Cross-calibration Method Research Articles

Radiometric Cross-Calibration of GF-4 PMS Sensor Based on Assimilation of Landsat-8 OLI Images

Earth observation data obtained from remote sensors must undergo radiometric calibration before use in quantitative applications. However, the large view angles of the panchromatic multispectral sensor (PMS) aboard the GF-4 satellite pose challenges for cross-calibration due to the effects of atmospheric radiation transfer and the bidirectional reflectance distribution function (BRDF). To address this problem, this paper introduces a novel cross-calibration method based on data assimilation considering cross-calibration as an optimal approximation problem. The GF-4 PMS was cross-calibrated with the well-calibrated Landsat-8 Operational Land Imager (OLI) as the reference sensor. In order to correct unequal bidirectional reflection effects, an adjustment factor for the BRDF was established, making complex models unnecessary. The proposed method employed the Shuffled Complex Evolution-University of Arizona (SCE-UA) algorithm to find the optimal calibration coefficients and BRDF adjustment factor through an iterative process. The validation results revealed a surface reflectance error of <5% for the new cross-calibration coefficients. The accuracy of calibration coefficients were significantly improved when compared to the officially published coefficients as well as those derived using conventional methods. The uncertainty produced by the proposed method was less than 7%, meeting the demands for future quantitative applications and research. This method is also applicable to other sensors with large view angles.

Radiometric cross Calibration of Gaofen-1 WFV Cameras Using Landsat-8 OLI Images: A Simple Image-Based Method

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.

First in-Flight Radiometric Calibration of MUX and WFI on-Board CBERS-4

Brazil and China have a long-term joint space based sensor program called China-Brazil Earth Resources Satellite (CBERS). The most recent satellite of this program (CBERS-4) was successfully launched on 7 December 2014. This work describes a complete procedure, along with the associated uncertainties, used to calculate the in-flight absolute calibration coefficients for the sensors Multispectral Camera (MUX) and Wide-Field Imager (WFI) on-board CBERS-4. Two absolute radiometric calibration techniques were applied: (i) reflectance-based approach and (ii) cross-calibration method. A specific site at Algodones Dunes region located in the southwestern portion of the United States of America was used as a reference surface. Radiometric ground and atmospheric measurements were carried out on 9 March 2015, when CBERS-4 passed over the region. In addition, a cross-calibration between both MUX and WFI on-board CBERS-4 and the Operational Land Imager (OLI) on-board Landsat-8 was performed using the Libya-4 Pseudo Invariant Calibration Site. The gain coefficients are now available: 1.68, 1.62, 1.59 and 1.42 for MUX and 0.379, 0.498, 0.360 and 0.351 for WFI spectral bands blue, green, red and NIR, respectively, in units of (W/(m2·sr·μm))/DN. These coefficients were determined with uncertainties lower than 3.5%. As a validation of these radiometric coefficients, cross-calibration was also undertaken. An evaluation of radiometric consistency was performed between the two instruments (MUX and WFI) on-board CBERS-4 and with the well calibrated Landsat-7 ETM+. Results show that the reflectance values match in all the analogous spectral bands within the specified calibration uncertainties.

A Practical Method for On-Orbit Estimation of Polarization Response of Satellite Ocean Color Sensor

Polarization response is an important factor influencing the accuracy of radiance measurement for satellite ocean color sensors, which would change with on-orbit time. In this paper, a practical method is proposed for on-orbit estimation of polarization response. First, the linear polarization components of the Stokes vector entering the sensor are estimated by a vector radiative transfer model of the coupled ocean–atmosphere system. Second, the real radiance entering the sensor is estimated by another high-accuracy ocean color sensor using the cross-calibration method. Finally, based on the estimated linear polarization components and real radiance, the polarization response coefficients are derived by the least squares method. The proposed method is tested by applying it to the Moderate Resolution Imaging Spectroradiometer on board the Aqua satellite, and the derived polarization factors are consistent with the prelaunch values, indicating the reliability of the proposed method. In addition, our results reveal that the contribution of aerosol scattering should be included in the estimation of the linear polarization components of the Stokes vector at the top of atmosphere, particularly for long wavelengths.

Cross-calibration of participatory sensor networks for environmental noise mapping

Abstract Participatory measurements appear as a promising technique for performing noise mapping and monitoring. However, the confidence in the quality of raw data collected through participatory measurements controls the faithfulness of the output noise maps. In this paper, a cross-calibration method is proposed, which aims at both selecting the best candidate sensors and improving the furnished raw data. The method rests upon four steps: (i) an outlier detection, (ii) the crowd sensors-based correction, (iii) a fixed sensors-based correction, and (iv) the L den estimation. The efficiency of the approach for different characteristics of the network of mobile sensors is evaluated on its ability to reconstruct an artificial reference sound field, which consists of the one-month L 10s evolution, on a twenty streets network. The main conclusions are: (i) the systematic errors of the sensors can be efficiently corrected by a cross-calibration method, and thus do not affect the L den estimation, (ii) the fixed sensor network helps estimating the average error of the network of mobile sensors, (iii) the dispersion in an individual sensor measurements, which is due for example to the operator, stands for a much more critical concern and should be flagged by a rigorous outlier detection method, as the one proposed in this paper, (iv) although individual measures are improved by the proposed cross-calibration, some errors remain on the L den estimation because of the shortness of the collected samples, (v) increasing the number of sensors does not improve the L den estimation as long as individual measurements dispersions remain too large.

An Investigation of a Novel Cross-Calibration Method of FY-3C/VIRR against NPP/VIIRS in the Dunhuang Test Site

Radiometric cross-calibration of Earth observation sensors is an effective approach to evaluate instrument calibration performance, identify and diagnose calibration anomalies, and quantify the consistency of measurements from different sensors. In this study a novel cross-calibration method is proposed, taking into account the spectral and viewing angle differences adequately; the method is applied to the FY-3C/Visible Infrared Radiometer (VIRR), taking the Suomi National Polar-Orbiting Partnership (NPP)/Visible Infrared Imaging Radiometer Suite (VIIRS) as a reference. The results show that the relative difference between the two sets increases from January to May 2014, and becomes lower for the data on 24 July, 11 September, and 16 September, within approximately 10%. This phenomenon is caused by the updating of the calibration coefficients in the VIRR datasets with results from a vicarious method on June 2014. After performing an approximate estimation of the uncertainty, it is demonstrated that this calibration has a total uncertainty of 5.5%–6.0%, which is mainly from the uncertainty of the Bidirectional Reflectance Distribution Function model.

Pressure balance cross-calibration method using a pressure transducer as transfer standard.

Piston gauges or pressure balances are widely used to realize the SI unit of pressure, the pascal, and to calibrate pressure sensing devices. However, their calibration is time consuming and requires a lot of technical expertise. In this paper, we propose an alternate method of performing a piston gauge cross calibration that incorporates a pressure transducer as an immediate in-situ transfer standard. For a sufficiently linear transducer, the requirement to exactly balance the weights on the two pressure gauges under consideration is greatly relaxed. Our results indicate that this method can be employed without a significant increase in measurement uncertainty. Indeed, in the test case explored here, our results agreed with the traditional method within standard uncertainty, which was less than 6 parts per million.

Radiometric cross-calibration of Gaofen-1 WFV cameras using Landsat-8 OLI images: A solution for large view angle associated problems

Abstract Four wide-field-of-view (WFV) instruments are onboard the Gaofen-1 (or GF-1) satellite, providing a combined swath of ~ 800 km. However, observations with large view angles pose new challenges for radiometric cross-calibration with a simple image-based method when using Landsat-8 Operational Land Imager (OLI) data as a reference. Based on radiative transfer modeling, a novel radiometric cross-calibration method has been proposed in this study to solve large view angle-associated problems. The Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol products were used to simulate the top of atmosphere (TOA) signal of the reference and target instruments, and the unequal bidirectional effects were corrected using MODIS bi-directional reflectance distribution function (BRDF) products. Extensive validations with both satellite data and in situ measurements revealed an uncertainty of ~ 8% for the newly produced cross-calibration coefficients when they were used to calibrate TOA reflectance for both close-nadir and off-nadir instruments. The improvements are discernable when compared with the official provided coefficients and that were derived using the image-based cross-calibration method. This study demonstrated not only the usefulness of Landsat-8 OLI data in sensor radiometric calibration but also the impressive accuracy of the MODIS BRDF and aerosol products in radiative transfer simulations. The proposed method can be used in the future to monitor and correct potential radiometric degradations of the GF-1 WFV instruments, and it also can be easily extended to other similar satellite missions to conduct radiometric cross-calibrations.

Aspects of the Cross-Calibration Method in Laser Goniometry

A simplified cross-calibration procedure used in laser dynamic goniometry is examined, in which the angular displacement increment of the studied angle sensor exceeds its resolution. The obtained method error for M uniform angular increments of the angle sensor is determined by the overall amplitude of the Mth harmonic and its aliquot harmonics of the goniometer systematic error. It is demonstrated experimentally that when the angular displacement increment of an angle sensor is less than 120°, the method error does not exceed 0.01′′.

Cross-Calibration of the HSI Sensor Reflective Solar Bands Using Hyperion Data

This paper describes a methodology that uses Hyperion imagery as reference data to calibrate the Chinese Hyperspectral Imager (HSI) onboard the HJ-1A satellite. Two test sites near Dunhuang in Gansu and in Inner Mongolia were used for the cross-calibration. To account for the uncertainties in the top of atmosphere (TOA) reflectance due to the bidirectional reflectance distribution function (BRDF) and relative spectral response (RSR) differences between the two sensors, a model is adopted to transfer the Hyperion TOA reflectance to the effective HSI TOA reflectance. The influence of BRDF is analyzed, and two BRDF correction approaches are applied to the two test sites, respectively. For the Dunhuang test site, the ground synchronously measured reflectance in different solar zenith angles is used for BRDF correction. For the Inner Mongolia test site, a kernel-driven model is applied. The influence of RSR mismatch is computed using a spectral profile adjustment factor (SPAF), which takes into account the spectral profile of the target and the RSR of each sensor. The SPAF is calculated according to the TOA reflectance simulated using moderate resolution atmospheric transmission. One-point calibration and multipoint calibration coefficients are computed, respectively. Ground reflectance data measured in June 2010 at the Inner Mongolia test site were used to validate the cross-calibration coefficients based on the Hyperion image. The results support the proposal that the cross-calibration method between two hyperspectral sensors is effective, and the use of multipoint calibration coefficient with nonzero offset has great potential for hyperspectral sensor calibration.

Automatic Cross-calibration of Multispectral Imagery with Airborne Hyperspectral Imagery Using Spectral Mixture Analysis

The analysis of remote sensing data depends on sensor specifications that provide accurate and consistent measurements. However, it is not easy to establish confidence and consistency in data that are analyzed by different sensors using various radiometric scales. For this reason, the cross-calibration method is used to calibrate remote sensing data with reference image data. In this study, we used an airborne hyperspectral image in order to calibrate a multispectral image. We presented an automatic cross-calibration method to calibrate a multispectral image using hyperspectral data and spectral mixture analysis. The spectral characteristics of the multispectral image were adjusted by linear regression analysis. Optimal endmember sets between two images were estimated by spectral mixture analysis for the linear regression analysis, and bands of hyperspectral image were aggregated based on the spectral response function of the two images. The results were evaluated by comparing the Root Mean Square Error (RMSE), the Spectral Angle Mapper (SAM), and average percentage differences. The results of this study showed that the proposed method corrected the spectral information in the multispectral data by using hyperspectral data, and its performance was similar to the manual cross-calibration. The proposed method demonstrated the possibility of automatic cross-calibration based on spectral mixture analysis.

Multi-points cross calibration and validation of HJ-1B and AIRS thermal infrared channels

On orbit radiometric calibration of thermal infrared sensors in China is mainly based on the single-point method with insitu measurements of large uniform targets and cross calibration with moderate resolution sensors which have high calibration accuracy. The single-point method is time-consuming and laborious, and the intercept can not be calculated. Accuracy of cross calibration with the moderate resolution sensors is restricted by the matching algorithm of entrance pupil radiances. Both of this two methods' insufficients have been affected the accuracy of on orbit radiometric calibration in China. According to the characteristics of AIRS hyperspectral data, multi-points cross calibration is performed between the AIRS and HJ-1B thermal infrared channles. The time and spatial varieties are eliminated through the similar imaging times and homogeneous regions. At the same time, radiative transfer calculation by MODTRAN is used for each pixel to normalize the observation geometry. Times series multi-points method with the single target, various times and homogeneous targets multi-points method with the single time, various targets are performed to obtain the calibration coefficients of HJ-1B thermal infrared channel. Onboard calibration coefficients calculated by the Look-up table method is used to validate the multi-points cross calibration results. After the elimination of abnormal points from the time series multi-points method and regulation of match factor with homogeneous targets multi-points method, the differences between the equivalent brightness temperature retrieved by the multi-points cross calibration and onboard calibration coefficients are 1.81 and 0.92 K, respectively. The results indicate that the homogeneous targets multi-points method for cross calibration of AIRS and HJ-1B thermal infrared channels has higher accurate.

Scientific impact of MODIS C5 calibration degradation and C6+ improvements

Abstract. The Collection 6 (C6) MODIS (Moderate Resolution Imaging Spectroradiometer) land and atmosphere data sets are scheduled for release in 2014. C6 contains significant revisions of the calibration approach to account for sensor aging. This analysis documents the presence of systematic temporal trends in the visible and near-infrared (500 m) bands of the Collection 5 (C5) MODIS Terra and, to lesser extent, in MODIS Aqua geophysical data sets. Sensor degradation is largest in the blue band (B3) of the MODIS sensor on Terra and decreases with wavelength. Calibration degradation causes negative global trends in multiple MODIS C5 products including the dark target algorithm's aerosol optical depth over land and Ångström exponent over the ocean, global liquid water and ice cloud optical thickness, as well as surface reflectance and vegetation indices, including the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI). As the C5 production will be maintained for another year in parallel with C6, one objective of this paper is to raise awareness of the calibration-related trends for the broad MODIS user community. The new C6 calibration approach removes major calibrations trends in the Level 1B (L1B) data. This paper also introduces an enhanced C6+ calibration of the MODIS data set which includes an additional polarization correction (PC) to compensate for the increased polarization sensitivity of MODIS Terra since about 2007, as well as detrending and Terra–Aqua cross-calibration over quasi-stable desert calibration sites. The PC algorithm, developed by the MODIS ocean biology processing group (OBPG), removes residual scan angle, mirror side and seasonal biases from aerosol and surface reflectance (SR) records along with spectral distortions of SR. Using the multiangle implementation of atmospheric correction (MAIAC) algorithm over deserts, we have also developed a detrending and cross-calibration method which removes residual decadal trends on the order of several tenths of 1% of the top-of-atmosphere (TOA) reflectance in the visible and near-infrared MODIS bands B1–B4, and provides a good consistency between the two MODIS sensors. MAIAC analysis over the southern USA shows that the C6+ approach removed an additional negative decadal trend of Terra ΔNDVI ~ 0.01 as compared to Aqua data. This change is particularly important for analysis of vegetation dynamics and trends in the tropics, e.g., Amazon rainforest, where the morning orbit of Terra provides considerably more cloud-free observations compared to the afternoon Aqua measurements.

Model of bubble velocity vector measurement in upward and downward bubbly two-phase flows using a four-sensor optical probe

The knowledge of bubble behaviors is of considerable significance for a proper understanding and modeling of two-phase flows. To obtain the information on the bubble motion, a novel model was developed, by which the bubble velocity vector can be directly calculated from six time intervals measured with a four-sensor probe. The measurements of local bubble velocity vector and void fraction were performed in both upward and downward bubbly flows by using a four-sensor optical probe. The area-averaged void fraction and bubble velocity obtained from the probe agree well with those measured by other cross-calibration methods, and the measurement errors are within 15% under various flow conditions. Experimental results of the bubble velocity vector reveal that the bubble lateral migration may be suppressed in upward flows, but be strengthened in downward flows as the liquid flow rate increases. Also, with an increase in gas flow rate, the bubble velocity distribution varies into the power–law profile in upward flows, but into an off-center peak profile in downward flows. In addition, the void fraction shows a core peak distribution at low void fraction for downward flows, but a wall peak distribution for upward flows. However, when the void fraction is relatively high, it displays an off-center peak distribution for downward flows but a core peak distribution for upward flows.

An Improved SWIR Atmospheric Correction Model: A Cross-Calibration-Based Model

The objectives of this study are to validate the applicability of a shortwave infrared atmospheric correction model (SWIR-based model) in deriving remote sensing reflectance from turbid Case II waters and to improve the model using an improved SWIR model (ISWIR-based model). In the ISWIR-based model, the aerosol reflectance and aerosol type at near-infrared wavelengths are determined from SWIR wavelengths using a 5 × 5 box-based cross-calibration method initialized in Case I waters. The remote sensing reflectance predicted by these models was compared to remote sensing reflectance measured from three different coastal waters. The results indicate that the ISWIR-based model provides a superior performance in atmospheric correction than the SWIR-based model. Using a SWIR-based model decreases MRE values from the SWIR-based model by 14.21%-42.7%. These findings indicate that the cross-calibration method can remove noise errors in MODIS SWIR data and improves the accuracy of remote sensing reflectance retrievals using MODIS SWIR wavelengths.

Investigating the Performance of Four Empirical Cross-Calibration Methods for the Proposed SWOT Mission

The proposed surface water and ocean topography (SWOT) mission aims at observing short scale ocean topography with an unprecedented resolution and accuracy. Its main proposed sensor is a radar interferometer, so a major source of topography error is the roll angle: the relative positions of SWOT’s antennas must be known within a few micrometers. Because reaching SWOT’s stringent requirements with onboard roll values is challenging, we carried out simulations as a contingency strategy (i.e., to be ready if roll is larger than anticipated) that could be used with ground-based data. We revisit the empirical calibration algorithms with additional solving methods (e.g., based on orbit sub-cycle) and more sophisticated performance assessments with spectral decompositions. We also explore the link between the performance of four calibration methods and the attributes of their respective calibration zones: size and geometry (e.g., crossover diamonds), temporal variability (e.g., how many days between overlapping SWOT images). In general, the so-called direct method (using a single SWOT image) yields better coverage and smaller calibrated roll residuals because the full extent of the swath can be used for calibration, but this method makes an extensive use of the external nadir constellation to separate roll from oceanic variability, and it is more prone to leakages from oceanic variability on roll (i.e., true topography signal is more likely to be corrupted if it is misinterpreted as roll) and inaccurate modeling of the true topography spectrum. For SWOT’s baseline orbit (21 days repeat and 10.9 days sub-cycle), three other methods are found to be complementary with the direct method: swath crossovers, external nadir crossovers, and sub-cycle overlaps are shown to provide an additional calibration capability, albeit with complex latitude-varying coverage and performance. The main asset of using three or four methods concurrently is to minimize systematic leakages from oceanic variability or measurement errors, by maximizing overlap zones and by minimizing the temporal variability with one-day to three-day image differences. To that extent, SWOT’s proposed “contingency orbit” is an attractive risk reduction asset: the one-day sub-cycle overlaps of adjoining swaths would provide a good, continuous, and self-sufficient (no need for external nadirs) calibration scheme. The benefit is however essentially located at mid to high-latitudes and it is substantial only for wavelengths longer than 100 km.

Vicarious Calibration of Beijing-1 Multispectral Imagers

For on-orbit calibration of the Beijing-1 multispectral imagers (Beijing-1/MS), a field calibration campaign was performed at the Dunhuang calibration site during September and October of 2008. Based on the in situ data and images from Beijing-1 and Terra/Moderate Resolution Imaging Spectroradiometer (MODIS), three vicarious calibration methods (i.e., reflectance-based, irradiance-based, and cross-calibration) were used to calculate the top-of-atmosphere (TOA) radiance of Beijing-1. An analysis was then performed to determine or identify systematic and accidental errors, and the overall uncertainty was assessed for each individual method. The findings show that the reflectance-based method has an uncertainty of more than 10% if the aerosol optical depth (AOD) exceeds 0.2. The cross-calibration method is able to reach an error level within 7% if the images are selected carefully. The final calibration coefficients were derived from the irradiance-based data for 6 September 2008, with an uncertainty estimated to be less than 5%.

Comment on “Transconvolution and the virtual positron emission tomograph: A new method for cross calibration in quantitative PET/CT imaging” [Med. Phys. 40, 062503 (15pp.) (2013)

Comment on “Transconvolution and the virtual positron emission tomograph: A new method for cross calibration in quantitative PET/CT imaging” [Med. Phys. 40, 062503 (15pp.) (2013)

Response to “Comment on ‘Transconvolution and the virtual positron emission tomograph (vPET): A new method for cross calibration in quantitative PET/CT imaging’ ” [Med. Phys. 40, 062503 (15pp.) (2013)

Response to “Comment on ‘Transconvolution and the virtual positron emission tomograph (vPET): A new method for cross calibration in quantitative PET/CT imaging’ ” [Med. Phys. 40, 062503 (15pp.) (2013)

SU-E-T-136: Novel Secondary Quality Assurance Procedure for the INTRABEAM System Ion Chamber Using Commercial OSLDs

Purpose: In recent events, our clinic has experienced a soft x‐ray ion chamber calibration drift of 8%. For the INTRABEAMTM system which relies on an ion chamber measurement to deduce a dose rate correction factor for treatment, such an ion chamber change results in a large deviation between the planned patient dose and the treatment dose. We present a cross calibration method between an ion chamber and commercially available optically stimulated luminescent dosimeters (OSLDs) to monitor the consistency of the soft x‐ray ion chamber. Methods: Our method uses the INTRABEAM™ system dose rate determined by a PTW soft x‐ray ion chamber (model 23342) under normal quality assurance procedures to produce the counts per dose rate for irradiated nanoDot™ OSLDs. The procedure replaces the ion chamber with an OSLD at the same position which is obtained with a fabricated stage of the same dimension as the ion chamber stage. Upon replacement of the ion chamber, the QA procedure is repeated irradiating a nanoDot™ OSLD for approximately two minutes with low energy kV x‐rays of energy 20 keV with 0.64 mm Al HVL. The QA procedure is repeated three times with a new OSLD for each irradiation. A ten minute waiting period is invoked prior to reading each nanoDot™ OSLD a total of three times. Results: This method produces an average counts per dose rate for all nanoDot™ OSLDs with a 1‐sigma standard deviation of 1.7%. The low standard deviation gives this method the ability to detect an ion chamber measurement change in dose rate greater than 3.5% incorporating the positional uncertainty and sensitivity change. Conclusion: In the advent of complete equipment failure, a system can degrade and report erroneous measurements. Our quick secondary check for equipment drift provides a necessary tool to ensure patient safety.