MODIS Land Surface Temperature Product Research Articles

Validation of Collection 6 MODIS land surface temperature product using in situ measurements

Land surface temperature (LST) is an important physical quantity at the land-atmosphere interface. Since 2016 the Collection 6 (C6) MODIS LST product is publicly available, which includes three refinements over bare soil surfaces compared to the Collection 5 (C5) MODIS LST product. To encourage the use of the C6 MODIS LST product in a wide range of applications, it is necessary to evaluate the accuracy of the C6 MODIS LST product. In this study, we validated the C6 MODIS LST product using temperature-based method over various land cover types, including grasslands, croplands, cropland/natural vegetation mosaic, open shrublands, woody savannas, and barren/sparsely vegetated. In situ measurements were collected from various sites under different atmospheric and surface conditions, including seven SURFRAD sites (BND, TBL, DRA, FPK, GCM, PSU, and SXF) in the United States, three KIT sites (EVO, KAL, and GBB) in Portugal and Namibia, and three HiWATER sites (GBZ, HZZ, and HMZ) in China. The spatial representativeness of the in situ measurements at each site was separately evaluated during daytime and nighttime using all available clear-sky ASTER LST products at 90 m spatial resolution. Only six sites during daytime are selected as sufficiently homogeneous sites despite the usually high spatial thermal heterogeneity, whereas during nighttime most sites can be considered to be thermally homogeneous and have similar LST and air temperature. The C6 MODIS LST product was validated using in situ measurements from the selected homogeneous sites during daytime and nighttime: except for the GBB site, large RMSE values (>2 K) were obtained during daytime. However, if only satellite LST with a high spatial thermal homogeneity on the MODIS pixel scale are used for LST validation, the best daytime accuracy (RMSE <1.3 K) for the C6 MODIS LST product is achieved over the BND and DRA sites. Except for the DRA site, the RMSE values during nighttime are <2 K at the selected homogeneous sites. Furthermore, the accuracy of the C6 MODIS LST product was compared with that of the C5 MODIS LST product during nighttime at the selected homogeneous sites. Except for the GBB site, there are only small differences (<0.4 K) between the RMSE values for the C5 and C6 MODIS LST products.

Evaluating the Estimation of Net Radiation Based on MODIS Data and CoLM: A Case Study in the Tibetan Plateau

Estimation of net radiation is essential for characterization of the land–atmosphere energy and water exchange processes of the Tibetan Plateau. Two methods are commonly used to estimate the net radiation. Method 1 relies on ground measurements and earth satellite observations, and method 2 relies on the use of a land surface model. Currently, there is no consensus on regarding which of these two methods is the most accurate. In this study, we evaluated net radiation calculated by MODIS land surface temperature products, combined with ground-based incoming shortwave and longwave radiation and albedo measurements, and compare this to performance of the common land model for modeling surface net radiation for the Muztaga site located in the northwest of the Tibetan Plateau at three timescales (half-hourly, hourly, and daily). The results show that the two methods provide similar results, and both methods provide estimates that are notably higher than ground-measured net radiation validations at three different timescales. Estimates of net radiation obtained at half-hourly and hourly timescales closely match the fluctuations in the reference data. However, the daily timescale is too coarse to capture the short-term fluctuations. Thus, this study demonstrates that the shorter timescales are preferred in determining surface net radiation. In addition, compared with method 2, method 1 is more accurate for each of the different timescales.

Retrieval of Land-surface Temperature from AMSR2 Data Using a Deep Dynamic Learning Neural Network

It is more difficult to retrieve land surface temperature (LST) from passive microwave remote sensing data than from thermal remote sensing data, because the emissivities in the passive microwave band can change more easily than those in the thermal infrared band. Thus, it is very difficult to build a stable relationship. Passive microwave band emissivities are greatly influenced by the soil moisture, which varies with time. This makes it difficult to develop a general physical algorithm. This paper proposes a method to utilize multiple-satellite, sensors and resolution coupled with a deep dynamic learning neural network to retrieve the land surface temperature from images acquired by the Advanced Microwave Scanning Radiometer 2 (AMSR2), a sensor that is similar to the Advanced Microwave Scanning Radiometer Earth Observing System (AMSR-E). The AMSR-E and MODIS sensors are located aboard the Aqua satellite. The MODIS LST product is used as the ground truth data to overcome the difficulties in obtaining large scale land surface temperature data. The mean and standard deviation of the retrieval error are approximately 1.4° and 1.9° when five frequencies (ten channels, 10.7, 18.7, 23.8, 36.5, 89 V/H GHz) are used. This method can effectively eliminate the influences of the soil moisture, roughness, atmosphere and various other factors. An analysis of the application of this method to the retrieval of land surface temperature from AMSR2 data indicates that the method is feasible. The accuracy is approximately 1.8° through a comparison between the retrieval results with ground measurement data from meteorological stations.

Estimation of the Land Surface Temperature over the Tibetan Plateau by Using Chinese FY-2C Geostationary Satellite Data.

During the process of land–atmosphere interaction, one of the essential parameters is the land surface temperature (LST). The LST has high temporal variability, especially in its diurnal cycle, which cannot be acquired by polar-orbiting satellites. Therefore, it is of great practical significance to retrieve LST data using geostationary satellites. According to the data of FengYun 2C (FY-2C) satellite and the measurements from the Enhanced Observing Period (CEOP) of the Asia–Australia Monsoon Project (CAMP) on the Tibetan Plateau (CAMP/Tibet), a regression approach was utilized in this research to optimize the split window algorithm (SWA). The thermal infrared data obtained by the Chinese geostationary satellite FY-2C over the Tibetan Plateau (TP) was used to estimate the hourly LST time series. To decrease the effects of cloud, the 10-day composite hourly LST data were obtained through the approach of maximal value composite (MVC). The derived LST was used to compare with the product of MODIS LST and was also validated by the field observation. The results show that the LST retrieved through the optimized SWA and in situ data has a better consistency (with correlation coefficient (R), mean absolute error (MAE), mean bias (MB), and root mean square error (RMSE) values of 0.987, 1.91 K, 0.83 K and 2.26 K, respectively) than that derived from Becker and Li’s SWA and MODIS LST product, which means that the modified SWA can be applied to achieve plateau-scale LST. The diurnal variation of the LST and the hourly time series of the LST over the Tibetan Plateau were also obtained. The diurnal range of LST was found to be clearly affected by the influence of the thawing and freezing process of soil and the summer monsoon evolution. The comparison between the seasonal and diurnal variations of LST at four typical underlying surfaces over the TP indicate that the variation of LST is closely connected with the underlying surface types as well. The diurnal variation of LST is the smallest at the water (5.12 K), second at the snow and ice (5.45 K), third at the grasslands (19.82 K) and largest at the barren or sparsely vegetated (22.83 K).

Geothermal Anomaly Mapping Using Landsat ETM+ Data in Ilan Plain, Northeastern Taiwan

Geothermal energy is an increasingly important component of green energy in the globe. A prerequisite for geothermal energy development is to acquire the local and regional geothermal prospects. Existing geophysical methods of estimating the geothermal potential are usually limited to the scope of prospecting because of the operation cost and site reachability in the field. Thus, explorations in a large-scale area such as the surface temperature and the thermal anomaly primarily rely on satellite thermal infrared imagery. This study aims to apply and integrate thermal infrared (TIR) remote sensing technology with existing geophysical methods for the geothermal exploration in Taiwan. Landsat 7 (L7) Enhanced Thematic Mapper Plus (ETM+) imagery is used to retrieve the land surface temperature (LST) in Ilan plain. Accuracy assessment of satellite-derived LST is conducted by comparing with the air temperature data from 11 permanent meteorological stations. The correlation coefficient of linear regression between air temperature and LST retrieval is 0.76. The MODIS LST product is used for the cross validation of Landsat derived LSTs. Furthermore, Landsat ETM+ multi-temporal brightness temperature imagery for the verification of the LST anomaly results were performed. LST Results indicate that thermal anomaly areas appear correlating with the development of faulted structure. Selected geothermal anomaly areas are validated in detail by field investigation of hot springs and geothermal drillings. It implies that occurrences of hot springs and geothermal drillings are in good spatial agreement with anomaly areas. In addition, the significant low-resistivity zones observed in the resistivity sections are echoed with the LST profiles when compared with in the Chingshui geothermal field. Despite limited to detecting the surficial and the shallow buried geothermal resources, this work suggests that TIR remote sensing is a valuable tool by providing an effective way of mapping and quantifying surface features to facilitate the exploration and assessment of geothermal resources in Taiwan.

Remotely sensed MODIS wetland components for assessing the variability of methane emissions in Indian tropical/subtropical wetlands

Temperature and area fluctuations in wetlands greatly influence its various physico-chemical characteristics, nutrients dynamic, rates of biomass generation and decomposition, floral and faunal composition which in turn influence methane (CH4) emission rates. In view of this, the present study attempts to up-scale point CH4 flux from the wetlands of Uttar Pradesh (UP) by modifying two-factor empirical process based CH4 emission model for tropical wetlands by incorporating MODIS derived wetland components viz. wetland areal extent and corresponding temperature factors (Ft). This study further focuses on the utility of remotely sensed temperature response of CH4 emission in terms of Ft. Ft is generated using MODIS land surface temperature products and provides an important semi-empirical input for up-scaling CH4 emissions in wetlands. Results reveal that annual mean Ft values for UP wetlands vary from 0.69 (2010–2011) to 0.71(2011–2012). The total estimated area-wise CH4 emissions from the wetlands of UP varies from 66.47Ggyr−1with wetland areal extent and Ft value of 2564.04km2 and 0.69 respectively in 2010–2011 to 88.39Ggyr−1with wetland areal extent and Ft value of 2720.16km2 and 0.71 respectively in 2011–2012. Temporal analysis of estimated CH4 emissions showed that in monsoon season estimated CH4 emissions are more sensitive to wetland areal extent while in summer season sensitivity of estimated CH4 emissions is chiefly controlled by augmented methanogenic activities at high wetland surface temperatures.

Land-atmosphere interaction patterns in southeastern South America using satellite products and climate models

In regions of strong Land-Atmosphere (L-A) interaction, soil moisture (SM) conditions can impact the atmosphere through modulating the land surface fluxes. The importance of the identification of L-A interaction regions lies in the potential improvement of the weather/seasonal forecast and the better understanding of the physical mechanisms involved. This study aims to compare the terrestrial segment of the L-A interaction from satellite products and climate models, motivated by previous modeling studies pointing out southeastern South America (SESA) as a L-A hotspot during austral summer. In addition, the L-A interaction under dry or wet anomalous conditions over SESA is analyzed. To identify L-A hotspots the AMSRE-LPRM SM and MODIS land surface temperature products; coupled climate models and uncoupled land surface models were used. SESA highlights as a strong L-A interaction hotspot when employing different metrics, temporal scales and independent datasets, showing consistency between models and satellite estimations. Both AMSRE-LPRM bands (X and C) are consistent showing a strong L-A interaction hotspot over the Pampas ecoregion. Intensification and a larger spatial extent of the L-A interaction for dry summers was observed in both satellite products and models compared to wet summers. These results, which were derived from measured physical variables, are encouraging and promising for future studies analyzing L-A interactions.L-A interaction analysis is proposed here as a meeting point between remote sensing and climate modelling communities of Argentina, within a region with the highest agricultural and livestock production of the continent, but with an important lack of in-situ SM observations.

Urban heat island mesoscale modelling study for the Budapest agglomeration area using the WRF model

The present paper evaluates modelled surface urban heat island intensities (SUHI) and other essential urban meteorology-related variables for Budapest(located in the central part of the Carpathian Basin). This analysis uses the Weather Research and Forecasting (WRF) model with updated surface characteristics databases (i.e. land use, albedo, topography, emissivity, soil texture). The horizontal resolution of the newly created land use-land cover (LULC) database is around 90m, and is compiled on the basis of the OpenStreetMap (OSM) and Google Earth satellite images. The urban classes of the new LULC database have been expanded to five, and the urban parameter spatial distribution database was also created. Three simulations with different LULC databases (MODIS, USGS, and newly defined OSM) are validated against remotely-sensed observations (MODIS Land Surface Temperature Product). The results from the WRF using the OSM (WRF/OSM) show the best agreement with the observations both in rural and urban areas, in contrast to the simulation using the USGS LULC, which results in the greatest errors. According to the mean normalised bias calculations, the WRF/OSM simulation estimates the lowest bias in urban areas, nevertheless, an overestimation can be detected over the surrounding rural areas, which results in weaker SUHI intensity.

Temporal Dynamics of Phlebotomine Sand Flies Population in Response to Ambient Temperature Variation, Bam, Kerman Province of Iran

BackgroundVariations in climate condition may have changed the dynamic of zoonotic cutaneous leishmaniasis (ZCL) and its agents such as sand flies and reservoir in the Bam Kerman the dry region of Iran. ObjectivesIn this study we intend to examine the seasonal and interannual dynamics of the phlebotomine mosquito as a function of ambient temperature in Bam, Kerman one of the main leshmaniasis prevalence area in Iran. MethodsThe MODIS land surface temperature product (LST; MODIS/Terra LST/E Monthly L3 Global 0.05Deg CMG [MOD11C3]) and land-based climatic data were used as explanatory variables. Monthly caught mosquitoes in Bam, Kerman, were used as a dependent variable. The temporal associations were first investigated by inspection of scatterplots and single-variable regression analysis. A multivariate linear regression model was developed to reveal the association between ambient temperature and the monthly mosquito abundance at a 95% confidence level (P < 0.05). FindingsThe findings indicated that the monthly variation of 0-10 cm of soil depth temperature is the main driver of phlebotomine mosquito temporal dynamics. The developed multivariate model also indicated that the ambient temperature variation was responsible for >0.80 of temporal dynamics of phlebotomine mosquitos in Bam.

Modelling directional effects on remotely sensed land surface temperature

Land surface temperature (LST) is a markedly directional variable and its remotely sensed measurement may be strongly affected by viewing and illumination geometries. This study proposes the use of LST products collocated in space and time, but obtained with different viewing angles, to calibrate a simple model capable of characterizing the LST angular variability. The exercise is performed using MODIS (Aqua and Terra) and SEVIRI (Meteosat) LST products, for an area covering Mediterranean Europe and Northern Africa and encompassing the full years of 2011, 2012, 2013 and 2014. The approach relies on a kernel model that is composed by an “emissivity kernel” and a “solar kernel”, associated to observation angle anisotropy and to shadowing/sunlit effects on the surface, respectively. The spatial distribution of the kernel coefficients is shown to reflect characteristics of the landscape, both in terms of vegetation cover and topography. Model performance is assessed through several comparison exercises over the 4-year period under analysis. Cross-validation results show that the angular correction by the kernel model leads to a decrease of the root mean square difference between SEVIRI and MODIS daytime (night-time) LST products, from the original uncorrected values of 3.5K (1.5K) to 2.3K (1.3K). Comparison of both MSG and MODIS LST products against in situ daytime measurements gathered over 2years at a validation site in Évora (Portugal) reveals that the angular correction leads to a decrease in root mean square error from 4.6K (2.0K) to 3.8K (1.9K) for MODIS (SEVIRI). The kernel model may be a useful tool to quantify the LST uncertainties associated with viewing and illumination angles.

A first satellite-based observational assessment of urban thermal anisotropy

Abstract Complex urban environments with diverse urban canyon structures and building materials mixed with human-modified green space substantially influence the directional variation in upwelling thermal radiance. The thermal anisotropy at a satellite pixel scale (e.g. 1 km) is hard to quantify with current techniques through airborne observations and modeling. The various anisotropic features over urban and rural surfaces lead to an uncertainty regarding how this anisotropy of land surface temperature (LST) influences estimates of the surface urban heat island (SUHI). We quantify LST anisotropy over Chicago and New York using MODIS LST products during the warm months (May-September) for a 10-year period. The impacts of atmospheric attenuation and daily weather variability on LST are removed in order to isolate the anisotropic signal. We then calculate the anisotropy for different levels of urban land use intensity. The daytime maximum anisotropic effects can be up to 9 K for the most urbanized areas, while the nighttime anisotropic effects are weaker but still discernible. Two anisotropic “hot spot” features are observed during daytime: one corresponds to the sensor-sun geometric effect with a higher proportion of sunlit surface components been seen a given view configuration, and the other occurs when sensor zenith angles are near nadir in the afternoon. Similar anisotropic features between the two cities confirm the broader applicability of our approach, while some unique features specific to each city emphasize that diverse urban surface properties and geographic settings can affect anisotropy. This study is the first of its kind to directly evaluate the influence of directional anisotropy on satellite-retrieved SUHI measurements, demonstrating that anisotropic effects can possibly modify the SUHI as measured with MODIS LST by 2.3 K, which is about 25 – 50% of the total magnitude of the SUHI over Chicago and New York. Our findings provide a statistical basis for the quantification of satellite-retrieved LST anisotropy over heterogeneous urban environments globally.

An Effective Interpolation Method for MODIS Land Surface Temperature on the Qinghai–Tibet Plateau

Land surface temperature (LST) is a key parameter in the processes of energy and water exchange between land and atmosphere. However, the MODIS LST products are often obscured by clouds and other atmospheric disturbances, resulting in severe data loss. Traditional interpolation methods cannot be effectively applied when there is large area of missing data. Thus, in this study, an effective LST interpolation method is developed to address this issue and is used to interpolate MODIS/Terra LST data on the Qinghai–Tibet Plateau in 2005. This method assumes that some pixels with spatially valid LSTs may follow a change trend over time similar to the null pixels, and the focus thus becomes to locate those similar pixels to interpolate each null pixel. First, LST images with a small amount of missing data, chosen as the reference images, were interpolated using a traditional interpolation method. Then, for each null pixel, other pixels with similar temporal changes of LST were identified by a similarity function. Finally, a transfer function for each null pixel was established based on those pixels most similar to it in the interpolated image and the corresponding reference image. The results were found to be much superior to those interpolated by traditional methods, such as regression Kriging, ordinary Kriging, and IDW. A specially designed experiment on an area that had ample valid LSTs confirmed that the proposed method can produce more favorable results than the other methods, and performed especially well when there was a significant lack of data.

Validating and Mapping Surface Water Temperatures in Lake Taihu: Results From MODIS Land Surface Temperature Products

Data from four in situ high-frequency monitoring sites in Lake Taihu, China were used to 1) assess the accuracy of MODIS land surface temperature (LST) products; 2) characterize the spatial and temporal variability in lake surface water temperature (LSWT) with Aqua MODIS thermal-infrared imagery; and 3) explore the causes of these variations. The validation showed that MODIS-derived LSWTs and in situ water temperatures were significantly correlated, with a coefficient of determination higher than 0.96 and a root mean square error between 1.2°C and 1.8°C. These results indicate MODIS LST products can be used to assess the spatial and temporal thermal behavior of Lake Taihu. A spatial analysis of daytime LSWT showed different thermal zones along the lake due to the differential heat storage capacity between the deep and shallow regions of the lake, such as the littoral and East Lake Taihu. In contrast, the nighttime LSWT showed little variation, and there was a uniform surface water temperature. Following a change in solar radiation, the annual cycle of the average LSWT (with a nearly 24°C range) started to increase in January and peaked in July. In Lake Taihu, the mean nighttime LSWT from July to September rapidly warmed over the period of 2002-2013 at an average rate of 0.059 ± 0.053°C/year. Our study promotes the use of MODIS LST products in limnology through a validation using a large amount of real-time synchronous water temperature data based on high-frequency observations, which were seldom used in previous studies.

Generating daily high spatial land surface temperatures by combining ASTER and MODIS land surface temperature products for environmental process monitoring.

There is a shortage of daily high spatial land surface temperature (LST) data for use in high spatial and temporal resolution environmental process monitoring. To address this shortage, this work used the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM), and the Spatial and Temporal Data Fusion Approach (STDFA) to estimate high spatial and temporal resolution LST by combining Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) LST and Moderate Resolution Imaging Spectroradiometer (MODIS) LST products. The actual ASTER LST products were used to evaluate the precision of the combined LST images using the correlation analysis method. This method was tested and validated in study areas located in Gansu Province, China. The results show that all the models can generate daily synthetic LST image with a high correlation coefficient (r) of 0.92 between the synthetic image and the actual ASTER LST observations. The ESTARFM has the best performance, followed by the STDFA and the STARFM. Those models had better performance in desert areas than in cropland. The STDFA had better noise immunity than the other two models.

Land Surface Temperature Retrieval from the Medium Resolution Spectral Imager (MERSI) Thermal Data

A single channel land surface temperature (LST) retrieval algorithm named Single Channel Water Vapor Dependent (SCWVD) method was presented for Medium Resolution Spectral Imager (MERSI) thermal infrared band aboard FengYun-3A (FY-3A) satellite. Water Vapor Content (WVC) is the only input parameter in the algorithm assuming the surface emissivity is known. NCEP reanalysis monthly mean datasets are used to develop the SCWVD algorithm. Some tests, including global numerical simulations and validations with both in-situ measurements and MODIS LST product at Lake Tahoe, USA, were carried out to evaluate the algorithm performance. Compared with NCEP data and U.S. standard mid-latitude summer atmosphere model, the retrieved LST from simulated MERSI brightness temperature with MODTRAN had a RMSE about 0.8 K. In the validation, MERSI Level 2 water vapor product was employed, and the MERSI band emissivity was evaluated using the MODIS band 31 and 32 emissivity with an empirical expression. The results show that the difference between the retrieved MERSI LST and the in-situ measurements is less than 1 K in most situations. The comparison with the MODIS LST products (V5) shows that the RMSE is about 2.3 K.

Land surface temperature retrieval for arid regions based on Landsat-8 TIRS data: a case study in Shihezi, Northwest China

Scientific interest in geophysical information about land surface temperature (LST) is ever increasing, as such information provides a base for a large number of applications, including environmental and agricultural monitoring. Therefore, the research of LST retrieval has become a hot topic. Recent availability of Landsat-8 satellite imagery provides a new data source for LST retrieval. Hence, exploring an adaptive method with reliable accuracy seems to be essential. In this study, basing on features of Landsat-8 TIRS thermal infrared channels, we re-calculated parameters in the atmospheric transmittance empirical models of the existing split-window algorithm, and estimated the ground emissivity with the help of the land cover classification map of the study area. Furthermore, a split-window algorithm was rebuilt by virtual of the estimation model of the updated atmospheric transmittance and the ground emissivity, and then a remote sensing retrieval for the LST of Shihezi city in Xinjiang Uygur autonomous region of Northwest China was conducted on the basis of this modified algorithm. Finally, precision validation of the new model was implemented by using the MODIS LST products. The results showed that the LST retrieval from Landsat-8 TIRS data based on our algorithm has a higher credibility, and the retrieved LST is more consistent with the MODIS LST products. This indicated that the modified algorithm is suitable for retrieving LST with competitive accuracy. With higher resolutions, Landsat-8 TIRS data may provide more accurate observation for LST retrieval.

Retrieval of land surface temperature from the Kalpana-1 VHRR data using a single-channel algorithm and its validation over western India

Indian geostationary satellite Kalpana-1 (K1) offers a potential to capture the diurnal cycle of land surface temperature (LST) through thermal infrared channel (10.5–12.5μm) observations of the Very High Resolution Radiometer (VHRR) sensor. A study was carried out to retrieve LST by adapting a generalized single-channel (SC) algorithm (Jiménez-Muñoz and Sobrino, 2003) for the VHRR sensor over India. The basis of SC algorithm depends on the concept of Atmospheric Functions (AFs) that are dependent on transmissivity, upwelling and downwelling radiances of the atmosphere. In the present study AFs were computed for the VHRR sensor through the MODTRAN simulations based upon varying atmospheric and surface inputs. The AFs were fitted with the atmospheric columnar water vapour content and a set of coefficients was derived for LST retrieval. The K1-LST derived with the SC algorithm was validated with (a) in situ measurements at two sites located in western parts of India and (b) the MODIS LST products. Comparison of K1-LST with the in situ measurements demonstrated that SC algorithm was successful in capturing the prominent diurnal variations of 283–332K in the LST at desert and agriculture experimental sites with a rmse of 1.6K and 2.7K, respectively. Inter comparison of K1-LST and MODIS LST showed a reasonable agreement between these two retrievals up to LST of 300K, however a cold bias up to 7.9K was observed in MODIS LST for higher LST values (310–330K) over the hot desert region.

Development and evaluation of a mosaic approach in the WRF‐Noah framework

AbstractThe current Weather Research and Forecasting (WRF)‐Noah modeling framework considers only the dominant land cover type within each grid cell, which here is referred to as the “dominant” approach. In order to assess the impact of subgrid‐scale variability in land cover composition, a mosaic/tiling approach (hereafter the “mosaic” approach) is implemented into the coupled WRF‐Noah modeling system. In the mosaic approach, a certain number (N) of tiles, each representing a land cover category, is considered within each grid cell. WRF simulations of a clear sky day and a rainfall period over a heterogeneous urban/suburban setting show that the two approaches generate differences in the surface energy balance, land surface temperature, near‐surface states, boundary layer growth, as well as rainfall distribution. Evaluation against a variety of observational data (including surface flux measurements, the MODIS land surface temperature product, and radar rainfall estimates) indicates that, compared to the dominant approach, the mosaic approach has a better performance. In addition, WRF‐simulated results with the mosaic approach are less sensitive to the spatial resolution of the grid: Larger differences are observed in simulations of different resolutions with the dominant approach. The effect of increasing the number of tiles (N) on the WRF‐simulated results is also examined. When N increases from 1 (i.e., the dominant approach) to 15, changes in the ground heat flux, sensible heat flux, surface temperature, and 2 m air temperature are more significant during nighttime. Changes in the 2 m specific humidity are more significant during daytime, and changes in the boundary layer height are most prominent during the morning and afternoon transitional periods.

The impact of temporal aggregation of land surface temperature data for surface urban heat island (SUHI) monitoring

The remotely sensed land surface temperature (LST) is widely used in studying the surface urban heat island (SUHI). Due to the influence of clouds, composite products usually are preferred; however, exactly what the impact of temporal aggregation is on LST and SUHI is still unknown. In this paper, we quantify this impact focusing on Houston, Texas and its surroundings using MODIS LST products from 2000 to 2010. The results show that 1) the SUHI values are more notably enhanced in the daytime than nighttime with an increasing trend with larger composite periods. 2) The influences of aggregation in the spring and summer are larger than autumn and winter for the daytime. 3) The temporal aggregation impacts the spatial pattern of the SUHI implying that the higher SUHI regions are more likely to have a larger gap between two composite scales and this is related to the amount and distribution of clouds.

An Atmospheric Correction Method for Medium Resolution Spectral Imager Thermal IR Soundings

Based on the thermal radiative transfer equation (RTE), a new atmospheric correction method named Single Band Water Vapor Dependent (SBWVD) method is developed for land surface temperature (LST) retrieval for the FY-3A Medium Resolution Spectral Imager (MERSI) with only one thermal infrared (TIR) channel. Assuming that the surface emissivity is known, water vapor content (WVC) is the only one parameter for input to the SBWVD algorithm to retrieve LST from MERSI TIR observations. FY-3A MERSI Level 2 water vapor product is employed to evaluate the performance of the proposed method, and a 2-D data interpolation procedure is applied in order to match the MERSI L1B data in spatial resolution. Some tests, including numerical simulation for MERSI sensor and the synchronous measurements of MERSI and the radiosondes for the radiative calibration of the FY-3A tests in Qinghai Lake, have been carried out for the proposed algorithm, respectively. The results show that the difference between the retrieved LST and the in-situ measurements is less than 0.6 K for most situations. The comparison with the MODIS LST products (V5) shows that the root mean square error (RMSE) is under 0.72 K. Thus, our proposed new algorithm is applicable for the atmospheric correction and LST retrieval using MERSI TIR channel observations.