Estimation of the Land Surface Emissivity in the Hinterland of Taklimakan Desert

An accurate accounting of land surface emissivity(ε) is important both for the retrieval of surface temperatures and the calculation of the longwave surface energy budgets.Since ε is one of the important parameterizations in land surface models(LSMs),accurate accounting also improves the accuracy of surface temperatures and sensible heat fluxes simulated by LSMs.In order to obtain an accurate emissivity,this paper focuses on estimating ε from data collected in the hinterland of Taklimakan Desert by two different methods.In the first method,ε was derived from the surface broadband emissivity in the 8–14 μm thermal infrared atmospheric window,which was determined from spectral radiances observed by field measurements using a portable Fourier transform infrared spectrometer,the mean ε being 0.9051.The second method compared the observed and calculated heat fluxes under nearneutral atmospheric stability and estimated ε indirectly by minimizing the root-mean-square difference between them.The result of the second method found a mean value of 0.9042,which is consistent with the result by the first method.Although the two methods recover ε from different field experiments and data,the difference of meanvalues is 0.0009.The first method is superior to the indirect method,and is also more convenient.

Land surface emissivity change in China from 2001 to 2010

Land surface emissivity is one of the important parameters in temperature inversion from thermal infrared remote sensing. Using MOD11C3 of Terra-MODIS L3 level products, spatio-temporal data sets of land surface emissivity in China for 10 years from 2001 to 2010 are obtained. The results show that the land surface emissivity in the northwest desert region is the lowest in China, with little seasonal variations. In contrast, there are significant seasonal variations in land surface emissivity in northeast China and northern Xinjiang, the Qinghai-Tibet Plateau, the Yangtze River Valley and the eastern and southern China. In winter, the land surface emissivity in the northeast China and northern Xinjiang is relatively high. The land surface emissivity of the Qinghai-Tibet Plateau region is maintained at low value from November to March, while it becomes higher in other months. The land surface emissivity of the Yangtze River Valley, eastern and southern China, and Sichuan Basin varies from July to October, and peaks in August. Land surface emissivity values could be divided into five levels low emissivity （0.6163-0.9638）, moderate-low emissivity （0.9639-0.9709）, moderate emissivity （0.9710-0.9724）, moderate-high emissivity （0.9725-0.9738）, and high emissivity （0.9739-0.9999）. The percentages of areas with low emissivity, moderate-low emissivity and moderate emissivity are, respectively, about 20%, 10% and 20%. The moderate-high emissivity region makes up 40%-50% of China＇s land surface area. The inter-annual variation of moderate-high emissivity region is also very clear, with two peaks （in spring and autumn） and two troughs （in summer and winter）. The inter-annual variation of the high emissivity region is very significant, with a peak in winter （10%）, while only 1% or 2% in other seasons. There is a clear association between the spatio-temporal distribution of China＇s land surface emissivity and temperature： the higher the emissivity, the lower the temperature, and vice versa. Emissivity is an inherent property of any object, but the precise value of its emissivity depends very much on its surrounding environmental factors.

Estimation of Land Surface Temperature from Landsat-8 OLI Thermal Infrared Satellite Data. A Comparative Analysis of Two Cities in Ghana

This study employs Landsat-8 Operational Land Imager (OLI) thermal infrared satellite data to compare land surface temperature of two cities in Ghana: Accra and Kumasi. These cities have human populations above 2 million and the corresponding anthropogenic impact on their environments significantly. Images were acquired with minimum cloud cover (<10%) from both dry and rainy seasons between December to August. Image preprocessing and rectification using ArcGIS 10.8 software were used. The shapefiles of Accra and Kumasi were used to extract from the full scenes to subset the study area. Thermal band data numbers were converted to Top of Atmospheric Spectral Radiance using radiance rescaling factors. To determine the density of green on a patch of land, normalized difference vegetation index (NDVI) was calculated by using red and near-infrared bands i.e. Band 4 and Band 5. Land surface emissivity (LSE) was also calculated to determine the efficiency of transmitting thermal energy across the surface into the atmosphere. Results of the study show variation of temperatures between different locations in two urban areas. The study found Accra to have experienced higher and lower dry season and wet season temperatures, respectively. The temperature ranges corresponding to the dry and wet seasons were found to be 21.0985oC to 46.1314oC, and, 18.3437oC to 30.9693oC respectively. Results of Kumasi also show a higher range of temperatures from 32.6986oC to 19.1077oC during the dry season. In the wet season, temperatures ranged from 26.4142oC to -0.898728oC. Among the reasons for the cities of Accra and Kumasi recorded higher than corresponding rural areas’ values can be attributed to the urban heat islands’ phenomenon.

Physics-based simultaneous retrieval of atmospheric temperature-humidity profiles and land surface temperature-emissivity by integrating Terra/Aqua MODIS measurements

Atmospheric temperature-humidity profiles and land or sea surface temperature are coupled actions in the earth system process. Based on the numerical perturbation form of the atmospheric radiative transfer equation, a physics-based algorithm is pre- sented to integrate four pairs of MODIS measurements from the Terra and Aqua satellites to retrieve simultaneously atmospheric temperature-humidity profile, land-surface temperature and emissivity. Three pairs of MODIS data at two field sites in China, Luancheng and Poyang Lake areas, have been chosen to test and validate the model. Two pairs of atmospheric tem- perature and humidity profiles, land surface temperature (LST), and land surface emissivity (LSE) have been retrieved simul- taneously for every pair of MODIS measurements respectively by the proposed physical algorithm for the study area. The synchronous field measurements at two field sites were conducted to validate the retrieval LST, the differences between the retrieved LST and the field measurements are in the range of -0.15 K and 1.11 K. The emissivity errors of MODIS bands 31 and 32, compared with the EOS MODIS LST/LSE data products (MOD11_L2/MYD11_L2 V5) by the physics-based day/night algorithm, are from 0.0018 to 0.44 and from 0.0058 to 1.24, respectively. Meanwhile, the retrieved atmospheric profiles fully agree with the standard atmospheric temperature-water vapor profiles and with the results from single MODIS data onboard Terra or Aqua satellite by the former two-step physical algorithm. Therefore, the proposed algorithm is robust enough to improve the retrieval accuracy of the atmospheric profiles and land surface parameters. And it will have four pairs of the retrieval results for one area each day by integrating these MODIS measurements from Terra and Aqua satellites.

New hybrid algorithm for land surface temperature retrieval from multiple-band thermal infrared image without atmospheric and emissivity data inputs

Land surface temperature(LST)retrieval from thermal infrared(TIR)remote sensing image requires atmospheric and land surface emissivity(LSE)data that are sometimes unattainable.To overcome this problem,a hybrid algorithm is developed to retrieve LST without atmospheric correction and LSE data input,by combining the split-window(SW)and temperature–emissivity separation(TES)algorithms.The SW algorithm is used to estimate surface-emitting radiance in adjacent TIR bands,and such radiance is applied to the TES algorithm to retrieve LST and LSE.The hybrid algorithm is implemented on five TIR bands of the Advanced Spaceborne Thermal Emission and Reflection Radiometer(ASTER).Analysis shows that the hybrid algorithm can estimate LST and LSE with an error of 0.5–1.5 K and 0.007–0.020,respectively.Moreover,the LST error of the hybrid algorithm is equivalent to that of the original ASTER TES algorithm,involving 1%–2%uncertainty in atmospheric correction.The hybrid algorithm is validated using ground-measured LST at six sites and ASTER LST products,indicating that the temperature difference between the ASTER TES algorithm and the hybrid algorithm is 1.4 K and about 2.5–3.5 K compared to the ground measurement.Finally,the hybrid algorithm is applied to at two places.

Validation of Landsat land surface temperature product in the conterminous United States using in situ measurements from SURFRAD, ARM, and NDBC sites

Since 1982,Landsat series of satellite sensors continuously acquired thermal infrared images of the Earth’s land surface.In this study,Landsat 5,7,and 8 land surface temperature(LST)products in the conterminous United States from 2009 to 2019 were validated using in situ measurements collected at 6 SURFRAD(Surface Radiation Budget Network)sites,6 ARM(Atmospheric Radiation Measurement)sites,and 9 NDBC(National Data Buoy Center)sites.The results indicate that a relatively consistent performance among Landsat 5,7,and 8 LST products is obtained for most sites due to the consistent LST retrieval algorithm in conjunction with the same atmospheric compensation and land surface emissivity(LSE)correction methods for Landsat 5,7,and 8 sensors.Large bias and root mean square error(RMSE)of Landsat LST product are obtained at some vegetated sites due to incorrect LSE estimation where LSE is invariant with the increasing of normalized difference vegetation index(NDVI).Except for the sites with incorrect LSE estimation,a mean bias(RMSE)of the differences between Landsat LST and in situ LST is 1.0 K(2.1 K)over snow-free land surfaces,−1.1 K(1.6 K)over snow surfaces,and−0.3 K(1.1 K)over water surfaces.