Visible And Infrared Radiometer Research Articles

Arctic Sea Ice Albedo Estimation from Fengyun-3C/Visible and Infra-Red Radiometer

The sea ice albedo can amplify global climate change and affect the surface energy in the Arctic. In this paper, the data from Visible and Infra-Red Radiometer (VIRR) onboard Fengyun-3C satellite are applied to derive the Arctic sea ice albedo. Two radiative transfer models, namely, 6S and FluxNet, are used to simulate the reflectance and albedo in the shortwave band. Clear sky sea ice albedo in the Arctic region (60°~90°N) from 2016 to 2019 is derived through the physical process, including data preprocessing, narrowband to broadband conversion, anisotropy correction, and atmospheric correction. The results are compared with aircraft measurements and AVHRR Polar Pathfinder-Extended (APP-x) albedo product and OLCI MPF product. The bias and standard deviation of the difference between VIRR albedo and aircraft measurements are −0.040 and 0.071, respectively. Compared with APP-x product and OLCI MPF product, a good consistency of albedo is shown. And analyzed together with melt pond fraction, an obvious negative relationship can be seen. After processing the 4-year data, an obvious annual trend can be observed. Due to the influence of snow on the ice surface, the average surface albedo of the Arctic in March and April can reach more than 0.8. Starting in May, with the ice and snow melting and melt ponds forming, the albedo drops rapidly to 0.5–0.6. Into August, the melt ponds begin to freeze and the surface albedo increases.

Comprehensive Evaluation and Comparison of AIRS, VASS, and VIRR Water Vapor Products Over Antarctica

AbstractThe detection capability of the water vapor mixing ratio (q) and total precipitable water vapor (PWV) products of the Atmospheric Infrared Sounder (AIRS), the Atmospheric Vertical Sounder System (VASS), and Visible and Infrared Radiometer (VIRR) in Antarctica is unclear due to the harsh environment and the scarcity of ground‐based stations. This study compares these products with data from nine radiosonde stations and 10 Global Navigation Satellite Systems Stations and evaluates them using the CCHZ‐DISO method. In the low atmosphere, the average wet bias of about 22% of VASS q is significantly greater than that of AIRS q (<15%), and the dry bias of AIRS is gradually corrected with increasing cloudiness, but the trend of exacerbated wet bias at 700 hPa requires attention. Meanwhile, VASS should optimize the inversion principle in the presence of clouds and refine the utilization weights of the channels heavily influenced by clouds under different cloud fraction conditions. Furthermore, when the atmospheric pressure is less than 500 hPa, VASS q exhibits a significant wet bias, and these data should be used cautiously. Both AIRS and VIRR detect PWV well in coastal areas, but VIRR PWV is significantly overestimated in the dry ice sheet plateau region. The CCHZ‐DISO value intuitively reveals that AIRS outperforms VASS and VIRR in both q and PWV detection. The findings of this study contribute to a more comprehensive understanding of the detection capability of the above satellite water vapor products to promote the wider application of satellite data in the Antarctic region.

Research on the establishment of NDVI long-term data set based on a novel method

This study compares the relationship between different NDVI (Normalized Difference Vegetation Index), the NDVI of AVHRR (Advanced Very High Resolution Radiometer) (NDVIa), the NDVI of MODIS (Moderate Resolution Imaging Spectrometer) (NDVIm), and the NDVI of VIRR (Visible and Infrared Radiometer) (NDVIv), and found that there is a significant correlation between the NDVIa and the NDVIm, and between the NDVIv and the NDVIa, the relationship between the three is NDVIv < NDVIa < NDVIm. Machine learning is an important method in artificial intelligence. It can solve some complex problems through algorithms. This research uses linear regression algorithm in machine learning to construct the Fengyun Satellite NDVI correction method. By constructing a linear regression model, the NDVI value of Fengyun Satellite VIRR is corrected to a level that is basically the same as NDVIm. The corrected correlation coefficients (R2) were significantly improved, and the corrected correlation coefficients were significantly improved, and the confidence levels were all significant correlations less than 0.01. It is proved that the corrected normalized vegetation index of Fengyun Satellite has significantly improved accuracy and product quality compared with the normalized vegetation index of MODIS.

Assessment of Global FY-3C/VIRR Sea Surface Temperature

Fengyun-3C (FY-3C) is a second-generation meteorological satellite of China that was launched on 23 September 2013. The on board Visible and Infrared Radiometer (VIRR) can be used to observe global sea surface temperature (SST). In this paper, the VIRR SST products are compared with MODIS SST products and buoy measurements from 2015 to 2019. The collocations of VIRR, MODIS, and buoy SST are generated separately during the day and night with the spatial window of 0.05° × 0.05°. The comparison results show that the biases of VIRR SST minus buoy SST during the day and night are −0.21 and −0.13 °C with a corresponding robust standard deviation (RSD) of 0.58 and 0.59 °C, respectively. The mean differences between VIRR and MODIS are −0.10 and 0.08 °C with RSDs of 0.53 and 0.58 °C for the daytime and nighttime, respectively. The consistency of VIRR SST accuracy from 2015 to 2019 and the dependence of VIRR SST error on SST and latitude are also investigated.

Optimum interpolation analysis for Fengyun-3C sea surface temperature data using oriented elliptic correlation scales

ABSTRACT Optimum interpolation (OI) analysis is performed on the sea surface temperature (SST) products retrieved from the Fengyun-3C (FY-3C) visible and infrared radiometer (VIRR). The oriented elliptic correlation scale is employed on the SST data and then compared with those from the traditional rectangular correlation scale in the OI analysis. The results were also evaluated against the daily SST data from the in-situ buoys and the Reynolds Optimum Interpolation 1/4 Degree Daily SST (OISST) during 2015. The statistical metrics indicate that the SSTs determined using the oriented elliptic correlation scales have higher accuracy than those from rectangular correlation scales, as the root-mean-square error (RMSE) from 0.42°C to 0.38°C, which are higher than those obtained with the comparison with OISST (RMSE of 0.28°C). This assessment demonstrates that the original SST data from FY-3C VIRR are of poor quality due to the presence of unresolved errors, whereas the oriented elliptic correlation scales for SST demonstrate better performance than the rectangular correlation scales in the OI analysis.

Development and evaluation of regional SST regression algorithms for FY-3C/VIRR data in the western north pacific

ABSTRACT Satellite remote sensing has been one of the most main methods to acquire the sea surface temperature (SST). In this study, the new regional algorithms to estimate SST in the western north Pacific are developed using the data from the visible and infrared radiometer (VIRR) aboard the Chinese FengYun-3 C (FY-3 C) meteorological satellite. The new regional algorithms are based on the nonlinear SST (NLSST) and triple window NLSST (TNLSST), and improve the accuracy of SST by adding atmospheric correction terms to the original forms of NLSST and TNLSST. For discriminating the original algorithms, the new regional algorithms are named as corrected NLSST (CNLSST) and corrected TNLSST (CTNLSST), respectively. The regional SST algorithms are evaluated with in-situ data in the western north Pacific. Compared with NLSST, CNLSST reduces the bias and root-mean-square error (RMSE) from 0.0259°C and 0.6376°C to 0.0162°C and 0.6185°C at daytime, and from −0.0348°C and 0.6365°C to −0.0291°C and 0.6036°C at night. Also, with comparison to TNLSST, CTNLSST reduces the bias and RMSE from −0.0822°C and 0.5691°C to −0.0550°C and 0.5424°C.

A Self-Adaptive Wildfire Detection Algorithm with Two-Dimensional Otsu Optimization

The gradual increase in wildfires has caused frequent trips and outages along electrical transmission lines, which is a serious threat to the operational stability of power grids. A self-adaptive wildfire detection algorithm has been developed and tested in this paper. Most of existing wildfire detection methods employed fixed thresholds to identify potential wildfire pixels while the background pixels were ignored. By calculating two-dimensional histogram of the brightness temperatures of mid-infrared channel, the threshold selection is self-adaptive and potential pixels containing scenes of fire can be distinguished automatically. Based on the two-dimensional Otsu method and contextual test algorithm, an improved wildfire detection algorithm that uses multitemporal Visible and Infrared Radiometer (VIRR) data is described. The wildfire detection results within three kilometers of electrical transmission lines demonstrate the effectiveness of the proposed method, which has accurate low-temperature wildfire detection ability.

A Precipitation Detection Method for MWTS-II Radiance Assimilation in Typhoon Simulation

FY-3C Microwave Temperature Sounder Ⅱ (MWTS-Ⅱ) lacks observations at 23.8 GHz, 31 GHz and 89 GHz, making it difficult to remove the data contaminated by precipitation in assimilation. In this paper, a fast forward operator based on the Community Radiative Transfer Model (CRTM) was used to analyze the relationship between the observation minus background simulation (O-B) and the cloud fractions in different MWTS-Ⅱ channels. In addition, based on the community Gridpoint Statistical Interpolation (GSI) system, the radiation brightness temperature of the MWTS-Ⅱ was assimilated in the regional Numerical Weather Prediction (NWP) model. In the process of assimilation, Visible and Infrared Radiometer (VIRR) cloud detection products were matched to MWTS-Ⅱ pixels for precipitation detection. For typhoon No. 18 in 2014, impact tests of MWTS-Ⅱ data assimilation was carried out. The results show that, though the bias observation minus analysis (O-A) of assimilated data can be reduced by quality control only with | O-B | &lt; 3K; however, the O-A becomes much smaller while the precipitation detection is performed with Fvirr &lt; 0.9 (VIRR cloud fraction threshold of 0.9). Besides, the change of the environmental field around the typhoon is more conducive to make the simulated track closer to the observation. The 72-hour typhoon track simulation error also shows that, after the precipitation detection, the error of simulated typhoon track is significantly reduced, which reflects the validity of a precipitation detection method based on a double criterion of | O-B | &lt; 3K and Fvirr &lt; 0.9.

An active fire detection algorithm based on multi-temporal FengYun-3C VIRR data

The Visible and Infra-Red Radiometer (VIRR) is an improved third-generation sensor used for Earth observation with channels ranging from visible to thermal bands and carried on board the Chinese FengYun-3C satellite. An active fire detection algorithm based on VIRR data has already been designed and tested in different study areas worldwide. Most of the previously related algorithms were developed merely focusing on the spatial and spectral features of pixels while the temporal attributes of these observed active fires were ignored. In this research, multi-temporal VIRR data were used to construct time series of pixels. The core content of the algorithm consists of the changes in the time-series profiles together with the observed data. By calculating the predicted mid-infrared (MIR) value and the stable MIR value of the target area, fire pixels can be easily distinguished. To assess the performance of this algorithm, a total of eight target areas distributed across the world were used for testing. Two stages of validation were carried out with data of different spatial resolutions. A rough comparison was carried out first. During this step, results from Collection 6 of MODIS Fire and Thermal Anomalies products (MOD14A1) and results generated from the previously used algorithm were used for comparison. The detailed validation work was conducted with the support of Landsat series (including ETM+ and OLI sensors) data even though the different imaging time may affect the actual validation results.

Evaluation of land surface temperature by comparing FY-3C/VIRR with Terra/MODIS and MSG/SEVIRI data

ABSTRACTLand surface temperature (LST) is a key parameter in the physics of land surface processes on a regional and global scale, as it combines the results of all surface–atmospheric interactions and energy fluxes between the atmosphere and the ground. Due to discrepancies in retrieval methods, input parameters, and sensor characteristics, various LST products are different from each other. As one of the important LST contributors, the operational LST product from the visible and infrared radiometer (VIRR) onboard Feng Yun 3C satellite (denoted as VIRR LST1) has not been comprehensively assessed, and the detailed LST retrieval accuracy is still unknown to the public. Taking this into consideration, a new group of VIRR LST (denoted as VIRR LST2) was derived using a generalized split-window method for operational use. Therefore, this study aimed to evaluate the consistency between VIRR LST1 and VIRR LST2 in detail using the LSTs derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Spinning Enhanced Visible and Infrared Imager (SEVIRI) data over the study area (from 11° W to 2° E and 32° N to 44° N). Several LST images in different seasons were used and registered in space, viewing time, and viewing geometry. After that, the LST differences in day-variation, land-cover types, and viewing zenith angles (VZAs) were also investigated. The results indicate that the differences between VIRR LST1 (or VIRR LST2) and MODIS LST were within 2.80 K, and the corresponding values between VIRR LST1 (or VIRR LST2) and SEVIRI LST were within 3.30 K. The VIRR LST1 was always underestimated, and VIRR LST2 was overestimated during the exuberant vegetation day. Furthermore, the mean differences between VIRR LST1 (or VIRR LST2) and MODIS LST for bare soil were larger than those for vegetation since the land surface emissivity for bare soil was often not well produced. On the other hand, the variation was not pronounced when comparing VIRR LST1 (or VIRR LST2) and SEVIRI LST, which could be caused by many more mixed pixels in the SEVIRI data set. In addition, the LST differences seemed to VZA dependent.

FengYun-3C VIRR Active Fire Monitoring: Algorithm Description and Initial Assessment Using MODIS and Landsat Data

The Visible and Infra-Red Radiometer (VIRR) is an improved third-generation earth observation sensor that has channels ranging from the visible to thermal bands and is carried on board the Chinese FengYun-3C satellite. The VIRR instrument provides full global coverage at a spatial resolution of 1 km. It is neither appropriate nor correct to simply duplicate the parameters used in algorithms designed for other sensors. Hence, this paper proposes an active fire-monitoring algorithm designed for the VIRR sensor. A newly proposed concept—the “infrared channel slope”—is employed in the algorithm, which can detect active fire pixels by identifying deviations in their mid-infrared and thermal infrared brightness temperatures. “Dynamic thresholds” are also used to allow the algorithm to adapt to different situations. Different fire events that occurred in different parts of the world between 2014 and 2016 are selected to test the robustness of the algorithm. The testing consists of two parts: a rough assessment using products of a similar spatial resolution and then a detailed assessment based on fine spatial resolution images. In the rough assessment part, the results are compared with the Moderate Resolution Imaging Spectroradiometer (MODIS) Fire and Thermal Anomaly product (MOD14A1) and the China Meteorological Administration’s Global Fire References (GFR) products from the National Satellite Meteorological Center. In the detailed assessment, imagery acquired by the Landsat-7/Enhanced Thematic Mapper Plus and the Landsat-8/Operational Land Imager is used. The MODIS and Landsat data are utilized as reference data to validate the accuracy while the comparison with the GFR products is carried out to demonstrate the advantages of this method. The assessment demonstrates that the algorithm produces comparable results to those obtained using MOD14A1 and is a significant improvement compared with GFR.

Cross-calibration of reflective bands of major moderate resolution remotely sensed data

The major global biogeophysical products obtained from remote sensing are usually composites of 8 or 16days, and are almost always retrieved from a single sensor. Subsequently, the applications of these products are limited in cases requiring higher temporal frequency. With the increasing number of freely available moderate resolution remote sensing datasets, multi-sensor synergy to increase temporal sampling is warranted. However, radiometric consistency of multi-sensor data is not as good as expected; therefore, first the constituency should be evaluated. A new cross-calibration approach of reflective bands for moderate resolution remotely sensed data is proposed in this paper. The new approach uses a time series of MODIS data from both Terra and Aqua to retrieve both the bidirectional reflectance distribution function (BRDF) and aerosol optical depth (AOD) simultaneously over an invariant desert target. The MODIS retrieved BRDF and AOD are then used to cross-calibrate the medium resolution spectral imager (MERSI) and visible and infra-red radiometer (VIRR) onboard FengYun3-A and B satellites (FY3A/B) and advanced very high resolution radiometer (AVHRR) onboard NOAA-16, 17, and 18. The cross-calibration method is validated in two ways: 1) by comparing with the vicarious calibration; and 2) the top of atmosphere (TOA) reflectance before and after calibration, which show that the calibration error of this new approach is consistent within 5%. Compared with most cross-calibration methods, our new method is generic and better consider on BRDF models; furthermore, the well-built BRDF of the calibration site and simultaneously retrieved AOD make the method fully automatic and cost effective for the calibration of most moderate resolution remote sensors, especially Chinese sensors.

Evaluation on Radiometric Capability of Chinese Optical Satellite Sensors.

The radiometric capability of on-orbit sensors should be updated on time due to changes induced by space environmental factors and instrument aging. Some sensors, such as Moderate Resolution Imaging Spectroradiometer (MODIS), have onboard calibrators, which enable real-time calibration. However, most Chinese remote sensing satellite sensors lack onboard calibrators. Their radiometric calibrations have been updated once a year based on a vicarious calibration procedure, which has affected the applications of the data. Therefore, a full evaluation of the sensors’ radiometric capabilities is essential before quantitative applications can be made. In this study, a comprehensive procedure for evaluating the radiometric capability of several Chinese optical satellite sensors is proposed. In this procedure, long-term radiometric stability and radiometric accuracy are the two major indicators for radiometric evaluation. The radiometric temporal stability is analyzed by the tendency of long-term top-of-atmosphere (TOA) reflectance variation; the radiometric accuracy is determined by comparison with the TOA reflectance from MODIS after spectrally matching. Three Chinese sensors including the Charge-Coupled Device (CCD) camera onboard Huan Jing 1 satellite (HJ-1), as well as the Visible and Infrared Radiometer (VIRR) and Medium-Resolution Spectral Imager (MERSI) onboard the Feng Yun 3 satellite (FY-3) are evaluated in reflective bands based on this procedure. The results are reasonable, and thus can provide reliable reference for the sensors’ application, and as such will promote the development of Chinese satellite data.

A bias correction method for FY-3C VIRR SST data

ABSTRACTA bias correction method was proposed for correcting the significant negative sea surface temperature (SST) biases in Fengyun-3C (FY-3C) visible and infrared radiometer (VIRR) products. The multichannel SST algorithm (MCSST) for the daytime SST and night-time SST of VIRR products are modified for estimating the debiased VIRR SSTs, and the optimal local matchup data are selected for each SST observations based on the distance to each parts of regressors in R-space. Compared with the probability density function matching technique for bias correction, the proposed method can better remove the negative biases and decrease the standard deviations of biases.

A cloud detection scheme for the Chinese Carbon Dioxide Observation Satellite (TANSAT)

Cloud detection is an essential preprocessing step for retrieving carbon dioxide from satellite observations of reflected sunlight. During the pre-launch study of the Chinese Carbon Dioxide Observation Satellite (TANSAT), a cloud-screening scheme was presented for the Cloud and Aerosol Polarization Imager (CAPI), which only performs measurements in five channels located in the visible to near-infrared regions of the spectrum. The scheme for CAPI, based on previous cloudscreening algorithms, defines a method to regroup individual threshold tests for each pixel in a scene according to the derived clear confidence level. This scheme is proven to be more effective for sensors with few channels. The work relies upon the radiance data from the Visible and Infrared Radiometer (VIRR) onboard the Chinese FengYun-3A Polar-orbiting Meteorological Satellite (FY-3A), which uses four wavebands similar to that of CAPI and can serve as a proxy for its measurements. The scheme has been applied to a number of the VIRR scenes over four target areas (desert, snow, ocean, forest) for all seasons. To assess the screening results, comparisons against the cloud-screening product from MODIS are made. The evaluation suggests that the proposed scheme inherits the advantages of schemes described in previous publications and shows improved cloud-screening results. A seasonal analysis reveals that this scheme provides better performance during warmer seasons, except for observations over oceans, where results are much better in colder seasons.

Inter-calibration of VIRR/FY-3A/B split-window channels with AIRS/Aqua and IASI/Metop-A measurements

ABSTRACTThis 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.

A method and its retrieval application for collocating the FY-3 microwave and VIS/IR data

As the second generation polar-orbiting meteorological satellite in China, FY-3 has been in operation for more than 7 years. Among the several Earth observing instruments, FY-3 is equipped with the Micro-Wave Radiation Imager (MWRI) and the Visible and Infrared Radiometer (VIRR), which can observe the global atmosphere, land, and ocean. A combined use of the multi-sensor remote sensing observations from FY-3 will enable us to extract comprehensive information of an Earth target. The multi-channel MWRI has a good ability to detect clouds and precipitation, although its spatial resolution changes with frequency. All VIRR channels have a high spatial resolution, but they can only obtain the information on the surface of a target. To get microwave signal at each VIRR pixel and enhance the calculated resolution of MWRI, in this study, a method is developed based on the principle of multi-information collocating, so that brightness temperature at each MWRI channel is collocated to a VIRR pixel.

Snow Cover Monitoring in Qinghai-Tibetan Plateau Based on Chinese Fengyun-3/VIRR Data

Snow cover monitoring in the Qinghai-Tibetan Plateau is very important to global climate change research. Because of the geographic distribution of ground meteorological stations in Qinghai-Tibetan Plateau is too sparse, satellite remote sensing became the only choice for snow cover monitoring in Qinghai-Tibetan Plateau. In this paper, multi-channel data from Visible and Infrared Radiometer (VIRR) on Chinese polar orbiting meteorological satellites Fengyun-3(FY-3) are utilized for snow cover monitoring, in this work, the distribution of snow cover is extracted from the normalized difference snow index(NDSI), and the multi-channel threshold from the brightness temperature difference in infrared channels. Then, the monitoring results of FY-3A and FY-3B are combined to generate the daily composited snow cover product. Finally, the snow cover products from MODIS and FY-3 are both verified by snow depth of meteorological station observations, result shows that the FY-3 products and MODIS products are basically consistent, the overall accuracy of FY-3 products is higher than MODIS products by nearly 1 %. And the cloud coverage rate of FY-3 products is less than MODIS by 2.64 %. This work indicates that FY-3/VIRR data can be reliable data sources for monitoring snow cover in the Qinghai-Tibetan Plateau.

Improved cloud phase retrieval approaches for China's FY-3A/VIRR multi-channel data using Artificial Neural Networks

Retrieving cloud phase accurately is important for cloud parameter studies, weather forecasting, and climate change research. Consequently, the purpose of this study is to develop better and more accurate cloud phase retrieval approaches to upgrade the current threshold technique used for China's second-generation polar-orbit meteorological satellite FengYun-3A (FY-3A). In this paper, improved cloud phase retrieval approaches using a supervised Back-Propagation Neural Network (BP-NN), and an unsupervised Self-Organizing Feature Map Neural Network (SOFM-NN) were proposed and investigated. The results of this study indicated that the two ANN approaches are satisfactory in discriminating cloud phase using FY-3A/Visible and InfRared Radiometer (VIRR) multi-channel data, and the average accuracy rates for the BP-NN approach are 93.50%, 93.81%, 94.25%, and 93.38% for the winter, spring, summer, and fall season categories, respectively, while for the SOFM-NN approach, rates are 91.93%, 92.08%, 92.63%, and 91.97%, respectively. The BP-NN approach performs slightly better than the SOFM-NN approach. Moreover, the two ANN approaches are found to perform more accurately than the current FY-3A operational product. Therefore, our work demonstrated that the ANN approaches provide an attractive alternative for cloud phase retrieval that could potentially be used to upgrade the current threshold technique used for the FY-3A operational product.

Estimation and Validation of Land Surface Temperatures from Chinese Second-Generation Polar-Orbit FY-3A VIRR Data

This work estimated and validated the land surface temperature (LST) from thermal-infrared Channels 4 (10.8 µm) and 5 (12.0 µm) of the Visible and Infrared Radiometer (VIRR) onboard the second-generation Chinese polar-orbiting FengYun-3A (FY-3A) meteorological satellite. The LST, mean emissivity and atmospheric water vapor content (WVC) were divided into several tractable sub-ranges with little overlap to improve the fitting accuracy. The experimental results showed that the root mean square errors (RMSEs) were proportional to the viewing zenith angles (VZAs) and WVC. The RMSEs were below 1.0 K for VZA sub-ranges less than 30° or for VZA sub-ranges less than 60° and WVC less than 3.5 g/cm2, provided that the land surface emissivities were known. A preliminary validation using independently simulated data showed that the estimated LSTs were quite consistent with the actual inputs, with a maximum RMSE below 1 K for all VZAs. An inter-comparison using the Moderate Resolution Imaging Spectroradiometer (MODIS)-derived LST product MOD11_L2 showed that the minimum RMSE was 1.68 K for grass, and the maximum RMSE was 3.59 K for barren or sparsely vegetated surfaces. In situ measurements at the Hailar field site in northeastern China from October, 2013, to September, 2014, were used to validate the proposed method. The result showed that the RMSE between the LSTs calculated from the ground measurements and derived from the VIRR data was 1.82 K.