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As a surface component, the tree trunk affects the top-of-canopy (TOC) emissivity and thermal infrared (TIR) radiance over a forest with fewer leaves, which is important for the inversion of land surface temperatures (LSTs) and further applications such as predicting forest fires and monitoring drought conditions. Therefore, the tree trunk effect was analyzed in this article using a thermal radiation directionality model, in which the forest structure was considered by the geometric optical (GO) theory and the spectral invariance theory was introduced into the GO framework for the single-scattering effect between components. The model used was evaluated using unmanned aerial vehicle (UAV)-based measurements with root-mean-square errors (RMSEs) lower than 0.25 °C for directional anisotropies (DAs) of brightness temperatures (BTs). Comparison with a 3-D radiative transfer model, discrete anisotropic radiative transfer (DART), also indicated an acceptable tool of the proposed model for the trunk effect with RMSEs lower than 0.003 °C and 1.2 °C for DAs of emissivity and BTs, respectively. In this study, the root-mean-squared difference (RMSD) levels between the vegetation–soil and vegetation–trunk–soil canopies, which were viewed as an equivalent indicator of the trunk effect, were provided for the TOC emissivity and BTs as well as their DAs, by combination with the changes in the leaf area index (LAI), stand density, trunk shape, and component temperatures, which can help identify the cases in which the trunk effect should be considered. According to a comprehensive analysis, for cases with sparse stand density ($\boldsymbol {\alpha < 0.04}$), the tree trunk should be considered for a BT RMSD level lower than 0.5 °C when the LAI value was lower than 0.6. The corresponding LAI value was 0.8 for an RMSD level of BT DA lower than 0.3 °C. Moreover, for the cases with low soil emissivity, the difference in the TOC emissivity with and without trunk can reach up to 0.035, and the RMSD was still larger than 0.01 when the stand density and LAI were 0.05 and 0.6, respectively. [ABSTRACT FROM AUTHOR]

In this study, two collection 6 (C6) Moderate Resolution Imaging Spectroradiometer (MODIS) level-2 land surface temperature (LST) products (MYD11_L2 and MYD21_L2) from the Aqua satellite were evaluated using temperature-based (T-based) and radiance-based (R-based) validation methods over barren surfaces in Northwestern China. The ground measurements collected at four barren surface sites from June 2012 to September 2018 during the Heihe Watershed Allied Telemetry Experimental Research (HiWATER) experiment were used to perform the T-based evaluation. Ten sand dune sites were selected in six large deserts in Northwestern China to carry out an R-based validation from 2012 to 2018. The T-based validation results indicate that the C6 MYD21 LST product has a better accuracy than the C6 MYD11 product during both daytime and nighttime. The LST is underestimated by the C6 MYD11 products at the four T-based sites during the daytime, with a mean bias of −2.82 K and a mean RMSE of 3.82 K, whereas the MYD21 LST product has a mean bias and RMSE of −0.51 and 2.53 K, respectively. The LST is also underestimated at night by the C6 MYD11 products at the four T-based sites, with a mean bias of −1.40 K and a mean RMSE of 1.72 K, whereas the MYD21 LST product has a mean bias and RMSE of 0.23 and 1.01 K, respectively. For the R-based validation, the MYD11 results are associated with large negative biases during both daytime and nighttime at three sand dune sites and biases within 1 K at the other seven sites, whereas the MYD21 results are more consistent at all ten sand dune sites, with a mean bias of 0.45 and 0.70 K for daytime and nighttime, respectively. The emissivities for these two products in MODIS bands 31 and 32 were compared with each other and then compared with the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) emissivity and laboratory emissivity. The results indicate that the emissivities in MODIS bands 31 and 32 of MYD11 at the four T-based and three of the R-based validation sites are overestimated and result in LST underestimation, whereas the emissivities of MYD21 are more consistent with the laboratory emissivity. Besides, an experiment was carried out to demonstrate that the physically retrieved dynamic emissivity of the MYD21 product can be utilized to improve the accuracy of the split-window (SW) algorithm for barren surfaces, making it a valuable data source for retrieving LST from different remote sensing data. [ABSTRACT FROM AUTHOR]

When ground-leaving radiance from low-temperature surface is similar to downwelling radiance from the sky, the singular values arise in the retrieved land surface emissivity (LSE) at some specific spectral bands. In addition, too many singular values eventually cause temperature/emissivity separation algorithms to fail. To reduce the occurrence of these singular points, we formulate two indices, including the land-atmosphere radiance contrast index (LACI) and neighbor band contrast index (NBCI). LACI characterizes the contrast between surface radiance and sky downwelling radiance. NBCI characterizes the contrast between the radiance at neighboring bands. These two indices are used as filters to select bands which participate in the iterative spectrally smooth calculation. Thus, we modify the iterative spectrally smooth temperature and emissivity separation (ISSTES) algorithm for low-temperature surfaces. Two methods have been used to evaluate the modified algorithm. First, numerical experiments are conducted to evaluate if the modified algorithm can accurately retrieve the “true” LSE from the simulated data. Second, an artificial low-temperature surface cooled by liquid nitrogen is measured to validate the modified algorithm. The results show that the modified algorithm can effectively avoid singular values, and behaves much better than the original algorithm with errors of less than 0.01 in retrieved emissivity when applied to low-temperature regions, while the modified algorithm brings limited improvement in retrieved temperature. [ABSTRACT FROM AUTHOR]

In this study, to improve the accuracy of land surface temperature (LST) products over barren surfaces, we present an operational algorithm to retrieve the LST from Moderate-Resolution Imaging Spectroradiometer (MODIS) thermal infrared data using physically retrieved emissivity products. The LST algorithm involved two steps. First, the emissivity in the two MODIS split-window (SW) channels was estimated using the vegetation cover method, with the bare soil component emissivity derived from the ASTER global emissivity data set. Then, the LST was retrieved using a modified generalized SW algorithm. This algorithm was implemented in the MUlti-source data SYnergized Quantitative (MuSyQ) remote sensing product system. The MuSyQ MODIS LST product and the Collection 6 MODIS LST product (MxD11_L2) were compared and validated using ground measurements collected from four barren surface sites in Northwest China during the Heihe Watershed Allied Telemetry Experimental Research (HiWATER) experiment from June 2012 to December 2015. In total, 2268 and 2715 clear-sky samples were used in the validation for Terra and Aqua, respectively. The evaluation results indicate that the MuSyQ LST products provide better accuracy than the C6 MxD11 product during both daytime and nighttime at all four sites. For the daytime results, the LST is underestimated by the C6 MxD11 products at all four sites, with a mean bias of −1.78 and −2.86 K and a mean root-mean-square error (RMSE) of 3.16 and 3.94 K for Terra and Aqua, respectively, whereas the mean biases of the MuSyQ LST products are within 1 K, with a mean bias of −0.26 and −1.03 K and a mean RMSE of 2.45 and 2.71 K for Terra and Aqua, respectively. For the nighttime results, the LST is also underestimated by the C6 MxD11 products at all four sites, with a mean bias of −1.60 and −1.26 K and a mean RMSE of 1.93 and 1.60 K for Terra and Aqua, respectively, whereas the mean biases of the MuSyQ LST products are 0.16 and 0.58 K and the mean RMSEs are 1.12 and 1.25 K for Terra and Aqua, respectively. The results indicate that the underestimation of the C6 MxD11 LST product at all four sites mainly results from the overestimation of the emissivities in MODIS bands 31 and 32. This study demonstrates that physically retrieved emissivity products are a useful source for LST retrieval over barren surfaces and can be used to improve the accuracy of global LST products. [ABSTRACT FROM AUTHOR]

Land surface temperature and emissivity (LST&E) are key variables for a wide variety of surface–atmosphere studies, and it is very important to validate the accuracy of different LST&E retrieval algorithms. In this paper, the accuracy of the Advanced Spaceborne Thermal Emission and Reflection (ASTER) temperature emissivity separation (TES) algorithm with the water vapor scaling (WVS) method and the ASTER standard LSE&E products (without WVS) was validated using 12 ASTER scenes from May 2012 to September 2012 with concurrent ground LST and emissivity measurements collected in an arid area in northwest China during the Heihe Watershed Allied Telemetry Experimental Research experiment. Both the National Center for Environmental Prediction (NCEP) and MOD07 atmospheric profile products were employed to perform the atmospheric correction. The results showed that the WVSTES LSTs retrieved from the two profiles both demonstrate good accuracies, with average biases of 0.34 and 0.24 K and average root-mean-square errors (RMSEs) of 1.52 and 1.46 K for the NCEP and MOD07 profiles, respectively. When the WVS was not applied, we obtained an average bias of 1.26 K and an average RMSE of 2.05 K for the AST08 product. The emissivities from the WVSTES algorithm with the two profiles both showed good agreements with the ground CE312 measurements, with mean differences of the five ASTER bands that were less than 0.01. AST05 showed anomalous emissivity spectra and underestimated the emissivity values of graybody surfaces when compared with the ground data, with mean differences of greater than 0.015, which were more obvious for cases with high water vapor. This paper demonstrated that the WVS method is critical for retrieving ASTER TES LST with accuracies within 1.5 K and emissivity within 0.015 for a wide range of atmospheric conditions and land surface types. [ABSTRACT FROM AUTHOR]

A new directional canopy emissivity model (CE-P) based on spectral invariants is proposed in this paper. First, we prove the existence of the spectral invariant properties in the thermal infrared (TIR) band using a Monte Carlo model. Based on it, the equation of the new model is derived from the perspective of absorption. In this expression, single-scattering and multiscattering effects are separated analytically in the TIR band. We find that the overall contribution of multiple scatterings is less than 0.005 when the component emissivities are over 0.90, and the overall contribution decreases with increasing leaf or soil emissivity. Furthermore, the new model can avoid the logical difficulty encountered when using the traditional cavity effect factor to simulate the emissivity of a sparse vegetation canopy. The results of 4SAIL and Discrete Anisotropic Radiative Transfer (DART) are selected to do cross validation. The CE-P can achieve a high accuracy compared with 4SAIL and DART, with an absolute bias less than 0.002 when the leaf (soil) emissivity is equal to 0.98 (0.94). Four widely used analytical models are selected for comparison. The resulting accuracies of these models are ordered from CE-P to REN15, FR97, FR02, and VALOR96 with the most serious error up to 0.002, 0.002, 0.007, 0.013, and 0.014, respectively. Three main conclusions are obtained through the sensitivity analysis: the multiscattering between vegetation and the background can be ignored when the leaf (soil) emissivity is no less than 0.94 (0.90), the second and higher order scattering within the vegetation can also be ignored when the leaf (soil) emissivity is no less than 0.94 (0.90), and the single-scattering effect within the canopy should be considered which can be calculated using three view factors. [ABSTRACT FROM AUTHOR]

An operational split-window (SW) algorithm was developed to retrieve high-temporal-resolution land surface temperature (LST) from global geostationary (GEO) satellite data. First, the MODTRAN 5.2 and SeeBor V5.0 atmospheric profiles were used to establish a simulation database to derive the SW algorithm coefficients for GEO satellites. Then, the dynamic land surface emissivities (LSEs) in the two SW bands were estimated using the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Emissivity Dataset (GED), fractional vegetation cover (FVC), and snow cover products. Here, the proposed SW algorithm was applied to Himawari-8 Advanced Himawari Imager (AHI) observations. LST estimates were retrieved in January, April, July, and October 2016, and three validation methods were used to evaluate the LST retrievals, including the temperature-based (T-based) method, radiance-based (R-based) method, and intercomparison method. The in situ night-time observations from two Heihe Watershed Allied Telemetry Experimental Research (HiWATER) sites and four Terrestrial Ecosystem Research Network (TERN) OzFlux sites were used in the T-based validation, where a mean bias of −0.70 K and a mean root-mean-square error (RMSE) of 2.29 K were achieved. In the R-based validation, the biases were 0.14 and −0.13 K and RMSEs were 0.83 and 0.86 K for the daytime and nighttime, respectively, over four forest sites, four desert sites, and two inland water sites. Additionally, the AHI LST estimates were compared with the Collection 6 MYD11_L2 and MYD21_L2 LST products over southeastern China and the Australian continent, and the results indicated that the AHI LST was more consistent with the MYD21 LST and was generally higher than the MYD11 LST. The pronounced discrepancy between the AHI and MYD11 LST could be mainly caused by the differences in the emissivities used. We conclude that the developed SW algorithm is of high accuracy and shows promise in producing LST data with global coverage using observations from a constellation of GEO satellites. [ABSTRACT FROM AUTHOR]