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.

Interband radiometric calibration from the mid-infrared to visible bands in the ocean specular region is an effective way to calibrate on-orbit remote sensing sensors. It assumes that the referenced band has highly accurate radiance and that the interband radiometric relationship can be obtained in the ocean specular region. Most current research employs only the radiative transfer (RT) equation to derive interband radiometric relationships. However, two variables—water-leaving radiance and whitecaps—are challenging to obtain yet crucial for radiative transfer calculations. Typically, water-leaving radiance is assigned a fixed value since empirical data, whereas whitecaps are estimated via the wind speed alone. These assumptions make the uncertainties of the calibrated bands large and different from those of real satellite-measured data, reducing the reliability of the interband relationship between the reference and calibrated bands and limiting the application of the interband radiometric calibration method. To address this issue, this study proposed a novel interband radiometric calibration method called coupled deep neural networks and radiative transfer (CDR), which integrates radiative transfer and a deep neural network (DNN) to provide a reliable relationship between referenced and to be calibrated bands without accurate water-leaving radiance and whitecaps. For the four visible bands of FY-3D/MERSI-II, the relative errors were found to be 2.12%, 4.62%, 1.89%, and 4.02%, respectively. Uncertainty analysis identified the referenced band as the largest uncertainty source, followed by chlorophyll concentration, polarization effects, and aerosol loading. The CDR algorithm can be used to calibrate historical long-term satellite data without additional measurements.

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.

Thermal infrared (TIR) remote sensing observation signal is influenced by both atmospheric and land surface conditions that are difficult to separate with conventional multichannel TIR data. Because of the advantage of channel wealth, hyperspectral TIR data can simultaneously estimate the land surface and atmospheric parameters using neural network models or integrating them with physical models. However, the commonly used neural network models do not fully explore the correlation between different channels by treating the input data as discrete features. Thus, this study aims to develop a new deep neural network (DNN) by combining the long short-term memory (LSTM) network and convolutional neural network (CNN) for estimating land surface temperature (LST), emissivity, atmospheric transmittance, upward radiance, and downward radiance more accurately. By applying on the thermal airborne hyperspectral imager (TASI) simulation dataset covering global atmospheric conditions with 32 channels in$8.0- 11.5\,\,\mu \text{m}$, the proposed model achieved results with the LST error of 0.95 K, the emissivity error of less than 0.012 for each channel, and the accuracy of three atmospheric parameters has also been improved compared with the current neural network models. Our model has been applied to a real TASI image, and its validity was further proved by the ground measurement validation data. Therefore, it can provide more reliable initial values for physical optimization models.

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.

The advanced hyperspectral imager (AHSI) is one of the sensors aboard the Chinese Gaofen-5 (GF-5) satellite, possessing characteristics of high spatial and spectral resolution, as well as width swath. To better understand the radiometric performance of GF-5/AHIS after its launch, this article presents an on-orbit radiometric calibration approach for AHSI visible and near-infrared (VNIR) and shortwave infrared (SWIR) sensors from field automatic observations with a field spectrometer in the absence of SWIR measurements. A spectrum extension method was proposed to extend the retrieved surface hyperspectral reflectance in the VNIR spectral ranges to SWIR by incorporating the historical hyperspectral reflectance library. The radiometric calibration coefficients of GF-5/AHSI were calculated by linear fitting of the observed digital number (DN) values with GF-5/AHSI and predicted at-sensor radiances with MODTRAN 5 based on extended hyperspectral surface reflectance. Comparisons with onboard calibration results were also performed, and the averaged relative differences were within 5% with$1\delta $standard deviations less than 10% for most bands, except for those in the atmospheric absorption and low signal-to-noise ratio bands. The comparison results indicate that the on-site radiometric calibration results are consistent with the onboard results, and the operational on-orbit radiometric calibration approach is reliable in the case that there are no measurements in the SWIR spectra range. The on-orbit radiometric performance of GF-5/AHSI rapidly degraded during the first several months after its launch and then tended to be relatively stable.

Land Surface Emissivity (LSE) is an important parameter in thermal infrared remote sensing, which is of great significance to temperature inversion. In this study, the Gradient Boost Regression Tree (GBRT) was proposed to directly retrieve LSEs of MODIS thermal infrared channels 29$(8.4-8.7\ \mu \mathrm{m}), 31(10.78-11.28\ \mu \mathrm{m})$, and 32 ($11.77-12.27\ \mu \mathrm{m}$) from the visible and near infrared (VNIR) data. We selected the variables related with LSE, including reflectivity, view zenith, solar zenith, land surface type, vegetation index (EVI), Normalized Difference Water Index (NDWI) and Leaf Area Index (LAI). The results of the test set showed that RMSEs of the estimated LSEs were 0.013 in channel 29, 0.005 in 31 and 0.004 in 32, which were more accurate than existing methods. Eight regions with different ground features were also selected to further evaluate the applicability of the model. In most areas, the RMSEs were below 0.015 in channel 29, below 0.005 in channel 31 and 32. In addition, the spatial distributions of the estimated LSEs and those extracted from MYD11B1 and MYD21A1D in H19V08 were compared, which were also reasonable. In general, it is feasible to use the selected variables with the GBRT model to directly retrieve the LSEs.

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.

The Chinese Gaofen-1(GF-1) high resolution satellite loaded with four Wide Field of View (WFV) cameras provides observations with high temporal and spatial resolutions. However, the radiometric calibration accuracy of the GF1/WFV should be given when being used to monitoring the earth. In this study, radiometric calibration of the GF1/WFV was carried out first with automatic instrumented Baotou site. Then, the determined radiometric calibration coefficients were cross validated with the MultiSpectral Imager (MSI) onboard the Sentinel-2 satellite. The preliminary results show that the radiometric performances of the four WFV cameras are relatively stable with averaged relative difference less than -1.85%. The standard deviation of the radiometric calibration coefficients during the period from April 2019 to June 2020 is 0.0056, 0.0081, 0.0072, and 0.0076 with respect to the blue, green, red, and near infrared channel. The results of cross validation with Sentinel-2/MSI suggest that the averaged relative difference is -3.16%, -4.28%, -1.15%, and -3.22% with respect to the blue, green, red, and near infrared channel. The results of cross-validation demonstrate that radiometric calibration of the GF-1/WFV cameras using automatic instrumented Baotou site is feasible and operational. And, it is also essential and necessary to update the on-orbit radiometric calibration coefficients of GF-1/WFV cameras during its' entire lifetime for further quantitative application.

As the growing interest in the use of the Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) low light data, there is an urgent need to obtain high accuracy product at low radiances. Currently the low light calibration accuracy was previously estimated at a moderate 15% using extended sources while the long-term stability has yet to be characterized. This paper gives a new method to quantitative analysis DNB data by using a specialized ground light point source at night, which is active designed light sources at selected site (Baotou of Inner Mongolia, China). It presents a possibility to resolve the need for SI traceable active light sources to monitor the calibration stability, radiometric and geolocation accuracy, and point spread functions of the DNB.

Land surface temperature (LST) is a crucial parameter in the energy and material balance of land surface system. The angle effect of LST makes the accuracy of LST restricted and limits the application of remote sensing LST product. In order to eliminate the influence of viewing angle, this study proposed a novel method to perform angular normalization by constructing a feature space of surface emission radiance and fractional vegetation coverage (Radiance-FVC space). The proposed approach is applied in Hetao Plain as an example. It is found that the Root Mean Square Error (RMSE) can reach 5.1K, and the angular normalization effect is more significant for pixels with larger viewing zenith angle.

The Radiometric Calibration Network (RadCalNet) provide SI-traceable Top-of-Atmosphere (TOA) spectrally-resolved reflectance for automated radiometric calibration of optical satellite sensors. Each RadCalNet site is equipped with automated ground instrumentation in order to provide continuous measurements of both surface reflectance and local environmental/atmospheric conditions needed for the derivation of TOA reflectance values. It is very valuable to use these data in calibrating and motoring optical satellite sensors with the recent launches of very large number of satellites. The work presented here shows the results of vicarious calibration of SuperView-1 satellite sensor using RadCalNet TOA reflectance product. The results also indicated that there was a good consistency among the vicarious calibration coefficients from different RadCalNet sites.

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.

The development of ground-based sampling strategies is vital to the validation of medium- or coarse-resolution satellite-derived land surface temperature (LST) products, extremely over heterogeneous ground with dramatic diurnal LST change. An optimal sampling strategy in support of LST validation across both spatial and diurnal scales (SDS) was proposed in this study. The SDS integrated prior knowledge of land-cover, multi-temporal feature information and spatial distribution of samples to improve the representativeness of the samples. The SDS were also compared with three sampling strategies including random, systematic, and land-cover base sampling. The results obtained by the remote sensing simulation data indicated that the SDS performed best with stable root mean square errors (RMSE) less than 0.1k when sample ratio was more than 2%, and the representativeness of samples selected by the SDS in both diurnal space and spatial space was superior to the current sampling strategies.

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.

When using field calibration site to perform on-orbit radiometric calibration for a space-borne remote sensor, the observed signal of the sensor may contain energy from adjacent pixels, due to existence of the earth atmosphere and sensor viewing characteristics. Hence the accuracy of radiometric calibration will be decreased to some extent. When calibration of high-resolution sensor is concerned, the non-uniformity of the ground target will be relatively more obvious. In addition, if the brightness contrast of neighboring targets is not small, the impact of adjacency effect will be more outstanding. How to quantitatively analyze and eliminate the influence of this kind of adjacency effect, becomes an actual demand to reduce the uncertainty of on-orbit radiometric calibration. Aiming at the adjacent effect caused by atmospheric multiple scattering, this paper analyzed radiation transfer mechanism first, then constructed a local atmospheric point spread function model using long time-series satellite-ground synchronous observation data, developed an adjacency effect correction method used in on-orbit vicarious calibration, which considers background reflectance spectral information. Test on Sentinel-2A imagery indicates that the proposed correction method can effectively alleviate the influence of adjacency effect in vicarious calibration.

Radiometric characteristics monitoring of optical remote sensing data is a very essential step that enables further quantitative study and application of the data. In this paper, long term vicarious radiometric calibration using ground reflected radiance-based approach was introduced in this study. A case study of Sentinel 2A multispectral imager (MSI) vicarious radiometric characteristics monitoring based on our proposed approach with an automatic observation system was conducted over a large scale desert at the Baotou calibration site in Inner Mongolia, China. And, twelve clear Sentinel 2A MSI scenes as well as ground measurements were successfully acquired during the year of 2017. Top of Atmospheric (TOA) radiance and reflectance were predicted with introduced approach from ground reflected radiance and atmospheric data. The long term radiometric calibration results suggests that the Sentinel 2A MSI display a stable radiometric performance over the calibration period. Vicarious and onboard radiometric calibration results were also cross compared with average relative error about 5%. Uncertainty analysis also show that the TOA radiance overall uncertainty is less than 3.5% due to the atmospheric characteristics, surface characteristics, and the calibration model uncertainties sources.

In this study, the automatic reflectance-based method is used to vicarious radiometrically calibrate the satellite optical sensors using the desert target located in the Baotou site in China. The ground reflected radiance of the desert target were collected automatically using an automatic observation system. The reflectance of the desert target was calculated with the radiance collected with the automatic observation system and the total irradiance simulated from MODTRAN code based on the atmospheric parameters. Then, the TOA radiance can be predicted with MODTRAN code based on the calculated desert reflectance. The automatic reflectance-based approach was applied to the Landsat 8/OLI sensors, and the TOA radiances calibrated by our method were also compared with the observed TOA radiance calibrated with on-board calibrator. Preliminary results show a good consistent and the mean relative difference of the multispectral channels is less than 5%. Uncertainty analysis also show that the TOA radiance overall uncertainty is less than 4% due to the source including the atmospheric characteristics, surface characteristics, and the selected calibration model.

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.

In this paper, a generalized regression neural network (GRNN) is used for land surface temperature (LST) retrieval from advanced microwave scanning radiometer-earth (AMSR-E) passive microwave data. To make neural network method more representative of the real situations, the simulated data under various atmospheric and surface conditions is generated with the aid of monochromatic radiative transfer model and the advances integral equation model, and is used to train GRNN, combined with AMSR-E measurements and MODIS LST product on the same platform (Aqua satellite). Because of the lack of simultaneous ground LST measurements in large scale, MODIS LSTs are taken as actual ground LST measurements. Through detailed analysis, the datasets in AMSR-E channels 23.8 V, 36.5 V, 89.0 V and 89.0 H GHz with the smallest root mean square error (RMSE) are used for LST retrieval, and the results show that more than 70% of errors are within 3 K, and the RMSE is 4.66 K.

Land surface temperature and emissivity separation (TES) is a key problem in thermal infrared remote sensing. Current TES methods were proposed and succeeded to apply for the retrieval of land surface temperature and emissivity for the materials with emissivity close to 1. This work addressed the performance of linear spectral emissivity constraint (LSEC) method proposed by wang et al. (2011) for the TES of hyperspectral TIR data for contrast samples (high- and low- emissivity materials). The simulated hyperspectral TIR data are used for analysis and generated with six MODTRAN standard atmospheric profiles by hyperspectral atmospheric radiative transfer model (4A/OP). The influence of initial emissivity estimation is considered in this paper. The results show that initial emissivity estimation has a great impact on the performance of LSEC. LSEC method performs a fairly good result when the initial emissivity is close to the true value, and the RMSEs of temperature and emissivity are smaller than 0.5K and 0.01 when initial emissivity is good. However, the performance is worst when the initial emissivity has a great deviation.

In this paper, great efforts are focused on the temperature and emissivity separation (TES) from hyperspectral thermal infrared data. However, instead of proposing new method, the performances of several published TES methods, including iterative spectrally smooth temperature emissivity separation method (ISSTES), automatic retrieval of temperature and emissivity using spectral smoothness method (ARTEMISS), spectral smoothness method (SpSm), downwelling radiance residual index method (DRRI) and linear spectral emissivity constraint method (LSEC) are analyzed under different instrument characteristics, including the shifting of spectral and the broadening of the full-width half-maximum (FWHM), with the simulated data. The results shows that LSEC has the most robust and accurate performance. DRRI also has a good performance, but a channel selection procedure is required before the use of this method. ISSTES, ARTEMISS and SpSm are more sensitive to the instrument characteristics with some larger errors than other two methods.

Remote sensing is an integrated Earth observation technology, and its standardization requires the multi-industry, multi-disciplinary and multi-field joint efforts and coordination. This paper discusses the management mechanism of remote sensing technology standardization, and reviews its current status in China. It's pointed that standardization work of remote sensing technology lags behind the development of remote sensing technology in the whole. Remote sensing technology standardization in China needs to be pushed urgently. Finally, some development suggestions of remote sensing technology standardization on the standard system, the international standardization and the publicizing and implementation of current standards are given.

Fractional vegetation cover (FVC) is a major biophysical parameter in earth surface system. In this paper, FVC is retrieved with a simple linear model between FVC and Normalized Difference Vegetation Index (NDVI). However, the parameters NDVI∞and NDVI0, corresponding to the values of NDVI for bare soil and full vegetation covered surface, used in the simple model are estimated with a vegetation phenology model using time series MODIS NDVI data. The results of the estimated FVC with our proposed method in the study area have been showed in the results section, which is compared with the FVC estimated with a single date MODIS NDVI data. Validation has also been proved that the retrieved FVC has a good agreement with the ground-measured truth FVC.

This work addressed the estimation of the directional reflectance in Middle Infra-Red (MIR) channel from the data acquired by the Stretched Visible and Infrared Spin Scan Radiometer (SVISSR) onboard Chinese geostationary Meteorological satellite FengYun 2C (FY-2C). SVISSR/FY-2C sensor acquires image covering the whole disk with a temporal resolution of 30 minutes. The MIR directional reflectance retrieval procedure can be seen as follows. Firstly, the atmospheric profiles data provided by European Centre for Medium-Range Weather Forecasts (ECMWF) were used to correct atmospheric influence with the radiative transfer code (MODTRAN 4.0). Secondly, the bi-directional reflectance in SVISSR/FY-2C MIR channel 4 (3.8 micron) was estimated from the combined MIR and TIR channel with day-night SVISSR/FY-2C data. Finally, a BRDF model referred to as the RossThick-LiSparse-R model was used to estimate the directional reflectance in MIR channel from the time-series bi-directional reflectance data. The results have been demonstrated that the method can be applied well to estimate the directional reflectance in MIR channel of SVISSR/FY-2C sensor.

In this paper, an improved method is proposed to simultaneously retrieve land surface temperature (LST), emissivity (LSE) and atmospheric profiles. This method employed the linear spectral emissivity constraint to efficiently reduce the number of retrieved variables. The proposed method was validated with some simulations. The initial guesses were derived from a neural network model. This method could greatly improve the accuracies of LST, LSE and atmospheric profiles. The RMSE of LST was decreased from 5.12 K (the initial guesses) to 1.59 K (the physical retrieved). The retrieved emissivity spectrum was in good agreement with the actual spectrum. An improvement of 1K in the tropospheric temperature was also been found. Those results showed that the proposed method is capable of improving the retrieval accuracies of land surface and atmospheric parameters with the remotely sensed thermal infrared data.

An operational statistical method suitable for nearly real-time estimate of land surface and atmospheric parameters was developed and applied to the Infrared Atmospheric Sounding Interferometer (IASI) observations. The proposed method utilized three steps to solve the ill-posed problems and to stabilize the solution in a fast speed regression manner: 1) the atmospheric profiles and land surface emissivity spectra were expressed by their eigenvectors to reduce the number of unknowns; 2) a ridge regression procedure was introduced to improve the conditioning of the problem and to lessen the influence of noises; 3) a set of optimal channels was selected to decrease the effect of forward model errors or uncertainties of trace gases, and to increase computational efficiency. The retrieval results using the independent simulated data indicate the proposed method is promising. The root mean squared error (RMSE) of land surface temperature is 3.5 K, the RMSE of land surface emissivity at the selected channels is 0.01, and the RMSE of atmospheric temperature and moisture profile are about 2.0K and 0.001g/g, respectively.

HJ-1B satellite is one of the small satellites in the constellation for disaster prediction and monitoring which will be launched in 2008. The infrared sensor, which is one of the payloads of HJ-1B satellite, contains the MIR and TIR channels. The improved capabilities of HJ-1B data offer an opportunity for the computation of subpixel fire temperature and fire area. The simulated HJ-1B MIR and TIR channel images are used in this paper for the algorithm test. Fires with various sizes and temperatures are simulated in a wide range of terrestrial biomes and climates conditions by MODTRAN 4. A bispectral method developed by L. Giglio and J. D. Kendall is adopted to retrieve the temperature and area of a subpixel fire within an otherwise homogeneous pixel. It is evident that HJ-1B satellite data are more sensitive to the smaller and the cooler fires than that of MODIS or AVHRR Data. For the HJ-1B data, if the fire area is about 450m2and fire temperature is about 1000K, it can also offer a capability of retrieving the fire temperature and area in a relatively high accuracy. It has been demonstrated that the accuracy will increase with the growing fire area or temperature. By sensitivity analysis it has been found that the uncertainties of the retrieved fire temperature and area using HJ-1B data are about 10.0% and 30% at the given simulation condition.