Radiometric Cross-calibration Research Articles

Radiometric Cross-Calibration of GF6-PMS and WFV Sensors with Sentinel 2-MSI and Landsat 9-OLI2

A panchromatic and multispectral sensor (PMS) and a wide-field-of-view (WFV) sensor were fitted aboard the Gaofen6 (GF6) satellite, which was launched on 2 June 2018. This study used the Landsat9-Operational Land Imager 2 and Sentinel2-Multispectral Instrument as reference sensors to perform radiometric cross-calibration on GF6-PMS and WFV data at the Dunhuang calibration site. The four selected sensor images were all acquired on the same day. The results indicate that: the calibration results between different reference sensors can be controlled within 3%, with the maximum difference from the official coefficients being 8.78%. A significant difference was observed between the coefficients obtained by different reference sensors when spectral band adjustment factor (SBAF) correction was not performed; from the two sets of validation results, the maximum mean relative difference in the near-infrared band was 9.46%, with the WFV sensor showing better validation results. The validation of calibration coefficients based on synchronous ground observation data and the analysis of the impact of different SBAF methods on the calibration results indicated that Landsat9 is more suitable as a reference sensor for radiometric cross-calibration of GF6-PMS and WFV.

Radiometric Cross Calibration of HY-1C/COCTS Based on Sentinel-3/OLCI

Radiometric Cross Calibration of HY-1C/COCTS Based on Sentinel-3/OLCI

Radiometric cross-calibration of Sentinel-2B MSI with HY-1C SCS based on the near simultaneous imaging of common ground targets.

To simplify the cross-calibration process and improve calibration frequency and accuracy, this paper proposes a cross-calibration method for the multispectral remote sensor Multi-Spectral Instrument (MSI) carried by Sentinel-2B using the hyperspectral remote sensor, that is, the satellite calibration spectrometer (SCS) carried by Hai Yang (HY)-1C, as the reference sensor and establishes the calibration process. Precise spectral response matching between SCS and MSI spectral channels is performed by the interpolation and iteration of hyperspectral data to eliminate the difference in band settings and significantly improve the accuracy of cross-calibration coefficients. The SNO-x inherited from the simultaneous nadir overpass (SNO) method is used as a prediction method to carry out cross-calibration imaging in mid- and low-latitude regions, which improves the cross-calibration frequency and broadens the dynamic range of calibration. The cross-calibration coefficients and offsets of MSI B1∼B7 and B8a were obtained by processing the earth observation images of the MSI and SCS on January 24, 2019. Then, the cross-calibration coefficients and offsets are applied to the ocean, farmland and other ground objects with different reflectance, and the reliability and accuracy of the cross-calibration results are evaluated with the Moderate-resolution Imaging Spectroradiometer (MODIS) carried by Terra as a reference. To improve the accuracy of the evaluation, the spectral band adjustment factor between the corresponding channels of MSI and MODIS is used to correct the measured reflectance of MODIS based on the satellite calibration coefficient. The reflectance directly obtained by processing the MSI image is used as the MSI-measured reflectance, the reflectance obtained based on the cross-calibration coefficient is used as the MSI-calculated reflectance, and the reflectance corrected by the spectral band adjustment factor (SBAF) is used as the MODIS-calculated reflectance. The results show that the mean root-mean-square relative error (RMSRE) between the MODIS-calculated reflectance and the MSI-calculated reflectance is 2.16% and that the mean RMSRE between the MODIS-calculated reflectance and the MSI-measured reflectance is 3.05%, indicating that the reflectance corrected based on calibration coefficients is closer to the MODIS-calculated reflectance. Finally, each uncertainty source in the cross-calibration is analyzed, and the comprehensive uncertainty is found to be 4.03%, indicating that SCS can be used as a reference for MSI cross-calibration.

Synthetic Aperture Radar Radiometric Cross Calibration Based on Distributed Targets

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 paper, a method for selecting stable distributed targets using time-series stability analysis was presented firstly. 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 adopt based on the characteristics of different targets. Thirdly, 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-1A/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 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sigma$</tex-math></inline-formula> ).

Radiometric Cross-Calibration of GF-6/WFV Sensor Using MODIS Images With Different BRDF Models

The wide field of view (WFV) imaging system of the GaoFen-6 (GF-6) satellite processes four popular spectral bands [blue, green, red, and near-infrared (NIR)] and four new spectral bands (costal, yellow, and two red edges). However, the corresponding reference spectral bands for these eight spectral bands from one reference sensor are insufficient in the cross-calibration process of the WFV; therefore, current cross-calibration methods should be improved. To address this problem, the Moderate-Resolution Imaging Spectroradiometer (MODIS), which has high radiometric performance with the aid of an onboard calibration system, is used as a reference sensor, and cross-calibration methods using the top of atmosphere (TOA) and the bottom of atmosphere (BOA) bidirectional reflectance distribution function (BRDF) models are developed and compared. The results reveal that the cross-calibration results in eight spectral bands with the BOA BRDF model that can obtain higher consistency with the official calibration coefficients (OCCs) compared with the TOA BRDF model. In addition, after various influencing factors are comprehensively analyzed, the spectral band adjustment factor (SBAF) correction in the shortwave infrared (SWIR) spectral bands can be neglected; moreover, by using five spectral bands (blue, green, red, NIR, and SWIR) of the MODIS sensor, the BOA BRDF model and the cubic polynomial interpolation method provide optimal cross-calibration schemes for WFV sensor. The total radiometric cross-calibration uncertainties of the proposed cross-calibration methods with the BOA BRDF model and the TOA BRDF model are less than 5.73% and 6.32%, respectively.

Cross-calibration of Chinese Gaofen-5 thermal infrared images and its improvement on land surface temperature retrieval

Thermal infrared (TIR) remote sensing technology is capable of acquiring large-scale land surface temperature (LST), which is a key factor in the energy exchange between land surface and atmosphere. The visible and infrared multispectral sensor (VIMS) equipped in the Chinese Gaofen-5 (GF-5) satellite can obtain four channels of TIR images with a 40 m spatial resolution. However, due to the change of working environment, the TIR sensor suffers a low-accurate radiometric calibration that needs improvement. This paper puts forward a new cross-calibration for GF-5/VIMS TIR images by linking the top of the atmosphere (TOA) radiance of the vertical angle MODIS observation with the GF-5/VIMS image to estimate the radiometric calibration coefficients, Gain and Offset. To verify the recalibration performance, a new nonlinear two-channel split-window (SW) algorithm and a light gradient boosting machine (LightGBM) method which is used to refine LST from the SW algorithm by minimizing the residuals, were developed for the recalibrated GF-5/VIMS TIR 3 and TIR 4 channels. The radiometric cross-calibration algorithm and the optimized SW algorithm were applied to real GF-5/VIMS TIR images. The validation results showed that the brightness temperature and LST were improved significantly, and the LST retrieval error was reduced greatly to 1.79 K after recalibration, indicating a large improvement of the LST retrieval for GF-5/VIMS TIR image using the proposed cross-calibration and SW algorithms.

Radiometric Cross-Calibration of GF-4/IRS Based on MODIS Measurements

GaoFen-4 (GF-4) infrared spectrum (IRS) camera has a special spectral band located in the mid-wave infrared spectrum with the wavelength ranging from 3.50 to 4.10 μm. It is very important for forest fire-detection and other quantitative remote sensing applications at a high spatial resolution of 400 m. Due to the similarity of the spectral characteristics, the thermal emissive band 20 (3.660-3.840 μm) and 22 (3.929-3.989 μm) of medium resolution imaging spectrometer (MODIS) are taken as the reference bands for the radiometric cross-calibration of the GF-4/IRS camera. The time difference between selected GF-4/IRS and MODIS images at the Qinghai Lake and Se lincuo Lake test sites is less than one hour. After spatial matching, single-band and dual-band spectral matching methods are proposed in this article. Then, the DN value of GF-4 image is linearly fitted to the radiance of the corresponding MODIS image to obtain the radiometric calibration coefficients of GF-4/IRS camera. The validation results show that the cross-calibration coefficients calculated by integrating the measurement of two MODIS bands with the mean value spectral matching approach has the highest radiometric calibration consistency with the official calibration coefficients.

A Novel Radiometric Cross-Calibration of GF-6/WFV With MODIS at the Dunhuang Radiometric Calibration Site

To obtain more abundant target information for vegetation remote sensing research, the wide field of view (WFV) of the GaoFen-6 (GF-6) satellite utilizes eight bands, including four new bands. Unfortunately, it finds the unavailable reference bands from well-calibrated sensors to implement radiometric cross-calibration of the WFV sensor for these new bands, which severely affects the radiometric quality estimation and the radiometric degradation detection of WFV. Therefore, taking the Dunhuang radiometric calibration site (DRCS) in China as the radiometric transfer platform and treating the moderate-resolution imaging spectroradiometer as a reference sensor, a novel radiometric cross-calibration method for WFV is proposed. The validation results show that compared with the official calibration coefficients (OCCs) published by the China centre for resources satellite data and application (CRESDA), the proposed method can obtain reliable radiometric cross-calibration results for each band even without available corresponding reference bands. Moreover, the influences of the interpolation method and the spectral band adjustment factor (SBAF) correction on the calibration results are discussed. The total radiometric cross-calibration uncertainty is less than 4.33%.

Radiometric Cross-Calibration of GF-4/PMS Based on Radiometric Block Adjustment

To obtain more abundant target information, the panchromatic and multispectral sensor (PMS) of the GaoFen-4 (GF-4) satellite utilizes five different integration times. The optimal approach for carrying out the integrated processing of absolute radiometric calibration and radiometric difference minimization of PMS images over these five integration times remains an important issue. Therefore, taking the Dunhuang radiometric calibration site (DRCS) in China as the radiometric-transfer platform and treating the Moderate-resolution Imaging Spectroradiometer (MODIS) and Landsat-8 Operational Land Imager (OLI) as the reference sensors, a new radiometric cross-calibration method of the PMS based on radiometric block adjustment (RBA) is proposed. The validation results show that the proposed method can simultaneously maintain high absolute radiometric calibration consistency with the official calibration coefficients published by the China Centre for Resources Satellite Data and Application (CRESDA) and can reduce the radiometric difference between PMS images over different integration times. In addition, the influences of the distribution of the radiometric control points (RCPs), radiometric tie points (RTPs) filter, spectral band adjustment factor (SBAF) correction, and correction of the bidirectional reflectance distribution function (BRDF) on the calibration results are discussed. The total cross-calibration uncertainty is less than 5.21%.

Radiometric Cross-Calibration of the Wide Field View Camera Onboard GaoFen-6 in Multispectral Bands

GaoFen6 (GF-6), successfully launched on June 2, 2018, is the sixth satellite of the High-Definition Earth observation system (HDEOS). Although GF-6 is the first high-resolution satellite in China to achieve precise agricultural observation, it will be widely used in many other domains, such as land survey, natural resources management, emergency management, ecological environment and so on. The GF-6 was not equipped with an onboard calibration instrument, so on-orbit radiometric calibration is essential. This paper aimed at the on-orbit radiometric calibration of the wide field of view camera (WFV) onboard GF-6 (GF-6/WFV) in multispectral bands. Firstly, the radiometric capability of GF-6/WFV is evaluated compared with the Operational Land Imager (OLI) onboard Landsat-8, Multi Spectral Instrument (MSI) onboard Sentinel-2 and Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Terra, which shows that GF-6/WFV has an obvious attenuation. Consequently, instead of vicarious calibration once a year, more frequent calibration is required to guarantee its radiometric consistency. The cross-calibration method based on the Badain Jaran Desert site using the bi-directional reflectance distribution function (BRDF) model calculated by Landsat-8/OLI and ZY-3/Three-Line Camera (TLC) data is subsequently applied to GF-6/WFV and much higher frequencies of calibration are achieved. Finally, the cross-calibration results are validated using the synchronized ground measurements at Dunhuang test site and the uncertainty of the proposed method is analyzed. The validation shows that the relative difference of cross-calibration is less than 5% and it is satisfied with the requirements of cross-calibration.

Radiometric Cross Calibration and Validation Using 4 Angle BRDF Model between Landsat 8 and Sentinel 2A

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.

Radiometric Cross-Calibration of GF-4/VNIR Sensor With Landsat8/OLI, Sentinel-2/MSI, and Terra/MODIS for Monitoring Its Degradation

Optical satellite sensor generally suffers from drifts and biases relative to their prelaunch calibration, caused by launch and the space environment. Radiometric cross-calibration is valuable for calibrating sensors without coincident surface measurements through transferring radiometric values between satellite sensors via a stable target. The data quality of visible and near-infrared (VNIR) sensor onboard Gaofen-4 (GF-4) satellite seriously depends on vicarious calibration, which is performed once every year, thus, a cross-calibration approach is proposed for monitoring its radiometric performance much more frequently. In this study, the GF-4/VNIR is cross-calibrated by Landsat8/OLI, Sentinel-2/MSI, and Terra/MODIS with the aid of the tandem scenes during 2017 and 2018 after correcting the discrepancy on viewing geometries and spectrum. The results show that there is test site dependency and sensor dependency, and cross-calibration coefficients in the four bands roughly have similar variation trend as a sinusoidal function during 2017 and 2018, varying from 0.00023 to 0.00079. Furthermore, uncertainty analysis of the cross-calibration is carried out to analyze the uncertainty contributions affecting the cross-calibration accuracy. Note that the calibration accuracy of referenced sensors, the uncertainty of the MODTRAN model, BRDF uncertainty are the three main factors affecting the cross-calibration accuracy, the total uncertainty is approximately 3.9%–6.64%.

Assessment of bidirectional reflectance effects on desert and forest for radiometric cross-calibration of satellite sensors

Cross-calibration is a wildly used approach to radiometrically calibrate satellite sensors to meet uncertainty requirements. However, its accuracy is greatly influenced by bidirectional reflectance distribution function (BRDF) effects. To understand such influences, we analyzed the long-term BRDF characteristics of two commonly used cross-calibration targets (‘bright’ desert and ‘dark’ forest) using long-term (2002 – 2016) Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF products. BRDF characteristics derived by MODIS observations of both land cover types had 365-day cycles. Furthermore, both solar and sensor zenith angles showed more influence on BRDF than relative azimuth angles, and BRDF variations were approximately 10 times sensitive when solar or sensor zenith angles surpassed the boundary of 75°, which is therefore not recommended for selecting image pairs between target and reference satellite sensors for cross-calibration. Due to cloud cover, three prevailing interpolation methods (nearest, linear, and spline interpolation) were also assessed in their capacity to fill temporally missing BRDF products. Linear interpolation was preferred when valid MODIS scenes were more than two in five days, while nearest interpolation outperformed the other two methods for no more than two valid MODIS scenes in five days. In addition, BRDF look up tables (LUTs) were established for radiometrically cross-calibrating satellite sensors when BRDF products were unavailable. The LUTs were further validated with Landsat-8/Operational Land Imager (OLI) top of atmosphere (TOA) radiance simulated by the Second Simulation of the Satellite Signal in the Solar Spectrum model (6S) with BRDF effects corrected using the BRDF LUTs and MODIS BRDF products for typical geometries at two sites. High consistency was achieved with mean biases less than 0.3%, which suggests that BRDF LUTs could be used as alternatives when BRDF products are unavailable. These results provide guidelines to improve the accuracy of cross-calibration including image-matchup selecting criteria, efficient BRDF effect removal, and selection of potential calibration sites.

Radiometric Cross-Calibration for Multiple Sensors with the Moon as an Intermediate Reference

The instrument cross-calibration is an effective way to assess the quality of satellite data. In this study, a new me-thod is proposed to cross-calibrate the sensors among satellite instruments by using a RObotic Lunar Observatory (ROLO) model and Apollo sample reflectance in reflective solar bands (RSBs). The ROLO model acts as a transfer radiometer to bridge between the instruments. The reflective spectrum of the Apollo sample is used to compensate for the difference in the instrument’s relative spectral responses (RSRs). In addition, the double ratio between the observed lunar irradiance and the simulated lunar irradiance is used to reduce the difference in instrument lunar viewing and illumining geometry. This approach is applied to the Moderate Resolution Imaging Spectroradiometer (MODIS), the Sea-Viewing Wide Field-of-View Sensor (SeaWiFS), and the Advanced Land Imager (ALI) on board three satellites, respectively. The mean difference between MODIS and SeaWiFS is less than 3.14%, and the difference between MODIS and ALI is less than 4.75%. These results indicate that the proposed cross-calibration method not only compensates for the RSR mismatches but also reduces the differences in lunar observation geometry. Thus, radiance calibration of any satellite instrument can be validated with a reference instrument bridged by the moon.

Derivation of Hyperspectral Profile of Extended Pseudo Invariant Calibration Sites (EPICS) for Use in Sensor Calibration

Reference of Earth-observing satellite sensor data to a common, consistent radiometric scale is an increasingly critical issue as more of these sensors are launched; such consistency can be achieved through radiometric cross-calibration of the sensors. A common cross-calibration approach uses a small set of regions of interest (ROIs) in established Pseudo-Invariant Calibration Sites (PICS) mainly located throughout North Africa. The number of available cloud-free coincident scene pairs available for these regions limits the usefulness of this approach; furthermore, the temporal stability of most regions throughout North Africa is not known, and limited hyperspectral information exists for these regions. As a result, it takes more time to construct an appropriate cross-calibration dataset. In a previous work, Shrestha et al. presented an analysis identifying 19 distinct “clusters” of spectrally similar surface cover that are widely distributed across North Africa, with the potential to provide near-daily cloud-free imaging for most sensors. This paper proposes a technique to generate a representative hyperspectral profile for these clusters. The technique was used to generate the profile for the cluster containing the largest number of aggregated pixels. The resulting profile was found to have temporal uncertainties within 5% across all the spectral regions. Overall, this technique shows great potential for generation of representative hyperspectral profiles for any North African cluster, which could allow the use of the entire North Africa Saharan region as an extended PICS (EPICS) dataset for sensor cross-calibration. This should result in the increased temporal resolution of cross-calibration datasets and should help to achieve a cross-calibration quality similar to that of individual PICS in a significantly shorter time interval. It also facilitates the development of an EPICS based absolute calibration model, which can improve the accuracy and consistency in simulating any sensor’s top of atmosphere (TOA) reflectance.

Radiometric Cross-Calibration of GF-1 PMS Sensor with a New BRDF Model

On-orbit radiometric calibration of a space-borne sensor is of great importance for quantitative remote sensing applications. Cross-calibration is a common method with high calibration accuracy, and the core and emphasis of this method is to select the appropriate reference satellite sensor. As for the cross-calibration of high-spatial resolution and narrow-swath sensor, however, there are some scientific issues, such as large observation angles of reference image, and non-synchronization (or quasi-synchronization) between the imaging date of reference image and the date of sensor to be calibrated, which affects the accuracy of cross-calibration to a certain degree. Therefore, taking the GaoFen-1 (GF-1) Panchromatic and Multi-Spectral (PMS) sensor as an example in this research, an innovative radiometric cross-calibration method is proposed to overcome this bottleneck. Firstly, according a set of criteria, valid MODIS (Moderate Resolution Imagine Spectroradiometer) images of sunny day in one year over the Dunhuang radiometric calibration site in China are extracted, and a new and distinctive bidirectional reflectance distribution function (BRDF) model based on top-of-atmosphere (TOA) reflectance and imaging angles of the sunny day MODIS images is constructed. Subsequently, the cross-calibration of PMS sensor at Dunhuang and Golmud radiation calibration test sites is carried out by using the method presented in this paper, taking the MODIS image with large solar and observation angles and Landsat 8 Operational Land Imager (OLI) with different dates from PMS as reference. The validation results of the calibration coefficients indicate that our proposed method can acquire high calibration accuracy, and the total calibration uncertainties of PMS using MODIS as reference sensor are less than 6%.

RADIOMETRIC CROSS-CALIBRATION OF GAOFEN-1 WFV CAMERAS WITH LANDSAT-8 OLI AND MODIS SENSORS BASED ON RADIATION AND GEOMETRY MATCHING

Abstract. Cross-calibration has the advantages of high precision, low resource requirements and simple implementation. It has been widely used in recent years. The four wide-field-of-view (WFV) cameras on-board Gaofen-1 satellite provide high spatial resolution and wide combined coverage (4 × 200 km) without onboard calibration. In this paper, the four-band radiometric cross-calibration coefficients of WFV1 camera were obtained based on radiation and geometry matching taking Landsat 8 OLI (Operational Land Imager) sensor as reference. Scale Invariant Feature Transform (SIFT) feature detection method and distance and included angle weighting method were introduced to correct misregistration of WFV-OLI image pair. The radiative transfer model was used to eliminate difference between OLI sensor and WFV1 camera through the spectral match factor (SMF). The near-infrared band of WFV1 camera encompasses water vapor absorption bands, thus a Look Up Table (LUT) for SMF varies from water vapor amount is established to estimate the water vapor effects. The surface synchronization experiment was designed to verify the reliability of the cross-calibration coefficients, which seem to perform better than the official coefficients claimed by the China Centre for Resources Satellite Data and Application (CCRSDA).

Spectroscopy of Very Hot Plasma in Non-flaring Parts of a Solar Limb Active Region: Spatial and Temporal Properties

Abstract In this work we investigate the thermal structure of an off-limb active region (AR) in various non-flaring areas, as it provides key information on the way these structures are heated. In particular, we concentrate on the very hot component ( &gt; 3 MK ) as it is a crucial element to distinguish between different heating mechanisms. We present an analysis using Fe and Ca emission lines from both the Solar Ultraviolet Measurement of Emitted Radiation (SUMER) on board the Solar and Heliospheric Observatory (SOHO) and the EUV Imaging Spectrometer (EIS) on board Hinode. A data set covering all ionization stages from Fe x to Fe xix has been used for the thermal analysis (both differential emission measure and emission measure, EM). Ca xiv is used for the SUMER-EIS radiometric cross calibration. We show that the very hot plasma is present and persistent almost everywhere in the core of the limb AR. The off-limb AR is clearly structured in Fe xviii. Almost everywhere, the EM analysis reveals plasma at 10 MK (visible in Fe xix emission), which is down to 0.1% of EM of the main 3 MK plasma. We estimate the power-law index of the hot tail of the EM to be between −8.5 and −4.4. However, the question about the possible existence of a small minor peak at around 10 MK remains open. The absence in some part of the AR of the Fe xix and Fe xxiii lines (which fall into our spectral range) enables us to determine an upper limit on the EM at these temperatures. Our results include a new Ca xiv 943.59 Å atomic model.

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 &lt;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.

Cross-Calibration between ASTER and MODIS Visible to Near-Infrared Bands for Improvement of ASTER Radiometric Calibration.

Radiometric cross-calibration between the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and the Terra-Moderate Resolution Imaging Spectroradiometer (MODIS) has been partially used to derive the ASTER radiometric calibration coefficient (RCC) curve as a function of date on visible to near-infrared bands. However, cross-calibration is not sufficiently accurate, since the effects of the differences in the sensor’s spectral and spatial responses are not fully mitigated. The present study attempts to evaluate radiometric consistency across two sensors using an improved cross-calibration algorithm to address the spectral and spatial effects and derive cross-calibration-based RCCs, which increases the ASTER calibration accuracy. Overall, radiances measured with ASTER bands 1 and 2 are on averages 3.9% and 3.6% greater than the ones measured on the same scene with their MODIS counterparts and ASTER band 3N (nadir) is 0.6% smaller than its MODIS counterpart in current radiance/reflectance products. The percentage root mean squared errors (%RMSEs) between the radiances of two sensors are 3.7, 4.2, and 2.3 for ASTER band 1, 2, and 3N, respectively, which are slightly greater or smaller than the required ASTER radiometric calibration accuracy (4%). The uncertainty of the cross-calibration is analyzed by elaborating the error budget table to evaluate the International System of Units (SI)-traceability of the results. The use of the derived RCCs will allow further reduction of errors in ASTER radiometric calibration and subsequently improve interoperability across sensors for synergistic applications.