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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 Francois et al. (1997) over continuous canopy and the RT model (FovMod) proposed by Ren et 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 1δ 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.

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 μm, 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.

The Sentinel-2B offers high spatial resolution optical imagery from 13 bands in the visible, near infrared, and short-wave infrared range of the electromagnetic spectrum, providing enhanced continuity to monitoring global terrestrial surfaces and coastal waters. In this article, its radiometry calibration is evaluated using 37 clear-sky observations in 2018 from the four sites of radiometric calibration network for assuring data quality. However, since the single calibration results acquired under different surface and atmospheric conditions have different biases and different uncertainties, it is difficult to determine which calibration sample is much more trustable. In view of this, by assuming that the calibration samples are independent of each other, a state-of-the-art reference value is derived by combining 37 calibration samples using a weighted average method, which has much lower uncertainty and approaches the “true” value. The reference value also could be used to compare each calibration result. The result shows that the reference value of the relative difference between the simulated and observed top-of-atmosphere reflectance is 4.29%, 4.95%, 4.54%, 5.34%, respectively, for bands 2, 3, 4, and 8, and the corresponding uncertainty is 1.09%, 1.10%, 1.10%, and 1.12%, respectively; the degree of equivalence for each sample is calculated by comparing each calibration result with the reference value. It is worth noting that the degrees of equivalence are lower than 5%, and the four samples on July 9, July 13, October 4, and October 11 perform worse than the other sample.

This article addressed the simultaneous retrieval of land surface temperature (LST) and emissivity (LST&E) from the time-series thermal infrared data. On the basis of the assumption that the time-series LSTs can be described by a piecewise linear function, a new method has been proposed to simultaneously retrieve LST&E from atmospherically corrected time-series thermal infrared data using LST linear constraint. A detailed analysis has been performed against various errors, including error introduced by the method assumption, instrument noise, initial emissivity, atmospheric downwelling radiance error, etc. The proposed method from the simulated data is more immune to noise than the existing methods. Even with a noise equivalent delta temperature of 0.5 K, the root-mean-square error of LST is observed to be only 0.13 K, and that of the land surface emissivity (LSE) is 1.8E-3. In addition, our proposed method is simple and efficient and does not encounter the problem of singular values unlike the existing methods. To validate the proposed method, a field experiment from June to September 2017 was conducted for sand target in Baotou site, China. The results show that the samples have an accuracy of LST within 0.87 K and that the mean values of LSE are accurate to 0.01.

Optical satellite sensor generally suffers from drifts and biases relative to their prelaunch calibration, caused by launch and the space environment. Radiometric cross-calibration is valuable for calibrating sensors without coincident surface measurements through transferring radiometric values between satellite sensors via a stable target. The data quality of visible and near-infrared (VNIR) sensor onboard Gaofen-4 (GF-4) satellite seriously depends on vicarious calibration, which is performed once every year, thus, a cross-calibration approach is proposed for monitoring its radiometric performance much more frequently. In this study, the GF-4/VNIR is cross-calibrated by Landsat8/OLI, Sentinel-2/MSI, and Terra/MODIS with the aid of the tandem scenes during 2017 and 2018 after correcting the discrepancy on viewing geometries and spectrum. The results show that there is test site dependency and sensor dependency, and cross-calibration coefficients in the four bands roughly have similar variation trend as a sinusoidal function during 2017 and 2018, varying from 0.00023 to 0.00079. Furthermore, uncertainty analysis of the cross-calibration is carried out to analyze the uncertainty contributions affecting the cross-calibration accuracy. Note that the calibration accuracy of referenced sensors, the uncertainty of the MODTRAN model, BRDF uncertainty are the three main factors affecting the cross-calibration accuracy, the total uncertainty is approximately 3.9%–6.64%.

Accurate inversion of land surface temperature (LST) from remote sensing data is an essential and challenging topic for earth observation applications. This paper successfully retrieves the LST from FY-3C/VIRR data with split-window method. With the simulated data, the algorithm coefficients are acquired with root mean square errors lower than 1.0 K for all subranges when view zenith angle (VZA) < 30° and the water vapor content (WVC) < 4.25 g/cm2 , as well as those in which the VZA < 30° and the LST < 307.5 K. In addition, a detailed sensitivity analysis is carried out. The analysis result indicates that the total LST uncertainty caused by the standard error of the algorithm, the uncertainties of land surface emissivity and WVC, and the instrument noise would be 1.22 K and 0.94 K for dry and wet atmosphere, respectively. Furthermore, LST retrieval method is applied to the visible and infrared radiometer measurements over the study area covering the geographical latitude of 31.671°N to 44.211°N and longitude of 10.739°W to 1.898°E, and the derived LST is cross-validated with Terra/MODIS LST product. The preliminary validation result shows that the split-window method determines the LST within 2.0 K for vegetation and soil areas.

Land surface temperature and emissivity separation (TES) is a key problem in thermal infrared (TIR) remote sensing. Along with the development of civil applications, increasing numbers of man-made low-emissivity materials can be found around our living environment. In addition, the characteristics and variation in properties of those materials should also be concerned. However, there are still few TES methods for low-emissivity materials reported in the literature. This paper addresses the performance of the automatic retrieval of temperature and emissivity using spectral smoothness (ARTEMISS) method proposed by Borel (2008) for the retrieval of temperature and emissivity from hyperspectral TIR data for low-emissivity materials. The results show that those modeling errors are less than 0.11 K for temperature and 0.3% for emissivity as shown in the ARTEMISS algorithm if atmospheric parameters and the mean emissivity of material spectra are known. A sensitivity analysis has been performed, and the results show that the retrieval accuracy will be degraded with the increase of instrument noises, the errors of the atmospheric parameters, and the coarser spectral resolution. ARTEMISS can give a reasonable estimation of the temperature and emissivity for high- and low-emissivity materials; however, the performance of the algorithm is more seriously influenced by the atmospheric compensation than by the instrument noises. Our results show that the errors of temperature and emissivity become approximately three times than that when the instrument spectral properties are $1{\text{ cm}}^{-1}$ of sampling interval and $2{\text{ cm}}^{-1}$ of FWHM, and $4{\text{ cm}}^{-1}$ of sampling interval and $8{\text{ cm}}^{-1}$ of FWHM, respectively.

Compared with thermal infrared ( $8{-}14\;\upmu{\rm{m}}$ ) spectrum, mid-infrared (MIR) spectrum is less sensitive to land surface emissivity (LSE) for estimating land surface temperature (LST). This work addressed the retrieval of LST from two adjacent MIR ( $3{-}5\;\upmu{\rm{m}}$ ) night-time airborne hyperspectral imager (AHS) simulated data with a split-window method, which can be expressed as a linear combination of the brightness temperature measured in two adjacent MIR channels with coefficients depending on LSE, view zenith angle (VZA), and water vapor content (WVC). Meanwhile, the LST retrieval accuracy for various channel combination was investigated and it was noted that the AHS channels 66 ( $3.5 {-} 4.25\;\upmu{\rm{m}}$ ) and 68 ( $4.25 {-} 5.0\;\upmu{\rm{m}}$ ) were the optimal channels for LST retrieval with a root-mean-square error (RMSE) less than 0.4 K for dry atmosphere and less than 0.5 K for wet atmosphere. Finally, the sensitivity analysis in terms of the instrumental noise, the uncertainties of LSE, and WVC were performed. It is worth noting that the combination of CH66 and CH68 performed well, and the LST retrieval errors were less than 0.5, 0.2, and 0.3 K caused by an noise equivalent delta temperature ( ${\rm{NE}}\Delta {\rm{T}}$ ) of 0.33 K, WVC error of 20%, and LSE error of 0.01, respectively.

This work addressed the retrieval of land surface emissivity (LSE) and land surface temperature (LST) by using Middle Infra-Red (MIR) and Thermal Infra-Red (TIR) channels 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 full disk with a temporal resolution of 30 minutes. The LST and LSE retrieval procedures can be shown as follows. Firstly, taking into the fact that land surface is non-lambertian characteristics, the time-series bi-directional reflectances in SVISSR/FY-2C MIR channel 4 (3.8 μm ) were estimated from the combined MIR and TIR channels with day-night SVISSR/FY-2C data. A diurnal temperature cycle (DTC) model was used to correct for the atmospheric effects. The atmospheric profile data provided by European Centre for Medium-Range Weather Forecasts (ECMWF) were adopted with the aid of the radiative transfer code (MODTRAN 4.0). Secondly, a Bidirectional Reflectance Distribution Function (BRDF) model named as RossThick-LiSparse-R model was used to estimate the hemispherical directional reflectance in MIR channel from the time-series bi-directional reflectance data. Then, the LSE in MIR channel can be retrieved according to Kirchhoff's law. The LSEs in TIR channels can be estimated based on the Temperature Independent Spectral Indices (TISI) concept. And the LST can be retrieved using the split-window algorithm. Finally, a cross-validation method was used to evaluate the retrieval accuracies with the Moderate-resolution Imaging Spectroradiometer (MODIS) MOD11B1 LST/LSE V5 product. The results demonstrated that the emissivities in 11 μm and 12 μm were underestimated approximately 0.003 and 0.004 compared with MOD11B1 LSE product over the study area. The FY-2C LST were overestimated approximately 1.65 K and 2.87 K during the night-time and day-time, respectively, compared with MOD11B1 LST product over the study area.

A physics-based regression algorithm was developed and applied to the Infrared Atmospheric Sounding Interferometer (IASI) observations to estimate atmospheric temperature and humidity profiles. The proposed algorithm utilized three steps to solve the ill-posed problems and to stabilize the solution in a fast speed regression manner: 1) a set of optimal channels was selected to decrease the effect of forward model errors or uncertainties of trace gases; 2) the principal component analysis technique was used to reduce the number of unknowns; 3) a ridge regression procedure was introduced to improve the ill-conditioned problem and to lessen the influence of correlation. To determine the optimal coefficients of the algorithm, a simulated dataset was generated with the spectral emissivities and atmospheric profiles fully covering all the possible situations for clear sky conditions. Then, the accuracy of the algorithm was evaluated against with both simulated and actual IASI data. The root mean squared error (RMSE) of atmospheric temperature profile for the simulated data is about 1.5 K in troposphere and stratosphere and is close to 4 K near the surface with no biases. The RMSE of atmospheric humidity profile for the simulated data is about 0.001-0.003 g/g at low altitude. Although the retrieval accuracy for the actual IASI data is not as good as those for the simulated data, the vertical distribution of atmospheric profiles can be well captured. Those results showed that the proposed algorithm is promising when the profile bias errors could be removed.