Radiometric Bias Research Articles

Understanding the Potential, Uncertainties, and Limitations of Spatiotemporal Fusion for Monitoring Chlorophyll a Concentration in Inland Eutrophic Lakes

The tradeoffs between the spatial and temporal resolutions for the remote sensing instruments limit their capacity to monitor the eutrophic status of inland lakes. Spatiotemporal fusion (STF) provides a cost-effective way to obtain remote sensing data with both high spatial and temporal resolutions by blending multisensor observations. However, remote sensing reflectance ( R rs ) over water surface with a relatively low signal-to-noise ratio is prone to be contaminated by large uncertainties in the fusion process. To present a comprehensive analysis on the influence of processing and modeling errors, we conducted an evaluation study to understand the potential, uncertainties, and limitations of using STF for monitoring chlorophyll a (Chla) concentration in an inland eutrophic water (Chaohu Lake, China). Specifically, comparative tests were conducted on the Sentinel-2 and Sentinel-3 image pairs. Three typical STF methods were selected for comparison, i.e., Fit-FC, spatial and temporal nonlocal filter-based fusion model, and the flexible spatiotemporal data fusion. The results show as follows: (a) among the influencing factors, atmospheric correction uncertainties and geometric misregistration have larger impacts on the fusion results, compared with radiometric bias between the imaging sensors and STF modeling errors; and (b) the machine-learning-based Chla inversion accuracy of the fusion data [ R 2 = 0.846 and root mean square error (RMSE) = 17.835 μg/l] is comparable with that of real Sentinel-2 data ( R 2 = 0.856 and RMSE = 16.601 μg/l), and temporally dense Chla results can be produced with the integrated Sentinel-2 and fusion image datasets. These findings will help to provide guidelines to design STF framework for monitoring aquatic environment of inland waters with remote sensing data.

A Monostatic Forward-Looking Staring Spotlight SAR Raw Data Generation and Hybrid-Domain Image Formation Modifications Based on Extended Azimuth Nonlinear Chirp Scaling Autofocusing

Forward-looking staring spotlight synthetic aperture radar (FSS-SAR) offers operational advantages over conventional squinted SAR counterparts. However, due to its complex observation configuration, pixel anomalies occur when processing raw data. For instance, in the forward-looking geometry, symmetrically distributed point scatterers along the flight path have the same Doppler history, resulting in azimuth ambiguities, whereas staring observation narrows and skews the 2D received spectrum, reducing signal orthogonality and causing spectral folding. As a result, not only is the Doppler frequency gradient too small to be precisely measured in the FSS-SAR mode, limiting the ability to achieve high azimuth angular resolution, but it also suffers from azimuth ambiguities when forming an image. To overcome such azimuth-variant characteristics known as inherent resolutional anomalies, a hybrid-domain image formation algorithm (IFA) based on azimuth scaling is modified here. As an approach, radiometric bias correction (RBC) techniques in conjunction with an extended azimuth nonlinear chirp scaling (ANCS) algorithm capable of equalizing range space variance in the azimuth direction, which benefits from an embedded autofocusing step, have been designed for raw data processing. Given the fact that the platform experiences trajectory deviations under a real-flight condition compared to an ideal trajectory, a pre-processing motion compensation (MOCO) step based on Kalman filtering is included within the IFA. The entire proposed IFA solution tackled the inherent resolutional anomalies of FSS-SAR and succeeded in reconstructing the focused image without azimuthal anomalies. To further assess the IFA, a modified Cartesian back-projection (BP) algorithm was developed along with other objective evaluation scenarios, all of which confirm that the proposed hybrid-domain extended ANCS algorithm is valid for the FSS-SAR application.

Validation of EMI-2 Radiometric Performance with TROPOMI over Dome C Site in Antarctica

(1) The Environmental Trace Gases Monitoring Instrument-2(EMI-2) is a high-quality spaceborne imaging spectrometer that launched in September 2021. To evaluate its radiometric calibration performance in-flight, the UV2 and VIS1 bands of EMI-2 were cross-calibrated by the corresponding bands (band3 and band4) of TROPOMI over the pseudo-invariant calibration site Dome C. (2) After angle limitation and cloud filtering of the Earth radiance data measured by EMI-2 and TROPOMI over Dome C, the top of atmosphere (TOA) reflectance time series were calculated. The spectral adjustment factors (SAF) were derived from the solar spectrum measured by the sensor to minimize the uncertainties caused by the different spectral response functions (SRF) of sensors. In addition, a correction method based on the radiative transfer model (RTM) SCIATRAN was used to suppress unaccounted angular dependence of atmospheric scattering. The radiation performance of EMI-2 is evaluated using the TOA reflectance ratio of EMI-2 and TROPOMI, combining the SAF correction and RTM-based correction methods. (3) It was shown that the time series trending of the TOA reflectance ratio between EMI-2 measurements and TROPOMI demonstrate flat characteristics and strong correlation. The mean reflectance ratios range from 0.998 to 1.09. The standard deviation of the reflection ratio is less than 3%. For 328 nm, 335 nm, 340 nm, 460 nm, and 490 nm, the mean values are close to one, and the relative radiometric bias estimated through EMI-2 and TROPOMI intercalibration is less than 3%, and for other wavelengths, the biases are less than 6%, except for 416 nm, which behaves higher than 7%. The cross-calibration results show that the radiometric calibration of EMI-2 is within the relative accuracy requirement.

Assessment and Correction of View Angle Dependent Radiometric Modulation due to Polarization for the Cross-Track Infrared Sounder (CrIS)

The Cross-track Infrared Sounder (CrIS) is an infrared Fourier-transform spectrometer that measures the Earth’s infrared radiance at high spectral resolution and high accuracy. The potential for polarization errors contributing significantly to the radiometric uncertainty of infrared remote sounders has been well recognized and documented, particularly due to polarization-dependent scene select mirrors operated in conjunction with grating-based instruments. The issue is equally applicable to FTS-based sensors. While the CrIS sensor utilizes an unprotected gold scene select mirror which has extremely low polarization in the infrared and the angle of incidence at the mirror is maintained for all calibration and Earth scene views, the radiometric bias due to polarization effects was determined to be non-negligible for cold scenes. A model for the polarization-induced calibration bias and the associated correction is presented for the CrIS instrument, along with details of the model parameter determination, and the impact of the correction on the calibrated radiances for a range of scene temperatures and types.

Building consistent time series night-time light data from average DMSP/OLS images for indicating human activities in a large-scale oceanic area

• Random Forest algorithm and a stepwise intercalibration approach were combined. • Multi-filter and bathymetric images were composited to improve intercalibration. • Noise impact on the intercalibration was greatly eliminated. • Consistent time series night-time light data specialized for large-scale oceanic area was build. • Total light index was implied as an effective indicator of ocean fishery activities. Human activities in the ocean have never been chronically and continuously investigated on a large scale. Night-time light (NTL) images collected by the Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) have been used as a proxy for monitoring the distribution and intensity of some human activities in the ocean from 1992 to 2013. However, systematic radiometric biases exist among the average visible-light DMSP/OLS NTL images (DMSP avg ) derived from different satellites. Moreover, the high randomness of fishing vessel locations and the large amount of noise impede the intercalibration of DMSP avg . To address these issues, this study has developed a method for generating a series of consistent NTL images from 1992 to 2013 for a large-scale oceanic area. A composite image was first constructed by combining the original DMSP avg , median, and standard deviation filter images derived from the DMSP avg , and a bathymetry image. Thereafter, Random Forest (RF) algorithm was employed to classify the composite image into effective and noisy pixels. Finally, a stepwise intercalibration method was adopted to reduce the systematic radiometric biases in the denoised images. The experimental results showed that RF had an overall accuracy of 96% and a Kappa coefficient of 0.775. Furthermore, the intercalibration was shown to significantly reduce the systematic radiometric biases owing to the noises being effectively discarded by the RF. Specifically, the Sum Normalized Different Index (SNDI) of the images intercalibrated by the proposed method can reach 0.61, which is 68.2% less than that of the original DMSP avg . In addition, the correlation coefficients between the intercalibrated DMSP avg and fishery catches in the exclusive economic zones (EEZs) of Japan and Malaysia can reach 0.949 and 0.901, respectively, which are higher than other values, such as the one intercalibrated using the Pseudo-Invariant Features (PIFs) method. In summary, the proposed method has been proven to be effective and feasible for generating consistent time-series NTL data for a large-scale oceanic area, and the derived Total Light Index (TLI) is an effective indicator of ocean fishery activities for ocean ecosystem research and related applications.

Evaluation of SNPP and NOAA-20 VIIRS Datasets Using RadCalNet and Landsat 8/OLI Data

In this study, we used RVUS data from RadCalNet as a benchmark to verify the radiometric accuracy and stability of operational and reprocessed SNPP/VIIRS data and the accuracy of NOAA-20/VIIRS data, as well as to assess the efficiency of the SNPP/VIIRS reprocessing algorithm. In addition, to remove the uncertainty of the RVUS site itself, we used Landsat 8/OLI as another benchmark with which to validate the accuracy and stability of VIIRS data through the RUVS site. The radiometric biases of the operational and reprocessed SNPP VIIRS bands were within ±4% and ±2%, respectively, as compared with the RUVS site and OLI, except for the M10 and M11 bands. In particular, the biases of the M5 and M7 bands were reduced by ~2% in this study. NOAA-20 VIIRS, on the other hand, was consistently lower than SNPP by ~2 to ~4% for all the bands. For the equivalent bands, the drift differences between operational and reprocessed SNPP/VIIRS and OLI were no larger than 0.24%/year and 0.1%/year, respectively. The reprocessing algorithm of SNPP VIIRS efficiently improved the radiometric accuracy and stability of the SNPP/VIIRS dataset to meet its specifications.

Characterization and Correction of Intersensor Calibration Convolution Errors Between S-NPP OMPS Nadir Mapper and Metop-B GOME-2

This article introduces a method to correct intersensor calibration convolution errors that occur in the convolution of spectral response functions (SRFs) between narrow-band and broad-band instruments. By using the intersensor calibration analysis between Ozone Mapping and Profiler Suite (OMPS) Nadir Mapper (NM) and Global Ozone Monitoring Experiment-2 (GOME-2) as an example, the root cause of convolution errors in the intersensor calibration is addressed through direct comparison of OMPS NM SRF and convolved OMPS SRF with GOME-2 SRF. The results reveal that distorted SRF of the narrow-band instrument is the major cause, which appears for GOME-2 at a wide range of channels. The convolution errors in reflectance, which were ignored in previous studies, can be greater than 2% for wavelength shorter than 320 nm and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sim 0.5$ </tex-math></inline-formula> % for wavelengths between 320 and 330 nm. This study thus presents a hybrid convolution error correction method that consists of theoretical approximation of the convolution errors and empirical estimates of residuals due to the deviation of the theoretical approximation from the actual convolution errors. According to the validation through simulation, after applying convolution error correction, the mean convolution errors are less than 0.02%, while the root mean square errors are reduced from more than 0.5% to less than 0.1%. In addition, the correction method is applied to the intersensor calibration radiometric bias assessment between the Meteorological Operational satellite–B (Metop-B) GOME-2 and the Suomi National Polar-orbiting Partnership (S-NPP) OMPS NM. The averaged intersensor calibration reflectance differences are decreased by more than 16% after convolution error correction.

A New 32-Day Average-Difference Method for Calculating Inter-Sensor Calibration Radiometric Biases between SNPP and NOAA-20 Instruments within ICVS Framework

Two existing double-difference (DD) methods, using either a 3rdSensor or Radiative Transfer Modeling (RTM) as a transfer, are applicable primarily for limited regions and channels, and, thus critical in capturing inter-sensor calibration radiometric bias features. A supplementary method is also desirable for estimating inter-sensor calibration biases at the window and lower sounding channels where the DD methods have non-negligible errors. In this study, using the Suomi National Polar-orbiting Partnership (SNPP) and Joint Polar Satellite System (JPSS)-1 (alias NOAA-20) as an example, we present a new inter-sensor bias statistical method by calculating 32-day averaged differences (32D-AD) of radiometric measurements between the same instrument onboard two satellites. In the new method, a quality control (QC) scheme using one-sigma (for radiance difference), or two-sigma (for radiance) thresholds are established to remove outliers that are significantly affected by diurnal biases within the 32-day temporal coverage. The performance of the method is assessed by applying it to estimate inter-sensor calibration radiometric biases for four instruments onboard SNPP and NOAA-20, i.e., Advanced Technology Microwave Sounder (ATMS), Cross-track Infrared Sounder (CrIS), Nadir Profiler (NP) within the Ozone Mapping and Profiler Suite (OMPS), and Visible Infrared Imaging Radiometer Suite (VIIRS). Our analyses indicate that the globally-averaged inter-sensor differences using the 32D-AD method agree with those using the existing DD methods for available channels, with margins partially due to remaining diurnal errors. In addition, the new method shows its capability in assessing zonal mean features of inter-sensor calibration biases at upper sounding channels. It also detects the solar intrusion anomaly occurring on NOAA-20 OMPS NP at wavelengths below 300 nm over the Northern Hemisphere. Currently, the new method is being operationally adopted to monitor the long-term trends of (globally-averaged) inter-sensor calibration radiometric biases at all channels for the above sensors in the Integrated Calibration/Validation System (ICVS). It is valuable in demonstrating the quality consistencies of the SDR data at the four instruments between SNPP and NOAA-20 in long-term statistics. The methodology is also applicable for other POES cross-sensor calibration bias assessments with minor changes.

Reprocessing of Suomi NPP CrIS Sensor Data Records to Improve the Radiometric and Spectral Long-Term Accuracy and Stability

Since early 2012, the cross-track infrared sounder (CrIS) on board the Suomi National Polar-orbiting Partnership (S-NPP) satellite has continually provided the hyperspectral infrared observations for profiling atmospheric temperature, moisture, and greenhouse gases. In this study, the CrIS sensor data record (SDR) data are improved for climate applications with its fine-tuning of calibration coefficients in an NOAA reprocessing project. A specific software system was developed to reprocess the CrIS SDR. This software system was updated with a new calibration algorithm, nonlinearity, and geolocation to improve the SDR data quality and long-term consistency. The calibration coefficients are refined with the latest updates, which were used to calibrate the latest operational SDR products and replace those in the engineering packet (EP) in the raw data record (RDR) data stream. The resampling wavelength was updated based on the metrology laser wavelength and resulted in zero sampling error in the spectral calibration. All the historical SDRs (from February 2012 to March 2017) were generated with the same calibration coefficients and same version of the processing software system, resulting in improved accuracy and stability in terms of spectral and radiometric calibration during the CrIS lifetime mission. The quality of the reprocessed CrIS SDR data at nominal spectral resolution (NSR) is assessed in terms of its radiometric and spectral calibration. Comparisons against the operational SDR data are carried out to demonstrate the improved long-term stability of the reprocessed CrIS SDR data. Overall radiometric biases are found to be small and highly stable over the instrument mission, the FOV-to-FOV differences are less than ~10 mK, and much better than that from the operational SDR data. It is shown that the CrIS metrology laser wavelength varies within 4 ppm as measured by the neon calibration system. The reprocessed SDR data have spectral errors less than 0.5 ppm, which is much better than the operational SDR data with about 4 ppm. This baseline version of the reprocessed SNPP CrIS SDR data is suitable for long-term climate monitoring and model assessments and can provide an infrared reference observation to assess other narrow- or broadband infrared instruments’ calibration accuracy.

Validation of GOES-16 ABI VNIR channel radiometric performance with NPP and NOAA-20 VIIRS over the Sonoran Desert

The advanced baseline imager (ABI) onboard Geostationary Operational Environmental Satellites-16 (GOES-16) provides high-quality visible and near-infrared (VNIR) imagery data. Radiometric performance of the GOES-16 ABI multiple VNIR bands (B1, B2, B3, B5, and B6) is evaluated over the Sonoran Desert by comparing measurements with Suomi National Polar-Orbiting Partnership (S-NPP) and NOAA-20 visible infrared imaging radiometer suite equivalent bands M3, M5, M7, M10, and M11, respectively. In order to minimize the uncertainties due to the difference in spectral response functions of similar bands from the different sensors, spectral band adjustment factor (SBAF) derived from Hyperion data over Sonoran Desert is used. The large viewing angle at the Sonoran Desert by ABI (56.34 deg) and a lack of comprehensive BRDF model at such viewing geometry are the main challenges for the comparison. Two schemes to address such challenges were developed. A data-derived (DD) method, based on confining the matched solar-zenith angle and view-zenith angle between ABI and VIIRS, interpolation in solar-zenithal and relative-azimuthal angles is developed to address this issue. It is shown that there is some residual bias for ABI B1 and its equivalent VIIRS M3 band, which is mainly due to unaccounted angular dependence of atmospheric scattering in this DD method. To address this issue, a radiative transfer modeling (RTM)-based method to account for atmospheric effects is developed to facilitate the comparison. The time series trending and mean bias of reflectance ratio between ABI and VIIRS measurements over the Sonoran Desert after SBAF and bidirectional reflectance distribution function corrections are derived to evaluate the radiometric performance of ABI with respect to net primary productivity and NOAA-20 VIIRS. The analysis shows that the radiometric biases of the five VNIR channels of GOES-16 ABI are all within 5% in comparison to the matched channels of NPP VIIRS after applying the RTM correction. The analysis also detects ∼6 % drop in the radiometric bias of GOES-16 ABI 0.64 μm channel after April 23, 2019, which can be traced to the implementation of a correction of the ABI B2 calibration coefficient around this date. Further, we evaluate the relative radiometric bias for the five VNIR channels between NPP and NOAA-20 VIIIRS using double difference method, and the comparison shows that NPP VIIRS has 2% to 3% higher bias than NOAA-20 VIIRS for these spectral bands.

Development of a Machine Learning-Based Radiometric Bias Correction for NOAA’s Microwave Integrated Retrieval System (MiRS)

We present the development of a dynamic over-ocean radiometric bias correction for the Microwave Integrated Retrieval System (MiRS) which accounts for spatial, temporal, spectral, and angular dependence of the systematic differences between observed and forward model-simulated radiances. The dynamic bias correction, which utilizes a deep neural network approach, is designed to incorporate dependence on the atmospheric and surface conditions that impact forward model biases. The approach utilizes collocations of observed Suomi National Polar-orbiting Partnership/Advanced Technology Microwave Sounder (SNPP/ATMS) radiances and European Centre for Medium-Range Weather Forecasts (ECMWF) model analyses which are used as input to the Community Radiative Transfer Model (CRTM) forward model to develop training data of radiometric biases. Analysis of the neural network performance indicates that in many channels, the dynamic bias is able to reproduce realistically both the spatial patterns of the original bias and its probability distribution function. Furthermore, retrieval impact experiments on independent data show that, compared with the baseline static bias correction, using the dynamic bias correction can improve temperature and water vapor profile retrievals, particularly in regions with higher Cloud Liquid Water (CLW) amounts. Ocean surface emissivity retrievals are also improved, for example at 23.8 GHz, showing an increase in correlation from 0.59 to 0.67 and a reduction of standard deviation from 0.035 to 0.026.

OLCI A/B Tandem Phase Analysis, Part 3: Post-Tandem Monitoring of Cross-Calibration from Statistics of Deep Convective Clouds Observations

This study is a follow-up of a full methodology for the homogenisation and harmonisation of the two Ocean and Land Colour Instrument (OLCI) payloads based on the OLCI-A/OLCI-B tandem phase analysis. This analysis provided cross-calibration factors between the two instruments with a very high precision, providing a ‘truth’ from the direct comparison of simultaneous and collocated acquisitions. The long-term monitoring of such cross-calibration is a prerequisite for an operational application of sensors harmonisation along the mission lifetime, no other tandem phase between OLCI-A and OLCI-B being foreseen due to the cost of such operation. This article presents a novel approach for the monitoring of the OLCI radiometry based on statistics of Deep Convective Clouds (DCC) observations, especially dedicated to accurately monitor the full across-track dependency of the cross-calibration of OLCI-A and OLCI-B. Specifically, the inflexion point of DCC reflectance distributions is used as an indicator of the absolute calibration for each subdivision of the OLCI Field-of-View. This inflexion point is shown to provide better precision than the mode of the distributions which is commonly used in the community. Excess of saturation in OLCI-A high radiances is handled through the analysis of interband relationships between impacted channels and reference channels that are not impacted by saturation. Such analysis also provides efficient insights on the variability of the target’s response as well as on the evolution of the interband calibration of each payload. First, cross-calibration factors obtained over the tandem period allows to develop and validate the approach, notably for the handling of the saturated pixels, based on the comparison with the ‘truth’ obtained from the tandem analysis. Factors obtained out of (and far from) the tandem period then provides evidence that the cross-calibration reported over the tandem period (1–2% bias between the instruments) as well as inter-camera calibration residuals persist with very similar proportions, to the exception of the 400 nm channel and with slightly less precision for the 1020 nm channel. For all OLCI channels, relative differences between the cross-calibration factors obtained from the tandem analysis and the factors obtained over the other period are below 1% from a monthly analysis, even below 0.5% from a multi-monthly analysis). This opens the way not only to an accurate long-term monitoring of the OLCI radiometry but also, and precisely targeted for this study, to the monitoring of the cross-calibration of the two sensors over the mission lifetime. It also provides complementary information to the tandem analysis as the calibration indicators are traced individually for each sensor across-track, confirming and quantifying inter-camera radiometric biases, independently for both sensors. Assumptions used in this study are discussed and validated, also providing a framework for the adaptation of the presented methodology to other optical sensors.

OLCI A/B Tandem Phase Analysis, Part 1: Level 1 Homogenisation and Harmonisation

Copernicus is a European system for monitoring the Earth in support of European policy. It includes the Sentinel-3 satellite mission which provides reliable and up-to-date measurements of the ocean, atmosphere, cryosphere, and land. To fulfil mission requirements, two Sentinel-3 satellites are required on-orbit at the same time to meet revisit and coverage requirements in support of Copernicus Services. The inter-unit consistency is critical for the mission as more S3 platforms are planned in the future. A few weeks after its launch in April 2018, the Sentinel-3B satellite was manoeuvred into a tandem configuration with its operational twin Sentinel-3A already in orbit. Both satellites were flown only thirty seconds apart on the same orbit ground track to optimise cross-comparisons. This tandem phase lasted from early June to mid October 2018 and was followed by a short drift phase during which the Sentinel-3B satellite was progressively moved to a specific orbit phasing of 140° separation from the sentinel-3A satellite. In this paper, an output of the European Space Agency (ESA) Sentinel-3 Tandem for Climate study (S3TC), we provide a full methodology for the homogenisation and harmonisation of the two Ocean and Land Colour Instruments (OLCI) based on the tandem phase. Homogenisation adjusts for unavoidable slight spatial and spectral differences between the two sensors and provide a basis for the comparison of the radiometry. Persistent radiometric biases of 1–2% across the OLCI spectrum are found with very high confidence. Harmonisation then consists of adjusting one instrument on the other based on these findings. Validation of the approach shows that such harmonisation then procures an excellent radiometric alignment. Performed on L1 calibrated radiances, the benefits of harmonisation are fully appreciated on Level 2 products as reported in a companion paper. Whereas our methodology aligns one sensor to behave radiometrically as the other, discussions consider the choice of the reference to be used within the operational framework. Further exploitation of the measurements indeed provides evidence of the need to perform flat-fielding on both payloads, prior to any harmonisation. Such flat-fielding notably removes inter-camera differences in the harmonisation coefficients. We conclude on the extreme usefulness of performing a tandem phase for the OLCI mission continuity as well as for any optical mission to which the methodology presented in this paper applies (e.g., Sentinel-2). To maintain the climate record, it is highly recommended that the future Sentinel-3C and Sentinel-3D satellites perform tandem flights when injected into the Sentinel-3 time series.

A Deep Learning Trained Clear-Sky Mask Algorithm for VIIRS Radiometric Bias Assessment

Clear-sky mask (CSM) is a crucial influence on the calculating accuracy of the sensor radiometric biases for spectral bands of visible, infrared, and microwave regions. In this study, a fully connected deep neural network (FCDN) was proposed to generate CSM for the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard Suomi National Polar-Orbiting Partnership (S-NPP) and NOAA-20 satellites. The model, well-trained by S-NPP data, was used to generate both S-NPP and NOAA-20 CSMs for the independent data, and the results were validated against the biases between the sensor observations and Community Radiative Transfer Model (CRTM) calculations (O-M). The preliminary result shows that the FCDN-CSM model works well for identifying clear-sky pixels. Both O-M mean biases and standard deviations were comparable with the Advance Clear-Sky Processor over Ocean (ACSPO) and were significantly better than a prototype cloud mask (PCM) and the case without a clear-sky check. In addition, by replacing CRTM brightness temperatures (BTs) with the atmosphere air temperature and water vapor contents as input features, the FCDN-CSM exhibits its potential to generate fast and accurate VIIRS CSM onboard follow-up Joint Polar Satellite System (JPSS) satellites for sensor calibration and validation before the physics-based CSM is available.

The Effects of VIIRS Detector-Level and Band-Averaged Relative Spectral Response Differences Between S-NPP and NOAA-20 on the Thermal Emissive Bands

The Joint Polar Satellite System-1 (renamed NOAA-20 after reaching the polar orbit) was successfully launched on November 18, 2017, into an afternoon orbit with a local equator crossing time of ∼1:30 p.m. , in the same orbital plane as the Suomi National Polar-orbiting Partnership (S-NPP) but with a time separation of 50 min. The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) will become the primary operational imager succeeding the VIIRS onboard S-NPP, which has been in orbit for more than six years. Although the VIIRS onboard S-NPP and NOAA-20 have identical designs, there are small differences in the relative spectral response (RSR) in most bands. Previous studies have shown that minor differences in the S-NPP RSRs at thermal emissive bands (TEBs) can lead to several effects at the detector level, such as striping in sensor data record (SDR) products. Such differences may explain the striping pattern found in S-NPP VIIRS sea surface temperature (SST) products. This article analyzes the detector-level and operational band-averaged RSRs for NOAA-20 VIIRS TEBs and examines the radiometric response to them using the line-by-line radiative transfer model at a very high spectral resolution for convolving with the RSRs. We also evaluate the impact of RSR differences between S-NPP and NOAA-20 for radiometric biases and potential striping in VIIRS TEB SDR brightness temperature. This article will contribute toward measurement consistency for long-term observations in the thermal infrared bands and ensure the quality of retrieval data produced by VIIRS, such as SST, fire, and other retrievals.

NOAA-20 VIIRS polarization effect and its correction.

The follow-on Visible Infrared Imaging Radiometer Suite (VIIRS) housed in the NOAA-20 satellite was launched on 18 November 2017. It has 22 spectral bands, among which 14 are reflective solar bands (RSBs) covering the wavelength range from 411 to 2258nm. Prelaunch polarization sensitivity measurements have revealed that NOAA-20 VIIRS RSBs are much more sensitive to polarization of the incident light than its predecessor, the VIIRS on the Suomi National Polar-orbiting Partnership. For the short wavelength bands, i.e., M1-M4, the polarization sensitivities are out of specifications, especially for band M1, for which the polarization factors can be as large as ∼6%. The polarization effect induces striping in imagery along the track and radiometric bias both along the scan and along the track, resulting in much larger uncertainties in the environmental data records (EDR). In this paper, the polarization effect correction algorithms are described and applied to the NOAA-20 VIIRS RSBs for ocean scenes where the top-of-atmosphere radiance can be separated into the ocean normalized water-leaving radiance, the basis of the ocean color EDR, and the sunlight reflected by the atmosphere, which can be mostly described by the Rayleigh scattering radiance. The errors of the sensor data records (SDR or Level-1B radiance) due to the polarization effect can be as large as ∼1% for bands M1 and M2, and those in the ocean normalized water-leaving radiances are about 13% and 10% for wavelengths at 411nm (band M1) and 445nm (band M2), respectively. The polarization effect also induces strong striping in both NOAA-20 VIIRS RSB SDR and normalized water-leaving radiances. It is demonstrated that with the polarization correction applied, the aforementioned errors and artifacts are successfully removed.

Recalibration of over 35 Years of Infrared and Water Vapor Channel Radiances of the JMA Geostationary Satellites

Infrared sounding measurements of the Infrared Atmospheric Sounding Interferometer (IASI), Atmospheric Infrared Sounder (AIRS), and High-resolution Infrared Radiation Sounder/2 (HIRS/2) instruments are used to recalibrate infrared (IR; ~11 µm) channels and water vapor (WV; ~6 µm) channels of the Visible and Infrared Spin Scan Radiometer (VISSR), Japanese Advanced Meteorological Imager (JAMI), and IMAGER instruments onboard the historical geostationary satellites of the Japan Meteorological Agency (JMA). The recalibration was performed using a common recalibration method developed by European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), which can be applied to the historical geostationary satellites to produce Fundamental Climate Data Records (FCDR). Pseudo geostationary imager radiances were computed from the infrared sounding measurements and regressed against the radiances from the geostationary satellites. Recalibration factors were computed from these pseudo imager radiance pairs. This paper presents and evaluates the result of recalibration of longtime-series of IR (1978–2016) and WV (1995–2016) measurements from JMA’s historical geostationary satellites. For the IR data of the earlier satellites (Geostationary Metrological Satellite (GMS) to GMS-4) significant seasonal variations in radiometric biases were observed. This suggests that the sensors on GMS to GMS-4 were strongly affected by seasonal variations in solar illumination. The amplitudes of these seasonal variations range from 3 K for the earlier satellites to &lt;0.4 K for the recent satellites (GMS-5, Geostationary Operational Environmental Satellite-9 (GOES-9), Multi-functional Transport Satellite-1R (MTSAT-1R) and MTSAT-2). For the WV data of GOES-9, MTSAT-1R and MTSAT-2, no seasonal variations in radiometric biases were observed. However, for GMS-5, the amplitude of seasonal variation in bias was about 0.5 K. Overall, the magnitude of the biases for GMS-5, MTSAT-1R and MTSAT-2 were smaller than 0.3 K. Finally, our analysis confirms the existence of errors due to atmospheric absorption contamination in the operational Spectral Response Function (SRF) of the WV channel of GMS-5. The method used in this study is based on the principles developed within Global Space-based Inter-calibration System (GSICS). Moreover, presented results contribute to the Inter-calibration of imager observations from time-series of geostationary satellites (IOGEO) project under the umbrella of the World Meteorological Organization (WMO) initiative Sustained and Coordinated Processing of Environmental Satellite data for Climate Monitoring (SCOPE-CM).

Crosstalk Effect and Its Mitigation in Aqua MODIS Middle‐Wave Infrared Bands

AbstractThe MODerate‐resolution Imaging Spectroradiometer (MODIS) has 36 bands, among which 6, bands 20–25, are middle‐wave infrared (MWIR) bands, covering a wavelength range from approximately 3.750 to 4.515 μm. Significant crosstalk effect in Aqua MODIS MWIR bands was found prelaunch and confirmed on‐orbit early in the mission. In this paper, the crosstalk effect in these bands is carefully investigated and quantitatively characterized using the instrument's scheduled lunar observations. The crosstalk correction algorithm that we have developed in our previous work is applied to all levels including black body calibration and L1B EV retrievals to mitigate the crosstalk effect in the bands. We show significant crosstalk contaminations among all MWIR bands and also show that these bands have significant crosstalk contaminations from the shortwave infrared bands. We demonstrate that the crosstalk effect has significant impact on onboard black body calibration as well as on EV retrievals. For L1B EV retrievals, these crosstalk effects induce significant radiometric bias as well as strong striping. The radiometric bias is larger than 1.8 K for all detectors of the 4.47‐μm band (band 24) for typical scene. Aqua MODIS band 24 detector 1 is most strongly impacted by crosstalk and has a bias that may be as large as 10 K. The crosstalk correction successfully mitigates the striping in the EV images and improves the accuracy of the EV retrievals in the MWIR bands.

Aqua MODIS Electronic Crosstalk Survey: Mid-Wave Infrared Bands

The Moderate Resolution Imaging Spectroradiometer (MODIS) on board NASA’s Aqua polar-orbiting satellite has long been known to be subjected to electronic crosstalk. Its signatures are clearly visible in images of the lunar disk by various bands, which are routinely obtained during scheduled roll maneuvers. Electronic crosstalk can potentially impact Level 1B products, causing image artifacts such as striping and radiometric bias. However, a comprehensive effort in mapping the sending/receiving bands and detectors, deriving the crosstalk coefficients, and assessing the impact of the crosstalk contamination on the Level 1B product for Aqua’s mid-wave IR bands (3.75– 4.52 $\mu \text{m}$ ) had been lacking. In this work, we surveyed lunar images by the Aqua MODIS bands that are connected to electronic output 2 of the SWIR/MWIR FPA, identifying all detectors affected by electronic crosstalk contamination and determined which bands and detectors are sending the contaminating signal. By assuming a linear model to describe the contamination, we derived linear crosstalk coefficients for the bands/detectors concerned from lunar images and used these to generate corrected Earth Level 1B images for the mid-wave IR bands and assessed the impact of the electronic crosstalk on the Level 1B imagery.

Consideration of Radiometric Quantization Error in Satellite Sensor Cross-Calibration

The radiometric resolution of a satellite sensor refers to the smallest increment in the spectral radiance that can be detected by the imaging sensor. The fewer bits that are used for signal discretization, the larger the quantization error in the measured radiance. In satellite inter-calibration, a difference in radiometric resolution between a reference and a target sensor can induce a calibration bias, if not properly accounted for. The effect is greater for satellites with a quadratic count response, such as the Geostationary Meteorological Satellite-5 (GMS-5) visible imager, where the quantization difference can introduce non-linearity in the inter-comparison datasets, thereby affecting the cross-calibration slope and offset. This paper describes a simulation approach to highlight the importance of considering the radiometric quantization in cross-calibration and presents a correction method for mitigating its impact. The method, when applied to the cross-calibration of GMS-5 and Terra Moderate Resolution Imaging Spectroradiometer (MODIS) sensors, improved the absolute calibration accuracy of the GMS-5 imager. This was validated via radiometric inter-comparison of GMS-5 and Multifunction Transport Satellite-2 (MTSAT-2) imager top-of-atmosphere (TOA) measurements over deep convective clouds (DCC) and Badain Desert invariant targets. The radiometric bias between GMS-5 and MTSAT-2 was reduced from 1.9% to 0.5% for DCC, and from 7.7% to 2.3% for Badain using the proposed correction method.