM) simulations to comprehensively characterize these events. The AOD data are sourced from the Japan Aerospace Exploration Agency (JAXA), Copernicus Atmosphere Monitoring Service (CAMS), and the Commonwealth Scientific and Industrial Research Organization (CSIRO), and they are statistically evaluated using mean, standard deviation, and root mean square error (RMSE) metrics. Our analysis indicates that the combined dataset provides a more robust representation of smoke haze events than individual datasets. Additionally, the study investigates aerosol distribution patterns and data correlation across the blended dataset and discusses possible improvements such as data imputation and aerosol plume scaling. The outcomes of this investigation contribute to enhancing our understanding of the impacts of smoke haze on various environmental factors and can assist in developing targeted mitigation and management strategies. Full article

(This article belongs to the Special Issue Climate and Environmental Changes Monitored by Satellite Remote Sensing III)

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16 pages, 8818 KiB

Open AccessArticle

Increased Warming Efficiencies of Lake Heatwaves Enhance Dryland Lake Warming over China

by Yuchen Wu, Fei Ji, Siyi Wang, Yongli He and Shujuan Hu

Remote Sens. 2024, 16(3), 588; https://doi.org/10.3390/rs16030588 - 04 Feb 2024

Viewed by 573

Abstract

Lake surface water temperature (LSWT) has significantly increased over China and even globally in recent decades due to climate change. However, the responses of LSWTs to climate warming in various climatic regions remain unclear due to the limited lake observations. Satellite-observed LSWT data from the Moderate Resolution Imaging Spectroradiometer (MODIS) dataset were extended using the air2water model. This research aimed to investigate summer LSWT trends across various climatic zones in China, shedding light on the complex interplay between surface air temperatures and LSWT from 1950 to 2020. The results demonstrate robust model performance, with high Nash–Sutcliffe efficiency coefficients, affirming its capability to simulate LSWT variability. Regional disparities in LSWT patterns are identified, revealing notable warming trends in dryland lakes, particularly in central Inner Mongolia. Notably, the study unveils a substantial increase in the intensity and duration of lake heatwaves, especially in semi-arid regions. Dryland lake heatwaves emerge as dominant contributors to intensified LSWT warming, showcasing stronger and longer-lasting events than humid regions. The research highlights a positive feedback loop between lake warming and heatwaves, further amplifying dryland LSWT warming. These findings underscore the vulnerability of dryland lakes to climate change and signal the potential ramifications of increased greenhouse gas concentrations. Full article

(This article belongs to the Special Issue Climate and Environmental Changes Monitored by Satellite Remote Sensing III)

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23 pages, 6688 KiB

Open AccessArticle

Integrated Remote Sensing Observations of Radiative Properties and Sources of the Aerosols in Southeast Asia: The Case of Thailand

by Arika Bridhikitti, Pakorn Petchpayoon and Thayukorn Prabamroong

Remote Sens. 2023, 15(22), 5319; https://doi.org/10.3390/rs15225319 - 10 Nov 2023

Viewed by 748

Abstract

Aerosols in Southeast Asia (SEA) are entangled with complex land–sea–atmosphere–human interactions, and it is difficult for scientists to understand their dynamic behaviors. This study aims to provide an insightful understanding of aerosols across SEA with respect to their radiative properties using several lines of evidence obtained from remote sensing instruments, including those from onboard Earth observation satellites (MODIS/Terra and MODIS/Aqua, CALIOP/CALIPSO) and from ground-based observation (AERONET). The findings, obtained from cluster analysis of aerosol optical properties, showed seven aerosol types which were dominant across the country, exhibiting diverse radiative forcing potentials. The light-absorbing (prone to warm the atmosphere) aerosols were likely found in mainland SEA, both for background and high-aerosol events. The light-scattering aerosols were associated with aging processes and hygroscopic growth. The neutral potential, which comprised a mixture of oceanic and local anthropogenic aerosols, was predominant in background aerosols in insular SEA. Further studies should focus on carbonaceous aerosols (organic carbons, black carbon, and brown carbon), the aging processes, and the hygroscopic growth of these aerosols, since they play significant roles in the regional aerosol optical properties. Full article

(This article belongs to the Special Issue Climate and Environmental Changes Monitored by Satellite Remote Sensing III)

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17 pages, 7223 KiB

Open AccessArticle

Analysis of Land Surface Temperature Sensitivity to Vegetation in China

by Zhonghua Qian, Yingxiao Sun, Zheng Chen, Fei Ji, Guolin Feng and Qianrong Ma

Remote Sens. 2023, 15(18), 4544; https://doi.org/10.3390/rs15184544 - 15 Sep 2023

Viewed by 973

Abstract

China has emerged as one of the global leaders in greening, achieved through human land use management practices, particularly afforestation projects. However, accurately calculating the energy balance processes of vegetated areas remains challenging because of the complexity of physical mechanisms, parameterization schemes, and driving dataset used in current research. In this study, we address these challenges by employing moving window methods in space inspired by “space-for-time”. This approach allows us to eliminate the influence of climate signals on vegetation development over long periods and determine the sensitivity of seasonal contributions of Land Surface Temperature (LST) to Leaf Area Index (LAI) in China from 2001 to 2018. Our findings reveal that the sensitivity of LST to LAI in the climatology period is approximately −0.085

K \cdot m^{2} \cdot m^{- 2}

, indicating a cooling effect. Moreover, the climatological trend remains negative, suggesting that Chinese vegetation greening is playing an increasingly important role in cooling the land surface. Considering the energy balance equation, we further investigate the underlying mechanisms. It is observed that the radiative feedback consistently contributes positively, while the non-radiative feedback always exerts a negative influence on the sensitivity. These results provide valuable insights into the complex interactions between vegetation greening and land surface temperature in China, providing informed land management and climate adaptation strategies. Understanding these trends and mechanisms is essential for sustainable and effective environmental planning and decision making. Full article

(This article belongs to the Special Issue Climate and Environmental Changes Monitored by Satellite Remote Sensing III)

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16 pages, 5346 KiB

Open AccessArticle

An ENSO Prediction Model Based on Backtracking Multiple Initial Values: Ordinary Differential Equations–Memory Kernel Function

by Qianrong Ma, Yingxiao Sun, Shiquan Wan, Yu Gu, Yang Bai and Jiayi Mu

Remote Sens. 2023, 15(15), 3767; https://doi.org/10.3390/rs15153767 - 28 Jul 2023

Viewed by 851

Abstract

This article presents a new prediction model, the ordinary differential equations–memory kernel function (ODE–MKF), constructed from multiple backtracking initial values (MBIV). The model is similar to a simplified numerical model after spatial dimension reduction and has both nonlinear characteristics and the low-cost advantage of a time series model. The ODE–MKF focuses on utilizing more temporal information and includes machine learning to solve complex mathematical inverse problems to establish a predictive model. This study first validates the feasibility of the ODE–MKF via experiments using the Lorenz system. The results demonstrate that the ODE–MKF prediction model could describe the nonlinear characteristics of complex systems and exhibited ideal predictive robustness. The prediction of the El Niño-Southern Oscillation (ENSO) index further demonstrates its effectiveness, as it achieved 24-month lead predictions and effectively improved nonlinear problems. Furthermore, the reliability of the model was also tested, and approximately 18 months of prediction were achieved, which was verified with the Clouds and the Earth’s Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) radiation fluxes. The short-term memory index Southern Oscillation (SO) was further used to examine the applicability of ODE–MKF. A six-month lead prediction of the SO trend was achieved, indicating that the predictability of complex systems is related to their inherent memory scales. Full article

(This article belongs to the Special Issue Climate and Environmental Changes Monitored by Satellite Remote Sensing III)

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