This paper addresses land surface temperature (LST) retrieval from Landsat-8 data using the generalized split-window (GSW) algorithm. First, radiative transfer modeling experiment is conducted using the moderate spectral resolution atmospheric transmittance algorithm and computer model fed with SeeBor V5.0 atmospheric profile database to build a data set of LST related to brightness temperatures in Thermal Infrared Sensor (TIRS) bands 10 and 11, land surface emissivities (LSEs), and total precipitable water (TPW). Then, the GSW algorithm's coefficients are obtained through linear regression, in which the simulated data are grouped into several sub-ranges to improve the accuracy. The impacts of noise equivalent temperature difference and uncertainty of LSEs and TPW on derived LST are evaluated. Next, the TIRS channels 10 and 11 are inter-calibrated against the channels of infrared atmospheric sounding interferometer on board Metop-B. After that, LST is retrieved from the re-calibrated and clear sky Landsat-8 data using the GSW algorithm, where LSEs are estimated from the measurements of operational land imager on Landsat-8 by the modified normalized difference vegetation index (NDVI) based emissivity method, and TPW is extracted from the european centre for medium-range weather forecasts reanalysis data. Finally, the retrieved LST is cross-validated with the MOD11_L2 V6 product. The results show that the GSW algorithm can accurately retrieve LST from Landsat-8 data, and errors mainly come from the uncertainty of LSEs and TPW. Against the MOD11_L2 V6 product, the LST errors are -1.45 ± 0.80 K and -0.49 ± 0.78 K before and after the correction of LSEs and TPW, respectively.

This work addresses the intercalibration of the Microwave Radiation Imager (MWRI) on the Chinese second-generation polar-orbiting meteorological satellite Fengyun 3C (FY-3C) against the Microwave Imager (GMI) on the Global Precipitation Measurement (GPM) Core Observatory, in which a modified Double Difference (DD) method is developed and the Brightness Temperatures (TBs) in FY-3C MWRI and GMI channels are simulated using the ocean microwave Radiative Transfer Model (RTM) fed with the fifth generation of European Centre for Medium-range Weather Forecast (ECMWF) atmospheric reanalysis (ERA5) data. With the modified DD method, the intercalibration of FY-3C MWRI in 2017 are obtained. The results show that the MWRI observations are underestimated, especially for the low frequency channels. The calibration biases (mean of DDs) in FY-3C MWRI channels are temperature dependent, and decrease with the frequency increment. The in-orbit calibration of the MWRI descending (MWRID) data is 1~2 K worse than that of the MWRI ascending (MWRIA) data. At the TBs of standard scene defined by the Global Space-based Inter-Calibration System (GSICS), the calibration errors (mean of DDs ± standard deviation at the mean) of MWRIA data in January 2017 are -6.7±0.4 K, -8.3±0.8 K, -3.0±0.7 K, -1.9±1.0 K, -2.5±1.1 K, -3.9±0.8 K, -2.1±1.5 K, -1.5±1.0 K and -0.4±2.3 K in the 10V/H, 18V/H, 23V, 36V/H and 89V/H channels, respectively, while the calibration errors of MWRID data in January 2017 are -7.6±0.8 K, -9.1±1.2 K, -4.4±0.8 K, -2.9±1.3 K, -3.6±1.2 K, -5.1±0.8 K, -2.6±1.4 K, -2.5±1.1 K and -1.2±2.5 K in the nine channels, respectively. Although calibration biases exist, the in-orbit calibration of FY-3C MWRI is generally stable in 2017. The results of the modified DD method are consistent with that of the DD method. The modified DD method is promising to be applied to the intercalibration with both target and reference radiometers on polar-orbiting satellites.

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.

Sea surface temperature (SST) is a key climate variable, which affects the behavior of the Earth’s atmosphere. In this paper, 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°×0.25° 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 K, 0.73 K 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 K, 0.17±1.15 K and 0.01±1.15 K against the GMI SST, respectively, while they are 0.48±1.24 K, 0.37±1.13 K 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.

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.

This work addressed the inter-calibration of the two split-window channels 4 and 5 (IR4, around 10.8 µm; IR5, around 12.0 µm) of Visible and Infrared Radiometer (VIRR) aboard FengYun 3 (FY-3) A and B satellites against the hyperspectral channels of Atmospheric Infrared Sounder (AIRS) on Aqua and Infrared Atmospheric Sounding Interferometer (IASI) on Metop-A over the Arctic area (180ºW–180ºE, 60ºN–90ºN) using the hyperspectral convolution (HSC) method in June and December of 2010–2013. First, the satellite data were re-sampled into the gridded study area using the area-weighted pixel-aggregation method. Then, besides the collocation condition, the measurements acquired by different instruments were matched with the following criteria: 1) the absolute observation time difference is less than 20 min (|∆time|< 20ʹ), and 2) |cosθ₁ /cosθ₂ – 1|< 0.015, where θ₁ and θ₂ are the at-surface view zenith angles (VZAs) of the VIRR/FY-3 measurement and the hyperspectral measurement, respectively. Finally, linear regression analyses on the matching measurements were carried out to obtain the inter-calibration coefficients, and re-calibrated VIRR/FY-3A/B measurements were achieved. The results showed that the calibrations of AIRS/Aqua and IASI/Metop-A channels were quiet consistent, whereas, against the hyperspectral channels, the calibrations of VIRR/FY-3A/B channels 4 and 5 were biased, and they presented certain seasonal and annual variations, especially for VIRR/FY-3A channel 4. In contrast to VIRR/FY-3A channels, the calibration discrepancies of VIRR/FY-3B channels 4 and 5 were smaller. Moreover, no obvious impact of solar illumination on VIRR/FY-3A/B measurements was observed.