scholarly journals EVALUATION OF MODIS-DERIVED LST PRODUCTS WITH AIR TEMPERATURE MEASUREMENTS IN CYPRUS

2019 ◽  
Vol 6 (1) ◽  
pp. 1 ◽  
Author(s):  
Andreas Marios Georgiou ◽  
Stefani Theofanis Varnava
Keyword(s):  
Surface Temperature ◽  
Air Temperature ◽  
Land Surface ◽  
Urban Areas ◽  
Remote Sensing Data ◽  
High Standard ◽  
Temperature Reconstruction ◽  
Seasonal Temperature ◽  
Limited Information ◽  
Near Surface

Air temperature data is usually obtained from measurements made in meteorological stations, providing only limited information about spatial patterns over wide areas. The use of remote sensing data can help overcome this problem, particularly in areas with low station density, having the potential to improve the estimation of air surface temperature at both regional and global scales. Land Surface (skin) Temperatures (LST) derived from Moderate Resolution Imaging Spectroradiometer (MODIS) sensor aboard the Terra and Aqua satellite platforms provide spatial estimates of near-surface temperature values. In this study, LST values from MODIS are compared to ground-based near surface air (Tair) measurements obtained from 4 observational stations during 2011 to 2015, covering coastal, mountainous and urban areas over Cyprus. Combining Terra and Aqua LST-8 Day and Night acquisitions into a mean 8-day value, provide a large number of LST observations and a better overall agreement with Tair. Comparison between mean monthly LSTs and mean monthly Tair for all sites and all seasons pooled together yields a very high correlations (r > 0.96) and biases ranging from 1.9oC to 4.1oC. MODIS capture overall variability with a slightly systematic overestimation of seasonal fluctuations of surface temperature. For the evaluation of intra-seasonal temperature variability, MODIS showed biases up to 6.7oC in summer with a tendency to overestimate the variability while in cold seasons, limited biases were presented (0.10oC ± 0.50oC) with a tendency to underestimate the variability. Finally, there was no indication of tendency for MODIS to systematically under- or overestimate the amplitude of the inter-annual variability analysis. The presented high standard deviation can be explained by the influence of surface heterogeneity within MODIS 1km2 grid cells, the presence of undetected clouds and the inherent difference between LST and Tair. Overall, MODIS LST data proved to be a reliable proxy for surface temperature and mostly for studies requiring temperature reconstruction in areas with lack of observational stations.

scholarly journals Near–surface air temperature and snow skin temperature comparison from CREST-SAFE station data with MODIS land surface temperature data

2015 ◽  
Vol 12 (8) ◽  
pp. 7665-7687 ◽  
Author(s):  
C. L. Pérez Díaz ◽  
T. Lakhankar ◽  
P. Romanov ◽  
J. Muñoz ◽  
R. Khanbilvardi ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Skin Temperature ◽  
Air Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Hydrological Cycle ◽  
Critical Role ◽  
Surface Air Temperature ◽  
Balance Model ◽  
Near Surface

Abstract. Land Surface Temperature (LST) is a key variable (commonly studied to understand the hydrological cycle) that helps drive the energy balance and water exchange between the Earth's surface and its atmosphere. One observable constituent of much importance in the land surface water balance model is snow. Snow cover plays a critical role in the regional to global scale hydrological cycle because rain-on-snow with warm air temperatures accelerates rapid snow-melt, which is responsible for the majority of the spring floods. Accurate information on near-surface air temperature (T-air) and snow skin temperature (T-skin) helps us comprehend the energy and water balances in the Earth's hydrological cycle. T-skin is critical in estimating latent and sensible heat fluxes over snow covered areas because incoming and outgoing radiation fluxes from the snow mass and the air temperature above make it different from the average snowpack temperature. This study investigates the correlation between MODerate resolution Imaging Spectroradiometer (MODIS) LST data and observed T-air and T-skin data from NOAA-CREST-Snow Analysis and Field Experiment (CREST-SAFE) for the winters of 2013 and 2014. LST satellite validation is imperative because high-latitude regions are significantly affected by climate warming and there is a need to aid existing meteorological station networks with the spatially continuous measurements provided by satellites. Results indicate that near-surface air temperature correlates better than snow skin temperature with MODIS LST data. Additional findings show that there is a negative trend demonstrating that the air minus snow skin temperature difference is inversely proportional to cloud cover. To a lesser extent, it will be examined whether the surface properties at the site are representative for the LST properties within the instrument field of view.

Recent Trends in Land Surface Temperature on the Tibetan Plateau

2006 ◽  
Vol 19 (12) ◽  
pp. 2995-3003 ◽  
Author(s):  
Yuichiro Oku ◽  
Hirohiko Ishikawa ◽  
Shigenori Haginoya ◽  
Yaoming Ma
Keyword(s):  
Tibetan Plateau ◽  
Surface Temperature ◽  
Air Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Precipitation Amount ◽  
Surface Air Temperature ◽  
The Tibetan Plateau ◽  
Near Surface ◽  
Diurnal Range

Abstract The diurnal, seasonal, and interannual variations in land surface temperature (LST) on the Tibetan Plateau from 1996 to 2002 are analyzed using the hourly LST dataset obtained by Japanese Geostationary Meteorological Satellite 5 (GMS-5) observations. Comparing LST retrieved from GMS-5 with independent precipitation amount data demonstrates the consistent and complementary relationship between them. The results indicate an increase in the LST over this period. The daily minimum has risen faster than the daily maximum, resulting in a narrowing of the diurnal range of LST. This is in agreement with the observed trends in both global and plateau near-surface air temperature. Since the near-surface air temperature is mainly controlled by LST, this result ensures a warming trend in near-surface air temperature.

Study on Urban Thermal Environment based on Diurnal Temperature Range

2020 ◽  
Author(s):  
Zheng Guo ◽  
Miaomiao Cheng
Keyword(s):  
Surface Temperature ◽  
Air Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Thermal Environment ◽  
Surface Air Temperature ◽  
Heat Island ◽  
Near Surface ◽  
Diurnal Range ◽  
Urban Thermal Environment

<p>Diurnal temperature range (includes land surface temperature diurnal range and near surface air temperature diurnal range) is an important meteorological parameter, which is a very important factor in the field of the urban thermal environmental. Nowadays, the research of urban thermal environment mainly focused on surface heat island and canopy heat island.</p><p>Based on analysis of the current status of city thermal environment. Firstly, a method was proposed to obtain near surface air temperature diurnal range in this study, difference of land surface temperature between day and night were introduced into the improved temperature vegetation index feature space based on remote sensing data. Secondly, compared with the district administrative division, we analyzed the spatial and temporal distribution characteristics of the diurnal range of land surface temperature and near surface air temperature.</p><p>The conclusions of this study are as follows:</p><p>1 During 2003-2012s, the land surface temperature and near surface air temperature diurnal range of Beijing were fluctuating upward. The rising trend of the near surface air temperature diurnal range was more significant than land surface temperature diurnal range. In addition, the rise and decline of land surface temperature and near surface air temperature diurnal range in different districts were different. In the six city districts, the land surface temperature and near surface air temperature diurnal range in the six areas of the city were mainly downward. The decline trend of near surface air temperature diurnal range was more significant than land surface temperature diurnal range.</p><p>2 During 2003-2012s, the land surface temperature and near surface air temperature diurnal range of Beijing with similar characteristics in spatial distribution, with higher distribution land surface temperature and near surface air temperature diurnal range in urban area and with lower distribution of land surface temperature and near surface air temperature diurnal range in the Northwest Mountainous area and the area of Miyun reservoir.</p>

Correlation of air temperature and land surface temperature of Landsat-8 in cities of South Korea

2020 ◽  
Author(s):  
Sungwon Choi ◽  
Donghyun Jin ◽  
Noh-hun Seong ◽  
Daeseong Jung ◽  
Kyung-soo Han
Keyword(s):  
Correlation Analysis ◽  
Surface Temperature ◽  
Air Temperature ◽  
Land Surface ◽  
Urban Areas ◽  
Temperature Data ◽  
Landsat 8 ◽  
In Situ Data ◽  
Urban Greenery ◽  
Important Position

<p>Recently, there are many problems in urban area such as urban thermal island phenomenon, changes in urban green area, changes in urban weather and various urban types. And surface temperature data have been utilized in many areas to identify these phenomena. This means that surface temperatures is an important position in urban greenery and weather. High temporal and spatial resolution satellite data are needed to continuously observe the phenomenon in urban areas. In addition, the surface temperature varies from type of indicator, topography, and various factors, so there is a limit to the in-situ data for observing changes throughout the city. Therefore, various organizations around the world are currently conducting surface temperature measurements using satellites. However, the use of data in clear pixel is essential for accurate surface temperature calculations using satellites, but the accuracy of results will be reduced if the data from in the pixel which conclude clouds.</p><p>Therefore, we tried to solve these problems by analyzing the correlation between the air temperature data and the Landsat-8 LST data. The variables used in the correlation analysis are air temperature, Landsat-8 LST, NDVI and NDWI, and the study period is 2014 to 2016 and the study area is South Korea's five cities (Seoul, Busan, Daejeon, Daegu, Gwangju). For correlation analysis, the air temperature data points provided by the Korea Meteorological Administration and the Landsat-8 pixels were matched, and the correlation coefficient calculated by the correlation analysis was applied to the Landsat-8 satellite to calculate the LST. We validated by direct comparison the re-produced Landsat-8 LST with observed Landsat-8 LST. And the result of validation showed a high correlation of 0.9. It shows that compensation for the satellite's shortcomings from clouds by using the correlation between temperature and LST.</p>

scholarly journals Evaluating sources of an apparent cold bias in MODIS land surface temperatures in the St. Elias Mountains, Yukon, Canada

2021 ◽  
Author(s):  
Ingalise Kindstedt ◽  
Kristin Schild ◽  
Dominic Winski ◽  
Karl Kreutz ◽  
Luke Copland ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Surface Temperature ◽  
Land Surface ◽  
Remote Sensing Data ◽  
Cold Bias ◽  
Near Surface ◽  
Wind Speeds ◽  
Brightness Temperatures ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Temperature Inversions

Abstract. Remote sensing data are a crucial tool for monitoring climatological changes and glacier response in areas inaccessible for in situ measurements. The Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) product provides temperature data for remote glaciated areas where weather stations are sparse or absent, such as the St. Elias Mountains (Yukon, Canada). However, MODIS LSTs in the St. Elias Mountains have shown a cold bias relative to available weather station measurements, the source of which is unknown. Here, we show that the MODIS cold bias likely results from the occurrence of near-surface temperature inversions rather than from the MODIS sensor’s large footprint size or from poorly constrained snow emissivity values used in LST calculations. We find that a cold bias in remote sensing temperatures is present not only in MODIS LST products, but also in Advanced Spaceborne Thermal Emissions Radiometer (ASTER) and Landsat surface temperature products, both of which have a much smaller footprint (90–120 m) than MODIS (1 km). In all three datasets, the cold bias was most pronounced in the winter (mean cold bias > 8 °C), and least pronounced in the spring and summer (mean cold bias < 2 °C). We also find this enhanced seasonal bias in MODIS brightness temperatures, before the incorporation of snow surface emissivity into the LST calculation. Finally, we find the MODIS cold bias to be consistent in magnitude and seasonal distribution with modeled temperature inversions, and to be most pronounced under conditions that facilitate near-surface inversions, namely low incoming solar radiation and wind speeds, at study sites Icefield Divide (60.68° N, 139.78° W, 2,603 m a.s.l) and Eclipse Icefield (60.84° N, 139.84° W, 3,017 m a.s.l.). These results demonstrate that efforts to improve the accuracy of MODIS LSTs should focus on understanding near-surface physical processes rather than refining the MODIS sensor or LST algorithm. In the absence of a physical correction for the cold bias, we apply a statistical correction, enabling the use of mean annual MODIS LSTs to qualitatively and quantitatively examine temperatures in the St. Elias Mountains and their relationship to melt and mass balance.

scholarly journals Comparison of MODIS-derived land surface temperatures with near-surface soil and air temperature measurements in continuous permafrost terrain

2011 ◽  
Vol 5 (3) ◽  
pp. 1583-1625 ◽  
Author(s):  
S. Hachem ◽  
C. R. Duguay ◽  
M. Allard
Keyword(s):  
Surface Temperature ◽  
Soil Temperature ◽  
Air Temperature ◽  
High Latitude ◽  
Land Surface ◽  
Temperature Measurements ◽  
Near Surface ◽  
Recent Climate ◽  
Continuous Permafrost ◽  
Promising Source

Abstract. In Arctic and sub-Arctic regions, meteorological stations are scattered and poorly distributed geographically; they are mostly located along coastal areas and are often unreachable by road. Given that high-latitude regions are the ones most significantly affected by recent climate warming, there is a need to supplement existing meteorological station networks with spatially continuous measurements such as those obtained by spaceborne platforms. In particular, land surface (skin) temperature (LST) retrieved from satellite sensors offer the opportunity to utilize remote sensing technology to obtain a consistent coverage of a key parameter for climate, permafrost, and hydrological research. The Moderate Resolution Imaging Spectroradiometer (MODIS) sensor aboard the Terra and Aqua satellite platforms offers the potential to provide spatial estimates of near-surface temperature values. In this study, LST values from MODIS were compared to ground-based near-surface air and soil temperature measurements obtained at herbaceous and shrub tundra sites located in the continuous permafrost zone of northern Québec, Canada, and the North Slope of Alaska, USA. LST values were found to be better correlated with near-surface air temperature (1–2 m above the ground) than with soil temperature (3–5 cm below the ground) measurements. A comparison between mean daily air temperature from ground-based station measurements and mean daily MODIS LST, calculated from daytime and nighttime temperature values of both Terra and Aqua acquisitions, for all sites and all seasons pooled together reveals a high correlation between the two sets of measurements (R>0.93 and mean difference of −1.86 °C). Mean differences ranged between −0.51 °C and −5.13 °C due to the influence of surface heterogeneity within the MODIS 1 km2 grid cells at some sites. Overall, it is concluded that MODIS offers a great potential for monitoring surface temperature changes in high-latitude tundra regions and provides a promising source of input data for integration into spatially-distributed permafrost models.

scholarly journals Urbanization Impacts on the Climate in Europe: Numerical Experiments by the PSU–NCAR Mesoscale Model (MM5)

2008 ◽  
Vol 47 (5) ◽  
pp. 1442-1455 ◽  
Author(s):  
K. Trusilova ◽  
M. Jung ◽  
G. Churkina ◽  
U. Karstens ◽  
M. Heimann ◽  
...  
Keyword(s):  
Land Cover ◽  
Surface Temperature ◽  
Land Surface ◽  
Urban Areas ◽  
Mesoscale Model ◽  
Urban Land ◽  
Actual State ◽  
Near Surface ◽  
Temperature And Precipitation ◽  
In The Beginning

Abstract The objective of this study is to investigate the effects of urban land on the climate in Europe on local and regional scales. Effects of urban land cover on the climate are isolated using the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5) with a modified land surface scheme based on the Town Energy Budget model. Two model scenarios represent responses of climate to different states of urbanization in Europe: 1) no urban areas and 2) urban land in the actual state in the beginning of the twenty-first century. By comparing the simulations of these contrasting scenarios, spatial differences in near-surface temperature and precipitation are quantified. Simulated near-surface temperatures and an urban heat island for January and July over a period of 6 yr (2000–05) agree well with corresponding measurements at selected urban areas. The conversion of rural to urban land results in statistically significant changes to precipitation and near-surface temperature over areas of the land cover perturbations. The diurnal temperature range in urbanized regions was reduced on average by 1.26° ± 0.71°C in summer and by 0.73° ± 00.54°C in winter. Inclusion of urban areas results in an increase of urban precipitation in winter (0.09 ± 00.16 mm day−1) and a precipitation reduction in summer (−0.05 ± 0.22 mm day−1).

scholarly journals Different response of surface temperature and air temperature to deforestation in climate models

2018 ◽  
Author(s):  
Johannes Winckler ◽  
Christian H. Reick ◽  
Sebastiaan Luyssaert ◽  
Alessandro Cescatti ◽  
Paul C. Stoy ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Air Temperature ◽  
Land Surface ◽  
Model Comparison ◽  
Climate Models ◽  
Surface Air Temperature ◽  
Atmospheric Layer ◽  
Nonlocal Effects ◽  
Local Effects ◽  
Near Surface

Abstract. Deforestation affects temperatures at the land surface and higher up in the atmosphere. Satellite-based observations typically register deforestation-induced changes in surface temperature, in-situ observations register changes in near-surface air temperature, and climate models simulate changes in both temperatures and the temperature of the lowest atmospheric layer. Yet a focused analysis of how these variables respond differently to deforestation is missing. Here, this is investigated by analyzing the biogeophysical temperature effects of large-scale deforestation in the climate model MPI-ESM, separately for local effects (which are only apparent at the location of deforestation) and nonlocal effects (which are also apparent elsewhere). While the nonlocal effects affect the temperature of the surface and lowest atmospheric layer equally, the local effects mainly affect the temperature of the surface. In agreement with observation-based studies, the local effects on surface and near-surface air temperature respond differently in the MPI-ESM, both concerning the magnitude of local temperature changes and the latitude at which the local deforestation effects turn from a cooling to a warming (at 45–55° N for surface temperature and around 35° N for near-surface air temperature). An inter-model comparison shows that in the northern mid latitudes, both for summer and winter, near-surface air temperature is affected by the 5local effects only about half as much compared to surface temperature. Thus, studies about the biogeophysical effects of deforestation must carefully choose which temperature they consider.

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