Land surface emissivity (LSE) is crucial for retrieving land surface temperature (LST) from Landsat 9 TIRS-2 thermal infrared data. However, the single-band LSE product (band 10) provided officially is insufficient for split-window (SW) algorithm requiring dual-band emissivity inputs. This letter proposes a land cover and channel transformed (LCCT-LSE) method to estimate band 11 LSE and enables LST retrieval using SW algorithm on Google Earth Engine. Cross-validation with MOD21 LSE products showed that the LCCT-LSE method achieved a mean absolute error (MAE) of 0.004 and a root mean square error (RMSE) of 0.005, outperforming classification-based method, NDVI threshold method, and vegetation cover (VCM) methods. In situ validation showed SW-retrieved LST attains MAE/RMSE of 1.27 K/ 2.13 K, with consistent accuracy across diverse land covers (water: 0.86 K, soil: 1.58 K, desert: 1.71 K, sand: 1.80 K, vegetation: 0.87 K). A comparison with the official Landsat 9 LST product indicated that the bias of retrieved LST is within 1K for all land cover classes (cropland, forest, grassland, shrubland, water, barren and impervious) in Beijing. These results demonstrated that the LCCT-LSE method is capable of estimating the LSE in Landsat 9 band 11 with a reliable and accurate result. This study provides a new insight for LST retrieval from Landsat 9 data.

The accurate acquisition of land surface temperature (LST) on complex mountainous surfaces has always been a difficult problem and hot topic in thermal infrared remote sensing inversion, and the uncertainty caused by the radiation angle effect is one of the important factors hindering the accurate inversion of LST. Researchers have proposed a variety of models to simulate and eliminate the influence of the angle effect, among which, the kernel-driven model has a bright prospect of development. However, fewer studies have been conducted to observe the effects of terrain and land cover on thermal radiation directionality (TRD) properties based on measured data. This paper intends to carry out observations on a small spatial scale of a complex mountainous area using an unmanned aerial vehicle (UAV) to investigate the specific effects of different slopes, aspects, and land cover on the TRD characteristics. The measured results show that the intensity of thermal radiation anisotropy is actively correlated with the complexity of surface structure, and the influence of slope on TRD bias is in the form of a staged “S”, where its intensity increases slowly and then rapidly before slowing down again; the dispersion of thermal radiation in TRDs of different aspects is affected by the duration and intensity of solar radiation, and there is a time lag effect. Meanwhile, in order to evaluate the accuracy of different radiation directionality models, this paper further evaluates the currently more recognized kernel-drive model named LSF-Chen and the thermal equivalent slope kernel-driven (TESKD) model based on the TRD measurements from UAVs. The results show that the correlation coefficients of simulation results and measurements are all greater than 0.6, and the RMSEs are all less than 2K, and that the two methods both have a better simulation of the TRD effect, but the TESKD is better overall in terms of accuracy and methodological details. Through this study, a new method of applying UAVs to capture the thermal direction of the complex surface in mountainous areas is proposed, which provides methodological support for the extraction and accurate simulation of the TRD characteristics on the complex mountainous areas.

The Sustainable Development Goals Science Satellite-1 (SDGSAT-1) is equipped with both low-light and thermal infrared imagers, which can detect infrared radiation and weak light information emitted by vessels. Compared to similar products, its spatial resolution has undergone a significant improvement. Existing remote sensing vessel detection methods only consider the use of single-source data for vessel detection, fail to effectively utilize the complementary information in multisource data, and have difficulty processing the complex vessel shapes of high-resolution satellite data, resulting in unsatisfactory detection results. In view of the sparse distribution of vessels at sea, this article proposes a new vessel target detection method using SDGSAT-1 high-resolution thermal infrared and low-light satellite data. Specifically, guided filtering is used to fuse thermal infrared and low-light data, the background and foreground are separated by partial sum of tensor core norms (PSTNN) model, and then the ordering points to identify the clustering structure (OPTICS) clustering algorithm and intercluster merging are used for detection. This article established a vessel dataset by choosing Shanghai Port, Hong Kong Port, and the Gulf of Mexico and then applied the algorithm. The detection accuracy and recall rate were found to be 97.67% and 97.80% respectively, which were significantly superior to other algorithms. This algorithm overcomes the complex background noise in the dataset and achieves good detection results.

This paper describes a novel on-orbit absolute radiometric calibration technique based on multiple ground observations for China HJ-2B thermal infrared sensor. Two types of natural surfaces were selected as references, i.e., the water bodies of Wuliangsuhai Lake and desert in Kubuqi. During the multiple ground observations, the 102F Fourier transform infrared spectrometer and SI-111 infrared radiometer were used to obtain the land surface emissivity and temperature. The atmospheric parameters were acquired from the ECMWF reanalysis data after temporal and spatial interpolation. With an atmospheric radiative transfer model (MODTRAN5.3), the measured radiance was calculated and the calibration coefficients were determined by combining satellite-observed digital number (DN). The results show that the proposed radiometric calibration accuracy is high in both two thermal infrared channels of HJ-2B, with top-of-atmosphere (TOA) radiance differences of 0.60% and 1.36%, and equivalent brightness temperature differences of 0.39K and 0.96K, respectively.

The urban land surface temperature (LST) is very important in urban development, changes and local climate in the city, etc. Many methods have been proposed to inverse LST from satellite remotely sensed data. In this study, an inversion method of combining deep learning and physical model was proposed to estimate the urban surface temperature from CHINESE SDGSAT-1 satellite, i.e., an improved temperature and emissivity separation (TES) algorithm based on sky view factor (SVF). Finally, two data sets were selected to evaluate the accuracy of the proposed algorithm. Results show that the root mean squared errors (RMSEs) using the proposed algorithm are approximately 0.39K for LST and 0.023 for emissivity, respectively. The proposed algorithm is applied to inverse the LST/LSE of Beijing and Wuhan, China. Compared with Landsat-8 satellite products, the proposed algorithm has consistent accuracy. The cross-validated RMSEs with the Landsat-8 surface temperature product in Wuhan and Beijing were 2.18 K and 1.11 K, respectively.

Urban land surface temperature (LST) plays a crucial role in observing and comprehending energy exchange within urban environments. Urban geometric structure and material composition are critical parameters to characterize urban thermal radiation accurately. In this article, an urban thermal radiation analytical model based on the sky view factor (UTRAM-SVF) was developed by considering the multiple scattering within the urban canopy and the radiation composition of urban components. The cross-comparison of the proposed method was conducted in four ways: the discrete anisotropic radiation transfer (DART) model, urban effective emissivity model based on SVF (UEM-SVF), Landsat 8 Thermal Infrared Sensor (TIRS) data, and Airborne Hyperspectral Scanner (AHS) TIR data. Compared with DART, the results revealed that the root-mean-square error (RMSE) of radiance by the UTRAM-SVF model is 0.04 W/(m$^{2}\cdot $sr$\cdot \mu $m). Furthermore, two field applications were conducted using Landsat 8 TIRS data and AHS TIR data. The differences of at-sensor radiance from UTRAM-SVF model and Landsat 8 TIRS data vary from 0.05 to 0.3 W/(m$^{2}\cdot $sr$\cdot \mu $m), and the RMSE is 0.17 W/(m$^{2}\cdot $sr$\cdot \mu $m). The results of AHS TIR images on the UTRAM-SVF model present that the average biases of at-sensor radiance are 0.07 and 0.19 W/(m$^{2}\cdot $sr$\cdot \mu $m) for two TIR channels. The cross-comparison results show that the proposed method outperformed the UEM-SVF model in evaluating radiance over the complex and heterogenous urban areas (SVF <0.4). The UTRAM-SVF model can be used to monitor urban thermal radiation based on high-resolution TIR data and can further be helpful to retrieve high-resolution urban LST.

The separation of land surface temperature (LST) and land surface emissivity (LSE) is an ill-posed problem in thermal infrared (TIR) remote sensing. By building a new observation matrix to compress the LSE unknows and a dictionary training method to reconstruct complete LSE spectra, a new dictionary-based sparse representation for emissivity (DSRE) method has been proposed to retrieve LST and LSE from the atmospherically corrected hyperspectral TIR data. The proposed method fully utilizes the sparsity of compressed sensing and the empirical knowledge of the trained emissivity dictionary. The sensitivity analysis shows that the modeling accuracies of the proposed method are 0.215Kand 0.0060 for LST and LSE, respectively. Even with the instrument noise of 0.3Kand the uncertainties in atmospheric transmittance, atmospheric upwelling, and downwelling radiance of 10 %, the retrieval accuracies are 0.811Kfor LST and 0.0241 for LSE, respectively. Then a field experiment was conducted to validate the proposed method, and a comparison was executed to three published methods, including ASTER temperature-emissivity separation (ASTERTES), linear spectral emissivity constraint TES (LSECTES), and iterative spectrally smooth TES (ISSTES). The accuracies of retrieved LST and spectral LSE are 1.41K/ 0.009, 2.57K/ 0.071, 1.59K/ 0.038, and 2.00K/ 0.077 for DSRE, ASTERTES, LSECTES, and ISSTES. In contrast to the three published methods, our proposed method is more accurate and effective than other published methods. Especially in the atmospheric absorption band, the proposed method has a strong anti-noise capability to the residuals of environmental downwelling radiance.

Directional brightness temperature (DBT) acquired by remote sensing instruments plays a significant role in characterizing the directional anisotropy of land surface, especially for row canopies. The difference between shaded vegetation and sunlit vegetation is ignored in the existing models. In this article, a four-component parameterized directional thermal radiance model (FCPMod) has been proposed to describe the DBT of the row canopy by considering the four components including the sunlit/shaded soil and sunlit/shaded leaf, the improved multiple scattering within the canopy, and the sensor’s field of view (FOV). First, the sensor’s FOV is divided into many tiny rectangles along the row direction and the probabilities of four components in each tiny rectangle are estimated based on the radiative transfer (RT) theory and the bidirectional gap probability. Second, the DBTs are weighted by the four components’ probabilities and brightness temperatures of tiny rectangles. Third, a modified multiple scattering model is proposed to improve the modeling accuracy by considering the contribution of the multiple scattering radiance between soil and canopy. The sensitivity analysis results show that the proposed method performed well compared to the FRA97 model proposed by Françoiset al.(1997) over continuous canopy and the RT model (FovMod) proposed by Renet al.(2013) over row canopy. Finally, the field validations on a maize row canopy show that the proposed FCPMod performed better than about 0.4 K compared with the FovMod.

This paper presents a series of temporal vicarious radiometric calibration results of ZY3-MUX sensor over 2016–2019 using the reflectance-based approach. The synchronous ground measuring data have been collected from Baotou sandy site in China given that its high-frequency standard product including surface reflectance and atmospheric parameters. The results show the average difference of radiometric calibration coefficients between calculated results and the official coefficients in each year of ZY3-MUX sensor are 4.6%, 4.6%, 0.64%, 6.28%, respectively. The long-term stability of the radiometric calibration using the data of four years shows a good consistent, and the average difference is 0.92%, 0.64%, 0.50% and 0.65% for ZY3-MUX sensor, respectively. In addition, uncertainty analysis shows that the overall uncertainty for ZY3-MUX radiometric calibration is 4.42%, 4.44%, 4.66% and 3.92%, which also confirms the credibility for radiation quality of Baotou sandy site.

This paper describes an SNO (Simultaneous Nadir Overpass) method based on observation elements matching for radiometric cross-calibration of China HJ-1B and MODIS thermal infrared channels. Firstly, a DTC (Diurnal Temperature Cycle) model with four parameters and ECMWF data are introduced for time matching. Then a BRDF model updated to the TIR domain is built for angle matching. Combining matching coefficients with the spectral matching factor, the TOA radiance of MODIS B31 can be converted into TOA radiance of HJ-1B. Finally, the radiometric calibration results using the images of Qinghai Lake show that the proposed method is effective. Compared with the strict SNO method, the accuracy is improved by 0.73K.

In this study, the ozone column retrieval algorithm is described using the ultraviolet solar radiation from space. The algorithm is based on differential optical absorption spectroscopy (DOAS) technique. Firstly, the differential slant column densities (SCD) of trace gases are retrieved. Secondly, SCDs are subsequently converted to vertical column densities (VCD) by radiative transfer model. Then, the ozone column is retrieved by this algorithm and the sample results show a good correlation with R2of 0.91 and RMSE of 0.89 mg/m2between retrieved and true values. It also can be shown the potential of the algorithm on further atmospheric molecule retrieval in hyperspectral quantitative remote sensing.

Directional reflectance of Baotou sandy calibration site was measured in September 2017. Directional reflectance factors were collected using the Multi-Angles Observation System (MAOS) designed by Academy of Opto-Electronics (AOE), Chinese Academy of Sciences. The directional reflectance was measured at a few viewing and azimuth angles in the 0-30° and 0-360° angular ranges, respectively. Anisotropy and directional effect of surface reflectance were analyzed based on the measured directional reflectance factors. The bidirectional reflectance distribution function (BRDF) model of the calibration site was also modelled, and the model fitting error was approximated to be within 2%. The BRDF model was also used to correct angular difference between ground measurements and satellite measurements in vicarious radiometric calibration carried out in Baotou sandy calibration site, and calibration results with and without angular correction were shown in this work.

This paper describes a four-component parameterized directional thermal radiance model for row crops, which consists of the thermal radiance of the sunlit/shaded soil and sunlit/shaded leaf, multiple scattering effects of canopy and sensor field of view. The light transmission process of the row crops canopy has been full depicted and the accuracy and sensitivity of the proposed model are discussed in detail. Finally, compared with the FRA97 and FovMod models, the results show that the root mean square error(RMSE) is 0.18K and 0.36K, respectively.

This paper addressed the retrieval of land surface temperature (LST) from combined mid-infrared and thermal infrared data of the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-Orbiting Partnership (S-NPP). To efficiently remove the effect of the direct solar radiance, a relationship between direct solar radiance and water vapor content, view zenith angle and solar zenith angle is proposed to improve the retrieve accuracy. Then, a split-window algorithm from combined mid-infrared and thermal infrared data is used to correct for the atmospheric effects and retrieve the LST with the aid of emissivity provide by VIIRS product. Finally, comparison of the standard VIIRS LST product and the retrieved LST from the proposed algorithm, a good agreement was shown. Analysis indicated the root mean square error (RMSE) of the LST over these land cover types is 2.04K for desert and 1.84K for vegetation, respectively.