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On-orbit absolute radiometric calibration is not only a prerequisite for the quantitative application of optical remote sensing satellite data but also a key step in ensuring the accuracy and reliability of satellite observation data. Due to the diversity of imaging conditions for optical remote sensing satellite sensors, on-orbit absolute radiometric calibration usually requires a large number of imaging tasks and manual labor to calibrate each imaging condition. This seriously limits the timeliness of on-orbit absolute radiometric calibration and is also an urgent problem to be solved in the context of the explosive growth of satellite numbers. Based on this, we propose a general on-orbit absolute radiometric calibration method compatible with multiple imaging conditions. Firstly, we use a large amount of laboratory radiometric calibration data to explore the mathematical relationship between imaging conditions (row transfer time, integration level and gain), radiance, and DN, and successfully build an imaging condition compatibility model. Secondly, we combine the imaging condition compatibility model with cross calibration to achieve a general on-orbit absolute radiometric calibration method. We use cross calibration to obtain the reference radiance and corresponding DN of the target satellites, which calculates the general coefficient by using row transfer time, integration level, and gain, and use the general coefficient to calibrate all imaging conditions. Finally, we use multiple imaging tasks of the JL1GF03D11 satellites to verify the effectiveness of the proposed method. The experiments show that the average relative difference was reduced to 2.79% and the RMSE was reduced to 1.51, compared with the laboratory radiometric calibration method. In addition, we also verify the generality of the proposed method by using 10 satellites of the Jilin-1 GF03D series. The experiment shows that the goodness of fit of the general coefficient is all greater than 95%, and the average relative difference between the reference radiance and the calibrated radiance of the proposed method is 2.46%, with an RMSE of 1.67. To sum up, by using the proposed method, all imaging conditions of optical remote sensing satellite sensor can be calibrated in one imaging task, which greatly improves the timeliness and accuracy of on-orbit absolute radiometric calibration. [ABSTRACT FROM AUTHOR]

The on-orbit radiometric calibration of spaceborne sensors is the foundation of quantitative remote sensing, and the calibration accuracy directly affects the quality of quantitative remote sensing products. The wide swath of the Chinese Ocean Color and Temperature Scanner (COCTS) onboard the HY-1C satellite makes it difficult to ignore errors caused by viewing angles during calibration. Thus, this article proposes a cross-calibration method based on the bottom-of-atmosphere (BOA) and top-of-atmosphere (TOA) spectral band adjustment factors (SBAFs). This method cross calibrates the COCTS sensor after correcting viewing geometry and radiometric differences. It is based on the Sentinel-3/ocean and land color instrument (OLCI) sensor. First, time-series data from the OLCI sensor were used to select calibration points in the Dunhuang area. Subsequently, the bidirectional reflectance distribution function (BRDF) for each corresponding spectral band of the OLCI sensor was constructed point by point. Next, the BOA and TOA SBAFs were calculated using the interpolated continuous spectra obtained from MOD09GA products. After correcting for the BRDF and SBAF, cross calibration between COCTS and OLCI was achieved. The results showed that the BOA SBAF model achieved higher precision calibration coefficients than the TOA SBAF model, with errors in each band below 5.76%. The proposed method can provide technical support for the cross calibration of wide-swath sensors.

SDGSAT-1, the inaugural Earth science satellite commissioned by the Chinese Academy of Sciences, successfully lifted off from China's Taiyuan Satellite Launch Center on 5 November 2021. Cross calibration has emerged as an indispensable methodology, providing an effective means for quality assessment, stability monitoring, and uncertainty analysis. This is achieved through the meticulous selection of an appropriate reference instrument. In our investigation, we conducted a comprehensive evaluation of the absolute radiometric calibration accuracy of the thermal infrared spectrometer (TIS), a pivotal component aboard SDGSAT-1. Our evaluation centered on a meticulous comparative analysis, where we juxtaposed the brightness temperature (BT) values recorded by TIS with the BT values derived from TIRS-2, computed following the spectral alignment with TIS. Our findings reveal that the current absolute BT bias for the B2 channel of TIS is less than 0.52 K. Meanwhile, the B3 channel exhibits a slightly larger fluctuation in absolute BT bias, yet remains within acceptable parameters, registering at less than 1 K. Notably, B3 emerges as the more dependable option for accurately gauging the temperature of the target region. Specifically, when the BT of B3 surpasses 290 K, the BT bias remains consistently below 0.3 K. In light of the approximately 40-min time difference in the water surface data utilized in our study, we conducted simulations to assess the channel BT of TIS under the scenario of a 0.25 °C water temperature variation. Our simulations conclusively demonstrate that the corresponding change in BT does not exceed 0.25 K.

Launched in November 2021, the sustainable development science satellite-1 carries the thermal infrared spectrometer (TIS) as a pivotal payload. Achieving precise radiometric calibration for geophysical parameter accuracy is crucial. Leveraging the frequent cross-calibration capability of the geostationary satellite FY-4A/advanced geostationary radiation imager (AGRI), this study addresses observational geometry challenges. The proposed radiance correction model for FY-4A/AGRI effectively reduces radiance discrepancies caused by differences in satellite zenith angles between TIS and AGRI. After integrating the corrected data into nadir-observed statistics, the long-term brightness temperature (BT) bias of TIS and AGRI was monitored. The results show that the daytime and nighttime BT bias exhibit similar trends, with the B2 band mainly showing negative bias. Overall, the BT bias for the B2 and B3 bands is within 1 K. The standard deviation of the BT bias for approximately 2250 sample points was calculated, revealing the values of 0.22 K and 0.40 K during the daytime, and 0.31 K and 0.57 K at night. The introduced model error remains below 0.31 K. Regarding the uncertainty of the BT bias introduced by spectral matching, when the spectral broadening is 1.15 times the wavelength interval, the uncertainty is less than 0.29 K for B2 and less than 0.38 K for B3. This work enhances our understanding of radiometric calibration challenges, providing insights into satellite-based geophysical parameter accuracy.

The very high resolution commercial satellite constellation of Maxar offers unique opportunities for a wide range of Earth science research and applications. The key to their widespread and effective use is stable and consistent calibration. In this article, we characterized the long-term calibration trends and cross-calibration coefficients for the four Maxar satellites (GeoEye-1, QuickBird-2, WorldView-2, and WorldView-3) using the Multi-Angle Implementation of Atmospheric Correction (MAIAC) processing technique. Utilizing MAIAC Moderate Resolution Imaging Spectroradiometer (MODIS) atmosphere and surface products, we calculated top-of-atmosphere (TOA) reflectance for the Blue, Green, Red, and near-infrared, or “NIR1” (NIR) bands over the Libya-4 desert site. To ensure data consistency, we applied geometric normalization to account for variations in TOA reflectance arising from different view geometries. In addition, a spatial transfer technique was employed to increase the number of samples and yield more robust statistical trend analysis. Our analysis revealed that half of the bands exhibited statistically significant calibration trends. These trends were found to be 2–3 times higher in magnitude compared with those observed in the early Collection 6 MODIS. After detrending, Maxar sensors were cross-calibrated to MODIS Aqua, considered as a calibration standard. In this process, German Aerospace Center (DLR) Earth Sensing Imaging Spectrometer hyperspectral measurements were used for spectral conversion required to align Maxar with MODIS bands. The cross-calibration analysis shows that GeoEye-1, WorldView-2, and WorldView-3 were systematically higher than MODIS Aqua by 2%–4% in the Blue, Green, and NIR bands, and by 7%–8% in the Red band. Detrending and cross-calibration to MODIS Aqua effectively transforms the Maxar constellation into a common sensor system enhancing spatiotemporal coverage and broadening the potential range of applications.

On-orbit absolute radiometric calibration is not only a prerequisite for the quantitative application of optical remote sensing satellite data but also a key step in ensuring the accuracy and reliability of satellite observation data. Due to the diversity of imaging conditions for optical remote sensing satellite sensors, on-orbit absolute radiometric calibration usually requires a large number of imaging tasks and manual labor to calibrate each imaging condition. This seriously limits the timeliness of on-orbit absolute radiometric calibration and is also an urgent problem to be solved in the context of the explosive growth of satellite numbers. Based on this, we propose a general on-orbit absolute radiometric calibration method compatible with multiple imaging conditions. Firstly, we use a large amount of laboratory radiometric calibration data to explore the mathematical relationship between imaging conditions (row transfer time, integration level and gain), radiance, and DN, and successfully build an imaging condition compatibility model. Secondly, we combine the imaging condition compatibility model with cross calibration to achieve a general on-orbit absolute radiometric calibration method. We use cross calibration to obtain the reference radiance and corresponding DN of the target satellites, which calculates the general coefficient by using row transfer time, integration level, and gain, and use the general coefficient to calibrate all imaging conditions. Finally, we use multiple imaging tasks of the JL1GF03D11 satellites to verify the effectiveness of the proposed method. The experiments show that the average relative difference was reduced to 2.79% and the RMSE was reduced to 1.51, compared with the laboratory radiometric calibration method. In addition, we also verify the generality of the proposed method by using 10 satellites of the Jilin-1 GF03D series. The experiment shows that the goodness of fit of the general coefficient is all greater than 95%, and the average relative difference between the reference radiance and the calibrated radiance of the proposed method is 2.46%, with an RMSE of 1.67. To sum up, by using the proposed method, all imaging conditions of optical remote sensing satellite sensor can be calibrated in one imaging task, which greatly improves the timeliness and accuracy of on-orbit absolute radiometric calibration.

To enhance the accuracy and stability of FY-4A/AGRI detection data, the MODIS, with highly accurate onboard calibration, is selected as the reference sensor for cross-radiation calibration calculations. The following are the data selection conditions: full considered time, observation geometries, field angles, cloud cover, etc. FY-4A/AGRI and Aqua-MODIS image data are selected as matching sample region locations, where the time difference between the observations for the same ground object is less than 15 min, the satellite zenith angle is less than 30°, and the field angle difference is less than 0.01. The 245 collected reflectance spectral curves are convolved with the spectral response functions of the two sensors, and the spectral band adjustment factors of the corresponding bands are calculated for spectral correction purposes. The cross-calibration coefficients for the red and near-infrared bands are calculated by linearly fitting the simulated top of the atmosphere reflectance values and digital number values from the AGRI sensor in a homogeneous area. In this paper, 16 cross-calibration calculations are performed on FY-4A/AGRI image data from August 2018 to September 2020, and the results are compared with the original calibration coefficients to test the feasibility of the proposed method. Additionally, 31 cross-calibration calculations are performed on image data from October 2020 to December 2022 to study the resulting AGRI sensor quality and performance changes. The NDVI of the FY-4A/AGRI image data was calculated before and after the cross-radiometric calibration using the maximum synthesis method. Additionally, the NDVI of the MODIS image data was compared and analyzed from three aspects: time, space, and the change trend. The results show that the spectral band adjustment factor calculated using the reflectance spectral curves of the ground objects in this paper can effectively correct for the spectral differences between the two sensors. Sixteen cross-calibration coefficients are less than 5.2% different from the original calibration coefficients, which fully proves the feasibility of the method used in this paper. All of the cross-calibration results show that the AGRI sensors have a certain degree of attenuation in the red and near-infrared bands, and the annual attenuation rates are approximately 1.37% and 2.55%, respectively. Cross-radiometric calibration has further improved the quality of the NDVI in FY-4A/AGRI imagery, enhancing the precision of its data application. [ABSTRACT FROM AUTHOR]

• The paper presents the electron fluxes measured by SATRAM on the Proba-V satellite in LEO. • A neural network has been apllied for particle identification and flux estimation at low fluxes. • A new simple, statistical method is provided to estimate fluxes at high fluxes. • The fluxes are compared with the fluxes measured by EPT on the Proba-V satellite. • Good agreement has been achieved.. We present the determination of electron fluxes measured by the Space Application of Timepix Radiation Monitor (SATRAM), a pixelated single-layer particle detector and the comparison with the Energetic Particle Telescope (EPT), a science-class radiation spectrometer. Both are attached to the Proba-V satellite of the European Space Agency. SATRAM hosts a Timepix chip with a 300 μ m thick silicon sensor divided into a 256 × 256 pixel matrix with 55 μ m pixel pitch. Simulations were conducted to determine the geometric factor of the sensor and the effective area that includes the shielding effects from all directions. The simulation was further used to study the influence of secondary particle production, track interruption, and backscattering on the number of detected particles. Particle identification is performed using two different methods. The first method requires that particle tracks are individually identifiable. A neural network was developed for this purpose, achieving an accuracy of 90.2% for particle identification. If individual particle tracks could not be identified, a statistical approach was used utilizing the energy deposition, average cluster energy, and the last known fraction of electrons. A comparison of the two instruments shows good agreement within one order of magnitude for the majority of the data. [ABSTRACT FROM AUTHOR]

The Polarization CrossFire (PCF) suite onboard the Chinese GaoFen-5(02) satellite has been sophisticatedly composed by the Particulate Observing Scanning Polarimeter (POSP) and the Directional Polarimetric Camera (DPC). Among them, DPC is a multi-angle sequential measurement polarization imager, while POSP is a cross-track scanning simultaneous polarimeter with corresponding radiometric and polarimetric calibrators, which can theoretically be used for cross comparison and calibration with DPC. After the data preprocessing of these two sensors, we first select local homogeneous cluster scenes by calculating the local variance-to-mean ratio in DPC's Level 1 product projection grids to reduce the influence of scale differences and geometry misalignment between DPC and POSP. Then, taking the observation results after POSP data quality assurance as the abscissa and taking the DPC observation results under the same wavelength band and geometric conditions as the same ordinate, a two-dimensional radiation/polarization feature space is established. Results show that the normalized top of the atmosphere (TOA) radiances of DPC and POSP processed data at the nadir are linearly correlated. The normalized TOA radiance root mean square errors (RMSEs) look reasonable in all common bands. The DPC and POSP normalized radiance ratios in different viewing zenith angle ranges at different times reveal the temporal drift of the DPC relative radiation response. The RMSEs, mean absolute errors (MAEs), relative errors (REs), and scatter percentage of DPC degree of linear polarization (DoLP) falling within the expected error (EE = ±0.02) of POSP measured DoLP are better than 0.012, 0.009, 0.066, and 91%, respectively. [ABSTRACT FROM AUTHOR]

To enhance the accuracy and stability of FY-4A/AGRI detection data, the MODIS, with highly accurate onboard calibration, is selected as the reference sensor for cross-radiation calibration calculations. The following are the data selection conditions: full considered time, observation geometries, field angles, cloud cover, etc. FY-4A/AGRI and Aqua-MODIS image data are selected as matching sample region locations, where the time difference between the observations for the same ground object is less than 15 min, the satellite zenith angle is less than 30°, and the field angle difference is less than 0.01. The 245 collected reflectance spectral curves are convolved with the spectral response functions of the two sensors, and the spectral band adjustment factors of the corresponding bands are calculated for spectral correction purposes. The cross-calibration coefficients for the red and near-infrared bands are calculated by linearly fitting the simulated top of the atmosphere reflectance values and digital number values from the AGRI sensor in a homogeneous area. In this paper, 16 cross-calibration calculations are performed on FY-4A/AGRI image data from August 2018 to September 2020, and the results are compared with the original calibration coefficients to test the feasibility of the proposed method. Additionally, 31 cross-calibration calculations are performed on image data from October 2020 to December 2022 to study the resulting AGRI sensor quality and performance changes. The NDVI of the FY-4A/AGRI image data was calculated before and after the cross-radiometric calibration using the maximum synthesis method. Additionally, the NDVI of the MODIS image data was compared and analyzed from three aspects: time, space, and the change trend. The results show that the spectral band adjustment factor calculated using the reflectance spectral curves of the ground objects in this paper can effectively correct for the spectral differences between the two sensors. Sixteen cross-calibration coefficients are less than 5.2% different from the original calibration coefficients, which fully proves the feasibility of the method used in this paper. All of the cross-calibration results show that the AGRI sensors have a certain degree of attenuation in the red and near-infrared bands, and the annual attenuation rates are approximately 1.37% and 2.55%, respectively. Cross-radiometric calibration has further improved the quality of the NDVI in FY-4A/AGRI imagery, enhancing the precision of its data application.

Synthetic aperture radar (SAR) radiometric calibration is an essential step in inferring meaningful physical information about the target. The majority of current calibration processes are directly based on artificial calibrators, but the number of calibrators is always restricted. Cross calibration, which has been widely used for optical and meteorological satellites, performs calibration by another calibrated satellite. Distributed targets are used as the calibration reference, allowing for frequent calibration. However, the potential of introducing cross calibration to SAR satellites has not been confirmed, and two issues must be resolved: 1) the number of calibration targets available for cross calibration is limited to a few known distributed targets; 2) the imaging parameters of calibrated and uncalibrated SARs are different, which may cause errors when cross calibration is applied. In this article, a method for selecting stable distributed targets using time-series stability analysis was presented first. The saline–alkali land and urban areas were selected as the calibration targets with low and high backscattering, respectively. Second, different stable pixel extraction methods were adopted based on the characteristics of different targets. Third, the Oh model was used to correct the scattering difference caused by the incidence angle difference, which greatly improved the accuracy of the backscattering coefficients of the calibration targets. The cross calibration experiments on the same series of satellite (Sentinel-1 A/B) data revealed that the accuracy of cross calibration was comparable to the accuracy of the artificial calibrator-based method, with a difference of less than 0.48 dB (1$\sigma$).

The Polarization CrossFire (PCF) suite onboard the Chinese GaoFen-5(02) satellite has been sophisticatedly composed by the Particulate Observing Scanning Polarimeter (POSP) and the Directional Polarimetric Camera (DPC). Among them, DPC is a multi-angle sequential measurement polarization imager, while POSP is a cross-track scanning simultaneous polarimeter with corresponding radiometric and polarimetric calibrators, which can theoretically be used for cross comparison and calibration with DPC. After the data preprocessing of these two sensors, we first select local homogeneous cluster scenes by calculating the local variance-to-mean ratio in DPC’s Level 1 product projection grids to reduce the influence of scale differences and geometry misalignment between DPC and POSP. Then, taking the observation results after POSP data quality assurance as the abscissa and taking the DPC observation results under the same wavelength band and geometric conditions as the same ordinate, a two-dimensional radiation/polarization feature space is established. Results show that the normalized top of the atmosphere (TOA) radiances of DPC and POSP processed data at the nadir are linearly correlated. The normalized TOA radiance root mean square errors (RMSEs) look reasonable in all common bands. The DPC and POSP normalized radiance ratios in different viewing zenith angle ranges at different times reveal the temporal drift of the DPC relative radiation response. The RMSEs, mean absolute errors (MAEs), relative errors (REs), and scatter percentage of DPC degree of linear polarization (DoLP) falling within the expected error (EE = ±0.02) of POSP measured DoLP are better than 0.012, 0.009, 0.066, and 91%, respectively.

Abstract The remote sensing techniques must be used to obtain long-term information in remote areas, like the Antarctic continent, to monitor the environmental productivity and its changes. The aim of this work was to analyze the surface reflectance profile patterns for the Antarctic biological soil crusts (algae, lichens, and mosses) in an area of Nelson Island (South Shetland Islands, maritime Antarctic), calculated from Landsat and Sentinel-2 images to identify its similarities and differences due to targets, sensors and acquired date. The surface reflectance values for Antarctic biological soil crusts are similar for those observed for biological soil crusts in other Earth extreme environments, like deserts. In Landsat images, the differences among biological soil crusts surface reflectance were identified at visible and near-infrared wavelengths and for Sentinel-2 images, the differences occur at visible, red-edge and shortwave infrared wavelengths, showing the feasibility of using surface reflectance products to identify these different crusts, despite its inherent pixel spectral mixture. Long-term biophysical parameters from such crusts as retrieved from orbital data is not possible due to very low cloud-free images over the Antarctic, which prevents building a consistent surface reflectance time-series which covers all biological soil crusts growth season.

Optical tracking systems, such as the Moiré Phase Tracking system (MPT), can be used to correct motion prospectively especially during magnetic resonance imaging (MRI, MR) in neurologic application. For that an MRI safe camera is mounted in the MRI bore to detect motion by tracking a specific MPT marker, which is rigidly attached on the subject’s head. To enable prospective updates of the imaging volume´s position and orientation, and therefore to correct motion from the subject, the motion information captured from the camera need to be transformed from the camera into the MR coordinate system. The process of finding the transformation between both coordinate systems is called cross calibration and is essential for the overall motion correction performance. For the procedure of the cross calibration, an MR visible phantom with an attached MPT-marker is measured simultaneously with MRI and the MPT camera in multiple specific alignment positions. To reduce cross calibration errors, it is essential to move the phantom precisely into specified alignment positions. Due to the long and narrow bore design of ultra-high field systems (tunnel length > 3 m), the phantom can not be moved simply from a person leaning inside the scanner bore. Thus, to rotate it after each measurement step, either a technician must work inside the tunnel during the complete period of the cross calibration or the table must be moved in and out of the bore multiple times. To improve this currently established cross calibration procedure, we have developed an MRI safe phantom rotation system, which can be controlled remotely and precisely from outside the MRI bore. Even for ultra-high field imaging, the rotation system is fully MRI compatible. Initial tests were performed at a 7T whole-body MRI system and have proven the benefit of our rotation system.

The National Oceanic and Atmospheric Administration (NOAA)-20 Visible Infrared Imaging Radiometer Suite (VIIRS) was launched on November 18, 2017, and it has been operational for more than two years and follows the first Joint Polar Satellite System (JPSS) series of the Suomi National Polar-orbiting Partnership (S-NPP) mission. VIIRS has 14 reflective solar bands (RSBs) covering a spectral range of 0.41– $2.3~\mu \text{m}$. The primary source of RSB calibration is the solar diffuser (SD), and the time-dependent SD degradation is monitored by the SD stability monitor (SDSM). The initial instability of the SD degradation (H-factor) was resolved by updating SDSM sun screen transmittance function combining yaw maneuver data and on-orbit SDSM data sets. After the H-factor improvements, the VIIRS RSB calibration coefficients (F-factors) are updated and applied to the operational Sensor Data Record (SDR) product generation. To validate the SD F-factors, the lunar F-factors are calculated by using a lunar irradiance model and comparing the trend differences between them. Over the two years of operation, decreasing trends have been calculated with the SD F-factors, whereas constant lunar F-factors were observed in bands M1–M4. With these discrepancies, the operational F-factors remained unchanged since April 2018 because the deep convective cloud (DCC) and cross-calibration comparison results did not show any further degradations in these bands. All the possible radiometric calibration sources, such as SD and lunar F-factors, DCC trends, and cross-calibration results, are monitored, compared, and applied by the NOAA VIIRS SDR science team for the best quality of the VIIRS SDR product. [ABSTRACT FROM AUTHOR]

The objective of this paper is to find an empirical hyperspectral absolute calibration model using Libya 4 pseudo invariant calibration site (PICS). The approach involves using the Landsat 8 (L8) Operational Land Imager (OLI) as the reference radiometer and using Earth Observing One (EO-1) Hyperion, with a spectral resolution of 10 nm as a hyperspectral source. This model utilizes data from a region of interest (ROI) in an “optimal region” of 3% temporal, spatial, and spectral stability within the Libya 4 PICS. It uses an improved, simple, empirical, hyperspectral Bidirectional Reflectance Distribution function (BRDF) model accounting for four angles: solar zenith and azimuth, and view zenith and azimuth angles. This model can perform absolute calibration in 1 nm spectral resolution by predicting TOA reflectance in all existing spectral bands of the sensors. The resultant model was validated with image data acquired from satellite sensors such as Landsat 7, Sentinel 2A, and Sentinel 2B, Terra MODIS, Aqua MODIS, from their launch date to 2020. These satellite sensors differ in terms of the width of their spectral bandpass, overpass time, off-nadir viewing capabilities, spatial resolution, and temporal revisit time, etc. The result demonstrates the efficacy of the proposed model has an accuracy of the order of 3% with a precision of about 3% for the nadir viewing sensors (with view zenith angle up to 5°) used in the study. For the off-nadir viewing satellites with view zenith angle up to 20°, it can have an estimated accuracy of 6% and precision of 4%.

This work describes a proposed radiometric cross calibration between the Landsat 8 Operational Land Imager (OLI) and Sentinel 2A Multispectral Instrument (MSI) sensors. The cross-calibration procedure involves (i) correction of the MSI data to account for spectral band differences with OLI and (ii) normalization of Bidirectional Reflectance Distribution Function (BRDF) effects in the data from both sensors using a new model accounting for the view zenith/azimuth angles in addition to the solar zenith/view angles. Following application of the spectral and BRDF normalization, standard least-squares linear regression is used to determine the cross-calibration gain and offset in each band. Uncertainties related to each step in the proposed process are determined, as is the overall uncertainty associated with the complete processing sequence. Validation of the proposed cross-calibration gains and offsets is performed on image data acquired over the Algodones Dunes site. The results of this work indicate that the blue band has the most significant offset, requiring use of the estimated cross-calibration offset in addition to the estimated gain. The highest difference was observed in the blue and red bands, which are 2.6% and 1.4%, respectively, while other bands shows no significant difference. Overall, the net uncertainty in the proposed process was estimated to be on the order of 6.76%, with the largest uncertainty component due to each sensor’s calibration uncertainty on the order of 5% and 3% for the MSI and OLI, respectively. Other significant contributions to the uncertainty include seasonal changes in solar zenith and azimuth angles, target site nonuniformity, variability in atmospheric water vapor, and/or aerosol concentration.

Chinese HY-1C/1D satellites will use a 5nm/10nm-resolutional visible-near infrared(VNIR) hyperspectral sensor with the solar calibrator to cross-calibrate with other sensors. The hyperspectral radiance data are composed of average radiance in the sensor's passbands and bear a spectral smoothing effect, a transform from the hyperspectral radiance data to the 1-nm-resolution apparent spectral radiance by spectral reconstruction need to be implemented. In order to solve the problem of noise cumulation and deterioration after several times of iteration by the iterative algorithm, a novel regression method based on SVM is proposed, which can approach arbitrary complex non-linear relationship closely and provide with better generalization capability by learning. In the opinion of system, the relationship between the apparent radiance and equivalent radiance is nonlinear mapping introduced by spectral response function(SRF),SVM transform the low-dimensional non-linear question into high-dimensional linear question though kernel function, obtaining global optimal solution by virtue of quadratic form. The experiment is performed using 6S-simulated spectrums considering the SRF and SNR of the hyperspectral sensor, measured reflectance spectrums of water body and different atmosphere conditions. The contrastive result shows: firstly, the proposed method is with more reconstructed accuracy especially to the high-frequency signal; secondly, while the spectral resolution of the hyperspectral sensor reduces, the proposed method performs better than the iterative method; finally, the root mean square relative error(RMSRE) which is used to evaluate the difference of the reconstructed spectrum and the real spectrum over the whole spectral range is calculated, it decreses by one time at least by proposed method. [ABSTRACT FROM AUTHOR]

The relationship between two reflectances of different bands is often encountered in cross calibration and parameter retrievals from remotely-sensed data. The asymmetric-order vegetation isoline is one such relationship, derived previously, where truncation error was reduced from the first-order approximated isoline by including a second-order term. This study introduces a technique for optimizing the magnitude of the second-order term and further improving the isoline equation’s accuracy while maintaining the simplicity of the derived formulation. A single constant factor was introduced into the formulation to adjust the second-order term. This factor was optimized by simulating canopy radiative transfer. Numerical experiments revealed that the errors in the optimized asymmetric isoline were reduced in magnitude to nearly 1/25 of the errors obtained from the first-order vegetation isoline equation, and to nearly one-fifth of the error obtained from the non-optimized asymmetric isoline equation. The errors in the optimized asymmetric isoline were compared with the magnitudes of the signal-to-noise ratio (SNR) estimates reported for four specific sensors aboard four Earth observation satellites. These results indicated that the error in the asymmetric isoline could be reduced to the level of the SNR by adjusting a single factor. [ABSTRACT FROM AUTHOR]

An analytical form of relationship between spectral vegetation indices (VI) is derived in the context of cross calibration and translation of vegetation index products from different sensors. The derivation has been carried out based on vegetation isoline equations that relate two reflectance values observed at different wavelength ranges often represented by spectral band passes. The derivation was first introduced and explained conceptually by assuming a general functional form for VI model equation. This process is universal by which two VIs of different sensors and/or different model equations can be related conceptually. The general process was then applied to the actual case of normalized difference vegetation index (NDVI) from two sensors in a framework of inter-sensor continuity. The derivation results indicate that the NDVI from one sensor can be approximated by a rational function of NDVI from the other sensor as a parameter. Similar result was obtained for the case of soil adjusted VI, enhanced VI, and two-band variance of enhanced VI.

The Moderate-Resolution Wide-Wavelength Imager (MWI), onboard the Tiangong-2 (TG-2) Space Lab, is an experimental satellite sensor designed for the next-generation Chinese ocean color satellites. The MWI imagery is not sufficiently radiometrically calibrated, and therefore, the cross-calibration is urgently needed to provide high quality ocean color products for MWI observations. We proposed a simple and effective cross-calibration scheme for MWI data using well calibrated Moderate Resolution Imaging Spectroradiometer (MODIS) imagery over aquatic environments. The path radiance of the MWI was estimated using the quasi-synchronized MODIS images as well as the MODIS Rayleigh and aerosol look up tables (LUTs) from SeaWiFS Data Analysis System 7.4 (SeaDAS 7.4). The results showed that the coefficients of determination (R2) of the calibration coefficients were larger than 0.97, with sufficient matched areas to perform cross-calibration for MWI. Compared with the simulated Top of Atmosphere (TOA) radiance using synchronized MODIS images, all errors calculated with the calibration coefficients retrieved in this paper were less than 5.2%, and lower than the lab calibrated coefficients. The Rayleigh-corrected reflectance (ρrc), remote sensing reflectance (Rrs) and total suspended matter (TSM) products of MWI, MODIS and the Geostationary Ocean Color Imager (GOCI) images for Taihu Lake in China were compared. The distribution of ρrc of MWI, MODIS and GOCI agreed well, except for band 667 nm of MODIS, which might have been saturated in relatively turbid waters. Besides, the Rrs used to retrieve TSM among MWI, MODIS and GOCI was also consistent. The root mean square errors (RMSE), mean biases (MB) and mean ratios (MR) between MWI Rrs and MODIS Rrs (or GOCI Rrs) were less than 0.20 sr−1, 5.52% and within 1 ± 0.023, respectively. In addition, the derived TSM from MWI and GOCI also agreed with a R2 of 0.90, MB of 13.75%, MR of 0.97 and RMSE of 9.43 mg/L. Cross-calibration coefficients retrieved in this paper will contribute to quantitative applications of MWI. This method can be extended easily to other similar ocean color satellite missions.

The microwave radiometer (MWR) on board the SAC-D/Aquarius mission is a Dicke radiometer operating at 23.8 (H-Pol) and 36.5 GHz (H/V-Pol), which can provide ancillary data for the various retrievals to be performed with Aquarius regarding ocean and land applications. In this study, we report calibration results obtained by a land cross-calibration between Windsat and MWR. Moreover, MWR drifts were monitored using vicarious cold methodology. Results were generated for the 2011–2012 period using version V5.0 S of MWR data. MWR and Windsat cross-calibration was carried out over selected homogeneous targets which include tropical and boreal forests, desert, grassland, and the Sahel. As a result, biases were identified and corrections were proposed. Drifts in MWR observations were identified by implementing the vicarious cold method, which is a statistical approach that estimates the coldest value of the brightness temperature (Tb) (over ocean) histogram. Time series of such cold values are closely related to drifts in the instrument. In general, it was observed that MWR drifts tend to stabilize within 1 K after June 2012, when the software of the on-board computer was updated. [ABSTRACT FROM PUBLISHER]

Since the launch of China’s first altimetry and scatterometry satellite, Haiyang-2A (HY-2A), various validation studies of HY-2A radar altimetry using preliminary data products have been conducted. Here, we present the first comprehensive result assessing HY-2A’s altimeter data quality and the altimetry system performance using an improved HY-2A Geophysical Data Record (GDR) product (Institute of Geodesy and Geophysics reprocessed GDR product version A, GDR_IGGA). The main improvements include altimeter timing and waveform retracking, and tropospheric, ionospheric, and sea-state bias (SSB) corrections, which resulted in more accurate HY-2A sea surface height observations. Jason-2 altimeter observations are used for the cross calibration of the HY-2A altimeter over the oceans between ±60° latitude bounds, primarily due to the limitation of Jason-2 coverage. The statistical results from single- and dual-satellite altimeter crossover analysis demonstrated that HY-2A fulfills its mission requirements. We uncovered a mean relative bias of −0.21 cm (with respect to Jason-2), and a standard deviation of 6.98 cm from dual-satellite crossover analysis. In addition, we present the results of a detailed analysis on variance reduction studies for the various geophysical corrections from HY-2A and compared them with Jason-2. The wave-number spectra of HY-2A and Jason-2 sea-level anomalies show similar spectral content, verifying the performance of HY-2A altimetry to be similar to Jason-2. Open issues and the remaining HY-2A data problems have been identified, allowing prospective future studies for further improvement of its accuracy. [ABSTRACT FROM AUTHOR]

WFV (Wide Field of View) cameras on-board Gaofen-1 satellite (gaofen means high resolution) provide unparalleled global observations with both high spatial and high temporal resolutions. However, the accuracy of the radiometric calibration remains unknown. Using an improved cross calibration method, the WFV cameras were re-calibrated with well-calibrated Landsat-8 OLI (Operational Land Imager) data as reference. An objective method was proposed to guarantee the homogeneity and sufficient dynamic coverage for calibration sites and reference sensors. The USGS spectral library was used to match the most appropriate hyperspectral data, based on which the spectral band differences between WFV and OLI were adjusted. The TOA (top-of-atmosphere) reflectance of the cross-calibrated WFV agreed very well with that of OLI, with the mean differences between the two sensors less than 5% for most of the reflectance ranges of the four spectral bands, after accounting for the spectral band difference between the two sensors. Given the calibration error of 3% for Landsat-8 OLI TOA reflectance, the uncertainty of the newly-calibrated WFV should be within 8%. The newly generated calibration coefficients established confidence when using Gaofen-1 WFV observations for their further quantitative applications, and the proposed simple cross calibration method here could be easily extended to other operational or planned satellite missions.

The accuracy of ocean surface wind vectors measured by satellite-borne scatterometers depends on measured backscattering coefficient (σ°). Hence, an in-flight calibration of a satellite scatterometer is essential as this is not guaranteed by its prelaunch absolute calibration. The postlaunch calibration of σ° is also required to monitor the time evolution of the accuracy of measured σ °. This is performed using relative calibration over land targets with minor spatiotemporal variation of σ°. A few such targets are the Amazon rainforest, Greenland, Antarctica, etc. In this paper, relative calibration of σ° from the OceanSAT-2 Scatterometer (OSCAT) has been carried out by comparing it with a similar quantity from the Quick Scatterometer (QuikSCAT) for November 2009. The differences between the average σ° of QuikSCAT and that of OSCAT have been calculated globally to check the overall consistency. Over the calibration sites, the differences are within ±0.25 dB. Histograms of differences in ascending/descending passes and fore/aft looks of OSCAT have also been analyzed over the calibration sites. These indicate that look bias in OSCAT σ° is within the range of ±0.5 dB. It is also evident that pass biases, i.e., differences between ascending and descending passes, exist over the Amazon rainforest for both QuikSCAT and OSCAT. This diurnal variation in σ° may go up to 1.25 dB in OSCAT. Further, computations of daily average and standard deviation over the calibration site show that mean OSCAT σ° is consistent with mean QuikSCAT σ°, whereas the standard deviation in OSCAT is marginally higher. Further, time-series analysis of OSCAT σ° shows its temporal stability. [ABSTRACT FROM PUBLISHER]