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

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.

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Impact of Multitemporal Land Use and Land Cover Change on Land Surface Temperature Due to Urbanization in Hefei City, China

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10120809 ◽

2021 ◽

Vol 10 (12) ◽

pp. 809

Author(s):

Jing Sun ◽

Suwit Ongsomwang

Keyword(s):

Land Use ◽

Land Cover ◽

Surface Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

Urban Areas ◽

Decomposition Analysis ◽

Lulc Change ◽

Main Components ◽

The Impact

Land surface temperature (LST) is an essential parameter in the climate system whose dynamics indicate climate change. This study aimed to assess the impact of multitemporal land use and land cover (LULC) change on LST due to urbanization in Hefei City, Anhui Province, China. The research methodology consisted of four main components: Landsat data collection and preparation; multitemporal LULC classification; time-series LST dataset reconstruction; and impact of multitemporal LULC change on LST. The results revealed that urban and built-up land continuously increased from 2.05% in 2001 to 13.25% in 2020. Regarding the impact of LULC change on LST, the spatial analysis demonstrated that the LST difference between urban and non-urban areas had been 1.52 K, 3.38 K, 2.88 K and 3.57 K in 2001, 2006, 2014 and 2020, respectively. Meanwhile, according to decomposition analysis, regarding the influence of LULC change on LST, the urban and built-up land had an intra-annual amplitude of 20.42 K higher than other types. Thus, it can be reconfirmed that land use and land cover changes due to urbanization in Hefei City impact the land surface temperature.

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Monitoring Drought over the Conterminous United States Using MODIS and NCEP Reanalysis-2 Data

Journal of Applied Meteorology and Climatology ◽

10.1175/2010jamc2328.1 ◽

2010 ◽

Vol 49 (8) ◽

pp. 1665-1680 ◽

Cited By ~ 46

Author(s):

Yunjun Yao ◽

Shunlin Liang ◽

Qiming Qin ◽

Kaicun Wang

Keyword(s):

United States ◽

Spatial Resolution ◽

Great Plains ◽

Land Surface ◽

Remote Sensing Data ◽

Moisture Stress ◽

Ncep Reanalysis ◽

Southern Great Plains ◽

Moderate Resolution ◽

Moderate Resolution Imaging Spectroradiometer

Abstract Monitoring land surface drought using remote sensing data is a challenge, although a few methods are available. Evapotranspiration (ET) is a valuable indicator linked to land drought status and plays an important role in surface drought detection at continental and global scales. In this study, the evaporative drought index (EDI), based on the estimated actual ET and potential ET (PET), is described to characterize the surface drought conditions. Daily actual ET at 4-km resolution for April–September 2003–05 across the continental United States is estimated using a simple improved ET model with input solar radiation acquired by Moderate-Resolution Imaging Spectroradiometer (MODIS) at a spatial resolution of 4 km and input meteorological parameters from NCEP Reanalysis-2 data at a spatial resolution of 32 km. The PET is also calculated using some of these data. The estimated actual ET has been rigorously validated with ground-measured ET at six Enhanced Facility sites in the Southern Great Plains (SGP) of the Atmosphere Radiation Measurement Program (ARM) and four AmeriFlux sites. The validation results show that the bias varies from −11.35 to 27.62 W m−2 and the correlation coefficient varies from 0.65 to 0.86. The monthly composites of EDI at 4-km resolution during April–September 2003–05 are found to be in good agreement with the Palmer Z index anomalies, but the advantage of EDI is its finer spatial resolution. The EDI described in this paper incorporates information about energy fluxes in response to soil moisture stress without requiring too many meteorological input parameters, and performs well in assessing drought at continental scales.

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Air Temperature Modelling Depended on Remote Sensing Techniques

Engineering and Technology Journal ◽

10.30684/etj.v38i3a.398 ◽

2020 ◽

Vol 38 (3A) ◽

pp. 352-360

Author(s):

Zainab T. Mohammed ◽

Riyad H. Al-Anbari ◽

Oday Z. Jasim

Keyword(s):

Air Temperature ◽

Land Surface ◽

Accuracy Assessment ◽

Day Length ◽

Temperature Estimation ◽

Digital Elevation ◽

Moderate Resolution ◽

Moderate Resolution Imaging Spectroradiometer ◽

Remote Sensing Techniques ◽

Elevation Model

Air temperature (T air) near the land surface is a fundamental descriptor of physical environmental conditions and one of the most widely used climatic variables in global change studies. In this study, the researcher trying to suggest a model for estimating air temperature in summer season for any region through integrating of Iraqi Agrometeorological network daily (T air) with the moderate resolution imaging spectroradiometer (MODIS) land surface temperature (LST), Duration Day Length (DDL) and Digital Elevation Model (DEM). In this model, using satellite images for the study area and data of air temperature for four weather stations located in Babylon governorate from 1- June to 30- September on year 2017 for modeling and accuracy assessment air temperature estimation. The standard error of this model is 1.72887° C, and the correlation equal to 0.69698.

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Assessing the Impact of Builtup Areas on Development of Urban Heat Island in Lahore, Pakistan

International Journal of Economic and Environmental Geology ◽

10.46660/ijeeg.vol10.iss4.2019.355 ◽

2020 ◽

Vol 10 (4) ◽

pp. 64-68

Author(s):

Safdar Ali Shirazi ◽

Khadija Shakrullah ◽

Saadia Sultan Wahla ◽

Mareena Khurshid

Keyword(s):

Urban Heat Island ◽

Land Surface ◽

Urban Areas ◽

Urban Forest ◽

Meteorological Data ◽

Geographical Information ◽

Heat Island ◽

Landsat 7 ◽

Urban Heat ◽

The Impact

The aim of present study is to evaluate and assess the impact of built-up areas on development of the urbanheat island (UHI).The study mainly focused on Lahore, which is one of the mega cities of Pakistan. In terms ofpopulation size, Lahore is the second largest city of Pakistan with 11.13 million inhabitants. The geospatial techniques(Remote Sensing and Geographical Information System) along with statistical applications were applied to find out theLand Cover Land Uses changes and consequent development of builtup areas over the period of 2000 and 2015. Tostudy the UHI, the meteorological data of each 30 minutes for 36 days starting from 30th June 2015 to 4th August 2015were collected through direct on site observation by using digital weather station. The results of UHI were crosschecked by obtaining land surface temperature by using thermal infrared (TIR) band 6 of the Landsat-7 TM. The resultsshow that the LCLU and built environment have direct impact on development of UHI. The areas where there wasmore vegetation cover had less temperature while in urban areas, the temperature was measured higher. Over the periodof 36 days, the average UHI remained 5.5°C and the highest intensity of UHI was observed as 8.3°C thus augmentedresearch rationale. The study suggests establishment of a thick network of automatic weather stations in Lahore togauge the urban heat island intensity and to plant indigenous trees on vacant swaths and develop urban forest tomitigate city’s rising temperature.

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Evaluation of cloud effects on air temperature estimation using MODIS LST based on ground measurements over the Tibetan Plateau

10.5194/acp-2016-747 ◽

2016 ◽

Author(s):

Hongbo Zhang ◽

Fan Zhang ◽

Guoqing Zhang ◽

Xiaobo He ◽

Lide Tian

Keyword(s):

Tibetan Plateau ◽

Air Temperature ◽

Land Surface ◽

Estimation Accuracy ◽

Daily Maximum ◽

The Tibetan Plateau ◽

Cloud Detection ◽

Air Temperatures ◽

Moderate Resolution ◽

Moderate Resolution Imaging Spectroradiometer

Abstract. Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) data have played a significant role in estimating the air temperature (Tair) due to the sparseness of ground measurements, especially for remote mountainous areas. Generally, two types of air temperatures are studied including daily maximum (Tmax) and minimum (Tmin) air temperatures. MODIS daytime and nighttime LST are often used as proxies for estimating Tmax and Tmin, respectively. The Tibetan Plateau (TP) has a high daily cloud cover fraction (> 45 %). The presence of clouds can affect the relationship between Tair and LST and can further affect the estimation accuracies. This study comprehensively analyzes the effects of clouds on Tair estimation based on MODIS LST using detailed half-hourly ground measurements and daily meteorological station observations collected from over the TP. Comparisons made between in-situ cloudiness observations and MODIS claimed clear-sky records show that erroneous rates of MODIS nighttime cloud detection are obviously higher than those achieved in daytime. Our validation of the MODIS LST values under different cloudiness constraining conditions shows that the accuracy of MODIS nighttime LST is severely affected by undetected clouds. Large errors introduced by undetected clouds are found to significantly affect the Tmin estimations based on nighttime LST through cloud effect tests. However, clouds are mainly found to affect Tmax estimation by affecting the essential relationship between Tmax and daytime LST. The obviously larger errors of Tmax estimation than those of Tmin could be attributed to larger MODIS daytime LST errors resulting from higher degrees of daytime LST heterogeneity within MODIS pixel than those of nighttime LST. Constraining all four MODIS observations per day to non-cloudy observations can efficiently screen samples to build a strong fit of Tmin estimation using MODIS nighttime LST. The present study reveals the effects of clouds on Tair estimation through MODIS LST and will thus help improve the estimation accuracy levels while alleviating the problems associated with severe data sparseness over the TP.

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Assessing the Impact of Land Cover Changes on Surface Urban Heat Islands with High-Spatial-Resolution Imagery on a Local Scale: Workflow and Case Study

Sustainability ◽

10.3390/su11195188 ◽

2019 ◽

Vol 11 (19) ◽

pp. 5188 ◽

Cited By ~ 1

Author(s):

Peng Ren ◽

Xinxin Zhang ◽

Haoyan Liang ◽

Qinglin Meng

Keyword(s):

Land Cover ◽

Spatial Resolution ◽

Land Surface ◽

High Spatial Resolution ◽

Spatial Configuration ◽

Central Area ◽

Local Scale ◽

Systematic Analysis ◽

Urban Heat ◽

The Impact

Low-altitude remote sensing platform has been increasingly applied to observing local thermal environments due to its obvious advantage in spatial resolution and apparent flexibility in data acquisition. However, there is a general lack of systematic analysis for land cover (LC) classification, surface urban heat island (SUHI), and their spatial and temporal change patterns. In this study, a workflow is presented to assess the LC’s impact on SUHI, based on the visible and thermal infrared images with high spatial resolution captured by an unmanned airship in the central area of the Sino-Singapore Guangzhou Knowledge City in 2012 and 2015. Then, the accuracy assessment of LC classification and land surface temperature (LST) retrieval are performed. Finally, the commonly-used indexes in the field of satellites are applied to analyzing the spatial and temporal changes in the SUHI pattern on a local scale. The results show that the supervised maximum likelihood algorithm can deliver satisfactory overall accuracy and Kappa coefficient for LC classification; the root mean square error of the retrieved LST can reach 1.87 °C. Moreover, the LST demonstrates greater consistency with land cover type (LCT) and more fluctuation within an LCT on a local scale than on an urban scale. The normalized LST classified by the mean and standard deviation (STD) is suitable for the high-spatial situation; however, the thermal field level and the corresponded STD multiple need to be judiciously selected. This study exhibits an effective pathway to assess SUHI pattern and its changes using high-spatial-resolution images on a local scale. It is also indicated that proper landscape composition, spatial configuration and materials on a local scale exert greater impacts on SUHI.

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Assessing the Impact of Builtup Areas on Development of Urban Heat Island in Lahore, Pakistan

International Journal of Economic and Environmental Geology ◽

10.46660/ojs.v10i4.355 ◽

2020 ◽

Vol 10 (4) ◽

pp. 64-68

Author(s):

Safdar Ali Shirazi ◽

Khadija Shakrullah ◽

Saadia Sultan Wahla ◽

Mareena Khurshid

Keyword(s):

Urban Heat Island ◽

Land Surface ◽

Urban Areas ◽

Urban Forest ◽

Meteorological Data ◽

Geographical Information ◽

Heat Island ◽

Landsat 7 ◽

Urban Heat ◽

The Impact

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EVALUATION OF THE SEASONAL NIGHTTIME LST-AIR TEMPERATURE DISCREPANCIES AND THEIR RELATION TO LOCAL CLIMATE ZONES (LCZ) IN STRASBOURG

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xliii-b3-2021-391-2021 ◽

2021 ◽

Vol XLIII-B3-2021 ◽

pp. 391-398

Author(s):

S. Del Pozo ◽

T. Landes ◽

F. Nerry ◽

P. Kastendeuch ◽

G. Najjar ◽

...

Keyword(s):

Spatial Resolution ◽

Air Temperature ◽

Land Surface ◽

Urban Areas ◽

Research Work ◽

Local Climate ◽

Urban Climatology ◽

Urban Energy Balance ◽

Local Climate Zones ◽

The City

Abstract. More and more uses and applications are being given to local climate zone (LCZ) maps, which describe the structure of the urban and semi-urban areas. Among others, it is worth highlighting its use in studies of urban heat islands (UHI), sustainability and urban energy balance. Even if the classes are well described in the literature, it is difficult to estimate the general precision of these classification maps because the classification is highly dependent of the urban typology of the city under study. However, LCZ maps represent a reference in the field of urban climatology. This research work aims to make use of these maps to explain the strong influence of LCZ classes on land surface temperature (LST) and, consequently, on air temperature (AT). This kind of investigations will help us to explain the outliers observed in previous work between LST and AT at specific locations in the city of Strasbourg for the period 2012–2019. The LST data were obtained from the thermal infrared data of both ASTER (with 90-m spatial resolution and 16-days temporal resolution) and MODIS satellite (with 1-km spatial resolution and daily revisit period). The reference ATs were obtained from different field measurement provided by a huge network of meteorological stations distributed in the city of Strasbourg. The comparison of measured ATs and remote LSTs provide the opportunity to thoroughly evaluate the relationship between these two parameters both during the day and night, for different land covers and for different times of the year. Finally, UHI maps of Strasbourg for every season are presented.

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High spatial resolution aerosol retrievals used for daily particulate matter monitoring over Po valley, northern Italy

Atmospheric Chemistry and Physics Discussions ◽

10.5194/acpd-15-123-2015 ◽

2015 ◽

Vol 15 (1) ◽

pp. 123-155 ◽

Cited By ~ 7

Author(s):

B. Arvani ◽

R. B. Pierce ◽

A. I. Lyapustin ◽

Y. Wang ◽

G. Ghermandi ◽

...

Keyword(s):

Spatial Resolution ◽

Urban Areas ◽

Atmospheric Correction ◽

Urban Air Pollution ◽

Northern Italy ◽

Po Valley ◽

Moderate Resolution ◽

Resolution Imaging ◽

Statistical Sense ◽

Moderate Resolution Imaging Spectroradiometer

Abstract. The Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 5.1 Aerosol Optical Depth (AOD) data retrieved at 0.55 μm with spatial resolution of 10 km (MYD04) and the new 1 km Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm from MODIS is investigated in this work. We focus on evaluating the ability of these two products to characterize the spatial distribution of aerosols within urban areas. This is done through the comparison with PM10 measurements from 126 of the Italian Regional Agency for Environmental Protection (ARPA) ground monitoring stations during 2012. The Po Valley area (northern Italy) was chosen as the study domain since urban air pollution is one of the most important concerns in this region. Population and industrial activities are located within a large number of urban areas within the valley. We find that the annual correlations between PM10 and AOD are R2 = 0.90 and R2 = 0.62 for MYD04 and for MAIAC respectively. When the depth of the planetary boundary layer (PBL) is used to normalize the AOD, we find a significant improvement in the PM–AOD correlation. The introduction of the PBL information is needed for AOD to capture the seasonal cycle of the observed PM10 over the Po valley and significantly improves the PM vs. AOD relationship, leading to a correlation of R2 = 0.98 for both retrievals when they are normalized by the PBL depth. The results show that the normalized MAIAC retrieval provides a higher resolution depiction of the AOD within the Po Valley and performs as well in a statistical sense as the normalized standard MODIS retrieval for the same days and locations.

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Evaluation of cloud effects on air temperature estimation using MODIS LST based on ground measurements over the Tibetan Plateau

Atmospheric Chemistry and Physics ◽

10.5194/acp-16-13681-2016 ◽

2016 ◽

Vol 16 (21) ◽

pp. 13681-13696 ◽

Cited By ~ 22

Author(s):

Hongbo Zhang ◽

Fan Zhang ◽

Guoqing Zhang ◽

Xiaobo He ◽

Lide Tian

Keyword(s):

Tibetan Plateau ◽

Air Temperature ◽

Land Surface ◽

Estimation Accuracy ◽

Daily Maximum ◽

The Tibetan Plateau ◽

Cloud Detection ◽

Air Temperatures ◽

Moderate Resolution ◽

Moderate Resolution Imaging Spectroradiometer

Abstract. Moderate Resolution Imaging Spectroradiometer (MODIS) daytime and nighttime land surface temperature (LST) data are often used as proxies for estimating daily maximum (Tmax) and minimum (Tmin) air temperatures, especially for remote mountainous areas due to the sparseness of ground measurements. However, the Tibetan Plateau (TP) has a high daily cloud cover fraction (> 45 %), which may affect the air temperature (Tair) estimation accuracy. This study comprehensively analyzes the effects of clouds on Tair estimation based on MODIS LST using detailed half-hourly ground measurements and daily meteorological station observations collected from the TP. It is shown that erroneous rates of MODIS nighttime cloud detection are obviously higher than those achieved in daytime. Large errors in MODIS nighttime LST data were found to be introduced by undetected clouds and thus reduce the Tmin estimation accuracy. However, for Tmax estimation, clouds are mainly found to reduce the estimation accuracy by affecting the essential relationship between Tmax and daytime LST. The errors of Tmax estimation are obviously larger than those of Tmin and could be attributed to larger MODIS daytime LST errors that result from higher degrees of LST heterogeneity within MODIS pixel compared to those of nighttime LST. Constraining MODIS observations to non-cloudy observations can efficiently screen data samples for accurate Tmin estimation using MODIS nighttime LST. As a result, the present study reveals the effects of clouds on Tmax and Tmin estimation through MODIS daytime and nighttime LST, respectively, so as to help improve the Tair estimation accuracy and alleviate the severe air temperature data sparseness issues over the TP.

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