Abstract:To date, little attention has been given to remote sensing-based algorithms for inferring urban surface evapotranspiration. A multi-source parallel model based on ASTER data was one of the first examples, but its accuracy can be improved. We therefore present a modified multi-source parallel model in this study, which has made improvements in parameterization and model accuracy. The new features of our modified model are: (1) a characterization of spectrally heterogeneous urban impervious surfaces using two endmembers (high-and low-albedo urban impervious surface), instead of a single endmember, in linear spectral mixture analysis; (2) inclusion of an algorithm for deriving roughness length for each land surface component in order to better approximate to the actual land surface characteristic; and (3) a novel algorithm for calculating the component net radiant flux with a full consideration of the fraction and the characteristics of each land surface component. HJ-1 and ASTER data from the Chinese city of Hefei were used to test our model's result with the China-ASEAN ET product. The sensitivity of the model to vegetation and soil fractions was analyzed and the applicability of the model was tested in another built-up area in the central Chinese city of Wuhan. We conclude that our modified model outperforms the initial multi-source parallel model in accuracy. It can obtain the highest accuracy when applied to vegetation-dominated (vegetation proportion > 50%) areas. Sensitivity analysis shows that vegetation and soil fractions are two important parameters that can affect the ET estimation. Our model is applicable to estimate evapotranspiration in other urban areas.

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A method for the retrieval of land surface temperature (LST) from the two thermal bands of Landsat 8 data is proposed in this paper. The emissivities of vegetation, bare land, buildings, and water are estimated using different features of the wavelength ranges and spectral response functions. Based on the Planck function of the Thermal Infrared Sensor (TIRS) band 10 and band 11, the radiative transfer equation is rebuilt and the LST is obtained using the modified emissivity parameters. A sensitivity analysis for the LST retrieval is also conducted. The LST was retrieved from Landsat 8 data for the city of Zoucheng, Shandong Province, China, using the proposed algorithm, and the LST reference data were obtained at the same time from a geosensor network (GSN). A comparative analysis was conducted between the retrieved LST and the reference data from the GSN. The results showed that water had a higher LST error than the other land-cover types, of less than 1.2°C, and the LST errors for buildings and vegetation were less than 0.75°C. The difference between the retrieved LST and reference data was about 1°C on a clear day. These results confirm that the proposed algorithm is effective for the retrieval of LST from the Landsat 8 thermal bands, and a GSN is an effective way to validate and improve the performance of LST retrieval.

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