Nothing Special   »   [go: up one dir, main page]
You seem to have javascript disabled. Please note that many of the page functionalities won't work as expected without javascript enabled.
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
8.3
4.2
Journals
Remote Sensing
Special Issues
Selected Papers from the 5th International Electronic Conference on Remote...
remotesensing-logo
Submit to Remote Sensing Review for Remote Sensing Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Selected Papers from the 5th International Electronic Conference on Remote Sensing

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (31 May 2024) | Viewed by 7487

Special Issue Editor

Dr. Alexander Kokhanovsky

E-Mail Website
Guest Editor
German Research Centre for Geosciences, Telegrafenberg, 14473 Potsdam, Germany
Interests: cloud remote sensing; aerosol remote sensing; trace gas remote sensing; snow remote sensing; radiative transfer
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue will comprise extended and expanded versions of proceedings papers from the 5th International Electronic Conference on Remote Sensing, which is to be held on 7–21 Nov 2023 on sciforum.net. In this 5th edition of the e-conference, contributors are invited to provide papers and presentations from the field of sensors and applications at large. Selected papers that will attract the most interest on the web, or that will provide a particularly innovative contribution, will be gathered for publication. These papers will be subjected to peer review and could possibly be published with the aim of the rapid and wide dissemination of research results, developments, and applications. We hope that this conference series will grow further in the future and become recognized as a new way and venue by which researchers can (electronically) present novel developments related to the field of remote sensing and their applications.

Dr. Alexander Kokhanovsky
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

  • remote sensing
  • atmospheric aerosol
  • trace gases
  • remote sensing of underlying surface
  • polarimetry
  • hyperspectral remote sensing
  • lidars
  • radars

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (4 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Other

26 pages, 9865 KiB  
Article
A Methodological Approach for Assessing the Post-Fire Resilience of Pinus halepensis Mill. Plant Communities Using UAV-LiDAR Data Across a Chronosequence
by Sergio Larraz-Juan, Fernando Pérez-Cabello, Raúl Hoffrén Mansoa, Cristian Iranzo Cubel and Raquel Montorio
Remote Sens. 2024, 16(24), 4738; https://doi.org/10.3390/rs16244738 - 19 Dec 2024
Viewed by 682
Abstract
The assessment of fire effects in Aleppo pine forests is crucial for guiding the recovery of burnt areas. This study presents a methodology using UAV-LiDAR data to quantify malleability and elasticity in four burnt areas (1970, 1995, 2008 and 2015) through the statistical [...] Read more.
The assessment of fire effects in Aleppo pine forests is crucial for guiding the recovery of burnt areas. This study presents a methodology using UAV-LiDAR data to quantify malleability and elasticity in four burnt areas (1970, 1995, 2008 and 2015) through the statistical analysis of different metrics related to height structure and diversity (Height mean, 99th percentile and Coefficient of Variation), coverage, relative shape and distribution strata (Canopy Cover, Canopy Relief Ratio and Strata Percent Coverage), and canopy complexity (Profile Area and Profile Area Change). In general terms, malleability decreases over time in forest ecosystems that have been affected by wildfires, whereas elasticity is higher than what has been determined in previous studies. However, a particular specificity has been detected from the 1995 fire, so we can assume that there are other situational factors that may be affecting ecosystem resilience. LiDAR metrics and uni-temporal sampling between burnt sectors and control aids are used to understand community resilience and to identify the different recovery stages in P. halepensis forests. Full article
(This article belongs to the Special Issue Selected Papers from the 5th International Electronic Conference on Remote Sensing)
►▼ Show Figures

Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Study area localization (EPSG: 25830). In light pink: contours of burnt areas in Aragón (Spain).</p> Full article ">Figure 2
<p>Flight footprints, located in Sierra de Luna (SL) and Montes de Zuera (MZ), corresponding to the fires in 1970, 1995, 2008 (MZ) and 2015 (SL). EPSG: 25830. In light pink: contours of burnt areas. In magenta: drone footprints.</p> Full article ">Figure 3
<p>Experimental scheme with quadrangular sectors (analysis plot) corresponding to the burnt and control sectors (EPSG: 25830). In light pink: contours of burnt areas. In magenta: drone footprints. In violet: analysis plots—burnt and control—of each fire.</p> Full article ">Figure 4
<p>Boxplot of Canopy Cover (percentage of ground covered by the vertical projection of the vegetation) in different sectors (BP/CP) by year of fire. In green: Control Sectors; in red: Burnt Sectors. Boxes are arranged chronologically (most recent on the left and oldest on the right). The orange cross represents the mean value.</p> Full article ">Figure 5
<p>Distribution of cumulative relative frequencies (in green: Control Sectors; in red: Burnt Sectors) by wildfire for Canopy Relief Ratio (<b>first row</b>), coefficient of variation (<b>second row</b>), height mean (<b>third row</b>), 99th percentile (<b>fourth row</b>) and Profile Area (<b>fifth row</b>). Graphics are arranged chronologically (most recent on the left and oldest on the right).</p> Full article ">Figure 5 Cont.
<p>Distribution of cumulative relative frequencies (in green: Control Sectors; in red: Burnt Sectors) by wildfire for Canopy Relief Ratio (<b>first row</b>), coefficient of variation (<b>second row</b>), height mean (<b>third row</b>), 99th percentile (<b>fourth row</b>) and Profile Area (<b>fifth row</b>). Graphics are arranged chronologically (most recent on the left and oldest on the right).</p> Full article ">Figure 6
<p>In lines: trend of magnitude of differences, expressed through the Kolmogorov–Smirnov statistic D. In bars: normalized differences of the analyzed metrics: mean height, 99th percentile, coefficient of variation, Canopy Relief Ratio and Profile Area.</p> Full article ">Figure 7
<p>Strata distribution of fires (1970, 1995, 2008 and 2015), arranged chronologically (most recent at the top and oldest at the bottom). Burnt sectors in red and control sectors in green.</p> Full article ">Figure A1
<p>Boxplot of Canopy Cover in different sectors (BP/CP) by year of fire in upper understory strata (0.5 m–1 m). In green, Control Sectors, and in red, Burnt Sectors. Boxes are arranged chronologically (most recent on the left and oldest on the right). The orange cross represents the mean value.</p> Full article ">Figure A2
<p>Boxplot of Canopy Cover in different sectors (BP/CP) by year of fire in shrub layer strata (1 m–3 m). In green, Control Sectors, and in red, Burnt Sectors. Boxes are arranged chronologically (most recent on the left and oldest on the right). The orange cross represents the mean value.</p> Full article ">Figure A3
<p>Boxplot of Canopy Cover in different sectors (BP/CP) by year of fire in undertree layer strata (3 m–5 m). In green, Control Sectors, and in red, Burnt Sectors. Boxes are arranged chronologically (most recent on the left and oldest on the right). The orange cross represents the mean value.</p> Full article ">Figure A4
<p>Boxplot of Canopy Cover in different sectors (BP/CP) by year of fire in tree layer strata (upper than 5 m). In green, Control Sectors, and in red, Burnt Sectors. Boxes are arranged chronologically (most recent on the left and oldest on the right). The orange cross represents the mean value.</p> Full article ">
29 pages, 11518 KiB  
Article
Evaluating the Two-Source Energy Balance Model Using MODIS Data for Estimating Evapotranspiration Time Series on a Regional Scale
by Mahsa Bozorgi, Jordi Cristóbal and Magí Pàmies-Sans
Remote Sens. 2024, 16(23), 4587; https://doi.org/10.3390/rs16234587 - 6 Dec 2024
Viewed by 845
Abstract
Estimating daily continuous evapotranspiration (ET) can significantly enhance the monitoring of crop stress and drought on regional scales, as well as benefit the design of agricultural drought early warning systems. However, there is a need to verify the models’ performance in estimating the [...] Read more.
Estimating daily continuous evapotranspiration (ET) can significantly enhance the monitoring of crop stress and drought on regional scales, as well as benefit the design of agricultural drought early warning systems. However, there is a need to verify the models’ performance in estimating the spatiotemporal continuity of long-term daily evapotranspiration (ETd) on regional scales due to uncertainties in satellite measurements. In this study, a thermal-based two-surface energy balance (TSEB) model was used concurrently with Terra/Aqua MODIS data and the ERA5 atmospheric reanalysis dataset to calculate the surface energy balance of the soil–canopy–atmosphere continuum and estimate ET at a 1 km spatial resolution from 2000 to 2022. The performance of the model was evaluated using 11 eddy covariance flux towers in various land cover types (i.e., savannas, woody savannas, croplands, evergreen broadleaf forests, and open shrublands), correcting for the energy balance closure (EBC). The Bowen ratio (BR) and residual (RES) methods were used for enforcing the EBC in the EC observations. The modeled ET was evaluated against unclosed ET and closed ET (ETBR and ETRES) under clear-sky and all-sky observations as well as gap-filled data. The results showed that the modeled ET presented a better agreement with closed ET compared to unclosed ET in both Terra and Aqua datasets. Additionally, although the model overestimated ETd across all different land cover types, it successfully captured the spatiotemporal variability in ET. After the gap-filling, the total number of days compared with flux measurements increased substantially, from 13,761 to 19,265 for Terra and from 13,329 to 19,265 for Aqua. The overall mean results including clear-sky and all-sky observations as well as gap-filled data with the Aqua dataset showed the lowest errors with ETRES, by a mean bias error (MBE) of 0.96 mm.day−1, an average mean root square (RMSE) of 1.47 mm.day−1, and a correlation (r) value of 0.51. The equivalent figures for Terra were about 1.06 mm.day−1, 1.60 mm.day−1, and 0.52. Additionally, the result from the gap-filling model indicated small changes compared with the all-sky observations, which demonstrated that the modeling framework remained robust, even with the expanded days. Hence, the presented modeling framework can serve as a pathway for estimating daily remote sensing-based ET on regional scales. Furthermore, in terms of temporal trends, the intra-annual and inter-annual variability in ET can be used as indicators for monitoring crop stress and drought. Full article
(This article belongs to the Special Issue Selected Papers from the 5th International Electronic Conference on Remote Sensing)
►▼ Show Figures

Figure 1

Figure 1
<p>Location of the study area and the 11 selected flux towers. Projection system in UTM-30N WGS-84.</p> Full article ">Figure 2
<p>Flowchart of the modeling framework estimating ET<sub>d</sub> time series. The orange rectangle denotes the pre-processing of MODIS vegetation indices through TIMESAT.</p> Full article ">Figure 3
<p>TSEB modelling scheme (adapted from [<a href="#B61-remotesensing-16-04587" class="html-bibr">61</a>]).</p> Full article ">Figure 4
<p>Scatterplots of the EBC calculated using the EC with the statistical metrics for the entire study period (<b>top left</b>) and the days compared with the mode (<b>bottom right</b>) at each flux tower.</p> Full article ">Figure 5
<p>Temporal variations in the modeled ET<sub>d</sub> estimated using Terra and Aqua datasets compared to in situ-measured ET at flux towers.</p> Full article ">Figure 6
<p>Scatterplots of the modeled ET<sub>d</sub> estimated using Terra against unclosed and closed ET (ET<sub>BR</sub> and ET<sub>RES</sub>).</p> Full article ">Figure 7
<p>Scatterplots of the modeled ET<sub>d</sub> estimated using Aqua against unclosed and closed ET (ET<sub>BR</sub> and ET<sub>RES</sub>).</p> Full article ">Figure 8
<p>Scatterplots of the LST obtained from MODSI Terra dataset and the LST calculated using half-hourly flux tower data at satellite overpass time.</p> Full article ">Figure 9
<p>Scatterplots of the LST obtained from MODSI Aqua dataset and the LST calculated using half-hourly flux tower data at satellite overpass time.</p> Full article ">Figure 10
<p>Mean monthly variability in estimated ET using Terra dataset.</p> Full article ">Figure 11
<p>Mean monthly variability in estimated ET using Aqua dataset.</p> Full article ">Figure 12
<p>Seasonal variation in estimated ET from 2000 to 2022 in the study area using Terra dataset.</p> Full article ">Figure 13
<p>Seasonal variation in estimated ET from 2002 to 2022 in the study area using Aqua dataset.</p> Full article ">Figure 14
<p>Temporal variation in annual cumulative of estimated <span class="html-italic">ET</span> from 2000 to 2022 in the study area using Aqua (<b>left</b>) and Terra (<b>right</b>) dataset.</p> Full article ">Figure 15
<p>Spatiotemporal variability in annual cumulative of estimated ET from 2000 to 2022 in the study area using Terra dataset.</p> Full article ">Figure 16
<p>Spatiotemporal variability in annual cumulative of estimated ET from 2000 to 2022 in the study area using Aqua dataset.</p> Full article ">
22 pages, 15350 KiB  
Article
Spatiotemporal Analysis of Land Surface Temperature in Response to Land Use and Land Cover Changes: A Remote Sensing Approach
by Gulam Mohiuddin and Jan-Peter Mund
Remote Sens. 2024, 16(7), 1286; https://doi.org/10.3390/rs16071286 - 5 Apr 2024
Cited by 5 | Viewed by 3293
Abstract
Rapid urbanisation in the global south has often introduced substantial and rapid uncontrolled Land Use and Land Cover (LULC) changes, considerably affecting the Land Surface Temperature (LST) patterns. Understanding the relationship between LULC changes and LST is essential to mitigate such effects, considering [...] Read more.
Rapid urbanisation in the global south has often introduced substantial and rapid uncontrolled Land Use and Land Cover (LULC) changes, considerably affecting the Land Surface Temperature (LST) patterns. Understanding the relationship between LULC changes and LST is essential to mitigate such effects, considering the urban heat island (UHI). This study aims to elucidate the spatiotemporal variations and alterations of LST in urban areas compared to LULC changes. The study focused on a peripheral urban area of Phnom Penh (Cambodia) undergoing rapid urban development. Using Landsat images from 2000 to 2021, the analysis employed an exploratory time-series analysis of LST. The study revealed a noticeable variability in LST (20 to 69 °C), which was predominantly influenced by seasonal variability and LULC changes. The study also provided insights into how LST varies within different LULC at the exact spatial locations. These changes in LST did not manifest uniformly but displayed site-specific responses to LULC changes. This study accounts for changing land surfaces’ complex physical energy interaction over time. The methodology offers a replicable model for other similarly structured, rapidly urbanised regions utilising novel semi-automatic processing of LST from Landsat images, potentially inspiring future research in various urban planning and monitoring contexts. Full article
(This article belongs to the Special Issue Selected Papers from the 5th International Electronic Conference on Remote Sensing)
►▼ Show Figures

Figure 1

Figure 1
<p>Location of the study area. (data sources: open street map, USGS for background map).</p> Full article ">Figure 2
<p>Different Landsat satellite-wise number of images.</p> Full article ">Figure 3
<p>Distribution of selected 425 images based on the day of the year and Landsat satellite type.</p> Full article ">Figure 4
<p>Graphical overview of the methodical overview.</p> Full article ">Figure 5
<p>Boxplot of minimum, mean and maximum LST (number of observation (N) = 425).</p> Full article ">Figure 6
<p>Calendar heatmap of mean LST (white boxes inside the figure represent no data).</p> Full article ">Figure 7
<p>Boxplot of yearly minimum, mean and maximum LST.</p> Full article ">Figure 8
<p>Total time series of minimum, mean and maximum LST (2000–2021).</p> Full article ">Figure 9
<p>LST (in °C) in different months from 2015 (images in March, July, August and November were heavily contaminated from cloud).</p> Full article ">Figure 10
<p>Monthly minimum, mean and maximum LST in 2015.</p> Full article ">Figure 11
<p>Example of how visual interpretation provides additional input.</p> Full article ">Figure 12
<p>LST (in °C) in 2000–2021.</p> Full article ">Figure 13
<p>Visible connection between LST (in °C) and LULC changes elaborating spatial changes: (<b>a</b>) LST in March 2003; (<b>b</b>) Google Earth RGB image in February 2003; (<b>c</b>) LST in March 2019; (<b>d</b>) Google Earth RGB image in February 2019.</p> Full article ">Figure 14
<p>LST and LULC changes in point 4 (The blue line is the line graph of LST data in the time series, and the green line is a statistical trendline showing the upward trend of LST at this specific point).</p> Full article ">Figure 15
<p>Correlation matrix between LST, IBI, MNDWI and NDVI in 400 random points.</p> Full article ">Figure 16
<p>Linear relationship between built-up area and LST (in °C).</p> Full article ">Figure 17
<p>Relationship between LST and NDVI and MNDWI: (<b>a</b>) LST vs. NDVI with all 400 samples; (<b>b</b>) LST vs. NDVI with only points that have ≥ 0.2 NDVI values (only vegetation samples); (<b>c</b>) LST vs. MNDWI with all 400 samples; (<b>d</b>) LST vs. MNDWI with only points that has ≤0.2 NDVI (without vegetation samples).</p> Full article ">Figure A1
<p>Location 400 random points considered for correlation test between LST and LULC.</p> Full article ">Figure A2
<p>Location of identified five points to examine LST and LULC interactions (background map is LST (in °C) from April 2015).</p> Full article ">

Other

Jump to: Research

16 pages, 5049 KiB  
Technical Note
Impact of Urbanization on Cloud Characteristics over Sofia, Bulgaria
by Ventsislav Danchovski
Remote Sens. 2024, 16(9), 1631; https://doi.org/10.3390/rs16091631 - 2 May 2024
Viewed by 1335
Abstract
Urban artificial surfaces and structures induce modifications in land–atmosphere interactions, affecting the exchange of energy, momentum, and substances. These modifications stimulate urban climate formation by altering the values and dynamics of atmospheric parameters, including cloud-related features. This study evaluates the presence and quantifies [...] Read more.
Urban artificial surfaces and structures induce modifications in land–atmosphere interactions, affecting the exchange of energy, momentum, and substances. These modifications stimulate urban climate formation by altering the values and dynamics of atmospheric parameters, including cloud-related features. This study evaluates the presence and quantifies the extent of such changes over Sofia, Bulgaria. The findings reveal that estimations of low-level cloud base height (CBH) derived from lifting condensation level (LCL) calculations may produce unexpected outcomes due to microclimate influence. Ceilometer data indicate that the CBH of low-level clouds over urban areas exceeds that of surrounding regions by approximately 200 m during warm months and afternoon hours. Moreover, urban clouds exhibit reduced persistence relative to rural counterparts, particularly pronounced in May, June, and July afternoons. Reanalysis-derived low-level cloud cover (LCC) shows no significant disparities between urban and rural areas, although increased LCC is observed above the western and northern city boundaries. Satellite-derived cloud products reveal that the optically thinnest low-level clouds over urban areas exhibit slightly higher cloud tops, but the optically thickest clouds are more prevalent during warm months. These findings suggest an influence of urbanization on cloudiness, albeit nuanced and potentially influenced by the city size and surrounding physical and geographical features. Full article
(This article belongs to the Special Issue Selected Papers from the 5th International Electronic Conference on Remote Sensing)
►▼ Show Figures

Figure 1

Figure 1
<p>Sofia valley and the locations of LBSF (cyan), NIMH (blue) and SU (pink). The city building boundaries are depicted by a red polyline; the magenta polyline is the valley. Rural area is denoted by difference between the magenta and the red polygons.</p> Full article ">Figure 2
<p>Diurnal and seasonal variations (heat map) of the mean LCL at LBSF, NIMH, and SU. The figure presents a summary of the dataset spanning the period from 2011 to 2020.</p> Full article ">Figure 3
<p>Heat maps of the mean difference in air temperature (<b>a</b>), relative humidity (<b>b</b>), and lifting condensation level (<b>c</b>) between SU and LBSF. Circles indicate statistically significant differences (conducted <span class="html-italic">t</span>-test with <span class="html-italic">p</span>-value 0.05). The figure presents a summary of the dataset spanning the period from 2011 to 2020.</p> Full article ">Figure 4
<p>Diurnal and seasonal variations in the mean CBH of low-level clouds at LBSF and SU, respectively. The figure presents a summary of the dataset spanning the period from 2011 to 2020.</p> Full article ">Figure 5
<p>The mean difference in CBH (measured by the ceilometers) of low-level clouds (CBH &lt; 2500 m) over SU (city center) and over LBSF (the airport at the city edge) as a heat map for different months and hours (<b>a</b>), where circles indicate statistically significant difference (<span class="html-italic">t</span>-test with <span class="html-italic">p</span>-value 0.05). A bivariate polar plot of the difference in CBH varying by wind speed (ws) and wind direction at 700 hPa (<b>b</b>). The figure presents a summary of the dataset spanning the period from 2011 to 2020.</p> Full article ">Figure 6
<p>Diurnal and seasonal variations of the mean low-level cloud persistence at LBSF and SU, respectively. The figure presents a summary of the dataset spanning the period from 2011 to 2020.</p> Full article ">Figure 7
<p>The mean difference in cloud persistence (measured by the ceilometers) of low clouds (CBH &lt; 2500 m) at SU (city center) and LBSF (the airport at the city edge). A heat map for different months and hours (<b>a</b>), where circles indicate statistically significant (test of equal proportions at <span class="html-italic">p</span>-value 0.05) differences. A bivariate polar plot of the difference in cloud persistence varying by wind speed (ws) and wind direction at 700 hPa (<b>b</b>). The figure presents a summary of the dataset spanning the period from 2011 to 2020.</p> Full article ">Figure 8
<p>BIAS of CBH determined from rawinsonde RH profiles against CBH obtained by the ceilometers—CL31(at the airport) and CHM15k (in the city center) as a function of the method parameters RH<sub>min</sub> and RH<sub>jump</sub>. The figure presents a summary of the dataset spanning the period from 2011 to 2020.</p> Full article ">Figure 9
<p>Seasonal variations in CERRA’s LCC. The magenta and red polylines enclose the valley and built-up areas, respectively. The figure presents a summary of the dataset spanning the period from 2011 to 2020.</p> Full article ">Figure 10
<p>The mean difference in CERRA’s low cloud cover (CBH &lt; 2500 m) over the built-up area and over the rural area. A heat map for different months and hours (<b>a</b>), where circles indicate statistically significant (test of equal proportions at <span class="html-italic">p</span>-value 0.05) differences. A bivariate polar plot of the difference in LCC varying by wind speed (ws) and wind direction at 700 hPa (<b>b</b>). The figure presents a summary of the dataset spanning the period from 2011 to 2020.</p> Full article ">Figure 11
<p>COT-CTP histograms for ice cloud top (<b>left side</b>) and water cloud top (<b>right side</b>) clouds detected over the rural and urban areas, respectively, during different seasons (MAM—spring, JJA—summer, SON—autumn, DJF—winter). The figure presents a summary of the dataset spanning the period from 2011 to 2020.</p> Full article ">
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-4
Back to TopTop