Comparison of satellite-derived land surface temperature and air temperature from meteorological stations on the Pan-Arctic scale

Satellite-based temperature measurements are an important indicator for global climate change studies over large areas. Records from Moderate Resolution Imaging Spectroradiometer (MODIS), Advanced Very High Resolution Radiometer (AVHRR) and (Advanced) Along Track Scanning Radiometer ((A)ATSR) are providing long-term time series information. Assessing the quality of remote sensing-based temperature measurements provides feedback to the climate modeling community and other users by identifying agreements and discrepancies when compared to temperature records from meteorological stations. This paper presents a comparison of state-of-the-art remote sensing-based land surface temperature data with air temperature measurements from meteorological stations on a pan-arctic scale (north of 60° latitude). Within this study, we compared land surface temperature products from (A)ATSR, MODIS and AVHRR with an in situ air temperature (Tair) database provided by the National Climate Data Center (NCDC). Despite analyzing the whole acquisition time period of each land surface temperature product, we focused on the inter-annual variability comparing land surface temperature (LST) and air temperature for the overlapping time period of the remote sensing data (2000–2005). In addition, land cover information was included in the evaluation approach by using GLC2000. MODIS has been identified as having the highest agreement in comparison to air temperature records. The time series of (A)ATSR is highly variable, whereas inconsistencies in land surface temperature data from AVHRR have been found

Retrievals of All-Weather Daily Air Temperature Using MODIS and AMSR-E Data

Satellite optical-infrared remote sensing from the Moderate Resolution Imaging Spectroradiometer (MODIS) provides effective air temperature (Ta) retrieval at a spatial resolution of 5 km. However, frequent cloud cover can result in substantial signal loss and remote sensing retrieval error in MODIS Ta. We presented a simple pixel-wise empirical regression method combining synergistic information from MODIS Ta and 37 GHz frequency brightness temperature (Tb) retrievals from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) for estimating surface level Ta under both clear and cloudy sky conditions in the United States for 2006. The instantaneous Ta retrievals showed favorable agreement with in situ air temperature records from 40 AmeriFlux tower sites; mean R2 correspondence was 86.5 and 82.7 percent, while root mean square errors (RMSE) for the Ta retrievals were 4.58 K and 4.99 K for clear and cloudy sky conditions, respectively. Daily mean Ta was estimated using the instantaneous Ta retrievals from day/night overpasses, and showed favorable agreement with local tower measurements (R2 = 0.88; RMSE = 3.48 K). The results of this study indicate that the combination of MODIS and AMSR-E sensor data can produce Ta retrievals with reasonable accuracy and relatively fine spatial resolution (~5 km) for clear and cloudy sky conditions

Comparison of MODIS Land Surface Temperature and Air Temperature over the Continental USA Meteorological Stations

The National Land Cover Database (NLCD) Impervious Surface Area (ISA) and MODIS Land Surface Temperature (LST) are used in a spatial analysis to assess the surface-temperature-based urban heat island's (UHIS) signature on LST amplitude over the continental USA and to make comparisons to local air temperatures. Air-temperature-based UHIs (UHIA), calculated using the Global Historical Climatology Network (GHCN) daily air temperatures, are compared with UHIS for urban areas in different biomes during different seasons. NLCD ISA is used to define urban and rural temperatures and to stratify the sampling for LST and air temperatures. We find that the MODIS LST agrees well with observed air temperature during the nighttime, but tends to overestimate it during the daytime, especially during summer and in nonforested areas. The minimum air temperature analyses show that UHIs in forests have an average UHIA of 1 C during the summer. The UHIS, calculated from nighttime LST, has similar magnitude of 1-2 C. By contrast, the LSTs show a midday summer UHIS of 3-4 C for cities in forests, whereas the average summer UHIA calculated from maximum air temperature is close to 0 C. In addition, the LSTs and air temperatures difference between 2006 and 2011 are in agreement, albeit with different magnitude

Application of Land Surface Temperature Extracted from Satellite Images for Zoning Reference Evapotranspiration

Reference evapotranspiration is one the most important climatic parameters to determine crop water requirement, climatic and hydrological studies and water resources management. The aim of this study was spatial estimating and zoning of reference evapotranspiration using the MODIS sensor images performed for Sefidroud basin. For this purpose, among the MODIS sensor products, Land Surface Temperature was selected and based on that, daily values of maximum and minimum air temperatures were modeled for studied area using Multiple Linear Regression and non-linear model of Support Vector Machines-based. Then, map of reference evapotranspiration zoning was prepared for studied area using Hargreaves-Samani method. Afterwards, by developing a linear regression model, the extracted reference evapotranspiration from Hargreaves-Samani method was converted to the FAO-Penman-Monteith method and based on that, map of reference evapotranspiration zoning on the basis of the FAO-Penman-Monteith method was extracted for the study area. Based on performed analyses, Support Vector Machine and Multiple Linear Regression models were selected to model minimum and maximum air temperatures, respectively and by applying cross validation method, the adjusted coefficient of determination for these models obtained was 0.81 and 0.92. The results showed that it is possible to make spatial estimation of reference evapotranspiration with an appropriate accuracy by considering an algorithm on the basis of combining satellite-based Land Surface Temperatures and statistical models. Zoning of reference evapotranspiration for any region can be extracted by only having some meteorological data in few point stations and by utilization from the satellite-extracted Land Surface Temperatures

Observed Differences between Near-Surface Air and Skin Temperatures Using Satellite and Ground-based Data

Accurate estimates of long-term land surface temperature (Ts) and near-surface air temperature (Ta) at finer spatio-temporal resolutions are crucial for surface energy budget studies, for environmental applications, for land surface model data assimilation, and for climate change assessment and its associated impacts. The Atmospheric Infrared Sounder (AIRS) and Moderate Resolution Imaging Spectroradiometer (MODIS) sensors onboard the Aqua satellite provide a unique opportunity to estimate both temperatures twice daily at the global scale. In this study, differences between Ta and Ts were assessed locally over regions of North America from 2009 to 2013 using ground-based observations covering a wide range of geographical, topographical, and land cover types. The differences between Ta and Ts during non-precipitating conditions are generally 2–3 times larger than precipitating conditions. However, these differences show noticeable diurnal and seasonal variations. The differences between Ta and Ts were also investigated at the global scale using the AIRS estimates under clear-sky conditions for the period 2003–2015. The tropical regions showed about 5–20 °C warmer Ts than Ta during the day-time, whereas opposite characteristics (about 2–5 °C cooler Ts than Ta) are found over most parts of the globe during the night-time. Additionally, Ts estimates from the AIRS and the MODIS sensors were inter-compared. Although large-scale features of Ts were essentially similar for both sensors, considerable differences in magnitudes were observed (\u3e6 °C over mountainous regions). Finally, Ta and Ts estimates from the AIRS and MODIS sensors were validated against ground-based observations for the period of 2009–2013. The error characteristics notably varied with ground stations and no clear evidence of their dependency on land cover types or elevation was detected. However, the MODIS-derived Ts estimates generally showed larger biases and higher errors compared to the AIRS-derived estimates. The biases and errors increased steadily when the spatial resolution of the MODIS estimates changed from finer to coarser. These results suggest that representativeness error should be properly accounted for when validating satellite-based temperature estimates with point observations

A Review on Different Modeling Techniques

In this study, the importance of air temperature from different aspects (e.g., human and plant health, ecological and environmental processes, urban planning, and modelling) is presented in detail, and the major factors affecting air temperature in urban areas are introduced. Given the importance of air temperature, and the necessity of developing high-resolution spatio- temporal air-temperature maps, this paper categorizes the existing approaches for air temperature estimation into three categories (interpolation, regression and simulation approaches) and reviews them. This paper focuses on high-resolution air temperature mapping in urban areas, which is difficult due to strong spatio-temporal variations. Different air temperature mapping approaches have been applied to an urban area (Berlin, Germany) and the results are presented and discussed. This review paper presents the advantages, limitations and shortcomings of each approach in its original form. In addition, the feasibility of utilizing each approach for air temperature modelling in urban areas was investigated. Studies into the elimination of the limitations and shortcomings of each approach are presented, and the potential of developed techniques to address each limitation is discussed. Based upon previous studies and developments, the interpolation, regression and coupled simulation techniques show potential for spatio-temporal modelling of air temperature in urban areas. However, some of the shortcomings and limitations for development of high-resolution spatio- temporal maps in urban areas have not been properly addressed yet. Hence, some further studies into the elimination of remaining limitations, and improvement of current approaches to high-resolution spatio-temporal mapping of air temperature, are introduced as future research opportunities

Evaluation of MODIS Land Surface Temperature Data to Estimate Near-Surface Air Temperature in Northeast China

Air temperature (Tair) near the ground surface is a fundamental descriptor of terrestrial environment conditions and one of the most widely used climatic variables in global change studies. The main objective of this study was to explore the possibility of retrieving high-resolution Tair from the Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) products, covering complex terrain in Northeast China. The All Subsets Regression (ASR) method was adopted to select the predictors and build optimal multiple linear regression models for estimating maximum (Tmax), minimum (Tmin), and mean (Tmean) air temperatures. The relative importance of predictors in these models was evaluated via the Standardized Regression Coefficients (SRCs) method. The results indicated that the optimal models could estimate the Tmax, Tmin, and Tmean with relatively high accuracies (Model Efficiency ≥ 0.90). Both LST and day length (DL) predictors were important in estimating Tmax (SRCs: daytime LST = 0.53, DL = 0.35), Tmin (SRCs: nighttime LST = 0.74, DL = 0.23), and Tmean (SRCs: nighttime LST = 0.72, DL = 0.28). Models predicting Tmin and Tmean had better performance than the one predicting Tmax. Nighttime LST was better at predicting Tmin and Tmean than daytime LST data at predicting Tmax. Land covers had noticeable influences on estimating Tair, and even seasonal vegetation greening could result in temporal variations of model performance. Air temperature could be accurately estimated using remote sensing, but the model performance was varied across different spatial and temporal scales. More predictors should be incorporated for the purpose of improving the estimation of near surface Tair from the MODIS LST production

A spatiotemporal analysis of the relationship between near-surface air temperature and satellite land surface temperatures using 17 years of data from the ATSR series

The relationship between satellite land surface temperature (LST) and ground-based observations of 2m air temperature (T2m) is characterised in space and time using >17 years of data. The analysis uses a new monthly LST climate data record (CDR) based on the Along-Track Scanning Radiometer (ATSR) series, which has been produced within the European Space Agency GlobTemperature project (http://www.globtemperature.info/). Global LST-T2m differences are analysed with respect to location, land cover, vegetation fraction and elevation, all of which are found to be important influencing factors. LSTnight (~10 pm local solar time, clear-sky only) is found to be closely coupled with minimum T2m (Tmin, all-sky) and the two temperatures generally consistent to within ±5 °C (global median LSTnight- Tmin= 1.8 °C, interquartile range = 3.8 °C). The LSTday (~10 am local solar time, clear-sky only)-maximum T2m (Tmax, all-sky) variability is higher (global median LSTday- Tmax= -0.1°C, interquartile range = 8.1 °C) because LST is strongly influenced by insolation and surface regime. Correlations for both temperature pairs are typically >0.9 outside of the tropics. The monthly global and regional anomaly time series of LST and T2m – which are completely independent data sets - compare remarkably well. The correlation between the data sets is 0.9 for the globe with 90% of the CDR anomalies falling within the T2m 95% confidence limits. The results presented in this study present a justification for increasing use of satellite LST data in climate and weather science, both as an independent variable, and to augment T2m data acquired at meteorological stations

Comparison of Satellite-Derived Land Surface Temperature and Air Temperature from Meteorological Stations on the Pan-Arctic Scale

Satellite-based temperature measurements are an important indicator for global climate change studies over large areas. Records from Moderate Resolution Imaging Spectroradiometer (MODIS), Advanced Very High Resolution Radiometer (AVHRR) and (Advanced) Along Track Scanning Radiometer ((A)ATSR) are providing long-term time series information. Assessing the quality of remote sensing-based temperature measurements provides feedback to the climate modeling community and other users by identifying agreements and discrepancies when compared to temperature records from meteorological stations. This paper presents a comparison of state-of-the-art remote sensing-based land surface temperature data with air temperature measurements from meteorological stations on a pan-arctic scale (north of 60° latitude). Within this study, we compared land surface temperature products from (A)ATSR, MODIS and AVHRR with an in situ air temperature (Tair) database provided by the National Climate Data Center (NCDC). Despite analyzing the whole acquisition time period of each land surface temperature product, we focused on the inter-annual variability comparing land surface temperature (LST) and air temperature for the overlapping time period of the remote sensing data (2000–2005). In addition, land cover information was included in the evaluation approach by using GLC2000. MODIS has been identified as having the highest agreement in comparison to air temperature records. The time series of (A)ATSR is highly variable, whereas inconsistencies in land surface temperature data from AVHRR have been found