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Climate and Environmental Changes Monitored by Satellite Remote Sensing

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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: closed (30 June 2022) | Viewed by 21477

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Dr. Hainan Gong

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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
Special Issues, Collections and Topics in MDPI journals

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Dear Colleagues,

During recent decades, the global climate has experienced unprecedented changes. Much evidence indicates that the global mean surface temperature is increasing rapidly, the Arctic sea ice cover is sharply declining, and extreme climate and environmental events are occurring frequently. Extreme climate and environmental events not only have a great impact on society and the economy, but also seriously affect people’s lives and health. Therefore, it is very important to understand why the global climate and environment are changing and what the key physical mechanisms behind these changes are. Moreover, another key question is to what extent recent changes in the global climate and environment are due to anthropogenic impacts as opposed to internal climate variability. Satellite remote sensing data can be very useful for monitoring the states and processes of the climate and environment at various spatiotemporal scales. Therefore, satellite remote sensing is crucial for advancing our understanding of the global climate and environmental changes and their impacts.

This Special Issue aims to invite contributions from studies that focus on understanding the climate and environmental changes at various spatiotemporal scales using satellite remote sensing observations and their derived products. Of interest to this Special Issue are a wide range of topics including, but not limited to:

Climate change impacts on the frequency and intensity of extreme weather and climate events, such as floods, droughts, typhoons, heat waves, and cold events, using satellite data or their derived products.
Climate change impacts on extreme environmental events, such as dust storms and severe air pollution, using satellite observations and their derived products.
Climate change impacts on Arctic sea ice changes using remote sensing data.
Separating the contributions of human activity and internal climate variability to the climate and environmental changes using long-term satellite remote sensing data or their derived products.

Original research papers and/or review papers that cover applications of satellite remote sensing technology or data for the improvement of understanding of the climate and environmental changes are all welcome.

Dr. Hainan Gong
Prof. Dr. Peng Zhang
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
remote sensing
extreme events
typhoon activity
air pollution
dust storms
heat waves
cold events
Arctic sea ice

Published Papers (10 papers)

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19 pages, 1657 KiB

Open AccessArticle

Hyperspectral Infrared Atmospheric Sounder (HIRAS) Atmospheric Sounding System

by Shuqun Li, Hao Hu, Chenggege Fang, Sichen Wang, Shangpei Xun, Binfang He, Wenyu Wu and Yanfeng Huo

Remote Sens. 2022, 14(16), 3882; https://doi.org/10.3390/rs14163882 - 10 Aug 2022

Cited by 6 | Viewed by 2399

Abstract

Accurate atmospheric temperature and moisture profiles are essential for weather forecasts and research. Satellite-based hyperspectral infrared observations are meaningful in detecting atmospheric profiles, especially over oceans where conventional observations can seldom be used. In this study, a HIRAS (Hyperspectral Infrared Atmospheric Sounder) Atmospheric Sounding System (HASS) was introduced, which retrieves atmospheric temperature and moisture profiles using a one-dimension variational scheme based on HIRAS observations. A total of 274 channels were optimally selected from the entire HIRAS spectrum through information entropy analyses, and a group of retrieval experiments were independently performed for different HIRAS fields of views (FOVs). Compared with the ECMWF reanalysis data version-5 (ERA5), the RMSEs of temperature (relative humidity) for low-, mid-, and high-troposphere layers were 1.5 K (22.3%), 1.0 K (33.2%), and 1.3 K (38.5%), respectively, which were similar in magnitude to those derived from other hyperspectral infrared sounders. Meanwhile, the retrieved temperature RMSEs with respect to the satellite radio occultation (RO) products increased to 1.7 K, 1.8 K, and 1.9 K for the low-, mid-, and high-troposphere layers, respectively, which could be attributed to the accurate RO temperature products in the upper atmospheres. It was also found that the RMSE varied with the FOVs and latitude, which may be caused by the current angle-dependent bias correction and unique background profiles. Full article

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

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20 pages, 5836 KiB

Open AccessArticle

Monitoring Heat Extremes across Central Europe Using Land Surface Temperature Data Records from SEVIRI/MSG

by Célia M. Gouveia, João P. A. Martins, Ana Russo, Rita Durão and Isabel F. Trigo

Remote Sens. 2022, 14(14), 3470; https://doi.org/10.3390/rs14143470 - 19 Jul 2022

Cited by 6 | Viewed by 1794

Abstract

The frequency and intensity of extreme hot events have increased worldwide, particularly over the past couple of decades. Europe has been affected by unprecedented mega heatwaves, namely the events that struck Western Europe in 2003 and Eastern Europe in 2010. The year 2018 was also reported as an unusually hot year, with record-breaking temperatures in many parts of Europe during spring and summer, associated with severe and unusual wildfires and significant crop losses in central and northern Europe. We show the ability of Land Surface Temperature (LST), retrieved from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard Meteosat Second Generation (MSG) to monitor heat extremes, using the 2018 European event as a showcase. The monitoring approach relies on monthly anomalies performed as departures from the median and the monthly number of hot days (NHD), both computed for satellite LST derived from MSG and MODIS, and for 2 m air temperature (T2m) from ERA5 reanalysis, using as threshold the 90th percentiles. Results show strong monthly LST anomalies during the spring and summer of 2018 extending over central and north Europe. Over a vast region in Central and Northern Europe, LST reached the last 15 years high record. Moreover, those outstanding warm LSTs persisted for more than four months. Results obtained using MODIS LST and ERA5 T2m show similar patterns, which, although slightly less intense, corroborate the exceptionality of the heat extremes observed over central and northern Europe during 2018. The spatial pattern of the number of monthly record high anomalies over the MSG observations period clearly depicts the regions in Northern and Central Europe affected by the complex phenomena that occurred in 2018, which resulted from the combined effect of an extreme heatwave in spring and summer with extensive dry conditions. Therefore, the results highlighted the suitability of MSG LST to evaluate and monitor heat extremes alone or combined with dry and bright conditions and prompts the potential of other climate data records from geostationary satellites to characterize these climate extremes that could become the norm in the near future over central and northern Europe. Full article

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

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21 pages, 8521 KiB

Open AccessArticle

Trend Changes of the Vegetation Activity in Northeastern East Asia and the Connections with Extreme Climate Indices

by Zijing Guo, Wei Lou, Cheng Sun and Bin He

Remote Sens. 2022, 14(13), 3151; https://doi.org/10.3390/rs14133151 - 30 Jun 2022

Cited by 10 | Viewed by 1884

Abstract

In the context of global warming, vegetation activity in northeastern East Asia (40–45°N, 105–130°E) (NEA) shows a significant growth trend on a multidecadal scale, but how vegetation changes on a decadal scale is unclear. In this study, we find a significant trend of vegetation greening in northeastern East Asia during 1982–1998 and a slowdown in the greening trend during 1998–2014. Trend analysis of the extreme climate indices reveals that the trends of precipitation-related extreme climate indices are similar to those of vegetation change, and further correlation analysis reveals that precipitation-related extreme climate indices have a strong positive correlation with the NDVI. The results indicate that the vegetation in northeastern East Asia is more sensitive to precipitation changes, especially extreme precipitation, compared with the temperature and related extreme indices. Furthermore, the analysis of large-scale atmospheric circulation changes suggests a role of Northwest Pacific subtropical high (NPSH) in the trend changes of precipitation-related extreme indices. The strengthening of NPSH before 1998 enhances the moisture transport to the NEA, providing abundant water vapor favorable for extreme precipitation events, while after 1998, the NPSH trend is much weakened, corresponding to a decrease in the moisture transport trend. Full article

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

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14 pages, 6571 KiB

Open AccessArticle

Linkage of Strong Intraseasonal Events of the East Asian Winter Monsoon to the Tropical Convections over the Western Pacific

by Tianjiao Ma, Wen Chen, Hainan Gong, Peng Hu, Yang Jiao, Xiadong An and Lin Wang

Remote Sens. 2022, 14(13), 2993; https://doi.org/10.3390/rs14132993 - 22 Jun 2022

Cited by 1 | Viewed by 1477

Abstract

The East Asian winter monsoon (EAWM) is the most important climate system for transporting Arctic cold air to the tropics in boreal winter. Rapid intensification of the EAWM, such as a cold surge, can lead to increased tropical convection over the western Pacific, but the possible effects from the intraseasonal variation of EAWM is unclear. Using high temporal and spatial resolution satellite data, including Outgoing Longwave Radiation (OLR) and Tropical Rainfall Measuring Mission (TRMM) precipitation, we show that strong intraseasonal EAWM events are associated with increased tropical convection over the western Pacific for about 6–8 days. Our statistical analysis shows that the lifetime of a strong intraseasonal EAWM event is about 2 weeks, with the beginning, peak, and ending phases occurring at days −6, 0, and 6, respectively. During days 0 to 8, increased convection is observed over the western tropical Pacific, due to the anomalous convergence associated with the strengthened northerly winds over the South China Sea. Over land, increased precipitation is observed over Vietnam, northwestern Kalimantan, and the southern Philippines. In addition, the East Asian local Hadley circulation is strengthened during these days, in association with the enhanced tropical convection. Full article

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

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19 pages, 4636 KiB

Open AccessArticle

Increased Compound Droughts and Heatwaves in a Double Pack in Central Asia

by Chuan Wang, Zhi Li, Yaning Chen, Yupeng Li, Xigang Liu, Yifeng Hou, Xuechun Wang, Zulipiya Kulaixi and Fan Sun

Remote Sens. 2022, 14(13), 2959; https://doi.org/10.3390/rs14132959 - 21 Jun 2022

Cited by 10 | Viewed by 2937 | Correction

Abstract

Compound droughts and heatwaves (CDHWs) are likely to cause more severe natural disasters than a single extreme event, and they have been exacerbated by rapid global warming. Based on high-resolution grid data, this study combines the daily-scale ERA5-Land dataset and the monthly-scale SPEI dataset with multiple indicators to analyze CDHWs. We calculated and analyzed the temporal and spatial modal distribution of CDHWs in Central Asia from 1981 to 2018, and in this paper, we discuss the sequence relationship between drought events, heatwave events, and CDHWs. The results show that the number of CDHWs in the study region have increased over time and expanded in terms of area, especially in eastern and southwestern Central Asia. The tsum (total frequency of CDHWs) was 0.5 times higher than the total heatwave frequency and it increased at a rate of 0.17/yr. The maximum duration of tmax (maximum duration of CDHWs in days) was 17 days. Furthermore, the occurrence rate of tmax was 96.67%, and the AH (CDHWs’ accumulated heat) had a rate of 97.78%, which, upon examination of the spatial trend pattern, accounted for the largest increase in terms of area. We also found that the TAH (CDHWs’ average temperature anomalies, SPEI < −0.5) shows obvious seasonality, with the increases in winter and spring being significantly greater than the increases in summer and autumn. The intensity of the CDHWs was stronger than that of a single extreme event, the temperature anomaly was higher than the average of 0.4–0.8 °C, and there was a north–south spatial pattern across the study region. In eastern and northwestern Central Asia, the AH and heatwaves (SPEI < −0.5) increased by 15–30 times per year on average. During the transition from the base period to the reference period, CDHWs increased by 25%, and the number of dry days prior to the CDHWs decreased by 7.35 days. The conclusion of our study can provide a theoretical basis for coping with climate change in arid zones. Full article

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

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23 pages, 8818 KiB

Open AccessArticle

The Impact of Central Heating on the Urban Thermal Environment Based on Multi-Temporal Remote Sensing Images

by Xinran Chen, Xingfa Gu, Yulin Zhan, Dakang Wang, Yazhou Zhang, Faisal Mumtaz, Shuaiyi Shi and Qixin Liu

Remote Sens. 2022, 14(10), 2327; https://doi.org/10.3390/rs14102327 - 11 May 2022

Cited by 2 | Viewed by 1598

Abstract

Research on the impact of anthropogenic heat discharge in a thermal environment is significant in climate change research. Central heating is more common in the winter in Northeast China as an anthropogenic heat. This study investigates the impact of central heating on the thermal environment in Shenyang, Changchun, and Harbin based on multi-temporal land surface temperature retrieval from remote sensing. An equivalent heat island index method was proposed to overcome the problem of the method based on a single-phase image, which cannot evaluate all the central heating season changes. The method improves the comprehensiveness of a thermal environment evaluation by considering the long-term heat accumulation. The results indicated a significant increase in equivalent heat island areas at night with 22.1%, 17.3%, and 19.5% over Shenyang, Changchun, and Harbin. The increase was significantly positively correlated with the central heating supply (with an R-value of 0.89 for Shenyang, 0.93 for Changchun, and 0.86 for Harbin; p < 0.05). The impact of central heating has a more significant effect than the air temperature. The results provide a reference for future studies of urban thermal environment changes. Full article

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

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17 pages, 5867 KiB

Open AccessArticle

Climatological Aspects of Active Fires in Northeastern China and Their Relationship to Land Cover

by Li Sun, Lei Yang, Xiangao Xia, Dongdong Wang and Tiening Zhang

Remote Sens. 2022, 14(10), 2316; https://doi.org/10.3390/rs14102316 - 11 May 2022

Cited by 5 | Viewed by 1364

Abstract

Biomass burning (BB) is a driving force for heavy haze in northeastern China (NEC) and shows distinct seasonal features. However, little is known about its climatological aspects, which are important for regional BB management and understanding BB effects on climate and environment. Here, the climatological characteristics of active fires and their dependence on land cover in NEC were studied using Moderate Resolution Imaging Spectroradiometer (MODIS) products. Moreover, the influence of meteorological factors on fire activities was explored. The number of fires was found to have increased significantly from 2003 to 2018; and the annual total FRP (FRP_tot) showed a generally consistent variation with fire counts. However, the mean fire radiative power for each spot (FRP_mean) decreased. Fire activity showed distinctive seasonal variations. Most fires and intense burning events occurred in spring and autumn. Spatially, fires were mainly concentrated in cropland areas in plains, where the frequency of fires increased significantly, especially in spring and autumn. The annual percentage of agricultural fires increased from 34% in 2003 to over 60% after 2008 and the FRP_tot of croplands increased from 12% to over 55%. Fires in forests, savannas, and grasslands tended to be associated with higher FRP_mean than those in croplands. Analysis indicated that the increasing fire count in NEC is mainly caused by agricultural fires. Although the decreasing FRP_mean represents an effective management of BB in recent years, high fire counts and FRP_tot in croplands indicate that the crop residue burning cannot be simply banned and a need instead for effective applications. More efforts should be made on clean utilization of straw. The accumulation of dry biomass, high temperature, and low humidity, and weak precipitation are conducive to the fire activities. This study provides a comprehensive analysis of BB in NEC and provides a reference for regional BB management and control. Full article

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

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17 pages, 9802 KiB

Open AccessArticle

A High-Performance Convolutional Neural Network for Ground-Level Ozone Estimation in Eastern China

by Sichen Wang, Yanfeng Huo, Xi Mu, Peng Jiang, Shangpei Xun, Binfang He, Wenyu Wu, Lin Liu and Yonghong Wang

Remote Sens. 2022, 14(7), 1640; https://doi.org/10.3390/rs14071640 - 29 Mar 2022

Cited by 12 | Viewed by 2449

Abstract

Having a high-quality historical air pollutant dataset is critical for environmental and epidemiological research. In this study, a novel deep learning model based on convolutional neural network architecture was developed to estimate ground-level ozone concentrations across eastern China. A high-resolution maximum daily average 8-h (MDA8) surface ground ozone concentration dataset was generated with the support of the total ozone column from the satellite Tropospheric Monitoring Instrument, meteorological data from the China Meteorological Administration Land Data Assimilation System, and simulations of the WRF-Chem model. The modeled results were compared with in situ measurements in five cities that were not involved in model training, and the mean R² of predicted ozone with observed values was 0.9, indicating the good robustness of our model. In addition, we compared the model results with some widely used machine learning techniques (e.g., random forest) and recently published ozone datasets, showing that the accuracy of our model is higher and that the spatial distributions of predicted ozone are more coherent. This study provides an efficient and exact method to estimate ground-level ozone and offers a new perspective for modeling spatiotemporal air pollutants. Full article

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

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1 pages, 179 KiB

Open AccessCorrection

Correction: Wang et al. Increased Compound Droughts and Heatwaves in a Double Pack in Central Asia. Remote Sens. 2022, 14, 2959

by Chuan Wang, Zhi Li, Yaning Chen, Yupeng Li, Xigang Liu, Yifeng Hou, Xuechun Wang, Zulipiya Kulaixi and Fan Sun

Remote Sens. 2023, 15(12), 3003; https://doi.org/10.3390/rs15123003 - 08 Jun 2023

Viewed by 650

Abstract

In the original article [...] Full article

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

14 pages, 3223 KiB

Open AccessTechnical Note

Enhanced Warming in Global Dryland Lakes and Its Drivers

by Siyi Wang, Yongli He, Shujuan Hu, Fei Ji, Bin Wang, Xiaodan Guan and Sebastiano Piccolroaz

Remote Sens. 2022, 14(1), 86; https://doi.org/10.3390/rs14010086 - 24 Dec 2021

Cited by 7 | Viewed by 3087

Abstract

Lake surface water temperature (LSWT) is sensitive to climate change. Previous studies have found that LSWT warming is occurring on a global scale and is expected to continue in the future. Recently, new global LSWT data products have been generated using satellite remote sensing, which provides an inimitable opportunity to study the LSWT response to global warming. Based on the satellite observations, we found that the warming rate of global lakes is uneven, with apparent regional differences. Indeed, comparing the LSWT warming in different climate zones (from arid to humid), the lakes in drylands experienced more significant warming (0.28 °C decade⁻¹) than those in semi-humid and humid regions (0.19 °C decade⁻¹) during previous decades (1995–2016). By further quantifying the impact factors, it showed that the LSWT warming is attributed to air temperature (74.4%), evaporation (4.1%), wind (9.9%), cloudiness (4.3%), net shortwave (3.1%), and net longwave (4.0%) over the lake surface. Air temperature is the main driving force for the warming of most global lakes, so the first estimate quantification of future LSWT trends can be determined from air temperature projections. By the end of the 21st century, the summer air temperature would warm up to 1.0 °C (SSP1-2.6) and 6.3 °C (SSP5-8.5) over lakes, with a more significant warming trend over the dryland lakes. Combined with their higher warming sensitivity, the excess summer LSWT warming in drylands is expected to continue, which is of great significance because of their high relevance in these water-limited regions. Full article

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

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