This study develops an integrated framework for all-sky surface longwave downward radiation (SLDR) estimate for the MERSI-II imager onboard the Fengyun-3D (FY-3D) satellite. The framework comprises a hybrid method for the clear-sky SLDR estimate and a cloud base temperature (CBT)-based single-layer cloud model (SLCM) for the cloudy-sky SLDR estimate. In-situ validation indicates that the hybrid method yields a bias/RMSE of -0.78/21.70 W/m2, whereas the SLCM achieves a bias/RMSE of 5.79/23.61 W/m2. The bias/RMSE of the all-sky SLDR is 3.37/22.93 W/m2. The estimated all-sky instantaneous SLDR was combined with ERA5 temporal information to derive daily SLDR using a bias-corrected sinusoidal integration method, yielding a bias of 0.04 W/m2and an RMSE of 16.77 W/m2. These results demonstrate the robustness of the proposed framework and its substantial potential in generating both instantaneous and daily SLDR products at 1 km spatial resolution.

This letter reports the first cloudy-sky land surface temperature (LST) retrieval from the MEdium Resolution Spectral Imager-II (MERSI-II) onboard Fengyun-3D (FY-3D). In this study, a new cloudy-sky LST retrieval algorithm was adapted for FY-3D/MERSI-II. The method consists of two key components: (1) estimating the hypothetical clear-sky LST using geographically weighted regression (GWR) downscaling and linear model correction, and (2) quantifying the temperature difference (ΔT) caused by cloud radiative effect (CRE) based on surface energy balance (SEB) theory. Validation against in situ LST measurements from the SURFRAD and HiWATER networks indicates that the retrieved cloud-sky LST exhibits a bias of 0.02 K and a root mean square error (RMSE) of 3.77 K. The results confirm that the algorithm achieves reliable retrieval of FY-3D/MERSI-II cloudy-sky LST, with the ΔT correction term making a notable contribution. This method is expected to be used to generate the operational FY-3D cloudy-sky LST product.

Land surface temperature (LST) is a direct result of earth-atmosphere interactions and has been widely used in earth system science and climate change. Thus, it is recognized as one of the essential climate variables (ECVs). Thermal infrared (TIR) remote sensing is the most effective way to obtain high-quality LST at a large scale. However, TIR cannot penetrate the Cloud and obtain the cloudy sky LST, which seriously hinders its applications. To address this challenge, we proposed a paradigm for estimating the seamless LST at regional and global scales. Validation results showed the root mean square error (RMSE) of the produced seamless LST achieved approximately 3K. The produced seamless LST at China landmass, East Asia, and global have been freely released to the public (https://elite.bnu.edu.cn).

This letter reported the first land surface temperature (LST) retrieval result from the thermal infrared spectrometer (TIS) onboard the Sustainable Development Science Satellite 1 (SDGSAT-1). The LST was retrieved from the three TIS thermal infrared (TIR) channels by a temperature and emissivity separation (TES) algorithm, which was adapted from an improved TES (iTES) algorithm. In situ validation showed the TIS iTES algorithm achieved a bias of 0.16 K and an RMSE of 3.01 K at SURFRAD sites. The radiance-based validation indicated the bias and RMSE of the retrieved LST are −0.49 and 2.71 K, respectively. In addition, cross-comparison with MYD21 LST showed that the retrieved LST had an average bias and RMSE of 1.15 and 1.80 K, respectively. These results demonstrated that the developed iTES algorithm is capable of retrieving LST from SDGSAT-1/TIS data with acceptable accuracy. This study provides a practical method of deriving LST from SDGSAT-1/TIS data and facilitates the application of SDGSAT-1/TIS data in studies related to thermal environment monitoring, surface energy balance, and climate change.

Sea surface temperature (SST) is an important variable in climate and weather research. We utilize two SST datasets to analyze the performance of the Fengyun-4A (FY-4A)/Advanced Geostationary Radiation Imager (AGRI) SST product retrieved from the nonlinear split-window algorithm. Validation results with the in situ SST Quality monitor (iQuam) (version 2.1) show that the overall bias and root mean square error (RMSE) are −0.38 and 1.04 K, respectively, meeting the demand for numerical weather prediction. There is a good agreement between the AGRI SST and the Advanced Himawari Imager (AHI) SST (version 2.0), with an overall bias of 0.09 and an RMSE of 0.95 K. The significant accuracy decreases in AGRI SST since August 2020 could be attributed to an updated operation calibration. This letter will benefit the scientific disciplines that require an SST as input by highlighting the accuracy and uncertainty of the AGRI SST product.

Soil directional emissivity plays a crucial role in canopy thermal-infrared (TIR) emissivity modeling over sparsely vegetated solo slopes. To our knowledge, the canopy emissivity model explicitly considers soil emissivity directionality, and topography does not exist. This study proposes a new canopy emissivity model under the framework of the four-stream approximation theory employed in the well-known 4SAIL model by incorporating soil directional emissivity and topography. The new model was validated by the discrete anisotropic radiative transfer (DART) model. The new model-simulated canopy emissivity data exhibited excellent consistency with the DART simulation data, and the bias, root mean square error (RMSE), and determination coefficient ($R^{2}$) were −0.001, 0.003, and 0.97, respectively, under the different leaf area indices (LAIs), slopes, and view zenith angles (VZAs). Sensitivity analysis revealed that LAI and soil nadir emissivity explained most of the variance, with total sensitivity indices of 52.9% and 30.3%, respectively. The effects of soil directional emissivity, topography, and leaf angle distribution (LAD) on canopy emissivity were subsequently investigated, and the results indicated that the differences could reach more than 0.02 when soil directional emissivity and/or topography were neglected; moreover, the influence of LAD functions is not significant. The model proposed in this article provides a practical method for modeling mountainous area canopy emissivity and can improve estimates of surface broadband emissivity (BBE) and land surface temperature (LST).

Polar regions play a crucial role in global climate change. Surface longwave downward radiation (SLDR) is a primary energy source for the polar surface and plays an essential role in studies of polar hydrology, temperature, and climate. Therefore, accurately estimating the SLDR over polar regions is highly important. However, the accuracies of existing polar SLDR datasets and SLDR inversion methods are insufficient to meet the requirements of relevant research. In this study, we developed a data-driven model for high spatial resolution clear-sky SLDRs estimated from Moderate Resolution Imaging Spectroradiometer (MODIS) imagery in polar regions. The model comprises two layers: the first layer incorporates three machine learning models, namely, eXtreme gradient boosting (XGBoost), convolutional neural network (CNN), and transformer, while the second layer consists of a stacking meta-model. The ground measurements collected from 51 sites were used to train and validate the developed model. The bias, RMSE, and R2 of the model training are zero, 14.15 W/m2, and 0.95, respectively, whereas the values for the validation are 0.49, 15.35 W/m2, and 0.9, respectively. We also compared the accuracies of the ERA5 and CERES-SYN SLDR data with the SLDR estimated by the developed model. The results indicate that the developed model is superior to the ERA5 and CERES-SYN SLDR models when evaluated at the validation sites. In addition, we analyzed the performance of the developed model under different elevations and seasons, demonstrating its robustness in different situations.

Surface longwave upward radiation (SLUR) is a key parameter in studying hydrological and climate models. This study develops a framework for estimating the all-sky SLUR from the Advanced Geostationary Radiation Imager (AGRI) onboard the Chinese geostationary weather satellite FengYun-4A. The framework is composed of a hybrid method for estimating clear-sky SLUR and a machine learning (ML) method for estimating cloudy-sky SLUR. According to the in-situ validation, the R2/bias/RMSE of the developed hybrid method is 0.95/0.59/18.41 W/m2, which is clearly superior to the AGRI official SLUR and ERA5 SLUR with a R2/bias/RMSE of 0.95/-7.79/19.04 W/m2and 0.92/-4.94/23.2 W/m2, respectively. The developed hybrid method performs better than the classical land surface temperature-broadband emissivity (LST-BBE) method. The R2, bias/RMSE of the developed cloudy sky SLUR estimate LightGBM model is 0.85/ 0.56/21.16 W/m2, which is also better than the accuracy of the LST-BBE method and comparable to the accuracy of the ERA5 SLUR. The R2, bias and RMES of the all-sky SLUR are 0.93, 0.57 and 19.58 W/m2, respectively. The developed framework is employed to determine hourly all-sky SLUR from AGRI data. This study provides a promising solution to obtain hourly all-sky SLUR from geostationary satellites.

All-weather Land Surface Temperature (LST) derived from passive microwave (PMW) sensors has significant implications for characterization on the physical processes of surface energy and water balance at local through global scales. However, the PMW sensors (e.g., the AMSR-E) suffer from swath gaps, cannot provide completely spatial-gapless observations. The existing Multi-temporal Feature Connection-CNN (MTFC-CNN) method caused obvious traces of ‘gaps’ when the sample number is small or features are not rich. This paper proposes a Sample Optimized-MTFC (SO-MTFC) seamless reconstruction method based on analyzing the periodicity and complementarity of AMSR-E swath gaps. Sample optimization includes two aspects: sample enhancement and single-cycle mask strategy. Taking China’s landmass as the study area, experimental results show that the original AMSR-E LSTs and the reconstructed AMSR-E LSTs are basically connected seamlessly. Validation against with the MODIS LSTs show that the daytime (nighttime) RMSEs of the original and the reconstructed AMSR-E LSTs are 3.87 K (2.57 K) and 4.76 K (2.96 K), respectively; while the corresponding daytime (nighttime) R2are 0.88 (0.94) and 0.73 (0.90), respectively. Validation against with the six in-situ LSTs show that the RMSEs and R2of reconstructed AMSR-E LSTs against in-situ LSTs are almost consistent with those of the original AMSR-E LST. The ablation study proves the effectiveness of the sample optimization. These findings indicated the SO-MTFC achieved a good reconstruction effect. Compared with the MTFC-CNN, the SO-MTFC got higher scores in visually and quantitatively. The SO-MTFC can potentially be implemented with other satellite PMW sensors to produce completely spatial-seamless PMW LST records on a global scale.

Thermal infrared (TIR) observations from geostationary satellites enable the retrievals of the diurnal variations in land surface temperatures (LST) linked to the land-atmosphere energy exchange and water cycle. However, cloud cover obstructs TIR signals and leads to missing gaps that generally account for more than 50% of TIR LST maps. This study proposed an effective method to estimate hourly all-weather LST from the Advanced Geosynchronous Radiation Imager (AGRI) data under the framework of surface energy balance (SEB) theory. An improved temperature and emissivity separation algorithm was first used to obtain the high-quality clear-sky LST, which plays a decisive role in ensuring the accuracy of recovered cloudy-sky LST. Then we proposed a unique way to solve the temperature difference (ΔLST) between the cloudy-sky LST and hypothetical clear-sky LST caused by cloud radiative effects (CRE). The bias (RMSE) of the estimated AGRI hourly cloudy-sky LST is 0.10 K (3.71 K) during the daytime, and -0.20 K (2.73 K) during the nighttime, respectively. The overall bias (RMSE) of the estimated AGRI all-weather LST is 0.02 K (2.84 K). The estimated hourly all-weather LST not only captures the rapid variation in diurnal LST but is also promising for temporal upscaling. The temporally upscaled daily mean LST shows a bias (RMSE) of 0.03 K (1.35 K). This study provides a promising solution to generate diurnal hourly all-weather LST for AGRI and other geostationary satellites.

Surface longwave upwelling radiation (SLUR) is a critical parameter for studying the water-energy balance between the land surface and atmosphere. In this study, we proposed a new hybrid method for estimating the clear-sky SLUR from the bottom-of-atmosphere (BOA) radiance (new BOA-hybrid method) of the Moderate Resolution Imaging Spectroradiometer (MODIS). There are two key parts in the developed new BOA-hybrid method. First, a physical four-channel algorithm was developed to estimate the atmospheric terms (atmospheric upwelling radiance and transmittance), which were used to calculate MODIS BOA radiance. Second, a linear model linking SLUR and MODIS BOA channel radiances was constructed using a large number of samples generated by extensive radiative transfer simulations. In situ measurements from twenty-seven sites in four flux networks were collected to validate the new BOA-hybrid method. The average bias and RMSE were 1.65 W/m2and 16.23 W/m2for the new BOA-hybrid method, which was superior to the original BOA-hybrid method whose bias and RMSE were 3.52 W/m2and 18.51 W/m2and slightly better than the classical hybrid method that estimates SLUR using the top-of-atmosphere (TOA) radiance (TOA-hybrid method) whose bias and RMSE were -1.64 W/m2and 17.44 W/m2, respectively. The performance of the new and original BOA-hybrid methods was similar when the total precipitable water vapor (TPW) was less than 1.5 cm. However, under a large TPW (i.e., TPW>3 cm), especially in the case of a large viewing zenith angle (i.e., VZA>45°), the accuracy of the original BOA-hybrid method decreased significantly, while the physical four-channel algorithm could effectively improve the accuracy of atmospheric correction and the RMSE of the SLUR estimated by BOA-hybrid could be reduced by more than 10 W/m2. In summary, the developed new BOA-hybrid method can estimate SLUR accurately and has great potential to produce a long-term high spatial resolution environmental data record of SLUR.

The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) land surface temperature (LST) product provides LST data with a high-spatial resolution of 70 m$\times70$m. In this letter, the quality of ECOSTRESS LST product was assessed using ground measurements collected from 17 sites, including seven surface radiation budget network (SURFRAD) sites, seven baseline surface radiation network (BSRN) sites, and three National Tibetan Plateau/Third Pole Environment Data Center (TPDC) sites. After outlier removal using the “$3\sigma $-Hampel identifier,” the overall bias and root mean square error (RMSE) of ECOSTRESS LST at SURFRAD, BSRN, and TPDC sites are −1.61 and 3.08 K, −0.75, and 3.50 K, and −0.82 and 4.18 K, respectively. This letter shows the accuracy and uncertainty of ECOSTRESS LST product, and will benefit research fields that require LST with high-spatial resolution.

Atmospheric correction is a key step toward estimating land surface temperature from the sensor with only one thermal infrared (TIR) channel. We use ground radiosounding profiles collected from 163 radiosonde observations to provide insights on how well the ERA5 reanalysis product performs in the atmospheric correction of Landsat series TIR data. Despite the poor performance of the ERA5 product for estimating atmospheric upward radiance, downward radiance, and transmittance of Landsat series TIR data in the Americas and Africa, the performance of the ERA5 product was superior to that of the M2I6NPANA (inst6_3d_ana_Np) dataset (MERRA2) and (Final) Operational Global Analysis data (FNL) products in Asia and Europe. The vertical distribution of air temperature and relative humidity profiles may explain the poor performance of ERA5 in the Americas and Africa. This letter shows the advantages and weaknesses of the ERA5 reanalysis product in the atmospheric correction of Landsat series TIR data and will benefit research fields that require an atmospheric profile as input.

As one of the four components of surface radiation balance, surface longwave downward radiation (SLDR) greatly affects the accurate characterization of hydrological, ecological, and biochemical processes. Since cloud-base temperature (CBT)-based single-layer cloud models (SLCMs) have advantages in both their strong physical mechanisms and abilities to produce high spatial resolution SLDR products, this study validated a CBT-based SLCM developed at the global scale usingin situobservations collected by the baseline surface radiation network (BSRN) in conjunction with Aqua and Terra Moderate Resolution Imaging Spectroradiometer (MODIS) cloud products and Modern-Era Retrospective Analysis For Research And Applications, Version 2 (MERRA-2) reanalysis data. Overall, the CBT-based SLCM achieved a relatively high SLDR estimation accuracy for the Terra and Aqua satellites, with biases better than −1.2 W/m2and root-mean-squared error (RMSE) values better than 29.9 W/m2. However, its random RMSE was slightly worse than those of two Clouds and the Earth’s Radiant Energy System (CERES) Single Scanner Footprint (SSF) SLDR products due to the larger spatial variability that exists at the Earth’s surface when it is quantified at a high spatial resolution (1 km). Additionally, the CBT-based SLCM outperformed two existing cloud-top temperature (CTT)-based SLCMs. In the future, we will continue to improve the performance of the CBT-based SLCM and will update the Global LAnd Surface Satellite (GLASS) SLDR product under cloudy sky conditions.

On the basis of preceding study of microwave (MW) land surface temperature (LST) downscaling, this letter proposed an all-weather LST fusion method based on random forest (RF) and evaluated it using the Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) LSTs in four areas of China that represent different landscapes. The results show that the RF method can effectively avoid the problem of oversmoothing patterns derived by the widely used Bayesian maximum entropy (BME) method and obtained LSTs more consistent with reality. Taking MODIS LST in the Yunnan–Guizhou Plateau (YGP) region and the border of Shanxi Province and Henan Province (BSH) region as a reference, the accuracy of RF method improved up to 13% and 11% compared with those of BME method under different cloud proportions. Taking field observations in the Heihe River Basin (HRB) and the Naqu area as references, the accuracy of RF-derived LST under cloudy conditions is basically consistent with that of MODIS LST in clear sky, differing by only 0.004–0.067 K. Due to the introduction of environmental variables, the performance of RF method is more stable than that of the BME method under different cloud proportions. In summary, RF is promising for fusing MW and thermal infrared (TIR) LSTs.

The high-frequency all-weather land surface temperature (LST) product generated from the thermal-infrared (TIR) observations of the geostationary meteorological satellite is of great significance to study the diurnal variations in the LST and the land surface energy balance. However, the TIR sensor cannot penetrate the clouds and obtain the desired LST under cloudy conditions. In this study, we developed a physical-based framework for generating high-frequency (hourly) all-weather LST data by synchronizing geostationary satellite TIR observations and simulations of the land surface model (LSM). There are three parts in the developed framework. First, the clear-sky LST was retrieved from the Advanced Himawari Imager (AHI) onboard the geostationary satellite Himawari-8 using our newly developed temperature and emissivity separation algorithm. Second, the Advanced Microwave Scanning Radiometer 2 (AMSR2) observations were assimilated into the Noah land surface model with multiple parameterization options (Noah-MP) model to generate the all-weather LST. Finally, the retrieved clear-sky AHI LST and Noah-MP assimilated LST were fused using the Ensemble Kalman filter (EnKF) algorithm. In situ measurements from three networks were collected to evaluate the Noah-MP assimilated LST and EnKF fused LST. The bias/RMSE of the Noah-MP assimilated LST and EnKF fused LST were –0.16/3.01 K and 0.15/2.68 K, respectively, under all-weather conditions. Compared to the Noah-MP free-run LST, the absolute values of the bias were reduced by 0.64 K and 0.68 K for the Noah-MP assimilated LST and EnKF fused LST, while the RMSEs were reduced by 0.33 K and 0.65 K, respectively. In addition, the spatial distribution of EnKF fused LST was in good agreement with the retrieved clear-sky AHI LST. The proposed framework in this study was demonstrated to be capable of obtaining accurate high-frequency (hourly) all-weather LST data.

The Chinese GF-1 satellite, the first satellite of the China High-resolution Earth Observation System launched in 2013, can be used to help estimate evapotranspiration (LE), which is important for myriad hydroclimatic and ecosystem science and applications. We propose a novel approach to use the GF-1 visible and near-infrared (VNIR) measurements at 16 m and 4-day resolutions to estimate LE. The NIR (near-infrared)–red spectral-domain (NRSD) model is coupled to a perpendicular soil moisture index (PSI) and a perpendicular vegetation index (PVI). We applied the model to the Huailai agricultural region of China with 55 scenes of GF-1 imagery during 2013–2017 and validated using ground measurements with footprint models for two eddy-covariance (EC) flux tower sites and one large aperture scintillometer (LAS) site. The results illustrate that the terrestrial daily LE can be estimated with squared correlation coefficients ($R^{2}$) of 0.77–0.84 ($p < 0.01$) and root-mean-square error (RMSE) values of 17.9–21.5 W/m2among all three sites. The site-calibrated statistics are improved by 0.14–0.25 for$R^{2}$and decreased by 4.2–8.3 W/m2for RMSE as compared to the commonly used universal PT-JPL model. A satisfactory performance is achieved across all experimental conditions, encouraging the application of the NRSD model to estimate LE for other broad regions.

In this letter, the National Oceanic and Atmospheric Administration Joint Polar Satellite System enterprise algorithm and the quadratic split-window (SW) algorithm were adapted to high spatial resolution thermal infrared (TIR) data of Chinese Gaofen-5 (GF5) to estimate the land surface temperature (LST) and sea surface temperature (SST), respectively. Lacking official calibration coefficients, GF5 TIR data were cross-calibrated by the well-characterized Visible Infrared Imaging Radiometer Suite (VIIRS) data. The coefficients of two SW algorithms were obtained by linear regression from the simulated data set generated via comprehensive radiative transfer modeling. The performance of the two algorithms was first evaluated by independent simulation data and then cross-validated by Moderate Resolution Imaging Spectroradiometer (MODIS) LST/SST, VIIRS LST/SST, and Advanced Himawari Imager (AHI) SST products. The preliminary results show good agreement between estimated GF5 LSTs/SSTs and referenced LST/SST products, with an average bias (root mean square error) of -0.26 (1.74), -2.48 (3.49), 0.18 (2.43), and -1.47 K (2.86 K) for VLSTO, VNP21, MYD11, and MYD21 LST products, -0.79 (1.55), -0.28(1.58), and -1.71 (2.21) K for VIIRS, MODIS, and AHI SST products. This is the first time that both LST and SST are retrieved from the real GF5 data. This letter provides a practical method to estimate LST and SST from Chinese Gaofen-5.

Parameterization schemes for estimating clear-sky surface downward longwave radiation (SDLR) are well recognized for their simplicity and acceptable accuracy, especially at the local scale. The near-surface temperature and/or water vapor are usually used to predict the clear-sky SDLR in a parameterization scheme. Air temperature inversion (ATI) alters the atmospheric state at the near-surface boundary layer and affects the accuracy of the clear-sky SDLR estimation. However, few studies have investigated the impact of ATI on the estimate of the clear-sky SDLR. This article investigated the impact of ATI on the estimate of the clear-sky SDLR using six widely used parameterization schemes. According to the evaluation results using ATI profiles from the Thermodynamic Initial Guess Retrieval (TIGR) database and the Surface Radiation Budget Network (SURFRAD) sites, all the parameterization schemes are sensitive to ATI, and their accuracy is degraded greatly as a whole. The SDLR is underestimated for the ATI profile both in the TIGR database and SURFRAD sites. The best three schemes can achieve the accuracy with bias values of approximately -10 W/m2and root-mean-square errors (RMSEs) less than 20 W/m2for the ATI profiles in the TIGR database. The reason the SDLR is underestimated for the ATI profiles is provided by a simulation study. An empirical method is proposed to correct the impact of ATI. The accuracy of all the parameterization schemes is remarkably improved at SURFRAD sites after correcting the impact of ATI, with the absolute values of bias and RMSEs less than 10 and 20 W/m2at SURFRAD sites.

This study proposes an improved temperature and emissivity separation (TES) algorithm for simultaneously retrieving the land surface temperature and emissivity (LST&E) from the Advanced Himawari Imager (AHI) data, including a modified water vapor scaling (WVS) method and a calibrated empirical relationship over vegetated surfaces. The modified WVS algorithm is comparable to the original WVS algorithm in deriving the LST&E but expands the application scope of the original WVS algorithm. The calibrated empirical relationship improved the LST&E and retrieval accuracy over vegetated surfaces by up to 0.165 K and 0.004, respectively. Comprehensive validation and evaluation are conducted in this study. In situ measurements from three networks are collected for the temperature-based validation. The bias and RMSE are 0.19 and 2.93 K in the daytime, and -0.43 and 1.95 K in the nighttime, respectively. Radiance-based LST validation shows that the bias and RMSE are 0.25 and 1.88 K, respectively. In addition, the AHI LST is evaluated using the MYD11 LST over large inland lakes, and the bias and RMSE are 0.25 and 1.12 K, respectively. The AHI LST is also compared to the MYD21 LST. The spatial distributions of the two LSTs are similar, and the LST differences are mostly within 4 K. The bias of the AHI LST ranges from -0.57 to 0.36 K, and the RMSE ranges from 1.7 to 2.64 K. The retrieved AHI LSE is compared with the latest MYD21 LSE. The biases and RMSEs are smaller than 0.005 and 0.014, respectively, for the three AHI bands. The improved TES algorithm is proven to be capable of obtaining accurate LST and LSE from AHI data.

Land surface temperature (LST) is a key parameter in weather forecast, global ocean circulation and climate change research. In this paper, the NOAA JPSS enterprise algorithm was adapted to retrieve LST from high spatial resolution thermal infrared data of Chinese Gaofen-5. The GF5 land surface emissivity (LSE) was determined by a new scheme. Visible Infrared Imaging Radiometer Suite (VIIRS) thermal radiance was used for cross-calibration of Gaofen-5 thermal data. The enterprise algorithm is tested by simulation data produced using CLAR and TIGR profiles. Moreover, VIIRS and Moderate Resolution Imaging Spectroradiometer (MODIS) LST products were used for cross-validation. The evaluation results show that the average bias and RMSE are -0.40 (-0.36) and 1.61K (0.90K), respectively. The validation results show that the bias (RMSE) are between -0.47K (2.18K) and 1.55K (2.59K). This study provides an alternative method to estimate LST from Chinese Gaofen-5 data.

The calculation of the SDLR is split into a clear-sky contribution calculated from bulk formula, and a cloud contribution depending on cloud fraction and cloud effective temperature (CET). This study used cloud base temperature (CBT) and cloud top temperature (CTT) as CET to estimate cloud contribution to SDLR, respectively. The retrieved SDLR are validated by the TIPEX-III surface budget network data. The validation results showed that accuracy of SDLR estimate was significantly improved when calculating cloud contribution using CBT compared with using CTT in Tibetan Plateau, especially when the surface elevation values for some sites are more than 4000 m, SDLR had higher accuracy with RMSE and BIAS of 27.46 W·m-2and -5.27 W·m-2, respectively. However, when estimating cloud contribution with CBT, some SDLR inversion results had relatively lower accuracy. Reasons may be as follows: (1) when the surface elevation values for some sites are relatively lower, the CALIPSO product probably cannot detect the real cloud base; (2) and also, the multi-layer cloud may have not been considered.

Land surface temperature (LST) is a key parameter for a wide number of applications. The Advanced Himawari Imager (AHI) onboard Himawari-8 has four thermal infrared (TIR) bands in the atmospheric window, which provides the possibility of separating LST and LSE from AHI TIR data. In this paper, the TES algorithm and water vapor scaling (WVS) method were combined to retrieve LST and LSE from the AHI TIR data. The retrieval results are evaluated by the ground measurements collected from one site in the Baseline Surface Radiation Network (BSRN) network. The average Bias and RMSE of the estimated LST are -0.096K and 0.705K, respectively. This study demonstrates the capability of the combinations of TES algorithm and WVS method in retrieving LST and LSE from AHI TIR data.

Surface upwelling longwave radiation (LWUP) is a vital component in calculating the Earth's surface radiation budget. Under the general framework of the hybrid method, we developed linear and dynamic learning neural network (DLNN) models for estimating the global 1-km instantaneous clear-sky LWUP from the top-of-atmosphere radiance of Moderate Resolution Imaging Spectroradiometer thermal infrared channels 29, 31, and 32. Extensive radiative transfer simulations were conducted to produce a large number of representative samples, from which the linear model and DLNN model were derived. These two hybrid models were evaluated using ground measurements collected at 19 sites from three networks (SURFRAD, ASRCOP, and GAME-AAN). According to the validation results, the linear model was more accurate than the DLNN model, with a bias and root-mean-square error (RMSE) of -0.31 W/m2and 19.92 W/m2obtained by averaging the mean bias and RMSE for the three networks. Additionally, the computational efficiency of the linear model was much higher than that of the DLNN model. We also compared our linear model to a hybrid method developed by a previous study and found ours to perform better.

Current satellite broadband emissivity (BBE) products do not correctly characterize the seasonal variation of vegetation abundance. This paper proposes a new method to estimate the BBE of vegetated surfaces to better describe the seasonal variation of vegetation abundance. The method takes advantage of the radiative transfer models' ability to calculate multiple scattering with a physical basis and uses the 4SAIL model to construct a lookup table (LUT) of BBE for vegetated surfaces. The BBE of the vegetated surface was derived from the LUT using three inputs: leaf BBE, soil BBE, and leaf area index (LAI). The validation results show that the accuracy of the new method exceeds 0.005 over fully vegetated surfaces. As a case study, this method was applied to data from 2003 to generate global vegetated surface BBE products for that year. An analysis of the results indicated that the derived BBE can correctly reflect seasonal variations in vegetation abundance that the data converted from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and MODIS spectral emissivity products have been unable to reveal. The new method was also compared to the vegetation cover method (VCM). The VCM can correctly characterize seasonal variations in vegetation abundance. However, the classification of bare soil and vegetation in the VCM may produce step discontinuity in the calculated BBE. The new method is being implemented to produce a new version of the Global LAnd Surface Satellite (GLASS) BBE product over vegetated surfaces.

Directionality is ignored in the satellite retrieval of surface thermal-infrared emissivity, which will unavoidably affect the estimates of surface broadband emissivity and surface longwave net radiation. The purpose of this work is to quantify the effects of emissivity directionality. First, three types of emissivity data are used to calculate hemispherical emissivity and the difference between directional broadband emissivity and hemispherical broadband emissivity. The emissivity directionality is highly significant, and the directional emissivity decreases with increasing view angles. A view angle within 45° -60° can be found whose directional emissivity is highly close to the hemispherical emissivity, and the difference between the calculated directional and hemispherical broadband emissivity is zero. The difference between the atmospheric downward radiation and blackbody radiation at surface temperature is then determined by extensive simulations. Finally, the error ranges of surface longwave net radiation are presented. If the sensor scan angle is within ±55°, the error can reach as high as 17.48 and 14.05 W/m2for water and bare ice, respectively; the error is less than 2.74 W/m2for snow with different radii; the error can reach 4.11 W/m2for sun crust; the error is less than 5.14 W/m2for minerals, sand, slime and gravel; and clay has the smallest error at 1.02 W/m2.

Surface broadband emissivity (BBE) in the thermal infrared spectrum is essential for calculating the surface total longwave net radiation in land surface models. However, almost all narrowband emissivities estimated from satellite observations are in the 3-14-μm spectral region. Previous studies converted these narrowband emissivities to BBE over different spectral ranges, such as 3-14, 8-12, 8-13.5, and 8-14 μm . Errors in the calculated total longwave net radiation must be quantified systematically using these BBEs. Moreover, the best spectral range for longwave net radiation must be determined. The key to addressing these issues is the use of the realistic emissivity spectra. By applying modern radiative transfer tools, we derived the emissivity spectra of water, snow, and minerals at 1-200 μm . Using these emissivity spectra, we first investigated the accuracy of replacing all-wavelength surface longwave net radiation with the surface longwave net radiation in the 3-100-, 4-100-, 2.5-100-, 2.5-200-, and 1-200-μm spectral domains. Surface longwave net radiation at 2.5-200 μm was found to be optimal, with a bias and root mean square (rms) of less than 0.928 and 0.993 W/m2, respectively. We calculated the errors when estimating surface longwave net radiation at 2.5-200 μm with BBE in different spectral ranges. The results show that BBE at 8-13.5 μm had the lowest error and the corresponding bias and rms were less than 0.002 and 1.453 W/m2, respectively. When the 2.5-200-μm surface longwave net radiation calculated by the 8-13.5-μm BBE was used to replace the all-wavelength surface longwave net radiation, the average bias and rms were 1.473 and 2.746 W/m2, respectively. Using the most representative emissivity spectra, we derived the conversion formulas for calculating BBE at 8-13.5 μm from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and the Moderate Resolution Imaging Spectrometer (MODIS) narrowband emissivity products.

This paper describes two new methods that were used to generate 26 years (1985–2010) of broadband emissivity (BBE) products with spatiotemporal continuity at the global scale from satellite data recorded by the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Advanced Very High Resolution Radiometer (AVHRR). On the basis of emissivity libraries, the study began with establishing relationships for converting channel emissivities of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and MODIS to BBEs for the 8–13.5-$\mu\hbox{m}$spectral window and then developed two new algorithms from simultaneous ASTER emissivity products to estimate BBEs over vegetated surfaces using the MODIS and AVHRR data. The MODIS-data-based algorithm (MDBA) uses linear equations with MODIS normalized difference vegetation index (NDVI) and seven channels' albedo; the AVHRR-data-based algorithm uses nonlinear equations with AVHRR red and near-infrared reflectances. The proposed algorithms were first validated with ASTER emissivity products. Results indicated that the root-mean-square errors of both the proposed algorithms were less than 0.015 and their biases were less than 0.003. Comparison with MODIS emissivity products from the day/night algorithm showed that the estimated BBEs using the MDBA were generally smaller than the MODIS products. Cross-comparisons were also made between the proposed algorithms and the NDVI threshold method. Finally, strategies for mapping global BBE products from the MODIS and AVHRR data are presented, and some examples are discussed. The global BBE products are planned to be released throughout the network in the near future.

Temperature and emissivity separation (TES) algorithms designed to work with mid-infrared (MIR) hyperspectral data are extremely limited. Two TES algorithms originally designed for long-wave infrared hyperspectral data, specifically, the iterative spectrally smooth (ISS) algorithm and the stepwise refining algorithm, are extended into MIR and renamed the extended iterative spectrally smooth (EISS) and extended stepwise refining algorithms (ESR), respectively. Numerical experiments are first conducted to evaluate their feasibility. The results of the numerical experiments indicate that the accuracy of the ESR algorithm is higher than that of the EISS algorithm. Moreover, the ESR algorithm is more robust than the EISS algorithm under sunlit conditions. Their accuracy is then validated with in situ measurements. Finally, the emissivity root mean square errors (RMSEs) of the EISS and ESR algorithms are compared with the data derived with the ISS algorithm using in situ measurements. Results show that the average emissivity RMSEs of 0.03 in 2000-2200 cm-1 and of 0.03-0.30 in 2400-3000 cm-1 for nighttime, and 0.02 in 2000-2200 cm-1 and 0.03 in 2500-3000 cm-1 for daytime, can be obtained from ground-based MIR hyperspectral data using the ESR algorithm.

Land surface temperature (LST) and land surface emissivity (LSE) are two key parameters in numerous environmental studies. In this paper, a stepwise refining temperature and emissivity separation (SRTES) algorithm is proposed based on the analysis of the relationship between surface self-emission and atmospheric downward spectral radiance in a narrow spectral region. The SRTES algorithm utilizes the residue of atmospheric downward spectral radiance in the calculated surface self-emission as a criterion and adopts a stepwise refining method to determine both the emissivity at the location of an atmospheric emission line in a narrow spectral region and the surface temperature. Three methods have been used to evaluate the SRTES algorithm. First, numerical experiments are conducted to evaluate if the SRTES algorithm can accurately retrieve the “true” LST and LSE from the simulated data. When a noise equivalent spectral error of$2.5\ e^{-9}\ \hbox{W/cm}^{2}/\hbox{sr}/\hbox{cm}^{-1}$is added into the simulated data, the retrieved temperature bias$(T_{\rm bias})$is 0.04$\pm$0.04 K, and the root-mean-square error (rmse) of the retrieved emissivity is below 0.002 except in the extremities of the 714–1250$\hbox{cm}^{-1}$spectral region. Second,in situmeasurements are used to validate the SRTES algorithm. The average rmse of the retrieved emissivity of ten samples is about 0.01 in the 750–1050$\hbox{cm}^{-1}$spectral region and is 0.02 in the 1051–1250$\hbox{cm}^{-1}$spectral region, but the rmse is larger when the sample emissivity is relatively low. Third, our new algorithm is compared with the iterative spectrally smooth temperature and emissivity separation (ISSTES) algorithm using both a simulated data set andin situmeasurements. The comparison demonstrates that the SRTES algorithm performs better than the ISSTES algorithms, and it can overcome some of the common drawbacks in the existing hyperspectral TES algorithms for the accurate retrieval of both temperature and emissivity.

it is well known that soil emissivity exhibits large uncertainty in thermal infrared spectral region. In order to find a way to derive soil emissivity accurately, we examine several existed typical temperature emissivity methods (e.g. NEM, ISSTES, ADE, MMD and TES). Based on the 58 soil spectra of the ASTER Spectral Library, several sets of thermal infrared hyperspectral data were simulated to assess the applicability, stability and accuracy of these methods respectively. This work also brings some improvements of the algorithms based on the results analysis, including: a new optimal maximum emissivity has been suggested for NEM, a better empirical relationship has been discovered to substitute the original mean-minimum maximum difference relationship in MMD method, the original NEM module has been replaced by ISSTES to acquire the accurate initial value of emissivity in TES. As a conclusion, we find the ISSTES is the best. Finally, we present an example of soil emissivity extraction using five methods mentioned above with ground-based measurement hyperspectral data. The distribution of derived emissivity spectrum verifies the results of algorithm analysis.

The paper firstly points out the defect of conventional temperature and emissivity separation algorithms when dealing with hyperspectral FTIR data: the conventional temperature and emissivity algorithms can not reproduce correct emissivity value when the difference of ground-leaving radiance and object's blackbody radiation at its true temperature and the instrument random noise are on the same order, and this phenomenon is very prone to occur in the extremity of 714-1250 cm-1in the field measurements. In order to settle this defect, a three-layer perceptron neural network has been introduced into the simultaneous inversion of temperature and emissivity from hyperspectral FTIR data. The soil emissivity spectra from the ASTER spectral library have been used to produce the training dataset, and the soil emissivity spectra from the MODIS spectral library have been used to produce the test dataset, the result of network test shows the MLP is robust. Meanwhile, ISSTES algorithm has also been used to retrieve the temperature and emissivity from the test dataset. By Comparison the result of MLP and ISSTES, we find MLP can overcome the disadvantage of conventional temperature and emissivity separation algorithms, although the RMSE of derived emissivity using MLP is lower than ISSTES as a whole. Hence, the MLP can be regarded as a beneficial complementarity to the conventional temperature and emissivity separation algorithms.

Based on the four step method, the paper puts forward a method for deriving mid-infrared emissivity. This method obtains thermal infrared emissivity and temperature with high accuracy by utilizing the ISSTES algorithm from thermal infrared data, then introducing the derived temperature into mid-infrared emissivity extraction, reducing the number of parameters need to be inversed in mid-infrared, forming redundant observation, and using the least square method to solve the equation at last. More attention has been paid into analyzing the impacts of instrument calibration error and simplification of radiative transfer equation on the extraction of mid-infrared emissivity. Finally, the paper gives out the reason for large error of emissivity inversion in some bands of mid infrared based on the simulated data.

The consistent end-to-end simulation of them is an important task, sometimes the only way for the adaptation and optimisation of a sensor and its observation conditions, the choice and test of algorithms for data processing, error estimation and the evaluation of the capabilities of the whole sensor system. It is essential to accomplish simulation of atmospheric radiative transfer, if a complete imaging simulating system is to be expected. Based on given resolution and directional capabilities of the instrument, and combination with land surface temperature and emissivity data obtained from airborne imagery, TOA (top of atmosphere) radiance images have been simulated pixel by pixel coupling the atmospheric radiative transfer analytic model extended from MODTRAN4 and the atmospheric adjacency effect model derived from point spread function (for atmospheric directional and adjacency effect). In this way, all major scattering and emission contribution of atmosphere were considered. Through analysing results, it indicates that analytic model and adjacency effect model is more adequate for thermal infrared imaging simulation than others existing models.