Moderate Spectral Resolution Atmospheric Transmittance Research Articles

Retrieving Land Surface Temperature From Chinese FY-3D MERSI-2 Data Using an Operational Split Window Algorithm

Medium resolution spectral imager II (MERSI-2) is a payload for the Chinese meteorological satellite Feng Yun 3 (FY-3). China's satellite remote sensing observation capabilities such as climate change research can be improved during MERSI-2's operation in orbit, and the sensor is the world's first imaging instrument that can obtain a global infrared split-window data with a spatial resolution of 250 m. We developed an operational spilt-window (SW) algorithm to retrieve land surface temperature (LST) accurately from the MERSI-2 data. The SW algorithm coefficients were derived from a simulation dataset that was established with the Moderate spectral resolution atmospheric Transmittance model version 5.2 and the thermodynamic initial guess retrieval dataset. In the practical retrieval, the precise algorithm coefficients were determined by view zenith angle and atmospheric water vapor content (WVC), the atmospheric WVC were obtained from the ERA5 dataset, and the land surface emissivity was dynamically estimated using the advanced spaceborne thermal emission and reflection radiometer global emissivity dataset, considering the fractional of vegetation cover and snow cover. The retrieved LST compared with in situ LST, which was highly consistent with the in situ LST and that the root-mean-square error of the two is within 3 K. The retrieved LST was compared with the MYD11_L2 and MYD21_L2 LST products, and the results indicated that MERSI-2 LST was more consistent with the MYD21 LST. The operational SW algorithm for FY-3D MERSI-2 developed in this study could retrieve LST accurately and has a wide range of popularization and application values.

Vicarious Radiometric Calibration of Ocean Color Bands for FY-3D/MERSI-II at Lake Qinghai, China.

To calibrate the low signal response of the ocean color (OC) bands and test the stability of the Fengyun-3D (FY-3D)/Medium Resolution Spectral Imager II (MERSI-II), an absolute radiometric calibration field test of FY-3D/MERSI-II at the Lake Qinghai Radiometric Calibration Site (RCS) was carried out in August 2018. The lake surface and atmospheric parameters were mainly measured by advanced observation instruments, and the MODerate spectral resolution atmospheric TRANsmittance algorithm and computer model (MODTRAN4.0) was used to simulate the multiple scattering radiance value at the altitude of the sensor. The results showed that the relative deviations between bands 9 and 12 are within 5.0%, while the relative deviations of bands 8, and 13 are 17.1%, and 12.0%, respectively. The precision of the calibration method was verified by calibrating the Aqua/Moderate-resolution Imaging Spectroradiometer (MODIS) and National Polar-orbiting Partnership (NPP)/Visible Infrared Imaging Radiometer (VIIRS), and the deviation of the calibration results was evaluated with the results of the Dunhuang RCS calibration and lunar calibration. The results showed that the relative deviations of NPP/VIIRS were within 7.0%, and the relative deviations of Aqua/MODIS were within 4.1% from 400 nm to 600 nm. The comparisons of three on-orbit calibration methods indicated that band 8 exhibited a large attenuation after launch and the calibration results had good consistency at the other bands except for band 13. The uncertainty value of the whole calibration system was approximately 6.3%, and the uncertainty brought by the field surface measurement reached 5.4%, which might be the main reason for the relatively large deviation of band 13. This study verifies the feasibility of the vicarious calibration method at the Lake Qinghai RCS and provides the basis and reference for the subsequent on-orbit calibration of FY-3D/MERSI-II.

Nonlinear Split-Window Algorithms for Estimating Land and Sea Surface Temperatures From Simulated Chinese Gaofen-5 Satellite Data

This paper proposes a different thermal channel combination split-window (DTCC-SW) method to estimate the land surface temperature (LST) and sea ST (SST) from the Chinese Gaofen-5 (GF-5) satellite thermal infrared (TIR) data. A nonlinear combination of two adjacent channels CH8.20(centered at $8.20~\mu \text{m}$ ) and CH8.63 (centered at $8.63~\mu \text{m}$ ) was proposed to estimate LST for low-emissivity surfaces. A nonlinear combination of two adjacent channels, CH10.80 (centered at $10.80~\mu \text{m}$ ) and CH11.95 (centered at $11.92~\mu \text{m}$ ), was developed to estimate LST and SST for high-emissivity surfaces under dry atmospheric conditions, and a nonlinear combination of two channels, CH8.63 and CH11.95, was used to estimate LST and SST for high-emissivity surfaces under wet atmospheric conditions. The numerical values of the DTCC-SW coefficients were obtained using a statistical regression method from synthetic data simulated with an accurate atmospheric radiative transfer model moderate spectral resolution atmospheric transmittance mode 5 over a wide range of atmospheric and surface conditions. The LST (SST), mean emissivity, and atmospheric water vapor content were divided into several tractable subranges to improve the fitting accuracy. The experimental results and the preliminary evaluation results showed that the root-mean-square error between the actual and estimated LSTs (SSTs) is less than 0.7 K (0.3 K), provided that the land surface emissivities are known, which indicates that the proposed DTCC-SW method can accurately estimate the LST and SST from the GF-5 TIR data.

Land Surface Temperature Retrieval From Landsat-8 Data With the Generalized Split-Window Algorithm

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.

Measurement and Correction of Atmospheric Effects at Different Altitudes for Remote Sensing of Sun-Induced Fluorescence in Oxygen Absorption Bands

Under sunlight illumination, the shape of the atmospheric oxygen bands [O2-B (687 nm) and O2-A (760 nm)] of the vegetation radiance is modified by chlorophyll fluorescence as a result of the filling-in process. However, for far-range measurements, atmospheric effects also modify this shape. In this paper, measurements of the depth of O2-A and O2-B absorption bands have been performed at different altitudes, from 324 to 3123 m, over different targets, including bare soil and wheat fields. It is observed that bands depth increases significantly with altitude. In the O2-B band, the total magnitude of variation is on the same order of magnitude as the change induced by vegetation, while it is much greater in the O2-A band. Three main factors affect the measurements of band depth: 1) the transmittance of the air column between ground and sensor, 2) the path radiance, and 3) the additional contribution of the ground surface outside the field of view scattered by the atmosphere (adjacency effect). We used the MODerate spectral resolution atmospheric TRANsmittance algorithm (MODTRAN) 4 to compute separately these atmospheric and environmental terms, and we corrected airborne measurements to retrieve ground-level band depths. A study was conducted to assess the sensitivity of the absorption band depth to several atmospheric parameters. The magnitudes of these effects were compared to the band depth variation induced by fluorescence. We identified temperature, air pressure, and aerosols as the most critical parameters. After correction of airborne measurements from atmospheric effects, it is found that the variation of band depth with altitude is greatly reduced. The residual variation, particularly in the case of O2-B band over vegetation, is attributed to an inaccurate assessment of the environment contribution that is scattered by the atmosphere to the sensor. These results show that the MODTRAN 4 is able to simulate oxygen band depth variations at moderate spectral resolution (i.e., 0.5–1 nm) and can be used to compute atmospheric and environmental factors for corrections of airborne measurements of oxygen band profiles. We also showed that an accurate evaluation of the contribution of environment is needed to correct the band depth from atmospheric effects in the O2-B band.

Retrieval of Sea and Land Surface Temperature From SVISSR/FY-2C/D/E Measurements

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.

Development of Split-Window Algorithm for Land Surface Temperature Estimation From the VIRR/FY-3A Measurements

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.

Estimating total suspended matter concentration in tropical coastal waters of the Berau estuary, Indonesia

This study presents the application of a semi-empirical approach, based on the Kubelka–Munk (K-M) model, to retrieve the total suspended matter (TSM) concentration of water bodies from ocean colour remote sensing. This approach is validated with in situ data sets compiled from the tropical waters of Berau estuary, Indonesia. Compared to a purely empirical approach, the K-M model provides better results in the retrieval of TSM concentration on both data sets (in situ and Medium Resolution Imaging Spectrometer (MERIS)). In this study, the K-M model was calibrated with in situ measurements of remote-sensing reflectance (R rs) and TSM concentration. Next, the inverse K-M model was successfully applied to images taken by the MERIS instrument by generating regional maps of TSM concentration. MERIS top-of-atmosphere radiances were atmospherically corrected using the Moderate Spectral Resolution Atmospheric Transmittance (MODTRAN) radiative transfer model. The best correlation between R rs measured in situ and R rs MERIS was found to be at a wavelength of 620 nm. The TSM concentrations retrieved using the K-M model showed a lower root mean square error (RMSE), a higher coefficient of determination and a smaller relative error than those retrieved by the purely empirical approach.

Correcting satellite‐based infrared sulfur dioxide retrievals for the presence of silicate ash

Previous studies have suggested that the presence of silicate ash may cause the 8.6‐μm sulfur dioxide (SO2) retrieval to overestimate total column abundances and contribute to variations between ultraviolet and 8.6‐μm SO2 estimates. Tested here is a volcanic aerosol forward model which allows the determination of transmission spectra. The forward model is embedded into a three‐layer atmospheric radiative transfer model, based upon the moderate spectral resolution atmospheric transmittance algorithm (MODTRAN) radiative transfer code. This model is used to investigate the effects of silicate ash on the 8.6‐μm SO2 retrieval from satellite data. Forward modeling of transmission through volcanic clouds enables us to evaluate the assumptions and errors of current retrievals. Four free variables, (1) effective radius, (2) variance, (3) number of particles, and (4) spectral refractive index, are used to determine the transmission and single scattering albedo at 7–13 μm. Here we present simplistic characterizations of three eruptions, containing sulfates, ice, or andesitic ash using this forward model. The ash correction model based on the aerosol forward model is tested on the ash‐rich lobe of the 19 February 2001 eruption plume of Mount Cleveland volcano using Moderate Resolution Imaging Spectroradiometer (MODIS) data. Uncorrected MODIS radiance values estimate 22 kt of SO2, while ash‐corrected radiances estimate 17 kt. The minimal reduction in total SO2 indicates that previous variations in ultraviolet and 8.6‐μm SO2 retrievals are unlikely to be due solely to the presence of ash.

Model Calculations of Solar Spectral Irradiance in the 3.7-μm Band for Earth Remote Sensing Applications

Abstract Since the launch of the first Advanced Very High Resolution Radiometer (AVHRR) instrument aboard the Television and Infrared Observational Satellite (TIROS-N), measurements in the 3.7-μm atmospheric window have been exploited for use in cloud detection and screening, cloud thermodynamic phase and surface snow/ice discrimination, and quantitative cloud particle size retrievals. The utility of the band has led to the incorporation of similar channels on a number of existing satellite imagers and future operational imagers. Daytime observations in the band include both reflected solar and thermal emission energy. Since 3.7-μm channels are calibrated to a radiance scale (via onboard blackbodies), knowledge of the top-of-atmosphere solar irradiance in the spectral region is required to infer reflectance. Despite the ubiquity of 3.7-μm channels, absolute solar spectral irradiance data come from either a single measurement campaign (Thekaekara et al.) or synthetic spectra. In the current study, the historical 3.7-μm band spectral irradiance datasets are compared with the recent semiempirical solar model of the quiet sun by Fontenla et al. The model has expected uncertainties of about 2% in the 3.7-μm spectral region. The channel-averaged spectral irradiances using the observations reported by Thekaekara et al. are found to be 3.2%–4.1% greater than those derived from the Fontenla et al. model for Moderate Resolution Imaging Spectroradiometer (MODIS) and AVHRR instrument bandpasses; the Kurucz spectrum, as included in the Moderate Spectral Resolution Atmospheric Transmittance (MODTRAN4) distribution, gives channel-averaged irradiances 1.2%–1.5% smaller than the Fontenla model. For the MODIS instrument, these solar irradiance uncertainties result in cloud microphysical retrieval uncertainties that are comparable to other fundamental reflectance error sources.

A case study of airborne and satellite remote sensing of a spring bloom event in the Gulf of Finland

The concentrations of chlorophyll- a (chl- a), total suspended solids (TSS) and the absorption coefficient of colored dissolved organic matter ( a CDOM(400)) are estimated in Case II waters using medium resolution imaging spectrometer (MERIS) satellite (full resolution [FR] level 1b, 300 m resolution) and AISA airborne spectrometer data acquired during a spring bloom in the Gulf of Finland, Baltic Sea on April 27, 2004. The accuracy of the estimation is analyzed using empirical band-ratio algorithms together with in situ observations that include water samples analyzed in a laboratory (variation ranges: 22–130 μg/l, 2.9–20 mg/l, and 1.29–2.61 m −1 for chl- a, TSS and a CDOM(400), respectively). Additional in situ estimates (transects) on these characteristics are available through absorption and scattering coefficients measured with an ac-9 absorption and attenuation meter installed in a flow-through system. The retrieval accuracy ( R 2) of all three water quality characteristics with MERIS data is close to or above 0.9, while the RMSE is 7.8 μg/l (22%), 0.74 mg/l (16%) and 0.08 m −1 (5%), for chl- a, TSS and a CDOM(400), respectively. The validity of the chl- a algorithm is tested using nine additional data points. The BIAS-error for these points is 5.2 μg/l and the RMSE is 10.6 μg/l. The effects of changes in the atmospheric characteristics on band-ratio algorithms in cases where no concurrent in situ reference data are available are analyzed using the MODerate spectral resolution atmospheric TRANSmittance algorithm and computer model (MODTRAN). The additional error due to these changes is estimated to be below 20% for the applied ratio algorithms. The water quality data available in the level 2 MERIS-product distributed by the European Space Agency did not include valid results for the date investigated here.