Accurate and stable radiometric calibration is a fundamental for quantitative remote sensing. This paper addresses the evaluation and correction of radiometric calibration biases in the middle infrared channel (the channel 6 centered at 3.8 μm) and the thermal infrared channels (the channels 7 and 8 centered at 10.8 μm and 12.0 μm respectively) of the MEdium Resolution Spectral Imager for the Rainfall Mission (MERSI-RM) on Fengyun-3G (FY-3G) satellite against the channels of the Moderate Resolution Imaging Spectroradiometer (MODIS) on Aqua satellite using the double difference (DD) method. First, an infrared radiative transfer model is constructed to simulate the observations in both middle infrared and thermal infrared channels, in which surface reflected solar irradiances are fully taken into account. Then, the matching samples between the MERSI-RM and MODIS observations in January, April, July and October of 2024 are collected in terms of the matching criteria. Next, the radiances at top of atmosphere (TOA) are simulated using the infrared radiative transfer model. Finally, the radiometric calibration biases in the MERSI-RM channels 6, 7 and 8 are evaluated and corrected. The results show that the impact of the simulation errors, the spectral response differences and geolocation errors on the final intercalibration results can be ignored. The radiometric calibration of the MERSI-RM channel 6 is quite consistent with that of the MODIS channel 20, while the radiometric calibrations in the MERSI-RM channels 7 and 8 are about 0.5 K underestimated. Although more or less calibration biases exist in the MERSI-RM channels, their on-orbit calibrations are generally stable in the four months of 2024.

This paper addresses the retrieval of land surface temperature (LST) from the measurements acquired by the Medium Resolution Spectral Imager – Low Light (MERSI-LL) on Fengyun 3E (FY-3E) satellite. First, a generalized split-window algorithm is developed using radiative transfer modelling experiments. Then, LSTs are retrieved from the FY-3E MERSI-LL measurements in Sept. of 2022 over a study area with longitude from 105°E to 135°E and latitude from 25°N to 50°N. Finally, the LSTs retrieved in this work are validated against the MOD11C1 V61 product, in which the LST product of the Advanced Geostationary Radiation Imager on Fengyun 4A (FY-4A) satellite serves as a connecting bridge. The results show that the accuracy of the LSTs retrieved from the FY-3E MERSI-LL measurements in this work is 0.01±0.75 K against the MOD11C1 V61 product.

This paper addresses the intercalibration of Fengyun 3D (FY-3D) Microwave Humidity Sounder II (MWHS-II) water vapor absorption channels against the Suomi National Polar-orbiting Partnership (S-NPP) Advanced Technology Microwave Sounder (ATMS) channels using the double difference method. First, a radiometric calibration transfer method is developed. Then, the matching samples in June of 2022 between the MWHS-II and ATMS observations are collected over both sea and land surfaces. Next, the brightness temperatures at top of atmosphere in the MWHS-II and ATMS channels are simulated, and the double differences are calculated. Finally, the radiometric calibration of the ATMS channels is transferred to the MWHS-II channels. The results show that radiometric calibration biases in the FY-3D MWHS-II channels 10, 11, 12, 13, 14 and 15 are -3.89±1.40 K, -2.80±1.00 K, -1.03±0.97 K, -0.42±1.11 K, 1.53±0.82 K, and -0.87±1.31 K, respectively.

Sea surface temperature (SST) is a key climate variable, which affects the behavior of the Earth’s atmosphere. In this article, a method coupled with microwave sea surface emissivities (SSEs) is developed to retrieve SST from the intercalibrated measurements acquired by the microwave radiation imager (MWRI) on Fengyun 3D (FY-3D) satellite. First, the spatiotemporal matching samples over sea surfaces in 0.25$^{\circ }\,\,\times0.25^{\circ }$between FY-3D MWRI measurements and the fifth generation of European center for medium-range weather forecast (ECMWF) atmospheric reanalysis (ERA5) data in January, April, July, and October 2020 are collected and used to determine the unknown coefficients of the SST retrieval algorithm. To improve the accuracy, besides grouping the samples by sea surface wind speed and SST, pseudo-SSEs are introduced into the SST retrieval algorithm. The root-mean-square errors (RMSEs) of the SST retrieval algorithm in the three steps are 1.18, 0.73, and 0.68 K, respectively. Then, the SSTs in 2020 between 60°S and 60°N are retrieved from the FY-3D MWRI measurements without precipitation and heavy clouds. Finally, the SSTs retrieved in this work are validated with the GMI SST and the iQuam in situ data. The errors of the retrieved SSTs in the three steps are 0.19 ± 1.23, 0.17 ± 1.15, and 0.01 ± 1.15 K against the GMI SST, respectively, while they are 0.48 ± 1.24, 0.37 ± 1.13, and 0.12 ± 1.10 K against the iQuam in situ data, respectively. The errors of both the retrieval algorithm and the derived SSTs in this work are obviously reduced after introducing the pseudo-SSEs into the algorithm, which proves that the SST retrieval algorithm developed in this work is valid and accurate.

This paper addresses the directional land surface emissivity (LSE) retrieval from the data acquired by the MEdium Resolution Spectral Imager (MERSI) on Fengyun 3D and 3E (FY-3D/E) satellites and the Moderate-resolution Imaging Spectroradiometer (MODIS) on Terra and Aqua satellites. First, a method to retrieve directional LSEs from multi-satellite data is developed based on the radiative transfer model. Then, the directional LSEs are retrieved from the combined MERSI/FY-3D/E, MODIS/Aqua and MODIS/Terra data in January 1~16 and July 1~16 of 2022 over a study area with longitude from 100°E to 130°E and latitude from 20°N to 50°N. Finally, the retrieved LSEs are, respectively, cross-validated with the MODIS/Terra land surface temperature (LST) and emissivity 8-day level 3 global 0.05° V61 (MOD11C2) product and the MODIS/Terra LST/3-band emissivity 8-day level 3 global 0.05° V61 (MOD21C2) product over the entire study area, and validated against the in-situ data at three true desert and semi-arid sites. The results show that the multi-satellite data provide more information of view angles and solar angles, which makes the determination of bi-directional reflectance distribution function model and the LSE retrieval more robust. The LSEs retrieved in this work have strong dependence on time and land cover types. Over the vegetated areas, the LSEs retrieved in this work basically agree with the MOD11C2 and MOD21C2 products, while over the true desert and semi-arid areas, the LSEs in the MOD11C2 and MOD21C2 products are obviously overestimated, especially the MOD11C2 product, but the LSEs in this work are consistent with the in-situ data. In general, the method developed in this work is valid and the retrieved LSEs are accurate.

Sea surface temperature (SST) plays an important role in tropical cyclogenesis and formation of sea fog and sea breezes. Satellite remote sensing is the only way to obtain a global and continuous coverage of SST. In this paper, a novel method combining microwave sea surface emissivity (SSE) is proposed to retrieve SST from the measurements acquired by Microwave Radiation Imager (MWRI) on Fengyun 3D (FY-3D), which has ten channels at 10.65, 18.7, 23.8, 36.5 and 89.0 GHz with both vertical and horizontal polarizations. The algorithm is applied to FY-3D MWRI data in 2020, and then the derived SSTs are validated against the NOAA iQuam in situ SST data and the Global Precipitation Measurement (GPM) Microwave Imager (GMI) SST product. Against the iQuam in situ data and the GMI SST, the errors of the retrieved SST in the final step are 0.04±1.13 K -0.07±1.18 K, respectively. The result indicates that the SST retrieval algorithm is valid and accurate in the SST retrieval.

In this paper, a method to retrieve directional land surface emissivity (LSE) from combined infrared data acquired by the advanced MEdium Resolution Spectral Imager (MERSI-2) on Fengyun 3D and 3E (FY-3D/E) and the Moderate-resolution Imaging Spectroradiometer (MODIS) on Terra and Aqua is developed. First, the observation differences due to different spectral response functions are removed by radiative transfer modeling, and FY-3D/E MERSI-2 observations and Aqua MODIS observations are transferred to effective MODIS measurements. Then, according to radiative transfer equation, the radiances at top of atmosphere are atmospherically corrected to ground level, in which the atmospheric parameters are calculated using the MODerate spectral resolution atmospheric TRANsmittance and radiance code (MODTRAN) and the fifth generation of European Centre for Medium-Range Weather Forecast (ECMWF) atmospheric reanalysis (ERA5) data. Next, based on the concept of temperature independent spectral indices (TISI), the bi-directional reflectances in the middle infrared (MIR) channel are derived. After that, according to the RossThick-LiSparse-R model and Kirchhoff’s law, the LSE in the MIR channel is retrieved. Finally, the LSE in the thermal infrared channel is deduced from the LSE in the MIR channel using the TISI concept again.

This letter addresses the intercalibration of the thermal infrared (TIR) channels 11 (8.0–9.0$\mu \text{m}$), 12 (10.3–11.3$\mu \text{m}$), 13 (11.5–$12.5~\mu \text{m}$), and 14 (13.2–13.8$\mu \text{m}$) of the Advanced Geostationary Radiation Imager (AGRI) on Chinese Fengyun 4A (FY-4A) satellite against the Advanced Himawari Imager (AHI) on the Himawari 8 using the double difference (DD) method with the data in January and July of 2020. To transfer the radiometric calibration from AHI to FY-4A AGRI, an accurate TIR radiative transfer model and intercalibration equations are constructed. The results show that AGRI channel 12 is well calibrated and keeps stable in the two months. However, in other AGRI TIR channels, radiometric calibration biases are obviously observed, especially AGRI channel 11, in which the calibration biases vary by hemisphere and month. The observations in AGRI channels 13 and 14 are about 0.83 and 0.50 K underestimated, respectively. In AGRI channel 11, the observations are averagely 2.36 and 4.08 K overestimated in Northern Hemisphere and Southern Hemisphere, respectively. Moreover, in Southern Hemisphere, the observations in AGRI channel 11 in July are averagely 0.55 K warmer than those in January. The intercalibration coefficients were obtained by linear regression, and finally, the radiometric calibration biases in the AGRI TIR channels were successfully removed.

Actual spectral response function (SRF) of the quadruple-sideband channel of the microwave sounder is axisymmetrical, and its distortion degree affects the difference in observations between the actual microwave sounder and the ideal design situation. Effects of the actual quadruple-sideband channel’s SRF on observations has been evaluated by applying the actual SRF of the quadruple-sideband channel of FengYun-3D (FY-3D) microwave temperature sounder-2 (MWTS-2) and rapid radiative transfer model (RTM) of China Meteorological Administration-Global Forecast System (CMA-GFS). Compared with ideal SRF, actual SRF could improve microwave-radiance simulation, and the observation-minus-background could be decreased by about 0.7 K. The improvements in the mid-high latitudes of the south hemisphere are more evident than in other latitudes. Effects of distortion types of actual SRF on observations have been analyzed by comparing simulated radiance differences between reference SRF and originally proposed SRFs with eight typical distortion types. Inner sideband drifting type and outer sideband drifting type have the greatest influence of about 1 K on microwave radiance. Inner–outer sideband symmetrical difference type has the least impact of approximately 0.02 K. Effects of the axisymmetry of SRF on observations have also been evaluated. Compared microwave radiance simulation by reference SRF with axisymmetrically and nonaxisymmetrically distorted SRFs, respectively, simulation errors induced by axisymmetrically distorted SRFs are less than that by nonaxisymmetrically distorted SRFs of about 0.1–0.4 K.

This paper addresses the intercalibration of the thermal infrared channels 11~14 of the Advanced Geostationary Radiation Imager (AGRI) on the Chinese Fengyun 4A (FY-4A) satellite against the Advanced Himawari Imager on the Himawari 8 (AHI-8) using the Double Difference (DD) method, in which an accurate thermal infrared radiative transfer model is constructed over both sea and land surfaces. The intercalibration results indicate that the on-orbit radiometric calibration biases are observed in FY-4A AGRI thermal infrared channels, especially for the channel 11 (8.0~9.0 µm), whose radiometric calibration biases are 4.36±0.26 K in the north hemisphere and 2.36±0.38 K in the south hemisphere. For the AGRI channels 12 (10.3~11.3 µm), 13 (11.5~ 12.5 µm) and 14 (13.2~ 13.8 µm), the on-orbit radiometric calibration biases are -0.14±0.31 K, - 0.91±0.41 K and -0.54±0.51 K, respectively. Through linear regression on the matching observations, the intercalibration coefficients are finally determined.

Accurate on-orbit radiometric calibration is the fundamental to quantitative remote sensing. Based on the thermal infrared radiative transfer model and Double Difference (DD) method, the on-orbit radiometric calibration in the thermal infrared channels of the advanced MEdium Resolution Spectral Imager (MERSI-2) on Chinese Fengyun 3D (FY-3D) are evaluated against the channels of Advanced Himawari Imager (AHI) on the Himawari 8 satellite. The results shows that the on-orbit radiometric calibration of FY-3D MERSI-2 channel 25 (12.0 µm) is quite consistent with the AHI channel 15, whereas the radiometric calibration biases (the mean of the double differences ± standard deviation) in FY-3D MERSI-2 channels 23 (8.55 µm) and 24 (10.8 µm) are −0.17±0.26 K and −0.28±0.30 K, respectively. The on-orbit radiometric calibration biases are finally corrected by linear fits on the matching observations.

Accurate radiometric calibration is fundamental to quantitative remote sensing. In this article, a thermal infrared radiative transfer model is first established, and then, the on-orbit radiometric calibration of the thermal infrared channels 23 (8.55$\mu \text{m}$), 24 (10.8$\mu \text{m}$), and 25 (12.0$\mu \text{m}$) of the advanced MEdium Resolution Spectral Imager (MERSI-2) on the Chinese Fengyun 3D (FY-3D) satellite is evaluated and corrected against the channels of the Advanced Himawari Imager (AHI) on Himawari 8 satellite using the double difference (DD) method. The results indicate that the on-orbit radiometric calibrations in FY-3D MERSI-2 channels 23, 24, and 25 are slightly biased and certain variations exist between January and July 2020. In January 2020, the on-orbit calibration of MERSI-2 channel 23 is statistically consistent with that of AHI channel 11, and the calibration bias (mean ±standard deviation at the mean) is 0.04 ±0.27 K, whereas they are$0.11~\pm ~0.49$K and$- 0.30\,\,\pm \,\,0.50$K in MERSI-2 channels 24 and 25, respectively. In July 2020, the on-orbit calibration of MERSI-2 channel 25 is very consistent with that of the AHI channels with a calibration bias of 0.06 ±0.31 K, while they are$- 0.18\,\,\pm \,\,0.23$K and$- 0.27\,\,\pm \,\,0.30$K in MERSI-2 channels 23 and 24, respectively. The radiometric calibration biases are finally corrected by the linear fits on the matching observations.

In order to cover the warm end of Earth-scene brightness temperature (TB) range of passive microwave radiometers, intercalibration over warm scenes is necessary. This article presents a methodology to intercalibrate the microwave radiation imager (MWRI) on the Chinese second-generation meteorological satellite Fengyun 3C (FY-3C) with the Global Precipitation Measurement (GPM) Microwave Imager (GMI) over the warm scenes of dense forests using the double-difference (DD) method. Based on the microwave radiative transfer model (RTM), an intercalibration method is developed, in which a modified land surface emissivity (LSE) model for dense forests is proposed. The forests with optically thick canopy are identified in terms of polarization TB differences and normalized difference vegetation index (NDVI) extracted from the latest vegetation product of Moderate-Resolution Imaging Spectroradiometer (MODIS). The matching TBs between FY-3C MWRI and GMI over dense forest warm scenes are collected and analyzed together with the TBs over ocean surfaces obtained by Zeng and Jiang (2020). The results show that: 1) FY-3C MWRI’s observations are generally underestimated, and the intercalibration biases are polynomial functions of observations; 2) the intercalibration biases at the warm end are relatively smaller than those at the cold end; and 3) the calibration in the ascending orbits (MWRIA) is relatively better than that in the descending orbits (MWRID). At the tropical rain forest scene TBs defined in this work, the intercalibration biases (mean ± standard deviation at the mean) in the FY-3C MWRI channels of 10 V, 10 H, 18 V, 18 H, 23 V, 36 V, 36 H, 89 V, and 89 H are, respectively, −1.3 ± 0.7, −1.9 ± 1.1, 1.6 ± 0.6, 2.5 ± 0.8, −0.2 ± 0.5, −2.0 ± 0.6, −2.4 ± 0.7, −0.2 ± 0.6, and −0.1 ± 0.6 K for the ascending orbits, while they are, respectively, −4.0 ± 0.8, −5.4 ± 1.2, −1.4 ± 0.7, −1.2 ± 0.8, −2.9 ± 0.5, −4.9 ± 0.7, −5.5 ± 0.7, −2.7 ± 0.8, and −2.3 ± 0.7 K for the descending orbits. The in-orbit calibration coefficients of GMI are successfully transferred to FY-3C MWRI.

Accurate calibration of satellite instrument is the premise of Earth geophysical parameter estimation. This paper presents the intercalibration of the Microwave Temperature Sounder (MWTS) on Fengyun 3D (FY-3D) against the Advanced Technology Microwave Sounder (ATMS) on Suomi National Polar-orbiting Partnership (S-NPP) satellite using microwave radiative transfer model fed with the fifth generation of European Centre for Medium-Range Weather Forecast (ECMWF) atmospheric reanalysis (ERA5) data. The results show that the calibration biases (mean ± standard deviation) are 1.83±1.45, 0.45±0.94, 1.87±0.60, 0.60±0.27, -0.02±0.37, 0.19±0.24, 1.69±0.28, 2.25±0.29, 1.97±0.33, 1.74±0.42, 2.84±0.42, 0.07±0.65, and 0.32±1.18 K in the thirteen MWTS channels, respectively. In addition, the results with the semi-empirical Land Surface Emissivity (LSE) model and the results with LSEs derived from ATMS observations are consistent, and the use of LSEs derived from ATMS observations for the rest of channels is feasible.

In order to cover the full Brightness Temperature (TB) range of microwave radiometer, warm-scene intercalibration is necessary and serves as a complement to cold-scene calibration over sea surfaces. This paper presents a method to intercalibrate the Microwave Radiation Imager (MWRI) on the Chinese meteorological satellite Fengyun 3C (FY-3C) against the Global Precipitation Measurement (GPM) Microwave Imager (GMI) over forest warm scenes using the Double Difference (DD) method based on a microwave radiative transfer model. Considering the complex and variable land properties, forests are selected as calibration sites in terms of normalized difference vegetation index and polarization TB differences. The intercalibration results show that calibration biases exist in all FY-3C MWRI channels, and the calibration of MWRI ascending (MWRIA) data is better than that of the MWRI descending (MWRID) data. The intercalibration coefficients of FY-3C MWRI are obtained using quadratic regression fed with coincident TBs.

This work addresses the development of Diurnal Temperature Cycle (DTC) model and its application of temporal normalization of Land Surface Temperature (LST) derived from the measurements acquired by the Advanced Himawari Imager on Himawari-8. The results show that the DTC model can describe the LST diurnal variation with root-mean-square error (RMSE) less than 0.45 K at the four selected typical locations, and with about 90% RMSEs less than 1.0 K over the whole study area. Finally, temporally normalized LST is produced using the DTC model.

This paper presents a method to intercalibrate the brightness temperature (TB) acquired by the Microwave Radiation Imager (MWRI) on Chinese meteorological satellite Fengyun 3C (FY-3C) against the measurements obtained by the Global Precipitation Measurement (GPM) Microwave Imager (GMI) based on an ocean microwave radiative transfer model, and the intercalibration coefficients of FY-3C MWRI are obtained. The results show that the MWRI measurements are underestimated, especially the TB in the low frequency bands, and the calibration bias decreases with the frequency increment. In addition, the calibration of FY-3C MWRI ascending data is much better than that of FY-3C MWRI descending data.

This work addresses Land Surface Temperature (LST) retrieval from the infrared measurements of Advanced Himawari Imager (AHI) on Himawari-8 satellite using the Generalized Split-Window (GSW) algorithm. First, a radiative transfer modeling experiment is conducted using the moderate spectral resolution atmospheric transmittance algorithm and computer model (MODTRAN) 4.0 fed with the SeeBor V5.0 atmospheric profile database to simulate the brightness temperatures in the AHI channels 14 (centered at about 11.2 μm) and 15 (centered at about 12.3 μm) related to Land Surface Emissivities (LSEs) and Total Precipitable Water (TPW). Then, the unknown coefficients of the GSW algorithm are obtained through multi-variable linear regression, in which the simulated data are grouped into several sub-ranges to improve algorithm accuracy. Next, LSTs are derived from the clear-sky AHI measurements in September 2016 over a study area with longitude from 100°E to 145°E and latitude from 15°N to 45°N, where LSEs are deduced from the MOD11C1 V6 product using the baseline fit method, and TPWs are extracted from the European Centre for Medium-range Weather Forecasts (ECMWF) reanalysis data. Finally, the derived LSTs are cross-validated with the MOD11C1 V6 product. The results show that the GSW algorithm developed in this work can accurately retrieve LST from the AHI measurements, and the error is 0.39±1.62 K against the MOD11C1 V6 product.

This paper addresses the intercalibration of the infrared channels 7~16 of Advanced Himawari-8 Imager (AHI8) against the hyperspectral channels of Infrared Atmospheric Sounding Interferometer (IASI) on Metop-B using the hyperspectral convolution method. Matching measurements in July of 2016 were collected over a tropical area (100° E~180 ° E, 10 ° S~ 10 ° N) with collocation, absolute observation time difference of less than 5 minutes, and |cosθ1/cosθ2-1| of less than 0.015, where θ1and θ2are, respectively, AHI8 and IASI/Metop-B viewing zenith angles. The results show that the AHI8 measurements are highly linearly related to the convolved IASI hyperspectral measurements, and intercalibration coefficients are obtained through linear regression on the matching measurements. Against the IASI hyperspectral channels, the in-orbit calibration biases in AHI8 infrared channels 7~16 are, 0.09, -0.11, -0.16, -0.04, -0.03, -0.19, 0.07, 0.10, -0.02 and 0.16 K, respectively.

This work presents Land Surface Temperature (LST) retrieval from Landsat-8 data using the Generalized Split-Window (GSW) algorithm. First, radiative transfer modeling experiments were conducted using the moderate spectral resolution atmospheric transmittance algorithm and computer model (MODTRAN) 4.0 fed with SeeBor V5.0 atmospheric profile database to simulate the brightness temperatures in Thermal InfraRed Sensor (TIRS) bands 10 (centered at 10.9 μm) and 11 (centered at 12.0 μm) related to Land Surface Emissivities (LSEs) and Total Precipitable Water (TPW). Then, the unknown coefficients of the GSW algorithm were obtained through multi-variable regressions, in which the simulated data were grouped into several subranges to improve fitting accuracy. Next, LSTs were derived from the clear-sky TIRS/Landsat-8 data in 2015, where LSEs were estimated from Operational Land Imager (OLI) measurements using the Normalized Difference Vegetation Index (NDVI) Based Emissivity Method (NBEM), and TPWs were extracted from the European Centre for Medium-range Weather Forecasts (ECMWF) reanalysis data. Finally, the derived LSTs were validated with MOD11_L2 V5 product. The results show that the GSW algorithm developed in this work can accurately retrieve LST from the Landsat-8 data, and the relative error is 0.18±0.68 K against the MOD11_L2 V5 product.

This paper presents the intercalibration of the infrared channels 1~19 of Infrared Atmospheric Sounder (IRAS) on FengYun 3B (FY-3B) against the hyperspectral channels of Infrared Atmospheric Sounding Interferometer (IASI) aboard Metop-A. Matching measurements were collected over the Arctic and Antarctic study areas with collocation, absolute observing time difference less than 20 minutes (|Δtime|1/cos2-1| less than 0.02, where θ1and θ2are, respectively, IRAS/FY-3B and IASI/Metop-A viewing zenith angles. The results show that IRAS/FY-3B measurements in channels 1~12, and 15 are linearly related to the corresponding convolved IASI/Metop-A measurements, and no obvious impact of direct solar illumination on measurements are observed. IRAS/FY-3B measurements in channels 13, 14 and 16~18 are strongly affected by solar illumination: the matching measurements with/without solar illumination are distributed around two different lines. Moreover, IRAS/FY-3B measurements in channels 18 and 19 present obvious nonlinear relation with the convolved IASI/Metop-A measurements. Intercalibration coefficients were obtained through linear/nonlinear regression on the matching measurements, and then IRAS/FY-3B data were re-calibrated.

This paper addressed the development of fast locally linear regression (LLR) algorithms to retrieve atmospheric temperature and humidity profiles from the measurements of Infrared Atmospheric Sounder (IRAS) on FengYun 3B (FY-3B), in which the matching samples were collected from IRAS/FY-3B measurements and AIRX2RET V5 product. Taking 2011 as example, first, the observation samples were obtained with collocation, the absolute observation time difference less than 15 minutes, and the absolute view zenith angle difference less than 2 degrees. Then, based on the matching samples, the fast LLR algorithms were developed and evaluated in contrast to the LLR algorithm, D matrix algorithm and neural network algorithm. Finally, the fast LLR algorithms were applied to retrieving atmospheric temperature and humidity profiles from IRAS/FY-3B measurements in 2011 and the first quarter of 2012, and then the results were, respectively, validated with the ECMWF reanalysis data, RAOB sounding data and AIRX2RET V5 product. The results indicate that, 1) the fast LLR algorithms is not only fast but also accurate, the retrieving errors for atmospheric temperature and humidity profiles are, respectively, reduced ~0.8 K and ~0.5 g/kg in contrast to the D matrix algorithm, and are comparable to the neural network algorithm. 2) the root mean square (rms) errors of the derived atmospheric temperature and humidity profiles in 2011 are, respectively, less than 2.5 K and 2.3 g/kg in contrast the ECMWF reanalysis data, and they are, respectively, 3.5 K and 2.0 g/kg in contrast to the RAOB sounding data. The rms errors of the derived atmospheric temperature and humidity profiles in 2011 are, respectively, 2.5 K and 1.6 g/kg in contrast to AIRX2RET V5 product, which are consistent with the algorithm.

In many applications, it is imperative to maintain high spectral and spatial resolution of remote sensing images. This letter addresses the issue by fusing low-spatial-resolution hyperspectral images (HSIs) and high-spatial-resolution multispectral images (MSIs) of the same scene collected by the coupled sensors and, thus, present a novel framework that generalizes well-established pan-sharpening algorithms. The main steps of the framework are dividing the spectrum of HSIs into several regions and fusing HSIs and MSIs in each region by the chosen pan-sharpening algorithm. Ratio image-based spectral resampling (RIBSR) is used to interpolate the missing data so that every region is covered by a multispectral band. Therefore, the framework allows most of pan-sharpening algorithms to be extended to HSI and MSI fusion. Synthetic data in accordance with sensor reality are used to test specific methods derived within the framework. Experimental results show that the proposed methods excel the state-of-the-art methods in terms of simplicity, feasibility, efficiency, and effectiveness.

This paper addresses the retrieval of sea surface temperature (SST) and land surface temperature (LST) from the measurements acquired by the Stretched Visible and Infrared Spin Scan Radiometer (SVISSR) on FengYun (FY) 2C/D/E satellites. First, the generalized split-window algorithms for SST and LST retrieval were developed using the moderate spectral resolution atmospheric transmittance algorithm and computer model (MODTRAN) fed with the parameter-adjusted standard model atmospheres. Then, the developed algorithms were applied to SST and LST retrieval from the SVISSR/FY-2C/D measurements in September 2007 and the SVISSR/FY-2E measurements in May 2010 over a large study area (latitude: 10° S-50° N; longitude: 60° E-130° E). Finally, the derived SVISSR/FY-2 SST and LST were, respectively, cross-validated with the MODIS/Terra SST and LST products. The results show the following: 1) the generalized split-window algorithms developed in this work are valid for both SST and LST retrieval from SVISSR/FY-2C/D/E measurements; 2) in contrast to the MODIS/Terra SST and LST products, the errors in nighttime retrieval are usually less than that in daytime retrieval, and the errors in SVISSR/FY-2 SST are generally less than the errors in SVISSR/FY-2 LST; and 3) for both daytime and nighttime, the total errors in SVISSR/FY-2C/D/E LST are, respectively, 0.3 ± 1.6 K, 0.3 ± 1.9 K, and 1.0 ± 1.8 K, while the total errors in SVISSR/FY-2C/D/E SST are, respectively, -0.6 ± 1.3 K, -0.2 ± 1.5 K, and 0.4 ± 1.8 K.

In recent years, dimensionality reduction (DR) and classification have become important issues of hyperspectral image analysis. In this paper, we propose a new spatial–spectral similarity measure, which maps the distances between two image patches in hyperspectral images. Including spatial information by using the spatial neighbors, the proposed similarity measure is based on the fact that the observed pixels in the images are spatially related, and the meaningful features can be extracted from both the spectral and spatial domains. First, the new similarity measure can effectively exploit the rich spectral and spatial structures of data, thus improving the original$k$-nearest neighbor ($k$NN) classification methods. Second, the new similarity measure can be incorporated into existing DR methods including linear or nonlinear techniques. With the merits of the proposed similarity measure, the modified DR methods become effective in dealing with the redundancy resulting from spectral signature as well as the spatial relation among pixels. A comparative study and analysis based on classification experiments using five real hyperspectral data sets, which were acquired by different instruments, is conducted to evaluate the proposed similarity measure. The experimental results demonstrate that the proposed measure is promising for combining spectral and spatial information when applied to DR and classification of hyperspectral data sets.

Under the linear spectral mixture model, hyperspectral unmixing can be considered as a convex geometry problem, in which the endmembers are located in the vertices of simplex enclosing the hyperspectral data set and the barycentric coordinates of observation pixels with respect to the simplex correspond to the abundances of endmembers. Based on distance geometry theory, in this paper we propose a new approach for abundance estimation of mixed pixels in hyperspectral images. With the endmember signatures, which is known a priori or can be obtained from the endmember extraction algorithms, the proposed method automatically estimates the abundances of endmembers at each pixel using convex geometry concepts and distance geometry constraints. In the algorithm, denoting the pairwise distances with Cayley-Menger matrix makes it easy to calculate the barycentric coordinates of the observation pixels. Another characteristic of this algorithm is that the optimal estimated points of observation pixels as well as the least distortion in geometric structure of original data set can be obtained with the distance geometry constraint. Simultaneously, the use of barycenter of simplex builds an accurate and efficient method to estimate endmembers with zero abundance and, as a result, the subsimplex containing the estimated points is obtained. A comparative study and analysis based on Monte Carlo simulations and real data experiments is conducted among the proposed algorithm and three state-of-the-art algorithms: fully constrained least squares (FCLS), FCLS computed using constrained sparse unmixing by variable splitting and augmented Lagrangian, and simplex-projection unmixing (SPU). The experimental results show that the proposed algorithm always provides the best unmixing accuracy and when the number of endmembers is not very large the algorithm has a lower computational complexity.

This paper presents the comparison of the in-orbit calibrations between the microwave sounders on the National Oceanic and Atmospheric Administration (NOAA) 15, 16, 17, 18 and 19 and FengYun 3A (FY-3A) and FY-3B satellites using the ray-matching method over the South Pole and North Pole study area in 2011. The results show that the in-orbit calibrations of NOAA Advanced Microwave Sounding Unit A (AMSU-A/NOAA) channels are identical with averaging errors less than 0.45K, except the channel 8, in which the averaging error is up to -1.53K. The in-orbit calibrations of FY-3 Microwave Temperature Sounder (MWTS/FY-3) are basically consistent with that of AMSU-A/NOAA-19 channels, and small influence of solar illumination on MWTS/FY-3B channel 4 was observed. Large in-orbit calibration discrepancies were found between FY-3 Microwave Humidity Sounder (MWHS/FY-3) channels and AMSU-B/NOAA-16 channels, especially in MWHS/FY-3A channel 5. Strong impacts of solar illumination on MWHS/FY-3 channels 3, 4 and 5 were observed.

Hyperspectral unmixing is an important issue to analyze hyperspectral data. Based on the present mixing models, this paper proposes a new nonlinear unmixing framework for hyperspectral imagery. The proposed framework transforms the hyperspectral unmixing problem to a constrained nonlinear least squares problem by introducing the abundance nonnegative constraint, abundance sum-to-one constraint and the bound constraints of nonlinear parameters. Accordingly, an alternating iterative optimization algorithm is developed to solve the arising nonlinear least squares problem. The method decomposes the nonlinear unmixing problem into two sub-problems, which obtain alternately the abundance vectors and nonlinear parameters of the observation pixels. The experimental results on synthetic and real hyperspectral dataset demonstrate that the proposed algorithm can effectively overcome the inherent limitations of the linear mixing model. Meanwhile, the proposed algorithm performs well for noisy data, and can also be used as an effective technique for the nonlinear unmixing of hyperspectral imagery.

This letter addressed the development of split-window algorithm to estimate land surface temperature (LST) from the measurements acquired by the Visible and Infrared Radiometer on FengYun 3A using radiative transfer modeling experiment with the moderate spectral resolution atmospheric transmittance algorithm and computer model and the SeeBor V5.0 database. To improve the accuracy, the total precipitable water and the mean of land surface emissivities (LSEs) and LST were divided into several subranges. The split-window algorithm was applied to the Northeastern China area (115°E-135°E, 40°N-55°N), and then, the estimated LSTs were cross-validated with the Terra Moderate Resolution Imaging Spectroradiometer (MODIS/Terra) LST and Emissivity (LST/E) V5 products: MOD11C1 V5 and MOD11_L2 V5. The results show that the LSTs in this work are averagely consistent with the MODIS/Terra LST/E V5 products with accuracy better than 1.0 K: The errors are 0.5 ± 0.9 K and 0.0 ± 0.9 K for daytime and nighttime, respectively, when the retrieved LSTs were compared to the MOD11C1 V5 product, while the errors are 0.6 ± 0.9 K and 0.0 ± 0.9 K for daytime and nighttime, respectively, when the results were compared to the MOD11_L2 V5 product.

This paper addressed the evaluation of Temperature Independent Spectral Indices of Emissivity (TISIE) in Land Surface Emissivity (LSE) retrievals over an eastern China area (110°E–125°E, 35°N–50°N) using Radiative Transfer Modeling (RTM) experiments with the products (MOD11C1, MOD11_L2 and MOD07) of the MODerate resolution Imaging Spectroradiometer (MODIS) on Terra in Sept. of 2007. The results show that the LSE errors due to the instrumental noises can be neglected, whereas the LSE errors due to the method and the uncertainties of Total Precipitable Water (TPW) are channel-dependent and region-dependent, and in general, they are so large that they cannot be neglected in both middle infrared and thermal infrared channels.

This paper proposed a new approach to estimate the abundance of each endmember at each pixel using distance geometry concepts and distance geometry constraints. It improves current hyperspectral unmixing algorithms in several aspects. Firstly, denoting the distance relationship with Cayley-Menger matrix makes it easy to calculate the barycentric coordinates of observation pixels, and the computation is independent of number of bands. Secondly, by the distance geometry constraint, the geometric structure of dataset is considered to obtain the optimal result with least geometric deformation. The synthetic and real data experimental results demonstrate that this algorithm is a fast and accurate algorithm for the hyperspectral unmixing.

This work addressed the Land Surface Emissivity (LSE) retrieval over west China region from combined Middle InfraRed (MIR) and Thermal InfraRed (TIR) data of the Stretched Visible and Infrared Spin Scan Radiometer (SVISSR) aboard the Chinese first generation geostationary satellites FengYun-2C (FY-2C) and FengYun-2D (FY-2D). To improve the retrieval accuracy, the SVISSR/FY-2 infrared channels 1 (~10.9μm), 2 (~11.9μm), 4 (~3.8μm) were intercalibrated with the well-calibrated AIRS/Aqua and MODIS/Terra channels in tropical regions using the Ray-Matching (RM) method and High Spectral Convolution (HSC) method. The atmospheric effects were removed from SVISSRFY-2 data using MODTRAN fed with ECMWF (European Center for Median-range Weather Forecast) data. Based on the concept of Temperature Independent Spectral Indices of Emissivity (TISIE) constructed with one channel in MIR and the other in TIR, and assuming that the TISIEs do not change between day and night, the land surface emitted and reflected radiances in SVISSR/FY-2 MIR channel in a clear-sky daytime can be first separated, and then the directional reflectances at different angles were obtained. Finally, LSEs were modeled with the RossThick- LiSparse-R BRDF (Bidirectional Reflectance Distribution Function) model and the Kirchhoff s law.

This paper addresses the intercalibration of the infrared channels 1 (10.9 mum), 2 (11.9 mum), and 4 (3.8 mum) of the Stretched Visible and Infrared Spin Scan Radiometer (SVISSR) onboard China's geostationary satellite FengYun 2C (FY-2C) against the channels of the MODerate resolution Imaging Spectroradiometer (MODIS) onboard Terra and of the Atmospheric InfraRed Sounder (AIRS) onboard Aqua. The effects of the wide spectral range of SVISSR channels 1, 2, and 4 were evaluated. Three methods, including the ray-matching method, the radiative transfer modeling method, and the high spectral convolution method, were developed in a tropical region using the SVISSR/FY-2C, MODIS/Terra, and AIRS/Aqua measurements in December 2006 and 2007. The results reveal that the onboard calibration of SVISSR/FY-2C was stable during that period; however, calibration discrepancies exist between MODIS and SVISSR channels and between AIRS and SVISSR channels. The intercalibration coefficients of SVISSR/FY-2C channels were obtained for each of the three methods.