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Land
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Spatiotemporal Variations of Land Surface Temperature
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Spatiotemporal Variations of Land Surface Temperature

A special issue of Land (ISSN 2073-445X). This special issue belongs to the section "Land–Climate Interactions".

Deadline for manuscript submissions: closed (20 April 2023) | Viewed by 15387

Special Issue Editors

Dr. Sibo Duan

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Guest Editor
Key Laboratory of Agricultural Remote Sensing, Ministry of Agriculture and Rural Affairs, Institute of Agricultural Resources and Regional Planning, Academy of Agricultural Sciences, Beijing 100081, China
Interests: thermal infrared remote sensing; land surface temperature; land surface emissivity; radiative transfer modeling
Special Issues, Collections and Topics in MDPI journals
Dr. Wenfeng Zhan

E-Mail Website1 Website2
Guest Editor
International Institute for Earth System Science, Nanjing University, Nanjing 210023, China
Interests: thermal remote sensing; remote sensing of urban environment; land surface temperature
Special Issues, Collections and Topics in MDPI journals
Dr. Wei Zhao

E-Mail Website
Guest Editor
Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610041, China
Interests: mountain water and heat exchange; soil moisture estimation and downscaling; mountain climate change
Special Issues, Collections and Topics in MDPI journals
Dr. Penghai Wu

E-Mail Website
Guest Editor
School of Resources and Environmental Engineering/Information Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui University, Hefei 230601, China
Interests: spatiotemporal fusion; the reconstruction of land surface temperature; regional eco-environmental change

Special Issue Information

Dear colleagues,

Land surface temperature (LST) is a key parameter for investigating the interactions between the land surface and the atmosphere, including the exchange of surface matter, surface energy balance, and surface physicochemical processes. It has been used in a wide variety of studies, including drought monitoring, urban thermal environment monitoring, and climate change studies. Accurate knowledge of the spatiotemporal variations in LST can help us better understand the thermal behavior of the Earth’s surface and its physical properties.

Several satellite-derived thermal infrared LST products are available to the scientific community—e.g., MODIS, ATSR/AATSR/SLSTR, and Landsat. These long time-series LST products provide a unique opportunity to analyze and characterize the spatiotemporal variations in LST. This Special Issue focuses on spatiotemporal variations in LST, the spatiotemporal fusion of LST, the trend analysis of time-series LST, and the modeling of diurnal and annual cycles of LST.

Dr. Si-Bo Duan
Dr. Wenfeng Zhan
Dr. Wei Zhao
Dr. Penghai Wu
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. Land is an international peer-reviewed open access monthly 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 2600 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

  • land surface temperature
  • spatiotemporal variations
  • spatiotemporal fusion
  • long time-series
  • trend analysis
  • annual temperature cycle
  • diurnal temperature cycle

Published Papers (6 papers)

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Research

18 pages, 2913 KiB  
Article
The Impact of Land Cover Change on Surface Water Temperature of Small Lakes in Eastern Ontario from 1985 to 2020
by Matthew D. Senyshen and Dongmei Chen
Land 2023, 12(3), 547; https://0-doi-org.brum.beds.ac.uk/10.3390/land12030547 - 24 Feb 2023
Viewed by 1628
Abstract
Land Cover Change (LCC) has been shown to significantly impact the magnitude and trend of Land Surface Temperature (LST). However, the influence of LCC near waterbodies outside of an urban environment remain less understood. Waterbodies serve as local climate moderators where nearby LCC [...] Read more.
Land Cover Change (LCC) has been shown to significantly impact the magnitude and trend of Land Surface Temperature (LST). However, the influence of LCC near waterbodies outside of an urban environment remain less understood. Waterbodies serve as local climate moderators where nearby LCC has the potential to decrease their cooling ability. Altered water surface temperatures can lead to altered species migration and distribution in aquatic species depending on a given species thermal boundary. In this study, using remotely sensed land cover and surface temperature data, we investigate the role that LCC around small lakes (500 m) plays on the surface water temperature change of nine small lakes in the Cataraqui Region Conservation Authority’s watershed, located in Eastern Ontario, from 1985 to 2020. The Continuous Change Detection Classification (CCDC) algorithm was used alongside the Statistical Mono-Window (SMW) algorithm to calculate LCC and LST, respectively. Results indicated a strong positive relationship (R2 = 0.81) between overall LCC and lake surface water temperature (LSWT) trends, where LSWT trends in all inland small lakes investigated were found to be positive. The land cover class sparse vegetation had a strong positive correlation with water temperature, whereas dense vegetation displayed a strong negative correlation. This 35-year study contributes to the broader understanding of the impact that LCC has on the surface water temperature trends of inland lakes. Full article
(This article belongs to the Special Issue Spatiotemporal Variations of Land Surface Temperature)
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25 pages, 12900 KiB  
Article
Combining Spatiotemporally Global and Local Interpolations Improves Modeling of Annual Land Surface Temperature Cycles
by Yangyi Chen, Wenfeng Zhan, Zihan Liu, Pan Dong, Huyan Fu, Shiqi Miao, Yingying Ji, Lu Jiang and Sida Jiang
Land 2023, 12(2), 309; https://0-doi-org.brum.beds.ac.uk/10.3390/land12020309 - 22 Jan 2023
Viewed by 1274
Abstract
Annual temperature cycle (ATC) models are widely used to characterize temporally continuous land surface temperature (LST) dynamics within an annual cycle. However, the existing ATC models ignore the spatiotemporally local correlations among adjacent LST pixels and are inadequate for capturing the complex relationships [...] Read more.
Annual temperature cycle (ATC) models are widely used to characterize temporally continuous land surface temperature (LST) dynamics within an annual cycle. However, the existing ATC models ignore the spatiotemporally local correlations among adjacent LST pixels and are inadequate for capturing the complex relationships between LSTs and LST-related descriptors. To address these issues, we propose an improved ATC model (termed the ATC_GL), which combines both the spatiotemporally global and local interpolations. Using the random forest (RF) algorithm, the ATC_GL model quantifies the complex relationships between LSTs and LST-related descriptors such as the surface air temperature, normalized difference vegetation index, and digital elevation model. The performances of the ATC_GL and several extensively used LST reconstruction methods were compared under both clear-sky and overcast conditions. In the scenario with randomly missing LSTs, the accuracy of the ATC_GL was 2.3 K and 3.1 K higher than that of the ATCE (the enhanced ATC model) and the ATCO (the original ATC model), respectively. In the scenario with LST gaps of various sizes, the ATC_GL maintained the highest accuracy and was less sensitive to gap size when compared with the ATCH (the hybrid ATC model), Kriging interpolation, RSDAST (Remotely Sensed Daily Land Surface Temperature), and HIT (Hybrid Interpolation Technique). In the scenario of overcast conditions, the accuracy of the ATC_GL was 1.0 K higher than that of other LST reconstruction methods. The ATC_GL enriches the ATC model family and provides enhanced performance for generating spatiotemporally seamless LST products with high accuracy. Full article
(This article belongs to the Special Issue Spatiotemporal Variations of Land Surface Temperature)
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22 pages, 7937 KiB  
Article
Effect of Deforestation on Land Surface Temperature in the Chiquitania Region, Bolivia
by Oswaldo Maillard, Roberto Vides-Almonacid, Álvaro Salazar and Daniel M. Larrea-Alcazar
Land 2023, 12(1), 2; https://0-doi-org.brum.beds.ac.uk/10.3390/land12010002 - 20 Dec 2022
Cited by 2 | Viewed by 3457
Abstract
Neotropical forests offer alternatives to surface cooling and their conservation is an effective solution for mitigating the effects of climate change. Little is known about the importance of tropical dry forests for temperature regulation in Chiquitania, a region with increasing deforestation rates. The [...] Read more.
Neotropical forests offer alternatives to surface cooling and their conservation is an effective solution for mitigating the effects of climate change. Little is known about the importance of tropical dry forests for temperature regulation in Chiquitania, a region with increasing deforestation rates. The impact that deforestation processes are having on the surface temperature in Chiquitania remains an open question. This study evaluated trends in forest cover loss based on land surface temperatures (°C) in forested and deforested areas in Chiquitania. We hypothesized a positive relationship between higher deforestation and a temperature increase, which would decrease the resilience of highly disturbed Chiquitano forests. We evaluated ten sampling sites (10 × 10 km), including five in forested areas with some type of protection and the other five in areas with populated centers and accelerated forest loss. We developed scripts on the Google Earth Engine (GEE) platform using information from the Normalized Difference Vegetation Index (NDVI, MOD13A2) and the daytime and nighttime Land Surface Temperature (LST, MYD11A1) from MODIS products for the period 2001–2021. The statistical significance of the trends of the time series averages of the MODIS products was analyzed using a nonparametric Mann–Kendall test and the degree of the relationship between the variables was determined using the Pearson statistic. Our results based on NDVI analysis showed consistent vegetation growth in forested areas across the study period, while the opposite occurred in deforested lands. Regarding surface temperature trends, the results for daytime LST showed a positive increase in the four deforested areas. Comparatively, daytime LST averages in deforested areas were warmer than those in forested areas, with a difference of 3.1 °C. Additionally, correlation analyses showed a significant relationship between low NDVI values due to deforestation in three sites and an increase in daytime LST, while for nighttime LST this phenomenon was registered in two deforested areas. Our results suggest a significant relationship between the loss of forest cover and the increase in land surface temperature in Chiquitania. This study could be the first step in designing and implementing an early climate–forest monitoring system in this region. Full article
(This article belongs to the Special Issue Spatiotemporal Variations of Land Surface Temperature)
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28 pages, 5261 KiB  
Article
Integrated Influencing Mechanism of Potential Drivers on Seasonal Variability of LST in Kolkata Municipal Corporation, India
by Dipankar Bera, Nilanjana Das Chatterjee, Faisal Mumtaz, Santanu Dinda, Subrata Ghosh, Na Zhao, Sudip Bera and Aqil Tariq
Land 2022, 11(9), 1461; https://0-doi-org.brum.beds.ac.uk/10.3390/land11091461 - 02 Sep 2022
Cited by 13 | Viewed by 2445
Abstract
Increasing land surface temperature (LST) is one of the major anthropogenic issues and is significantly threatening the urban areas of the world. Therefore, this study was designed to examine the spatial variations and patterns of LST during the different seasons in relation to [...] Read more.
Increasing land surface temperature (LST) is one of the major anthropogenic issues and is significantly threatening the urban areas of the world. Therefore, this study was designed to examine the spatial variations and patterns of LST during the different seasons in relation to influencing factors in Kolkata Municipality Corporation (KMC), a city of India. The spatial distribution of LST was analyzed regarding the different surface types and used 25 influencing factors from 6 categories of variables to explain the variability of LST during the different seasons. All-subset regression and hierarchical partitioning analyses were used to estimate the explanatory potential and independent effects of influencing factors. The results show that high and low LST corresponded to the artificial lands and bodies of water for all seasons. In the individual category regression model, surface properties gave the highest explanatory rate for all seasons. The explanatory rates and the combination of influencing factors with their independent effects on the LST were changed for the different seasons. The explanatory rates of integration of all influencing factors were 89.4%, 81.4%, and 88.7% in the summer, transition, and winter season, respectively. With the decreasing of LST (summer to transition, then to winter) more influencing factors were required to explain the LST. In the integrated regression model, surface properties were the most important factor in summer and winter, and landscape configuration was the most important factor in the transition season. LST is not the result of single categories of influencing factors. Along with the effects of surface properties, socio-economic parameters, landscape compositions and configurations, topographic parameters and pollutant parameters mostly explained the variability of LST in the transition (11.22%) and summer season (15.22%), respectively. These findings can help to take management strategies to reduce urban LST based on local planning. Full article
(This article belongs to the Special Issue Spatiotemporal Variations of Land Surface Temperature)
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16 pages, 4784 KiB  
Article
A Slight Temperature Warming Trend Occurred over Lake Ontario from 2001 to 2018
by Xiaoying Ouyang, Dongmei Chen, Shugui Zhou, Rui Zhang, Jinxin Yang, Guangcheng Hu, Youjun Dou and Qinhuo Liu
Land 2021, 10(12), 1315; https://0-doi-org.brum.beds.ac.uk/10.3390/land10121315 - 29 Nov 2021
Cited by 2 | Viewed by 1732
Abstract
Satellite-derived lake surface water temperature (LSWT) measurements can be used for monitoring purposes. However, analyses based on the LSWT of Lake Ontario and the surrounding land surface temperature (LST) are scarce in the current literature. First, we provide an evaluation of the commonly [...] Read more.
Satellite-derived lake surface water temperature (LSWT) measurements can be used for monitoring purposes. However, analyses based on the LSWT of Lake Ontario and the surrounding land surface temperature (LST) are scarce in the current literature. First, we provide an evaluation of the commonly used Moderate Resolution Imaging Spectroradiometer (MODIS)-derived LSWT/LST (MOD11A1 and MYD11A1) using in situ measurements near the area of where Lake Ontario, the St. Lawrence River and the Rideau Canal meet. The MODIS datasets agreed well with ground sites measurements from 2015–2017, with an R2 consistently over 0.90. Among the different ground measurement sites, the best results were achieved for Hill Island, with a correlation of 0.99 and centered root mean square difference (RMSD) of 0.73 K for Aqua/MYD nighttime. The validated MODIS datasets were used to analyze the temperature trend over the study area from 2001 to 2018, through a linear regression method with a Mann–Kendall test. A slight warming trend was found, with 95% confidence over the ground sites from 2003 to 2012 for the MYD11A1-Night datasets. The warming trend for the whole region, including both the lake and the land, was about 0.17 K year−1 for the MYD11A1 datasets during 2003–2012, whereas it was about 0.06 K year−1 during 2003–2018. There was also a spatial pattern of warming, but the trend for the lake region was not obviously different from that of the land region. For the monthly trends, the warming trends for September and October from 2013 to 2018 are much more apparent than those of other months. Full article
(This article belongs to the Special Issue Spatiotemporal Variations of Land Surface Temperature)
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14 pages, 1297 KiB  
Article
The Impact of Seasonality and Land Cover on the Consistency of Relationship between Air Temperature and LST Derived from Landsat 7 and MODIS at a Local Scale: A Case Study in Southern Ontario
by Michael Burnett and Dongmei Chen
Land 2021, 10(7), 672; https://0-doi-org.brum.beds.ac.uk/10.3390/land10070672 - 26 Jun 2021
Cited by 11 | Viewed by 2810
Abstract
Land surface temperature (LST) and air temperature (Tair) have been commonly used to analyze urban heat island (UHI) effects throughout the world, with noted variations based on vegetation distribution. This research has compared time series LST data acquired from the Moderate [...] Read more.
Land surface temperature (LST) and air temperature (Tair) have been commonly used to analyze urban heat island (UHI) effects throughout the world, with noted variations based on vegetation distribution. This research has compared time series LST data acquired from the Moderate Resolution Imaging Spectroradiometer (MODIS) platforms, Landsat 7 Enhanced Thematic Mapper (ETM+) and Tair from weather stations in the Southern Ontario area. The influence of the spatial resolution, land cover, vegetated surfaces, and seasonality on the relationship between LST and in situ Tair were examined. The objective is to identify spatial and seasonal differences amongst these different spatial resolution LST products and Tair, along with the causes for variations at a localized scale. Results show that MODIS LST from Terra had stronger relationships with Landsat 7 LST than those from Aqua. Tair demonstrated weaker correlations with Landsat LST than with MODIS LST in sparsely vegetated and urban areas during the summer. Due to the winter’s ability to smooth heterogenous surfaces, both LST and Tair showed stronger relationships in winter than summer over every land cover, except with coarse spatial resolutions on forested surfaces. Full article
(This article belongs to the Special Issue Spatiotemporal Variations of Land Surface Temperature)
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