As a key parameter describing physics of land surface processes on local and global scales, land Surface Temperature (LST) is the result of all interactions and energy flows between land surface and the atmosphere. Temperature changes rapidly on temporal and spatial scales, and thus a complete description of LST require measurements involving spatial and temporal frequencies. Hence, climatological, meteorological, and hydrogeological studies require having access to wide scale information about spatial changes of air temperature. Since the LST product of SLSTR uses linear split-window algorithm, the present study has used nonlinear split-window algorithm to estimate LST in Sentinel-3 images. Linearity of the radiation transfer equation in linear algorithm and some approximations used in split-window algorithms (such as transfer approximation as a linear function of vapor value) result in considerable errors because of which nonlinear algorithm is used in the present study. Using linear split-window algorithm to estimate LST in tropical climates also leads to a high level of error. The present study seeks to estimate LST using a nonlinear split-window algorithm and data retrieved from Sentinel-3 in different seasons of 2018 and 2019. The results are also evaluated using temperature product of MODIS and SLSTR.
Materials & Method
A time series of sentinel-3 images retrieved from 2018 to 2019 was used as research data. Data were collected by Sentinel-3 SLSTR sensors operated by the European Space Agency (ESA). Obviously, images shall be radio-metrically corrected before calculating physical land surface parameters such as temperature, emissivity, reflectance and radiance, albedo, and etc. To reach this goal, it is necessary to omit or minimize the effect of atmosphere, epipolar geometry of sensor, sunlight, topography, and surface characteristics while estimating surface parameters in these images. The current study seeks to estimate LST applying a nonlinear split-window algorithm on Sentinel-3 data collected during different seasons of 2018 and 2019 and to evaluate the results using temperature product of MODIS, SLSTR, and in-situ data. Pearson Correlation Coefficient and Root Mean Square Error (RMSE) were also used as relative and quantitative criteria to evaluate the accuracy of the proposed method and determine the deference between temperature calculated by the proposed method and temperature product of MODIS and SLSTR sensor. Hence, four frames of LST product collected by MODIS, and SLSTR in April, June, and October, 2018 and January, 2019 were used to evaluate the proposed method.
Results & Discussion
The proposed method was also indirectly evaluated using temperature products of MODIS and SLSTR sensor. Applying parameters of mean and root mean square error, the evaluation has shown that the results obtained from the proposed method in the one-year reference period were more similar to the results obtained from MODIS sensor. Comparing nonlinear Split-Window algorithm and MODIS products, RMSE ranged from 1.21 to 2.46 and the highest and lowest accuracy belonged to winter and summer, respectively. Comparing this algorithm with the SLSTR product, RMSE ranged from 0.76 to 2.24 and the highest and lowest accuracy belonged to winter and summer, respectively. Proper performance of the algorithm in winter is due to the relative balance of atmospheric water vapour in this season. Comparing nonlinear modelling of atmospheric water vapour in the non-linear algorithm of a Split-window and the linear algorithm in SLSTR and MODIS products, the small difference between temperature calculated by the algorithm and the products can be justified. However, due to temperature fluctuations in summer, results obtained by the proposed method were not reliable enough compared to both temperature products. Generally, results obtained from the proposed method showed a higher correlation with the temperature product of SLSTR sensor, which is due to the similar spectral bands used in calculating the surface temperature. Relative comparison of the Split-Window and the MODIS product’s nonlinear algorithm showed a coefficient of determination ranging from 0.76 to 0.96, while comparing this algorithm with the SLSTR product showed a determination coefficient of 0.80 to 0.98. Comparing temperature obtained from the nonlinear Split-Window algorithm with SLSTR and MODIS temperature products, the proposed algorithm was relatively stable no matter which season was taken into account.
The present study seeks to estimate Land Surface Temperature using a nonlinear Split-Window algorithm and Sentinel-3 data collected in different seasons. Values obtained from the algorithm were validated using in-situ dataset retrieved from the meteorological station. They were also evaluated using temperature product of MODIS and SLSTR. To increase the accuracy level, temperature product of MODIS and SLSTR were also evaluated and compared with the in-situ dataset and provided good results. Generally, there is a significant difference between temperature values estimated by the NSW algorithm for different seasons especially summer. However, a similar trend was observed in temperature changes reported by SLSTR and MODIS, and the proposed algorithm in different seasons of the study area. Although, the nonlinear Split-Window algorithm showed a higher accuracy in spring and winter, overall results indicated that the proposed method was relatively stable no matter which season was taken into account. It can be concluded that LST estimation with nonlinear Split-window method and Sentinel-3 satellite data has an acceptable level of accuracy and thus, can be used in large scale environmental crises such as climate changes.