Accurate Radiometric Calibration Research Articles

Uncertainty budget for detector-based absolute radiometric calibration with GLAMR

The accuracy of the absolute radiometric calibration (RadCal) for remote sensing instruments is essential to their wide range of applications. The uncertainty associated with the traditional source-based RadCal method is assessed at a 2% (k=1) or higher level for radiance measurement. To further improve the accuracy to meet the demands of climate studies, a detector-based approach using tunable lasers as a light source has been devised. The Goddard Laser for Absolute Measurement of Radiance, known as the GLAMR system, is a notable example of the incorporation of such technology. Using transfer radiometers calibrated at the National Institute of Standards and Technology as calibration standards, the absolute spectral response function of a remote sensing instrument is measured with its uncertainty traceable to the International System of Units. This paper presents a comprehensive uncertainty analysis of the detector-based absolute RadCal using the GLAMR system. It identifies and examines uncertainty sources during the GLAMR RadCal test, including those from the GLAMR system, the testing configuration, and data processing methodologies. Analysis is carried out to quantify the contribution of each source and emphasize the most influential factors. It is shown that the calibration uncertainty of GLAMR RadCal can be better than 0.3% (k=1) in the wavelength range of 350–950 nm and 0.6% (k=1) between 950 and 2300 nm, with the exception of regions with strong water absorption. In addition, recommendations are made to refine the calibration process to further reduce the uncertainty.

End-to-end simulations to optimize imaging spectroscopy mission requirements for seven scientific applications

CNES is currently carrying out a Phase A study to assess the feasibility of a future hyperspectral imaging sensor (10 m spatial resolution) combined with a panchromatic camera (2.5 m spatial resolution). This mission focuses on both high spatial and spectral resolution requirements, as inherited from previous French studies such as HYPEX, HYPXIM, and BIODIVERSITY. To meet user requirements, cost, and instrument compactness constraints, CNES asked the French hyperspectral Mission Advisory Group (MAG), representing a broad French scientific community, to provide recommendations on spectral sampling, particularly in the Short Wave InfraRed (SWIR) for various applications.This paper presents the tests carried out with the aim of defining the optimal spectral sampling and spectral resolution in the SWIR domain for quantitative estimation of physical variables and classification purposes. The targeted applications are geosciences (mineralogy, soil moisture content), forestry (tree species classification, leaf functional traits), coastal and inland waters (bathymetry, water column, bottom classification in shallow water, coastal habitat classification), urban areas (land cover), industrial plumes (aerosols, methane and carbon dioxide), cryosphere (specific surface area, equivalent black carbon concentration), and atmosphere (water vapor, carbon dioxide and aerosols). All the products simulated in this exercise used the same CNES end-to-end processing chain, with realistic instrument parameters, enabling easy comparison between applications. 648 simulations were carried out with different spectral strategies, radiometric calibration performances and signal-to-noise Ratios (SNR): 24 instrument configurations × 25 datasets (22 images + 3 spectral libraries).The results show that spectral sampling up to 20 nm in the SWIR range is sufficient for most applications. However, 10 nm spectral sampling is recommended for applications based on specific absorption bands such as mineralogy, industrial plumes or atmospheric gases. In addition, a slight performance loss is generally observed when radiometric calibration accuracy decreases, with a few exceptions in bathymetry and in the cryosphere for which the observed performance is severely degraded. Finally, most applications can be achieved with a realistic SNR, with the exception of bathymetry, shallow water classification, as well as carbon dioxide and methane estimation, which require the optimistic SNR level tested. On the basis of these results, CNES is currently evaluating the best compromise for designing the future hyperspectral sensor to meet the objectives of priority applications.

Land surface emissivity retrieval from SDGSAT-1: comparison of LSE products with different spatial resolutions

ABSTRACT Land surface emissivity (LSE) represents an essential parameter for understanding land change and surface energy balance. While numerous algorithms have been proposed to retrieve LSE, they are seldom applied to remote sensing data with high spatial resolution. The SDGSAT-1 satellite provides an opportunity to retrieve LSE with high spatial resolution that can be regarded as references for validating LSE products generated from coarse-spatial-resolution satellite data. However, no study has yet explored LSE-retrieval based on SDGSAT-1. This research seeks to fill this gap through evaluating the radiometric calibration accuracy of SDGSAT-1, using five methods to retrieve pixel-level emissivity of Guangzhou, and comparing such results with emissivity generated from other satellites (e.g. Landsat 8, ECOSTRESS, and MODIS) that transited Guangzhou during the similar period. Our results indicated that SDGSAT-1 performed well in radiometric calibration. Heterogeneous vegetation and man-made surfaces registered significant emissivity differences in varying spatial resolutions, whereas relatively homogeneous water bodies registered smaller differences. Decreasing spatial resolution amplifies the emissivity differences of the same land cover among distinctive sensors. The five emissivity-retrieval methods performed exceptionally well based on SDGSAT-1, registering average absolute differences less than 0.026 as compared with Landsat 8; with decreasing spatial resolutions, the maximum difference between SDGSAT-1 and MODIS LSE product reached 0.124.

Towards Accurate Radiometric Calibration of INSAT-3D and INSAT-3DR IMAGER: Addressing Uncertainty and Error Sources

ABSTRACT The proper radiometric calibration of the INSAT-3D and −3DR IMAGER is of utmost importance for effective weather forecasting and monitoring. Accurate and reliable images are crucial for predicting weather patterns, tracking storms, and assessing the impact of natural disasters. This research paper presents an evaluation of the radiometric calibration of the INSAT-3D and −3DR IMAGER using a reflectance-based method referenced to in-situ ground site measurements. Calibration campaigns were conducted at the Great Rann of Kutch (GROK) in Gujarat, India during February 2022 to ensure synchronous measurements of surface reflectance and atmospheric variables. In this study, the radiative transfer model called 6 SV (Second Simulation of a Satellite Signal in the Solar Spectrum Vector) was employed to simulate the Top-Of-Atmospheric (TOA) radiance. The calibration coefficients for the Visible (VIS) and Short Wave Infrared (SWIR) bands of the IMAGER were determined by comparing the TOA radiance obtained from the radiative transfer model with the Level 1B product of satellite data. The results convincingly demonstrate that the reflectance-based calibration method yields highly accurate and reliable radiometric calibration for both the VIS and SWIR bands of the INSAT-3D and −3DR IMAGER, with an uncertainty significantly smaller than the expected sensor degradation. Specifically, it was found that the VIS band of INSAT-3D underestimates the radiance value by 9.05%, while the VIS band of INSAT-3DR underestimates it by 4.88%. The study also discusses various sources of uncertainty and emphasizes the need for comprehensive uncertainty quantification to ensure the radiometric accuracy of the IMAGER data. Overall, the calibration results indicate that the absolute radiometric accuracy for the VIS and SWIR bands of the IMAGER on-board INSAT-3D and INSAT-3DR satellites is better than 4%. This research contributes to enhancing the reliability and usability of the INSAT-3D and −3DR IMAGER data for weather forecasting and monitoring applications.

A dataset of JLS-5-20M black soil image in Northeast China

This is a dataset of the surface reflectance data covering some black soil areas in the three northeastern provinces of China, based on the L1 data of the JL1GP satellites’ multispectral imager with a spatial resolution of 5-20 meters. Specifically, we adopted the on-orbit absolute calibration technology based on multiple stable sites to ensure that the on-orbit absolute radiometric calibration accuracy of the JL1GP satellites’ multispectral imagers is no less than 7%. Then, we selected the L1 data of JL1GP satellite multispectral imager that imaged in the three provinces of Northeast China with clear weather and no clouds are as the input image of atmospheric correction process. Finally, the L1-level image data are atmospherically corrected and orthorectified by the atmospheric correction algorithm based on the atmospheric radiative transfer model, to generate the reflectance product dataset covering some black soil areas in the three northeastern provinces of China with an absolute accuracy of no less than 7%. This dataset takes advantage of the domestic JL1GP satellites’ advantages of high spatial resolution, a large number of bands, and stable imaging quality, hence it can provide reliable support for quantitative remote sensing applications in the black soil areas of the three northeastern provinces in China.

Radiometric Cross-Calibration of Wide-Field-of-View Cameras Based on Gaofen-1/6 Satellite Synergistic Observations Using Landsat-8 Operational Land Imager Images: A Solution for Off-Nadir Wide-Field-of-View Associated Problems

The Gaofen-1 satellite is equipped with four wide-field-of-view (WFV) instruments, enabling an impressive spatial resolution of 16 m and a combined swath exceeding 800 km. These WFV images have shown their valuable applications across diverse fields. However, achieving accurate radiometric calibration is an essential prerequisite for establishing reliable connections between satellite signals and biophysical, as well as biochemical, parameters. However, observations with large viewing angles (>20°) pose new challenges due to the bidirectional reflectance distribution function (BRDF) effect having a pronounced impact on the accuracy of cross-radiation calibrations, especially for the off-nadir WFV1 and WFV4 cameras. To overcome this challenge, a novel approach was introduced utilizing the combined observations from the Gaofen-1 and Gaofen-6 satellites, with Landsat-8 OLI serving as a reference sensor. The key advantage of this synergistic observation strategy is the ability to obtain a greater number of image pairs that closely resemble Landsat-8 OLI reference images in terms of geometry and observation dates. This increased availability of matching images ensures a more representative dataset of the observation geometry, enabling the derived calibration coefficients to be applicable across various sun–target–sensor geometries. Then, the geometry angles and bidirectional reflectance information were put into a Particle Swarm Optimization (PSO) algorithm incorporating radiative transfer modeling. This PSO-based approach formulates cross-calibration as an optimization problem, eliminating the reliance on complex BRDF models and satellite-based BRDF products that can be affected by cloud contamination. Extensive validation experiments involving satellite data and in situ measurements demonstrated an average uncertainty of less than eight percent for the proposed cross-radiation calibration scheme. Comparisons of top-of-atmosphere (TOA) results calibrated using our proposed scheme, the previous traditional radiative transfer modeling using MODIS BRDF products for BRDF correction (RTM-BRDF) method, and official coefficients reveal the superior accuracy of our method. The proposed scheme achieves a 36.99% decrease in root mean square error (RMSE) and a 38.13% increase in mean absolute error (MAE) compared to official coefficients. Moreover, it achieves comparable accuracy to the RTM-BRDF method while eliminating the need for MODIS BRDF products, with a decrease in RMSE exceeding 14% for the off-nadir WFV1 and WFV4 cameras. The results substantiate the efficacy of the proposed scheme in enhancing cross-calibration accuracy by improving image match-up selection, efficiently removing BRDF effects, and expanding applicability to diverse observation geometries.

Research on the Effects of Detector Shape on Spectral Calibration of Infrared Hyperspectral Interferometer Based on Simulation Data

ABSTRACT Infrared hyperspectral instrument is a complex optical instrument. The requirement for the application of the infrared hyperspectral interferometer is fine calibration accuracy. The accuracy of the spectral frequency of the infrared hyperspectral interferometer is a key influencing factor of the radiometric calibration accuracy. In order to meet the quantitative application, the spectral calibration accuracy is generally within 10ppm. For infrared hyperspectral instruments in geostationary orbit, the focal plane has the characteristics of large array and small detector size. When the detector size of an infrared hyperspectral interferometer is in the geostationary orbit whether the detector as a point detector will affect the spectral calibration accuracy. In this paper, we simulate and analyze the influence of the shape of the detector (square and point detector) on the spectral calibration accuracy. This paper first builds an infrared hyperspectral observation and calibration simulation model. The brightness temperature deviation between the calibration spectrum and the input spectrum is close to 0 K, which shows the rationality of building the simulation model. Then, in the process of spectral calibration, two methods, square detectors and point detectors, are adopted, respectively. The spectral calibration of the square detector needs to calculate the line shape function of the instrument according to the detector position. Regarding point detector spectral calibration, this paper proposes a spectral calibration parameter optimization algorithm based on the optimization algorithm, which does not depend on the physical parameters of the instrument, but only uses the observed simulated spectrum and the reference spectrum. The results show that the spectral calibration accuracy of the point detector based on the optimization method can be close to 0ppm, which is consistent with the spectral calibration accuracy of the square detector.

The Uncertainty of SNO Cross-Calibration for Satellite Infrared Channels

The on-orbit radiometric calibration is a fundamental task in quantitative remote sensing applications. A widely used calibration method is the cross-calibration based on Simultaneous Nadir Observation (SNO), which involves using high-precision reference instruments to calibrate lower-precision onboard instruments. However, despite efforts to match the observation time, spatial location, field geometry, and instrument spectra, errors can still be introduced during the matching processes and linear regression analysis. This paper focuses on the error generated by sample matching and the error fitting method generated by the sample fitting method. An error propagation analysis is performed to develop a generic model for assessing the uncertainty of the SNO cross-calibration method itself in meteorological satellite infrared channels. The model is validated using the payload parameters of the Hyperspectral Infrared Atmospheric Sounder (HIRAS) and the Medium Resolution Spectral Imager (MERSI) instruments aboard the FengYun-3D (FY-3D). Simulation experiments are performed considering typical bright temperatures, different background fields, and varying matching threshold conditions. The results demonstrate the effectiveness of the proposed model in capturing the error propagation chain in the SNO cross-calibration process. The model provides valuable insight into error analysis in the SNO cross-calibration method and can assist in determining the optimal sample matching threshold for achieving radiometric calibration accuracy.

Freeform reflector light source used for space traceable spectral radiance calibration on the solar reflected band.

For purpose of improving the accuracy of in-orbit radiometric calibration of Chinese Space-based Radiometric Benchmark (CSRB) reference payload on the reflected solar band and reduce resource consumption, this paper proposed a freeform reflector radiometric calibration light source system based on the beam shaping characteristics of the freeform surface. The design method of initial structure discretization based on Chebyshev points was used to design and solve the freeform surface, and the feasibility of the design method was verified by optical simulation. The designed freeform surface is machined and tested, and the test results show that the surface roughness root mean square (RMS) of the freeform reflector is 0.61 μm, which indicates that the continuity of the machined surface is good. The optical characteristics of the calibration light source system are measured, the results show that the irradiance uniformity and radiance uniformity are better than 98% in the effective illumination area of 100 mm × 100 mm on the target plane. The constructed freeform reflector calibration light source system can meet the requirements of large area, high uniformity and light weight for onboard calibration of the payload of the radiometric benchmark, improving the measurement accuracy of spectral radiance on the reflected solar band.

On-Orbit Vicarious Radiometric Calibration and Validation of ZY1-02E Thermal Infrared Sensor

The ZY1-02E satellite carrying a thermal infrared sensor was successfully launched from the Taiyuan Satellite Launch Center on 26 December 2021. The quantitative characteristics of this thermal infrared camera, for use in supporting applications, were acquired as part of an absolute radiometric calibration campaign performed at the Ulansuhai Nur and Baotou calibration site (Inner Mongolia, July 2022). In this paper, we propose a novel on-orbit absolute radiometric calibration technique, based on multiple ground observations, that considers the radiometric characteristics of the ZY1-02E thermal infrared sensor. A variety of natural surface objects were selected as references, including bodies of water, bare soil, a desert in Kubuqi, and sand and vegetation at the Baotou calibration site. During satellite overpass, the 102F Fourier transform thermal infrared spectrometer and the SI-111 infrared temperature sensor were used to measure temperature and ground-leaving radiance for these surface profiles. Atmospheric water vapor, aerosol optical depth, and ozone concentration were simultaneously obtained from the CIMEL CE318 Sun photometer and the MICROTOP II ozonometer. Atmospheric profile information was acquired from radiosonde instruments carried by sounding balloons. Synchronous measurements of atmospheric parameters and ECMWF ERA5 reanalysis data were then combined and input to an atmospheric radiative transfer model (MODTRAN6.0) used to calculate apparent radiance. Calibration coefficients were determined from the measured apparent radiance and satellite-observed digital number (DN), for use in calculating the on-orbit observed radiance of typical surface objects. These values were then compared with the apparent radiance of each object, using radiative transfer calculations to evaluate the accuracy of on-orbit absolute radiometric calibration. The results show that the accuracy of this absolute radiometric calibration is better than 0.6 K. This approach allows the thermal infrared channel to be unrestricted by the limitations of spectrum matching between a satellite and field measurements, with strong applicability to various types of calibration sites.

Quick Quality Assessment and Radiometric Calibration of C-SAR/01 Satellite Using Flexible Automatic Corner Reflector

C-SAR/01, the successor of China’s Gaofen-3 Satellite, which launched on 23 November 2021, is the first synthetic aperture radar (SAR) satellite to be launched in China’s civil space infrastructure plan and has served as an invaluable data resource. Radiometric calibration and validation are prerequisites for the quantitative application of SAR data. In this study, the radiometric calibration experiments of C-SAR/01 data of the ultra-fine strip (UFS) and fine strip I (FSI) modes were conducted applying flexible automatic triangular trihedral corner reflectors deployed in Xilinhot SAR satellite calibration and validation site. Accordingly, the image quality and radiometric calibration accuracy were evaluated. The results show that the spatial resolution, peak sidelobe ratio, and integrated sidelobe ratio of UFS and FSI mode data of C-SAR/01 are better than those of the design indexes, and the calibration results from the integral method are more stable than those from the peak method. Furthermore, the standard deviation of the calibration constant for UFS mode data is 0.234 dB, with the relative and absolute calibration accuracies obtained as 0.233 and 0.532 dB, respectively, whereas the standard deviation calibration constant for FSI mode data is 0.198 dB, with its relative and absolute calibration accuracies evaluated as 0.199 and 0.333 dB, respectively.

Absolute radiometric calibration with lunar surface reflectance as reference for earth-observing VNIR camera

The absolute radiometric accuracy of earth-observing camera is crucial for the applications of natural resources, environment, agriculture and other industries. To continue the progress in this filed, a lunar surface reflectance based radiometric calibration approach is given in this paper. We chose IIM, M3, SP lunar models as references and GF-4 VNIR camera as sensor under calibrating. The lunar calibration sites were MS-2 site, Apollo-16 site and CE-3 site. The equivalent reflectance models of lunar were retrieved by Multiplying and integrating with the spectral response function of VNIR camera. Absolute radiometric calibrations with the equivalent reflectance models of lunar were carried out for 520–590nm, 630–690nm and 770–890nm spectral bands. The ground-based validation experiments were conducted with low, medium and high reflectance targets. The calibration accuracy was evaluated by comparing the relative errors of derived radiance after radiometric calibration with the benchmarks of TOA radiance transferred by in-situ measured reflectance. Lunar-based calibration models, lab and on-orbit filed-based models were used to compare the relative errors between proposed method and traditional way. The results showed that using IIM lunar model had better radiometric accuracy than other models, and SP model had the similar performance with traditional on-orbit filed-based model. The results indicated that using lunar to calibrate the earth-observing camera had the capability to improve the radiometric calibration accuracy.

On-Orbit Radiometric Calibration of Hyperspectral Sensors on Board Micro-Nano Satellite Constellation Based on RadCalNet Data

The stability and accuracy of the on-orbit radiometric calibration of hyperspectral sensors are prerequisites for the quantitative application of satellite hyperspectral data. The Zhuhai-1 micro-nano satellite constellation is composed of eight hyperspectral satellite missions. The Orbita Hyperspectral Sensor (OHS) on board each satellite has a gradient filter spectroscopic design. When observing the Earth, eight integration stages can be set for each band according to different lighting conditions. Due to high manufacturing costs, OHSs are not equipped with on-board calibration devices. Therefore, it is very difficult to accurately calibrate OHSs for all of the integration stages. On the other hand, it is extremely important to ensure radiometric consistency between different OHSs within the Zhuhai-1 micro-nano satellite constellation. To carry out the rapid radiometric calibration of the Zhuhai-1 constellation, an on-orbit radiometric calibration model considering all of the integration stages related to hyperspectral sensors was built based on the BOA reflectance and atmosphere parameters published by the Committee on Earth Observation Satellites (CEOS) radiometric calibration network (RadCalNet). The RadCalNet product was used to derive the TOA radiance base in the Second Simulation of the Satellite Signal in the Solar Spectrum (6S) radiative transfer (RT) model. In this paper, we analyzed the radiometric stability of the same sensor and the consistency of different calibration results regarding four RadCalNet sites, and the on-orbit radiometric performance evaluation of OHSs was also carried out. The data retrieved from OHSs regarding hyperspectral surface reflectance were preliminarily validated using site-synchronous surface reflectance measurements.

Evaluation of FY-3E/HIRAS-II Radiometric Calibration Accuracy Based on OMB Analysis

Before infrared hyperspectral data are used in satellite data assimilation systems or retrieval systems, the quantitative analysis of data deviation is necessary. Based on RTTOV’s (Radiative Transfer for TOVS) simulation data of FY-3E/HIRAS-II (Hyperspectral Infrared Atmospheric Sounder) and the observation data of HIRAS-II, we counted the bias of observation minus simulation (OMB) during an on-orbit test; analyzed the characteristics and reasons for the bias from the perspective of the FOV (field of view), the scanning angle of the instrument, the day and night, and the target temperature change; and analyzed the stability of the radiometric calibration accuracy. We also combined the results of the MetOp-C/IASI (infrared atmospheric sounding interferometer), a similar high-precision instrument, with the bias of OMB to compare and evaluate the FY-3E/HIRAS-II radiometric calibration accuracy. In the end, we found that the mean OMB bias of the long-wave and medium-wave infrared bands is within ±2 K, and the bias standard deviation is better than 2 K; the bias of each FOV is consistent and the bias of most channels is better than 2 K. The OMB bias of each channel is consistent with the changes in the angle of the instrument. The bias trend of long-wave and medium-wave infrared channels is more consistent with the deviation of the day and night; the bias of the short-wave infrared channel at night is lower than in the daytime. When counting the bias as the target temperature changed, the results showed that there are no obvious temperature dependencies in the long-wave and medium-wave infrared channels. This reflects that the instrument’s non-linear effect is well ordered. We further evaluated the stability of the radiometric calibration accuracy through statistics from the OMB standard deviation of each channel of FY-3E/HIRAS-II. Most channel accuracy stability values were better than 0.1 K. We calculated that IASI and HIRAS-II OMB have double differences, and the results show that the double difference in most channels is better than 1 K. It shows that the HIRAS-II and IASI observations are highly consistent. Through the statistics of the OMB bias during the on-orbit test period of FY-3E/HIRAS-II, we fully evaluated its radiometric calibration accuracy and laid the foundation for FY-3E/HIRAS-II data to be used in the retrieval application and assimilation system.

Radiometric Calibration Scheme for COCTS/HY-1C Based on Image Simulation From the Standard Remote-Sensing Reflectance

The data quality of the satellite-retrieved water-leaving reflectance (Rrs) depends on the accuracy of radiometric calibration and the performance of atmospheric correction. A radiometric calibration scheme (RCS) has been developed to ensure the accuracy of Rrs through the gain adjustment factors (GAFs) to adjust the satellite calibrated data. The GAF is obtained from the ratio of the simulated reflectance at the top of atmosphere to the calibrated values. The simulated reflectance is computed by a satellite image simulation model (SISM) based on a dataset of climatological global Rrs images according to the same geometric angles of the image pixels. The dataset, taken as a kind of the pseudo-invariant calibration sites for in situ measurements, is generated from the average of standard satellite-retrieved Rrs during more than two decades (1997-2019). The SISM inputs the aerosol properties retrieved from the satellite level 1B data (L1B) and uses the same algorithms of the data-processing system. The results show that the accuracy of the calibration of the website downloaded Chinese Ocean Color and Temperature Scanner on the Haiyang-1C satellite (COCTS/HY-1C) is beyond the requirement of the operational data-processing system (higher than 10%). The daily GAFs can be used to recalibrate the L1B data and monitor the daily sensor degradations. The influences of GAFs are assessed on different meteorological conditions, indicating that the values decrease with the increase of the aerosol optical depths (AODs) but the average of the GAF image is little affected by the meteorological conditions. The uncertainty of GAFs was tested by the different inputs of Rrs values and the results show that they are actually little affected by errors of the Rrs inputs. Therefore, the RCS, taking the advantage of vicarious calibration, offers a tool to recalibrate the COCTS/HY-1C L1B data for the data reprocessing system.

Airborne SAR Radiometric Calibration Based on Improved Sliding Window Integral Method.

To verify the performance of the high-resolution fully polarimetric synthetic aperture radar (SAR) sensor carried by the Xinzhou 60 remote-sensing aircraft, we used corner reflectors to calibrate the acquired data. The target mechanism in high-resolution SAR images is more complex than it is in low-resolution SAR images, the impact of the point target pointing error on the calibration results is more obvious, and the target echo signal of high-resolution images is more easily affected by speckle noise; thus, more accurate extraction of the point target position and the response energy is required. To solve this problem, this paper introduces image context information and proposes a method to precisely determine the integration region of the corner reflector using sliding windows based on the integral method. The validation indicates that the fully polarimetric SAR sensor on the Xinzhou 60 remote-sensing aircraft can accurately reflect the radiometric characteristics of the ground features and that the integral method can obtain more stable results than the peak method. The sliding window allows the position of the point target to be determined more accurately, and the response energy extracted from the image via the integral method is closer to the theoretical value, which means that the high-resolution SAR system can achieve a higher radiometric calibration accuracy. Additionally, cross-validation reveals that the airborne SAR images have similar quality levels to Sentinel-1A and Gaofen-3 images.

Assessing Radiometric Calibration of FY-4A/AGRI Thermal Infrared Channels Using CrIS and IASI

The quality of thermal infrared (TIR) radiances from the FY-4A advanced geostationary radiation imager (AGRI) and their derived products is very important for weather predictions. The demand for more accurate weather forecasting models is increasing, so it is necessary to evaluate and improve the calibration accuracy of AGRI. This study aims to evaluate the AGRI TIR radiometric calibration accuracy by comparing two well-calibrated hyperspectral radiometers, the cross-track infrared sounder (CrIS), and the infrared atmospheric sounding interferometer (IASI). Most of the AGRI TIR channels are calibrated with an absolute brightness temperature (Tb) bias less than 0.5 K currently. The Tb bias of AGRI and CrIS\IASI depends on the target temperature, and it is smaller and more stable for higher Tb and increases for lower Tb. CH_14 shows the largest Tb bias (−0.53 K) and standard deviation (0.64 K), CH_10 and CH_11 record slightly positive Tb biases, and the other four channels exhibit negative Tb biases. The diurnal mean Tb bias is very consistent except for CH_11, which varies greatly among different time bins. The Tb bias of CH_09 is very consistent after the calibration update in February 2018. CH_10 shows a gradual bias decrease with bias from 1.0 to 0.6 K. CH_11 shows a gradual bias decrease with bias from 0.4 to 0.1 K with large fluctuations. CH_14 shows a gradual bias decrease with large fluctuation and like CH_11. CH_12 and CH_13 exhibit seasonal variations where the Tb bias reaches its maximum around June and minimum around February. In general, the Tb bias shows different spatial patterns in different channels and CH_11 shows obvious spatial distribution characteristics that is monthly dependent.

In-Flight Spectral Response Function Retrieval of a Multispectral Radiometer Based on the Functional Data Analysis Technique

The spectral response function (SRF) is a crucial parameter in multispectral radiometers, and it influences the radiometric calibration accuracy and quantitative application capabilities. The in-flight SRF often has errors due to prelaunch contamination or postlaunch degradation. This study proposes an innovative new method to retrieve SRFs of multispectral radiometers based on intercomparisons with hyperspectral sounders via the functional data analysis (FDA) technique. Under the FDA framework, all variables, including the hyperspectral radiance and SRF, are regarded as functions rather than discrete data by expanding in the Fourier functional basis. The forward convolution equation is processed directly into a functional integration model rather than a normally pointwise summation; this ensures that the unknown quantities are transformed from numerous SRF samples to several function parameters, thus avoiding the ill-posed problem. The proposed algorithm is verified with both simulated and real data from multiple thermal infrared bands of the FY-3 IRAS and FY-4 AGRI using collocations with METOP-B IASI. All these results demonstrate our algorithm's qualitative and quantitative effectiveness for infrared SRF retrieval. Although the demonstrations are particularly relevant to infrared spectra, the algorithm is universal and also applicable to other spectral bands.

Perennial ryegrass biomass retrieval through multispectral UAV data

Frequent biomass measurement is a key activity for optimal perennial ryegrass (Lolium perenne) management in intensive forage-based dairy operations. Due to the necessary high-frequency (i.e., weekly or monthly) pasture monitoring and continuous trend of larger dairy farms, such activity is perceived as an operational bottleneck. Consequently, substantial effort is directed to the development of accurate and automated technological solutions for biomass assessment. The popularization of unmanned aerial vehicles (UAVs) combined with multispectral cameras should allow for an optimal observational system able to deploy machine learning algorithms for near real-time biomass dry-matter (DM) mapping. For successful operation, these systems should deliver radiometrically accurate orthomosaics and robust models able to generalize across different periods. Nevertheless, the accuracy of radiometric calibration and generalization ability of these models is seldom evaluated. Also, such pipelines should require minimum processing power and allow for fast deployment. This study has established a two-year experiment comparing reflectance measurements between a handheld spectrometer and a commercial multispectral UAV camera. Different algorithms based on regression-tree architecture were contrasted regarding accuracy, speed, and model size. Model performances were validated, providing error-metrics for baseline accuracy and temporal validation. The results have shown that the standard procedure for multispectral imagery radiometric calibration is sub-optimal, requiring further post-processing and presenting low correlation with handheld measurements across spectral bands and dates. Nevertheless, after post-calibration, the use of spectral imagery has presented better baseline error than the point-based sensors, respectively displaying an average of 397.3 and 464.2 kg DM/ha when employed alongside the best performing algorithm (i.e., Cubist). When trained and validated across different years, model performance was largely reduced and deemed unfit for operational purposes. The Cubist/M5 family of algorithms have exhibited advantageous characteristics such as compact model structure, allowing for a higher level of model interpretability, while displaying a smaller size and faster deployment than the Random Forest, Boosted, and Bagged Regression Trees algorithms.

Radiometric calibration accuracy and stability of GOES-16 ABI Infrared radiance

The Advanced Baseline Imager (ABI) is the primary instrument onboard NOAA’s current generation of Geostationary Operational Environment Satellites R-series (GOES-R) satellites, measuring the reflected and emitted energy from the Earth. It consists of 16 channels, 10 in the thermal infrared (IR) and 6 in the solar reflective spectrum. Being the first in the GOES-R series satellites, GOES-16 was launched on November 19, 2016, and became operational as GOES-East at 75.2°W since December 18, 2017. We examine the radiometric calibration accuracy and stability of GOES-16 ABI IR radiance since its first light in January 2017, including the effects of two major updates of the GOES-R Ground Segment processing for the IR channels in October 2017, and June 2018. Using measurements by multiple hyperspectral radiometers from low Earth orbit satellites as references, it is found that, when converted to scene brightness temperature of 300 K, the calibrated ABI IR radiance is accurate within 0.13 K for Ch16 (13.3 μm), within 0.06 K for channel 12 (9.6 μm), and within 0.05 K for the other IR channels. This is an order of magnitude better than the requirement of 1 K. Since June, 2018, the radiometric calibration of GOES-16 ABI IR channels has been temporally stable, spatially uniform within the ABI full-disk field of view, absent of diurnal and seasonal variations, and invariant within various timelines. Other than short term disruptions as noted in the Calibration Event Log, GOES-16 ABI IR Level 1b products since June 19, 2018, is a reliable reference for satellite intercomparison or intercalibration studies.