Band Radiance Research Articles

Razvoj metode bi-normaliziranog omjera opožarenosti na događaju katastrofalnog šumskog požara 14. srpnja 2022. godine kod Vodica, Hrvatska

The use of multispectral sensors, such as MSI, on the Sentinel-2 satellite for rapid assessment of the area and the burning degree after large forest fires utilises standard method of determining the Normalized Burn Ratio (NBR) before and after the fire, from which the difference of the Normalized Burn Ratio (dNBR) is calculated. Applied to areas with sparse vegetation, the standard method of NBR gives poor results. To cope with low vegetation areas, a so-called “relativizing dNBR” was introduced where an empirical expression divides the difference of the NBRs in two channels by a quotient slightly greater than the pre-fire NBR. In this paper, a forest fire near Vodice, Croatia, which occurred on July 14, 2022, and which grew into a catastrophic fire that left about 30 km2 of burnt area, was studied. As this is an area of Mediterranean vegetation, relatively sparse, instead of the relativization, an attempt was made to introduce a new formula for calculating the normalized ratio of burning, which included two differences in spectral radiances (instead of one). In addition to the standard difference in the near-infrared and short-wave infrared part of the spectrum, a difference between the near-infrared and the green visible part of the spectrum was used simultaneously. These new, bi-Normalized Burn Ratio, retain information about the spectral radiance in the green visible band and react to surfaces where at least a little vegetation cover remained after the fire, which ultimately gives lower burn categories, with the same burned area detected compared to the standard method.

An On-Orbit Relative Sensor Normalization for Unbalance Images from the Ice Pathfinder Satellite (BNU-1)

The Ice Pathfinder satellite (code: BNU-1) is the first Chinese microsatellite, designed for monitoring polar climate and environmental changes. The major payload of BNU-1 is the wide-field camera which provides multispectral satellite images with a 73.69 m spatial resolution and a 739 km swath width. However, the color misrepresentation issue can be observed as the BUN-1 image appears yellowish as it gets farther towards the center field of view (FOV). The blue band of the image appears to be higher near the center FOV and declines generously towards both the edge areas of the image, which may cause the color misrepresentation issue. In this study, we develop a relative sensor normalization method to reduce the radiance errors of the blue band of BNU-1 images. This method uses the radiometric probability density distribution of the BNU-1 panchromatic band as a reference, correcting the probability density distribution of the blue band radiance first. Then, the mean adjustment is used to correct the mean of the blue band radiance after probability density function (PDF) correction, obtaining the corrected radiance in the blue band. Comparisons with the ground measurements and the Landsat8 image reveal the following: (1) The radiances of snow surfaces also have good consistency with ground observations and Landsat-8 images in the red, green, and blue bands. (2) The radiance errors of the uncorrected BNU-1 images are eliminated. The RMSE decreases from 80.30 to 32.54 W/m2/μm/sr. All these results indicate that the on-orbit relative correction method proposed in this study can effectively reduce the radiance errors of the BNU-1 images.

Enhancement of Nighttime Fire Detection and Combustion Efficiency Characterization Using Suomi-NPP and NOAA-20 VIIRS Instruments

We present the second-generation Fire Light Detection Algorithm (FILDA-2), which includes advances in fire detection and retrievals of radiative power (FRP), fire visible energy fraction (VEF), and fire modified combustion efficiency (MCE) at nighttime from the holistic use of multiple-spectral radiances measured by the Visible Infrared Imaging Radiometer Suite (VIIRS) aboard Suomi-NPP (VNP) and NOAA-20/JPSS-1 (VJ1) satellites. Key enhancements include: (1) a new fast algorithm that maps VIIRS Day/Night Band (DNB) radiances to the pixel footprints of VIIRS Moderate (M) and Imagery (I) bands; (2) identification of potential fire pixels through the use of the DNB anomalies and I-Band thermal anomalies; (3) dynamic thresholds for contextual testing of fire pixels; and (4) pixel-specific estimates of FRP, VEF, and MCE. The global benchmark test demonstrates that FILDA-2 can detect approximately 25-30% more smaller and cooler fires than the operational VIIRS Active Fire 375 m I-band algorithm with the added benefit of providing daily global pixel-level characterizations of MCE for nighttime surface fires. The MCE derived by FILDA-2 is in good agreement with limited ground-based observations near the fires. Additionally, FILDA-2 reduces angular dependence in FRP estimates and significantly reduces the ”bow-tie” (double-counting) effect in fire detection compared to the AF-I product. The cross-validation of FILDA-2 products from VNP and VJ1 retrievals confirms good consistency in FRP and MCE retrievals globally. FILDA-2 is being implemented by NASA to generate a new VIIRS data product for fire monitoring, chemical-speciated fire emission estimates, and fire line characterization.

Aerosol Optical Properties above Productive Waters of Gorky Reservoir for Atmospheric Correction of Sentinel-3/OLCI Images

The main challenge that one has to face during the atmospheric correction (AC) of productive inland waters is the inability to correctly separate aerosol radiance from water-leaving radiance in the near-infrared range (NIR) bands. This leads both to incorrect estimates of the aerosol parameters and the remote-sensing reflectance (Rrs). For the Gorky Reservoir, where we are developing regional bio-optical models, the situation is complicated by the lack of field measurements of aerosol optical properties due to the significant remoteness of AERONET stations. The standard AC algorithms, as shown earlier, greatly overestimated the aerosol radiance in all spectral bands up to red bands during the period of intense cyanobacteria blooms, while the algorithm with a fixed aerosol optical depth (AOD) obtained in a clean water area gave encouraging results. Therefore, it was important to investigate the characteristics of the atmosphere above the reservoir and validate the proposed approach for regular use of Sentinel-3 imagery of the Gorky Reservoir. To solve these issues, regular in situ aerosol measurements using the handheld sun photometer SPM were performed. The measured AOD and the Angstrom exponent were compared with the estimates of these parameters from two Sentinel-3/OLCI Level-2 products, namely, Synergy (SYN) and Water Full Resolution products (OL_2_WFR). It was found that AOD and the Angstrom exponent from these standard products were overestimated by 2–3 times and almost 2 times in all cases. Atmospheric correction with fixed AOD, defined by measurements, allows us to completely get rid of negative Rrs, and its shapes and values became typical for the Gorky Reservoir. Despite the overestimation of AOD in traditional AC and its large variations in general, it was found that the minimum AOD spectrum is close to the measured spectrum. Therefore, the AOD spectra, which correspond to the two percentiles of the distribution, can be used for preliminary AC with a fixed AOD of the Sentinel-3/OLCI imaginary. The relative errors of the Rrs retrievals using the two percentile AOD compared to the measured AOD were 3–35% in the green and red bands of Sentinel-3/OLCI.

An Objective Quality Control of Surface Contamination Observations for ABI Water Vapor Radiance Assimilation

AbstractA quality control (QC) process which handles surface impacts is an important step toward successful assimilation of the Advanced Baseline Imager (ABI) water vapor (WV) band radiances. If the QC is too relaxed, many surface contaminated radiances get assimilated. If the QC is too stringent, useful radiances are rejected. Either way can result in reduced or even compromised observation impacts. A new machine learning‐based QC scheme for the three ABI WV bands is developed and optimized to help understand the importance and effectiveness of the scheme. Unlike previous schemes which are dependent on the background, this scheme extracts and blends the surface information from 7 ABI bands (bands 8–10, 13–16) to determine if a WV radiance is affected by the surface. Simulation studies show that the new QC scheme is effective in retaining radiances that are either unaffected by the surface or have very small surface contamination. It is highly effective in rejecting radiances with large surface contamination. Numerical experiments from a single case study of Hurricane Harvey (2017) were carried out to optimize the QC and to understand the potential impacts on forecasts. The use of the new QC scheme shows that radiances from each WV band have substantially added value. Combining them has a positive impact on hurricane track forecasts compared with existing QC schemes. Hence, it is critical that an optimized QC scheme is used for infrared WV radiance assimilation. It provides a balance between positive impacts from useful radiances and negative impacts from surface contaminated radiances.

Detection of marine aerosols using ocean colour sensors

Ocean colour sensors are equipped with atmospheric correction bands operating in the near infrared region of the spectrum. At these wavelengths, the ocean surface, because of high infrared absorption by water, acts as a dark background and the sensor detected radiance is due to solar radiation backscattered by the atmospheric air molecules and aerosols called Rayleigh and aerosol path radiances respectively. From the radiances in the atmospheric correction bands, after accounting for Rayleigh path radiance, it is possible to determine aerosol parameters like aerosol optical depth (AOD) and particle size distribution index. 
 
 While sensors like IRS P3 MOS-B, IRS P4 OCM, NOAA-AVHRR, etc detect AOD in the total atmospheric column, IRS P3 MOS-A sensor can detect AOD in two atmospheric layers by exploiting the differential absorption property of oxygen at four narrow band channels at the O2 A band around 760nm. The method of determination from ocean colour sensors and the results from IRS P3 MOS-B, IRS P4-OCM and IRS P3 MOS-A radiance data are presented and discussed.

Trends in Downwelling Longwave Radiance Over the Southern Great Plains

AbstractDownwelling longwave radiation is an important part of the surface energy budget. Spectral trends in the downwelling longwave radiance (DLR) provide insight into the radiative drivers of climate change. In this research, we process and analyze a 23‐year DLR record measured by the Atmospheric Emitted Radiance Interferometer (AERI) at the U.S. Department of Energy Atmospheric Radiation Program Southern Great Plains (SGP) site. Two AERIs were deployed at SGP with an overlapping observation period of about 10 years, which allows us to examine the consistency and accuracy of the measurements and to account for discrepancies between them due to errors associated with the instruments themselves. We then analyzed the all‐sky radiance trends in DLR, which are associated with the surface warming trend at SGP during this same period and also the complex changes in meteorological conditions. For instance, the observed radiance in the CO2 absorption band follows closely the near‐surface air temperature variations. The significant positive radiance trends in weak absorption channels, such as in the wings of the CO2 band and in the weak absorption channels in the H2O vibration‐rotational band, show earlier detectability of climate change. The magnitude of the radiance trend uncertainty in the DLR record mainly results from internal climate variability rather than from measurement error, which highlights the importance of continuing the DLR spectral measurements to unambiguously detect and attribute climate change.

Validation of CrIS Radiometric Performance through Its Comparison to ABI

Radiometric intercomparison between satellite remote sensing instruments has become an increasingly common practice to monitor the stability and even the accuracy of their radiance products. The assessment also enables the evaluation of calibration improvements made to these products, as well as the identification and resolution of remaining calibration inadequacies. In this paper, the radiance products of the Cross-track Infrared Sounder (CrIS), an interferometer-based hyperspectral IR sounder in low Earth orbit (LEO), is compared with the level-1b (L1b) radiance products of the infrared (IR) bands of the Advanced Baseline Imager (ABI), an imaging radiometer in geostationary (GEO) orbit. Two CrIS instruments are currently in operation on S-NPP and NOAA-20 satellites, respectively, and two ABI instruments are in operation on GOES-16 and GOES-17 satellites, respectively. Radiometric intercomparisons are performed between each CrIS-ABI pair. An established procedure by GSICS for such GEO-LEO instrument comparison is principally followed to emulate the radiance of ABI IR bands from CrIS spectra of the collocated pixels to be compared with the actual ABI radiance. Results show that the long-term time series of CrIS-ABI radiance bias have been stable within 0.2 K for nearly all ABI IR bands within a spectral range from 3.7 μm to 13.3 μm, except those with known calibration issues. Miscellaneous calibration events that had occurred to either instrument and altered the biases are identified and explained. While the main goal of the work is to support the on-orbit Cal/Val of CrIS, including the future JPSS-2/3/4 CrIS, such observations can also be referenced to further improve the calibration of ABI.

Subpixel Analysis of Primary and Secondary Infrared Emitters with Nighttime VIIRS Data

Biomass burning is a coupled exothermic/endothermic system that transfers carbon in several forms to the atmosphere, ultimately leaving mineral ash. The exothermic phases include flaming and smoldering, which produce the heat that drives the endothermic processes. The endothermic components include pre-heating and pyrolysis, which produce the fuel consumed by flaming and smoldering. These components can be broadly distinguished from each other based on temperature. For several years, we have researched the subpixel analysis of two temperature phases present in fire pixels detected in nighttime VIIRS data. Here, we present the flaming subtractive method, with which we have successfully derived temperatures and source areas for two infrared (IR) emitters and a cooler background. This is developed as an add-on to the existing VIIRS nightfire algorithm version 3 (VNF v.3) which uses Planck curve fitting to calculate temperatures and source areas for a single IR emitter and background. The flaming subtractive method works with data collected in four spectral ranges: near-infrared (NIR), short-wave infrared (SWIR), mid-wave infrared (MWIR) and long-wave infrared (LWIR). With sunlight eliminated, the NIR and SWIR radiances can be fully attributed to the primary IR emitter. The analysis begins with Planck curve modeling for the primary emitter based on the NIR and SWIR radiances, yielding temperature, source area and primary emitter radiances in all spectral bands. The primary emitter radiances are subtracted from each spectral band and then the residual radiance is analyzed for a secondary IR emitter and the background. Spurious results are obtained in pixels lacking a discernable secondary emitter. These misfit pixels revert back to the single IR emitter analysis of VNF v.3. In tests run for two California megafires, we found that the primary emitters straddle the temperature ranges for flaming and smoldering, the exothermic portions of biomass burning, which are apparently commingled on the ground. The secondary emitter temperatures span 350–750 K, corresponding to pre-heating and slow pyrolysis. The natural gas flare test case had few numbers of successful secondary emitter retrievals and a wide range of secondary emitter temperatures. The flaming subtractive analysis is the key addition to VNF version 4, which will commence production later in 2021. In 2022, we will seek validation of the VNF v.4 from nighttime Landsat and other data sources.

First retrieval of absorbing aerosol height over dark target using TROPOMI oxygen B band: Algorithm development and application for surface particulate matter estimates

Constraint of the vertical distribution of aerosol particles is crucial for the study of aerosol plume structure, aerosol radiative effects, and ultimately monitoring surface air pollution. We developed an algorithm to retrieve the aerosol optical central height (AOCH) of absorbing aerosols by using, for the first time, the oxygen (O2) A and B absorption band measurements from the TROPOspheric Monitoring Instrument (TROPOMI) over dark targets. For the retrieval, narrow band radiance at seven channels ranging from ultraviolet (UV) to shortwave infrared (SWIR) are convolved from TROPOMI hyperspectral measurements. Subsequently, cloudy pixels are screened out by using the slope of spectral reflectance, while aerosol types (dust and smoke) are classified by the wavelength dependence of aerosol path radiance in conjunction with UV aerosol index. Surface reflectance over land is derived from the MODIS surface bi-directional reflectance climatology, and over water from the GOME-2 surface Lambert-equivalent reflectivity (LER) database. The aerosol optical depth (AOD) and AOCH are retrieved through an approach of look-up-table accounting for AERONET-based dust and smoke optical properties. For multiple smoke and dust plume events around the world, our retrieved AOCH values agree with space-borne lidar CALIOP counterparts, with a mean bias of <0.15 km and a correlation coefficient of 0.85–0.87. Due in part to adding the O2 B band, our retrieval represents an aerosol extinction peak height better than the TROPOMI operational Level 2 aerosol layer height retrieved from only the O2 A band. The latter shows 0.5–2 km low bias, especially over land. Finally, the high potential of AOCH for improving surface PM2.5 estimates is also illustrated with a case study in which the high bias of surface PM2.5 in MERRA-2 data is corrected after being scaled by the retrieved AOCH.

All-sky total and direct surface Shortwave Downward Radiation (SWDR) estimation from satellite: Applications to MODIS and Himawari-8

The earth’s surface shortwave downward radiation (SWDR), which is generally defined as the sum of the incoming solar energy over the earth’s surface in the shortwave spectrum (0.3–3.0 μm), is a key component of the surface radiation budget (SRB). Spatiotemporally continuous SWDR is needed because it is one of the required parameters driving many land models and applications. In this study, a look-up table (LUT)-based, easy-to-use approach to derive all-sky total and direct surface SWDR is proposed. The only inputs for the new approach include solar and observing angles, blue band radiance, elevation, and surface climatological albedo. The applications of the new method to both Moderate Resolution Imaging Spectrometer (MODIS) and Himawari-8 measurements are presented, and validation versus ground measurements from 21 sites shows that both the instantaneous and daily SWDR can be accurately derived from the above-mentioned method. A further comparison with the Himawari-8 L2 SWDR product reveals that our newly proposed method performs better, with an overall root mean square error (RMSE) of 91.77 (13.99) W/m2 and a bias of −3.91 (-1.55) W/m2 for the instantaneous (daily) total SWDR. The simplicity and reliable accuracy of the new approach make it suitable for producing all-sky surface total and direct SWDR from space-based measurements at the globe scale.

Can multi-angular polarimetric measurements in the oxygen-A and B bands improve the retrieval of aerosol vertical distribution?

Based on the multi-angle measurements at the top of the atmosphere including radiance and polarization in O2 A and B absorption bands simulated by forward model UNL-VRTM, the information content and posterior uncertainties about the altitude of peak aerosol extinction (Hpeak) and half-width (w) characterizing quasi-Gaussian aerosol vertical profiles are analyzed for different observation combinations. Although the information content described as degree of freedom for signal (DFS) for Hpeak and w increases with the number of angles, this increase reaches a limit when more than around six angles are used, regardless of surface type (ocean, vegetation or soil). Due to high surface reflectance of vegetation in O2 A band, the DFS of Hpeak from multi-angle measurements could be lower than 0.8, whereas the DFS > 0.9 for soil and ocean. At a single angle, polarization measurement is more sensitive to Hpeak than radiance when Hpeak is near the surface. Compared with single-angle radiance in O2 A band, adding multi-angle radiances reduces retrieval uncertainty for Hpeak by 10–20%, larger than that from only adding polarization at a single angle (< 10%), especially over vegetation. Over vegetated surface, the multi-angle radiances in O2 A and B band have comparable information, whereas for single angle, O2 B band has richer information than O2 A, especially for lower Hpeak. Multi-angle radiances and polarization in the O2 A band as well as radiances in O2 B band reduce the posterior uncertainty of w more than Hpeak, especially at large AOD, while the polarized measurements in O2 B band are more useful for Hpeak retrieval. The most significant impact of O2 B band radiances is to mitigate aerosol height retrieval uncertainties due to uncertainties in the surface parameters, whereas polarized measurements reduce errors from all error sources including measurements and forward model parameters.

Mapping of thermal power plant emitted atmospheric carbon dioxide concentration using AVIRIS-NG data and atmospheric radiative transfer model simulations

Our study demonstrates the potential of Airborne Visible/Infrared Imaging Spectrometer Next Generation (AVIRIS-NG) hyperspectral data and radiative transfer modeling to retrieve the atmospheric carbon dioxide (CO2) concentration from point source emission of one of India’s major coal-fed super thermal power plants at Kota, Rajasthan, India. AVIRIS-NG data with synchronous in situ measurements were collected as a part of the first ISRO-NASA joint airborne hyperspectral science campaign in India. The method utilized in our study includes theoretical simulations of at-sensor radiance in AVIRIS-NG spectral bands through the atmospheric radiative transfer model MODTRAN. Simulations were performed for the specific scene-sensor-atmospheric conditions pertaining to AVIRIS-NG overpass over Kota site. To eliminate the interfering effect of atmospheric water vapor with CO2 concentrations, simulations pertaining to variable water vapor values varying from 0.25 to 4.5 g / cm2 were also carried out, which in turn were utilized to normalize the water vapor effects. Based upon the simulation results pertaining to the specific absorption bands of CO2 in the shortwave infrared spectral range of AVIRIS-NG radiance data, a theoretical model was established between continuum interpolated band ratio (CIBR) and CO2 concentration. The CIBR − CO2 model coefficients for each of the water vapor subranges were then applied to AVIRIS-NG L1 radiance data to obtain the CO2 concentration map of the study area. A distinct CO2 plume could be detected from the coal-fed Kota Super Thermal Power Station with the CO2 concentration (XCO2) of the order of more than 500 ppmv near the power plant stacks. A range of XCO2 from 400 to 550 ppmv was observed within the scene. Our study has provided promising results in mapping the atmospheric CO2 content from the point source using the high-resolution airborne hyperspectral AVIRIS-NG data.

An analytical model of zenith radiance under different atmosphere and earth-surface conditions for JHKs bands

The application of star sensor navigation to aircraft and ships is a significant development direction. Due to the intense sky background radiation in the daytime, it is difficult for a visible-light camera to detect stars effectively in near-earth space, the development of a short-wavelength infrared (SWIR, especial for JHKs bands) detector provides the possibility for it. However, in the system design, simulation verification, and software integration of star sensor based on SWIR, a mass of sky background data of JHKs bands under different atmosphere and earth-surface conditions is needed as input. Although the existing atmospheric radiative transfer software can generate large amounts of discrete sky background data in some directions, it has a complicated operation, low efficiency, and time-consuming disadvantage, which is challenging to meet the real-time application in engineering. To use the sky background radiation data more efficiently and conveniently, an analytical model of zenith radiance for JHKs bands is urgent to build. Zenith radiance reference data are generated by libRadtran. Atmospheric absorption function and atmospheric scattering function are used to construct the analytical model of zenith radiance. The Levenberg-Marquardt (L-M) method is used to solve the parameters of the analytical model of zenith radiance. The Adaptive Moment Estimation (Adam) method is used to optimize the parameters of the analytical model of zenith radiance. The mean relative error between the zenith radiance's analytical function value and the zenith radiance's reference data is calculated to evaluate the accuracy of the constructed analytical model of zenith radiance. The results show that the mean relative errors are no greater than 2.54%, 2.15%, 2.26%; the root mean square errors are no greater than 1.0414, 0.2462, 0.0642; the correlation coefficients are no less than 0.9985, 0.9976, 0.9979 for all combination of cases corresponding to JHKs bands. It indicates that zenith radiance's analytical model constructed is exact and can provide a useful reference for the star sensor's demonstration design and application assessment based on SWIR technology.

SenSCOPE: Modeling mixed canopies combining green and brown senesced leaves. Evaluation in a Mediterranean Grassland

The coupling of radiative transfer, energy balance, and photosynthesis models has brought new opportunities to characterize vegetation functional properties from space. However, these models do not accurately represent processes in ecosystems characterized by mixtures of green vegetation and senescent plant material (SPM), in particular grasslands. These inaccuracies limit the retrieval of vegetation biophysical and functional properties. Green and senesced plants feature contrasting spectral properties and carry out different functions that current coupled models do not represent separately. Besides, senescent pigments' absorption features change as SPM decomposes, and neither is this process well parameterized in radiative transfer models. This manuscript aims at overcoming these limitations. On the one hand, we have developed senSCOPE, a version of the Soil-Canopy Observation of Photosynthesis and Energy fluxes (SCOPE) that separately represents light interaction and physiology of green and senesced leaves. On the other, we have characterized new specific absorption coefficients of senescent pigments (Ks) from optical measurements of SPM from a Mediterranean grassland. Sensitivity analyses revealed that compared to SCOPE, senSCOPE 1) predicts variables that respond more linearly to the faction of green leaf area; and 2) keeps high levels of absorbed photosynthetically active radiation in the green leaves, which leads to significant differences in leaf photosynthesis, non-photochemical quenching, and transpiration. Moreover, we compared SCOPE vs. senSCOPE's capability to provide estimates of functional and biophysical parameters of vegetation. We assimilated different combinations of reflectance factors (R), chlorophyll sun-induced fluorescence radiance in the O2-A band (F760), gross primary production (GPP), and thermal radiance (Lt) measured in a Mediterranean grassland. Besides, we compared the role of three different sets of Ks coefficients in the inversion of senSCOPE, two estimated from SPM. The performance of the inversions was assessed using field data and a pattern-oriented model evaluation approach. Unlike SCOPE, senSCOPE provided unbiased estimates of chlorophyll content (Cab) during the dry season. The use of SPM-specific Ks improved the representation of R in the near-infrared wavelengths; and, consequently, the estimation of leaf area index (LAI). Compared with field LAI, the coefficient of determination R2 increased from ~0.4 to ~0.6, depending on the inversion constraints. Compared with SCOPE, the new model and coefficients together reduced the root mean squared error between observed and modeled R (~40%), F760 (~30%), and GPP (~5%). Both models failed to represent Lt; in this case, senSCOPE featured larger uncertainties. The modeling approach we propose improves the simulation and retrieval of vegetation properties and function in grasslands. Further work is needed to test the applicability of senSCOPE in different ecosystems, improve the simulation of the thermal spectral domain, and better characterize the optical parameters of SPM. To do so, new databases of SPM optical and biophysical properties should be produced.

Improvement in tropospheric moisture retrievals from VIIRS through the use of infrared absorption bands constructed from VIIRS and CrIS data fusion

Abstract. An operational data product available for both the Suomi National Polar-orbiting Partnership (S-NPP) and National Oceanic and Atmospheric Administration-20 (NOAA-20) platforms provides high-spatial-resolution infrared (IR) absorption band radiances for Visible Infrared Imaging Radiometer Suite (VIIRS) based on a VIIRS and Crosstrack Infrared Sounder (CrIS) data fusion method. This study investigates the use of these IR radiances, centered at 4.5, 6.7, 7.3, 9.7, 13.3, 13.6, 13.9, and 14.2 µm, to construct atmospheric moisture products (e.g., total precipitable water and upper tropospheric humidity) and to evaluate their accuracy. Total precipitable water (TPW) and upper tropospheric humidity (UTH) retrieved from hyperspectral sounder CrIS measurements are provided at the associated VIIRS sensor's high spatial resolution (750 m) and are compared subsequently to collocated operational Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) and S-NPP VIIRS moisture products. This study suggests that the use of VIIRS IR absorption band radiances will provide continuity with Aqua MODIS moisture products.

The Cluster Low-Streams Regression Method for Fast Computations of Top-of-the-Atmosphere Radiances in Absorption Bands

Atmospheric composition sensors provide a huge amount of data. A key component of trace gas retrieval algorithms are radiative transfer models (RTMs), which are used to simulate the spectral radiances in the absorption bands. Accurate RTMs based on line-by-line techniques are time-consuming. In this paper we analyze the efficiency of the cluster low-streams regression (CLSR) technique to accelerate computations in the absorption bands. The idea of the CLRS method is to use the fast two-stream RTM model in conjunction with the line-by-line model and then to refine the results by constructing the regression model between two- and multi-stream RTMs. The CLSR method is applied to the Hartley-Huggins, O2 A-, water vapour and CO2 bands for the clear sky and several aerosol types. The median error of the CLSR method is below 0.001 %, the interquartile range (IQR) is below 0.1 %, while the performance enhancement is two orders of magnitude.

Green Band Generation for Advanced Baseline Imager Sensor Using Pix2Pix With Advanced Baseline Imager and Advanced Himawari Imager Observations

Green bands in satellite remote sensing play an important role in monitoring water and vegetation information. Due to the lack of observed green band, the Geostationary Operational Environmental Satellite (GOES-16) Advanced Baseline Imager (ABI) sensor uses a synthetic one. This study presents an ABI green band generation method using the Pix2Pix based on conditional generative adversarial networks (CGANs) and convolutional neural network techniques with data observed in the visible range of the GOES-16/ABI sensor. Our model was constructed from the radiance data sets in the red, blue, and green bands of the Advanced Himawari Imager (AHI) onboard Himawari-8/9 satellites from August 27, 2018 to May 1, 2019, and applied to generate a GOES-16 ABI green band using the ABI blue band radiance data. A comparison between the AHI and the Pix2Pix-generated AHI green bands displayed high accuracy, evaluated through bias = 0.120, root mean square error (RMSE) = 0.983 in digital number (DN) units, and correlation coefficient (CC) = 0.999. Furthermore, comparison between the Pix2Pix-generated and synthetic ABI green bands resulted in a good agreement (bias = 1.029 and RMSE = 2.892 in DN units, CC = 0.993). The statistical comparison between the green band, and red or blue band resulted in the exceptional performance of the Pix2Pix-generated ABI green band compared to the synthetic ABI green band. Consequently, our Pix2Pix-based model can be effectively used to generate nonexistent green band of ABI sensor and be applied in a variety of scientific applications requiring green band.

Bias Correction of the Ratio of Total Column CH4 to CO2 Retrieved from GOSAT Spectra

The proxy method, using the ratio of total column CH4 to CO2 to reduce the effects of common biases, has been used to retrieve column-averaged dry-air mole fraction of CH4 from satellite data. The present study characterizes the remaining scattering effects in the CH4/CO2 ratio component of the Greenhouse gases Observing SATellite (GOSAT) retrieval and uses them for bias correction. The variation of bias between the GOSAT and Total Carbon Column Observing Network (TCCON) ratio component with GOSAT data-derived variables was investigated. Then, it was revealed that the variability of the bias could be reduced by using four variables for the bias correction—namely, airmass, 2 μm band radiance normalized with its noise level, the ratio between the partial column-averaged dry-air mole fraction of CH4 for the lower atmosphere and that for the upper atmosphere, and the difference in surface albedo between the CH4 and CO2 bands. The ratio of partial column CH4 reduced the dependence of bias on the cloud fraction and the difference between hemispheres. In addition to the reduction of bias (from 0.43% to 0%), the precision (standard deviation of the difference between GOSAT and TCCON) was reduced from 0.61% to 0.55% by the correction. The bias and its temporal variation were reduced for each site: the mean and standard deviation of the mean bias for individual seasons were within 0.2% for most of the sites.

DART: Improvement of thermal infrared radiative transfer modelling for simulating top of atmosphere radiance

Land surface temperature (LST) is increasingly needed for studying the functioning of the Earth's surface at local to global scale. Radiative transfer (RT) models that simulate top of atmosphere (TOA) radiance are essential tools to derive accurate LST from thermal infrared (TIR) signals of Earth observation (EO) satellites. DART (Discrete Anisotropic Radiative Transfer) is one of the most accurate and comprehensive three-dimensional models that simulate RT in the Earth-atmosphere system. Up to version 5.7.3, the mean absolute error (MAE) of DART atmospheric TIR radiance of six standard atmospheres (USSTD76, TROPICAL, MIDDLATSUM, MIDDLATWIN, SUBARCSUM, SUBARCWIN) over 3.5 μm - 20 μm was 3.1 K compared to the reference atmospheric RT model MODTRAN, which is much larger than the 1 K accuracy needed by most LST applications. Also, the radiance error reached 2.6 K for some TIR bands whereas the noise equivalent differential temperature (NeDT) of satellite TIR sensor is usually less than 0.4 K. Recently, the DART atmospheric RT modelling was greatly improved by (1) introducing the equivalent absorption cross-section of five most absorbing gases (H2O, CO2, O3, CH4, N2O), and (2) implementing a double-layer thermal emission method. The MAE of DART atmospheric TIR radiance of six standard atmospheres and actual atmospheres over France and the Mediterranean Sea is now better than 1.0 K. The band radiance error is less than 0.2 K in the EO satellite TIR bands. DART is still accurate if the temperature profiles of standard atmospheres are offset by less than 6 K and if the viewing zenith angle is less than 50°. In short, the improved DART meets the requirements of both LST applications, and present and future TIR EO satellite missions. It is already available to scientists (https://dart.omp.eu).