Radiometric Calibration Research Articles

Fusing MODIS and AVHRR products to generate a global 1-km continuous NDVI time series covering four decades

ABSTRACT Satellite normalized difference vegetation index (NDVI) time series, essential for ecological and environmental applications, is still limited by a trade-off between the spatiotemporal resolution and time coverage despite various global products. The Advanced Very High-Resolution Radiometer (AVHRR) instrument can provide the longest continuous time series since 1982, but with the drawback of coarse spatial resolution and poor data quality. To address this issue, a spatiotemporal fusion-based long-term NDVI product (STFLNDVI) since 1982 was generated in this study at a 1-km spatial resolution with monthly intervals, by fusing with the Moderate Resolution Imaging Spectroradiometer (MODIS) data. A multi-step processing fusion framework, containing temporal filtering, normalization, spatiotemporal fusion, and residual error correction, was employed to combine the superior characteristics of the two products, respectively. Simulated comparison with MODIS data and real-data assessments with true 1 km AVHRR data both confirm the ideal accuracy of the fusion product in spatial distribution and temporal variation, providing stable long-term results similar to MODIS data. We believe that the STFLNDVI product will be of great significance in characterizing the spatial patterns and long-term variations of global vegetation and the historical radiometric calibrations inAVHRR data gaps around the Arctic and instrument differences between MODIS and AVHRR should be further considered in the future.

The Characterization of the Railroad Valley Playa Test Site Using the DESIS Imaging Spectrometer from the Space Station Orbit

The reflectance-based vicarious calibration approach uses measurements at well-understood test sites to provide top-of-atmosphere reference reflectance values suitable for inter-calibration approaches and does not require coincident views. The challenge is that results from such data may suffer from high variability from day to day. Data from high-quality sensors, such as the imaging spectrometers on the International Space Station (ISS) platform, provide an opportunity to use improved fine spectral information about the test sites with various sun/sensor geometries and site surface and atmospheric conditions to improve the test sites’ characterization. The results here are based on data from the DLR Earth Sensing Imaging Spectrometer (DESIS) instrument installed on the ISS since 2018 combined with output from the Radiometric Calibration Network (RadCalNet) site at Railroad Valley Playa (RRV) to decouple the effects of sun/sensor geometry from the RadCalNet predictions. The approach here uses the precessing orbit of the ISS to allow similar sensor view zenith angles at varying sun angles over short periods that limit the impact of any sensor changes and highlight the bi-directional effects of the surface reflectance and atmospheric conditions. DESIS data collected at (i) similar solar angles but varying view angles, (ii) similar sensor angles and varying solar angles, and (iii) similar scatter angles are compared. The DESIS results indicate that the top-of-atmosphere reflectance spectra for RRV at similar solar zenith angles but with varying sensor viewing angles provide more consistent data than those with varying solar zenith but with similar sensor viewing angles. In addition, comparisons of reflectance spectra of the site performed in terms of the sensor view scatter angle show good agreement, indicating that a directional reflectance correction would be straightforward and could offer a significant improvement in the use of RadCalNet data. The work shows that observations from imaging spectroscopy data from DESIS, and eventually Earth Surface Mineral Dust Source Investigation (EMIT), Surface Biology and Geology (SBG), and the climate-quality sensor CLARREO Pathfinder (CPF), provide the opportunity for the development of a model-based, SI-traceable prediction of at-sensor radiance over the RRV site that would serve as the basis for similar site characterizations with error budgets valid for arbitrary view and illumination angles.

Simulation of a simultaneous traceable spectroradiometric calibration of an imaging spectrometer

Spectroradiometric calibration aims to determine the instrumental spectral response function (ISRF) parameters and radiometric coefficients of an instrument’s spectral bands across all spatial pixels. Typically, this is done by making separate spectral and radiometric calibration measurements. We present a method for the simultaneous traceable spectroradiometric calibration of an imaging spectrometer, using the Spectroscopically Tunable Absolute Radiometric, calibration and characterisation, Optical Ground Support Equipment (STAR-cc-OGSE) facility. We performed the forward simulation of calibration data acquisition by convolving input spectra with the sensor model’s response and simulated a slit scattering function (SSF)-based calibration, allowing for both ISRF coefficients and the absolute spectral responsivities to be accurately retrieved from a single series of measurements. We show how the SSF method minimizes uncertainties compared to the traditional spectroradiometric calibration approach.

Hinode EIS: Updated In-flight Radiometric Calibration

Abstract We present an update to the in-flight radiometric calibration of the Hinode Extreme Ultraviolet Imaging Spectrometer (EIS), revising and extending our previous studies. We analyze full-spectral EIS observations of quiet Sun (QS) and active regions (ARs) from 2007 until 2022. Using CHIANTI version 10, we adjust the EIS relative effective areas for a selection of dates with emission measure analyses of off-limb QS. We find generally good agreement (within typically ±15%) between measured and expected line intensities. We then consider selected intensity ratios for all of the dates and apply an automatic fitting method to adjust the relative effective areas. To constrain the absolute values from 2010 and later, we force agreement between EIS and Solar Dynamics Observatory Atmospheric Imaging Assembly 193 Å observations. The resulting calibration, with an uncertainty of about ±20%, is then validated in various ways, including flare line ratios from Fe xxiv and Fe xvii, emission measure analyses of cool AR loops, and several density-dependent line ratios.

Assessment of Radiometric Calibration Consistency of Thermal Emissive Bands Between Terra and Aqua Moderate-Resolution Imaging Spectroradiometers

Moderate-Resolution Imaging Spectroradiometer (MODIS) sensors onboard the Terra and Aqua spacecraft have been in orbit for over 24 and 22 years, respectively, providing continuous observations of the Earth’s surface. Among the instrument’s 36 bands, 16 of them are thermal emissive bands (TEBs) with wavelengths that range from 3.75 to 14.24 μm. Routine post-launch calibrations are performed using the sensor’s onboard blackbody and space view port, the moon, and vicarious targets that include the ocean, Dome Concordia (Dome C) in Antarctica, and quasi-deep convective clouds (DCC). The calibration consistency between the satellite measurements from the two instruments is essential in generating a multi-year data record for the long-term monitoring of the Earth’s Level 1B (L1B) data. This paper presents the Terra and Aqua MODIS TEB comparison for the upcoming Collection 7 (C7) L1B products using measurements over Dome C and the ocean, as well as the double difference via simultaneous nadir overpasses with the Infrared Atmospheric Sounding Interferometer (IASI) sensor. The mission-long trending of the Terra and Aqua MODIS TEB is presented, and their cross-comparison is also presented and discussed. Results show that the calibration of the two MODIS sensors and their respective Earth measurements are generally consistent and within their design specifications. Due to the electronic crosstalk contamination, the PV LWIR bands show slightly larger drifts for both MODIS instruments across different Earth measurements. These drifts also have an impact on the Terra-to-Aqua calibration consistency. This thorough assessment serves as a robust record containing a summary of the MODIS calibration performance and the consistency between the two MODIS sensors over Earth view retrievals.

A radiometric calibration method for defined lunar areas based on SP model

A radiometric calibration method for defined lunar areas based on SP model

A Study of NOAA-20 VIIRS Band M1 (0.41 µm) Striping over Clear-Sky Ocean

The Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the National Oceanic and Atmospheric Administration-20 (NOAA-20) satellite was launched on 18 November 2017. The on-orbit calibration of the NOAA-20 VIIRS visible and near-infrared (VisNIR) bands has been very stable over time. However, NOAA-20 operational M1 (a dual gain band with a center wavelength of 0.41 µm) sensor data records (SDR) have exhibited persistent scene-dependent striping over clear-sky ocean (high gain, low radiance) since the beginning of the mission, different from other VisNIR bands. This paper studies the root causes of the striping in the operational NOAA-20 M1 SDRs. Two potential factors were analyzed: (1) polarization effect-induced striping over clear-sky ocean and (2) imperfect on-orbit radiometric calibration-induced striping. NOAA-20 M1 is more sensitive to the polarized lights compared to other NOAA-20 short-wavelength bands and the similar bands on the Suomi NPP and NOAA-21 VIIRS, with detector and scan angle-dependent polarization sensitivity up to ~6.4%. The VIIRS M1 top of atmosphere radiance is dominated by Rayleigh scattering over clear-sky ocean and can be up to ~70% polarized. In this study, the impact of the polarization effect on M1 striping was investigated using radiative transfer simulation and a polarization correction method similar to that developed by the NOAA ocean color team. Our results indicate that the prelaunch-measured polarization sensitivity and the polarization correction method work well and can effectively reduce striping over clear-sky ocean scenes by up to ~2% at near nadir zones. Moreover, no significant change in NOAA-20 M1 polarization sensitivity was observed based on the data analyzed in this study. After the correction of the polarization effect, residual M1 striping over clear-sky ocean suggests that there exists half-angle mirror (HAM)-side and detector-dependent striping, which may be caused by on-orbit radiometric calibration errors. HAM-side and detector-dependent striping correction factors were analyzed using deep convective cloud (DCC) observations (low gain, high radiances) and verified over the homogeneous Libya-4 desert site (low gain, mid-level radiance); neither are significantly affected by the polarization effect. The imperfect on-orbit radiometric calibration-induced striping in the NOAA operational M1 SDR has been relatively stable over time. After the correction of the polarization effect, the DCC-based striping correction factors can further reduce striping over clear-sky ocean scenes by ~0.5%. The polarization correction method used in this study is only effective over clear-sky ocean scenes that are dominated by the Rayleigh scattering radiance. The DCC-based striping correction factors work well at all radiance levels; therefore, they can be deployed operationally to improve the quality of NOAA-20 M1 SDRs.

Prelaunch Reflective Solar Band Radiometric Performance of JPSS-3 and -4 VIIRS

The Joint Polar Satellite System 3 (JPSS-3) and -4 (JPSS-4) Visible Infrared Imaging Radiometer Suite (VIIRS) instruments are the last in the series (S-NPP VIIRS launched in October 2011, JPSS-1 VIIRS launched in November 2017, and JPSS-2 VIIRS launched in November 2022) of highly advanced polar-orbiting environmental satellites. Both instruments underwent a comprehensive sensor-level thermal vacuum (TVAC) testing at the Raytheon Technologies El Segundo facility to characterize the spatial, spectral, and radiometric aspects of the VIIRS sensor performance. This paper focuses on the radiometric performance of the 14 reflective solar bands (RSBs) that cover the wavelength range from 0.41 to 2.3 µm. Key instrument calibration parameters such as instrument gain, signal-to-noise ratio (SNR), dynamic range, and radiometric calibration uncertainty were derived from the TVAC measurements for both the primary and redundant electronics at three instrument temperature plateaus: cold, nominal, and hot. This paper shows that all the JPSS-3 and -4 VIIRS RSB detectors have been well characterized, with key performance metrics comparable to the previous VIIRS instruments on-orbit. The radiometric calibration uncertainty of the RSBs is within the 2% requirement, except in the case of band M1 of JPSS-4. Comparison of the radiometric performance to sensor requirements, as well as a summary of key instrument testing and performance issues, is also presented.

Reconstruction of electron velocity distribution function and Gibbs entropy from electron cyclotron emission in magnetized plasmas

Abstract We propose a method for reconstructing the fluctuation components of the electron velocity distribution function, and the electron entropy, which is a functional of the fluctuation components of the electron velocity distribution function, using the harmonic spectrum from pure X-mode electron cyclotron emission (ECE) in optically thin plasmas. This formulation employs the maximum entropy method in velocity space using the Hankel transform, which converts from v⊥ space to p space (where, v⊥ and p are the electron velocity perpendicular to the background magnetic field and the index of the wavenumber in velocity space). Numerical tests validated the effectiveness of the proposed method, which is applicable across a wide range of magnetized plasma conditions, including conditions with both non-relativistic and relativistic electrons, except in cases of harmonic overlap or under optically thick conditions. Notably, this method does not require radiometer calibration for ECE measurements. This method fascilitates the experimental evaluation of electron entropy transport in fusion plasma experiments. Moreover, when combined with measurements in k-space (spatial distribution), this approach enables entropy distribution acquisition in phase space (k-p space).

Assessing Geometric and Radiometric Accuracy of DJI P4 MS Imagery Processed with Agisoft Metashape for Shrubland Mapping

The rise in inexpensive Unmanned Aerial Systems (UAS) and accessible processing software offers several advantages in forest ecosystem monitoring and management. The increase in usability of such tools can result in the simplification of workflows, potentially impacting the quality of the generated data. This study offers insights into the precision and reliability of the DJI Phantom 4 Multispectral (P4MS) UAS for mapping shrublands using the Agisoft Metashape (AM) for image processing. Geometric accuracy was evaluated using ground control points (GCPs) and different configurations. The best configuration was then used to produce orthomosaics. Subsequently, the orthomosaics were transformed into reflectance orthomosaics using various radiometric correction methods. These methods were further assessed using reference panels. The method producing the most accurate reflectance values was then chosen to create the final reflectance and Normalised Difference Vegetation Index (NDVI) maps. Radiometric accuracy was assessed through a multi-step process. Initially, precision was measured by comparing reflectance orthomosaics and NDVI derived from images taken on consecutive days. Finally, reliability was evaluated by comparing the NDVI with NDVI from a reference camera, the MicaSense Altum AL0, produced with images acquired on the same days. The results demonstrate that the P4MS is both precise and reliable for shrubland mapping. Reflectance maps and NDVI generated in AM exhibit acceptable geometric and radiometric accuracy when geometric calibration is performed with at least one GCP and radiometric calibration utilises images of reflectance panels captured at flight height, without relying on incident light sensor (ILS) data.

Evaluation of Soil Moisture Retrievals from a Portable L-Band Microwave Radiometer

A novel Portable L-band radiometer (PoLRa), compatible with tower-, vehicle- and drone-based platforms, can provide gridded soil moisture estimations from a few meters to several hundred meters yet its retrieval accuracy has rarely been examined. This study aims to provide an initial assessment of the performance of PoLRa-derived soil moisture at a spatial resolution of approximately 0.7 m × 0.7 m at a set of sampling pixels in central Illinois, USA. This preliminary evaluation focuses on (1) the consistency of PoLRa-measured brightness temperatures from different viewing directions over the same area and (2) whether PoLRa-derived soil moisture retrievals are within an acceptable accuracy range. As PoLRa shares many aspects of the L-band radiometer onboard NASA’s Soil Moisture Active Passive (SMAP) mission, two SMAP operational algorithms and the conventional dual-channel algorithm (DCA) were applied to calculate volumetric soil moisture from the measured brightness temperatures. The vertically polarized brightness temperatures from the PoLRa are typically more stable than their horizontally polarized counterparts across all four directions. In each test period, the standard deviations of observed dual-polarization brightness temperatures are generally less than 5 K. By comparing PoLRa-based soil moisture retrievals against the simultaneous moisture values obtained by a handheld capacitance probe, the unbiased root mean square error (ubRMSE) and the Pearson correlation coefficient (R) are mostly below 0.05 m3/m3 and above 0.7 for various algorithms adopted here. While SMAP models and the DCA algorithm can derive soil moisture from PoLRa observations, no single algorithm consistently outperforms the others. These findings highlight the significant potential of ground- or drone-based PoLRa measurements as a standalone reference for the calibration and validation of spaceborne L-band synthetic aperture radars and radiometers. The accuracy of PoLRa-yielded high-resolution soil moisture can be further improved via standardized operational procedures and appropriate tau-omega parameters.

Towards an Improved Approach of Clay Minerals Mapping in the Northwestern Region of Algeria

Abstract The availability of hyperspectral images has significantly facilitated clay minerals identification and mapping. Based on the Hyperion L1R hyperspectral dataset, this research aims to improve previous scientific work related to identifying and mapping clay minerals in the Djebel Meni region (northwestern of Algeria). Firstly, we enhance the dataset quality through pre-processing techniques like the removal of bad bands, radiometric calibration, and atmospheric correction. Secondly, the Spectral Information Divergence (SID) algorithm was employed by introducing endmembers derived with the Sequential Maximum Angle Convex Cone (SMACC) algorithm initially and then using Jet Propulsion Laboratory (JPL) spectral signatures of Illite, Kaolinite, and Montmorillonite. The classification results show a correspondence between areas occupied by endmembers and JPL spectral signatures, which helps in matching endmembers with specific minerals. Finally, we conducted a comparative analysis of the two classifications outcomes against a reference map. This last is generated using SID algorithm, which takes ground truth spectral signatures as input. Our proposed approach using the SID classifier has given an overall accuracy score of 97.13% and 84.17% using endmembers image and JPL library respectively. The Kappa coefficient is respectively, with endmembers image and JPL, 0.93 and 0.57. These results show that the SID classification with the endmembers image is better than the SID classification with the JPL library because extracting the endmembers from the image is more accurate than doing that from the pure minerals analyzed in the laboratory. These promising results suggest that our approach could be extended to the identification of clay minerals in diverse hyperspectral datasets.

Geometric preprocessing for measurements of comet 67P acquired by Rosetta's Visible and InfraRed Thermal Imaging Spectrometer, Mapping channel (VIRTIS-M).

Comet 67P/Churyumov-Gerasimenko (hereafter 67P) was the primary target of ESA's Rosetta mission. Hyperspectral images acquired by the Mapping channel of the Visible and InfraRed Thermal Imaging Spectrometer aboard Rosetta can be used to derive physical and compositional surface properties by detailed spectrophotometric analyses. This calls for a precise spatial co-registration between measurements and geometry information. In this work, we improve the wavelength-dependent co-registration and also the spatial consistency of the radiometric calibration. This is accomplished by applying a feature-based image matching method comparing measured 67P nucleus images from the entire mission to corresponding photometric simulations. The derived geometric distortions suggest previously unaccounted optical aberrations of the instrument, in conjunction with non-systematic spacecraft pointing and perspective errors, and discrepancies between the true nucleus shape at data acquisition time and the used digital shape model.

Calibration and Validation of NOAA-21 Ozone Mapping and Profiler Suite (OMPS) Nadir Mapper Sensor Data Record Data

The Ozone Mapping and Profiler Suites (OMPS) Nadir Mapper (NM) is a grating spectrometer within the OMPS nadir instruments onboard the SNPP, NOAA-20, and NOAA-21 satellites. It is designed to measure Earth radiance and solar irradiance spectra in wavelengths from 300 nm to 380 nm for operational retrievals of the nadir total column ozone. This study presents calibration and validation analysis results for the NOAA-21 OMPS NM SDR data to meet the JPSS scientific requirements. The NOAA-21 OMPS SDR calibration derives updates of several previous OMPS algorithms, including the dark current correction algorithm, one-time wavelength registration from ground to on-orbit, daily intra-orbit wavelength shift correction, and stray light correction. Additionally, this study derives an empirical scale factor to remove 2.2% of systematic biases in solar flux data, which were caused by pre-launch solar calibration errors of the OMPS nadir instruments. The validation of the NOAA-21 OMPS SDR data is conducted using various methods. For example, the 32-day average method and radiative transfer model are employed to estimate inter-sensor radiometric calibration differences from either the SNPP or NOAA-20 data. The quality of the NOAA-21 OMPS NM SDR data is largely consistent with that of the SNPP and NOAA-20 OMPS data, with differences generally within ±2%. This meets the scientific requirements, except for some deviations mainly in the dichroic range between 300 nm and 303 nm. The deep convective cloud target approach is used to monitor the stability of NOAA-21 OMPS reflectance above 330 nm, showing a variation of 0.5% over the observed period. Data from the NOAA-21 VIIRS M1 band are used to estimate OMPS NM data geolocation errors, revealing that along-track errors can reach up to 3 km, while cross-track errors are generally within ±1 km.

Open-Source Software for Satellite-Based Crop Health Monitoring

PurposeTimely information on agricultural crops is essential to support informed crop management decisions. Satellite remote sensing technology offers a new paradigm for monitoring agricultural crops at large scale. Daily access to satellite imagery from Planet Scope allows opportunities to continuously monitor the Earth’s surface in 3-m resolution with various spectrums. However, satellites on agricultural applications are still in less information to agriculture producers due to the limited access to analytic software. The paper aims to fill the gap between the satellite resources and the agricultural users, specifically to develop satellite image analytic software publicly available for agricultural crop health monitoring.MethodsOpen-source software, iCalendar, was developed for high-throughput image analysis and visualization of a vegetation index (VI) and a time-series crop growth. Application programing interface (API) was used to automate the image queuing, activation, and download based on user-defined time and area of interest.ResultsThe API was refined to filter unusable data such as multiples of clipped images and duplicates of tile images. iCalendar resulted in the seamless data processing and automated user-friendly visualization of a seasonal VI profile and a corresponding field map calendar, that are key sources of crop production cycle. Machine learning-based segmentation algorithm and radiometric calibration showed the detection and correction of abnormalities in the image.ConclusionThe satellite image-based plant monitoring system through open-source software iCalendar provided a significant improvement to spatiotemporal field observation and allows growers and researchers in-season crop health assessment, thereby promoting new management strategies for crop productivity and protection.

Stacked Ensemble with Machine Learning Regressors on Optimal Features (SMOF) of hyperspectral sensor PRISMA for inland water turbidity prediction.

Leveraging hyperspectral data across various domains yields substantial benefits, yet managing many spectral bands and identifying the essential ones poses a formidable challenge. This study identifies the most relevant bands within a hyperspectral data cube for turbidity prediction in inland water. Nine machine learning regressors Cat Boost, Decision Trees, Extra Trees, Gradient Boost, Light Gradient Boost (LightGBM), Recursive Feature Elimination (RFE), Random Forest, Support Vector Regressor (SVR), and Xtreme Gradient Boost (XGBoost) have been used to compute the feature importance of the hyperspectral bands for predicting turbidity. Random Forest has outperformed the other models with a mean absolute percentage error (MAPE) of 1.61%, and the R2 of the linear fit is 0.96. Band 77, with a central wavelength of 1067.61nm, is the most dominating band regarding feature importance. We have also developed a novel framework for turbidity prediction: Stacked Ensemble with Machine Learning Regressors on Optimal Features (SMOF). It employs a stacking ensemble of the nine regressors mentioned above with Random Forest as both base and meta-model, leveraging feature selection outputs. With this framework, the MAPE (%) reached 1.21, while the R2 stood at 0.95. The present study also presents a simple statistical algorithm to detect noisy bands in the Hyperspectral Precursor of the Application Mission (PRISMA) data cube. The approach assesses quadrat-wise intra-band spatial coherence using Renyi's entropy thresholding for noisy band segregation. Radiometric calibration error and absorption due to water vapour are the two primary sources of noise within the data cube. Moreover, this research implements the open-source Water Colour Simulator (WASI) to simulate inland water spectra with varied proportions of turbidity. Overall, the study presents an approach to identify noisy bands and integrates the potential wavelengths for turbidity prediction of inland waters.

Monitoring the Subsidence in Wan’an Town of Deyang Based on PS-InSAR Technology (Sichuan, China)

In recent years, land subsidence has become a crucial factor affecting urban safety and sustainable development, especially in Wan’an Town. To accurately monitor and analyze the land subsidence in Wan’an Town, this study uses the PS-InSAR technique combined with an improved DEM for detailed research on land subsidence in Wan’an Town. PS-InSAR, or Permanent Scatterer Interferometric SAR, is suitable for high-precision monitoring of surface deformation. The natural neighbor interpolation method optimizes DEM data, improving its spatial resolution and accuracy. In this study, multiple periods of SAR imagery data of Wan’an Town were collected and preprocessed through radiometric calibration, phase unwrapping, and other steps. Using the PS-InSAR technique, the phase information of permanent scatterers (PS points) on the surface was extracted to establish a deformation model and preliminarily analyze the land subsidence in Wan’an Town. Concurrently, the DEM data were optimized using the natural neighbor interpolation method to enhance its accuracy. Finally, the optimized DEM data were combined with the surface deformation information extracted through the PS-InSAR technique for a detailed analysis of the land subsidence in Wan’an Town. The research results indicate that the DEM data optimized by the natural neighbor interpolation method have higher accuracy and spatial resolution, providing a more accurate reflection of the topographical features of Wan’an Town. The research found that the optimized DEM provided a more accurate reflection of Wan’an Town’s topographical features. By combining PS-InSAR data, subsidence information from 2016 to 2024 was calculated. The study area showed varying degrees of subsidence, with rates ranging from 6 mm/year to 10 mm/year. Four characteristic deformation areas were analyzed for causes and influencing factors. The findings contribute to understanding urban land subsidence, guiding urban planning, and providing data support for geological disaster warning and prevention.

Optical System Design of a Self-Calibrating Real Entrance Pupil Imaging Spectrometer

Presently, on-orbit calibration methods have several problems, such as low calibration accuracy and broken traceability links, so an urgent need exists to unify traceable and high-precision on-orbit radiometric calibration loads as benchmarks for cross-transfer radiometric calibration. Considering the deficiencies of current on-orbit calibration, this paper proposes adjusting the size of the variable diaphragm at the entrance pupil and the integration time to attain large dynamic attenuation, converting the radiometric calibration into absolute geometric calibration of the attenuation device, and realizing a self-calibrating real entrance pupil imaging spectrometer (SCREPIS) that can be directly used to view the Earth and the Sun and quickly obtain apparent reflectance data. An initial structural design method based on the distance between individual mirrors is proposed according to the instrument design requirements. The design of a real entry pupil image-side telecentricity off-axis three-reflector front optical system with a 7° field of view along the slit direction, a 3.7 systematic F-number, and a 93 mm focal length is finally realized, and the system image plane energy is verified to change proportionally to the variable diaphragm area. Finally, the front system and rear Offner optical system are jointly simulated and optically designed. The system provides instrumental support for cross-calibration and theoretical support and a technical basis for planning space-based radiation references.

Identification of Global Extended Pseudo Invariant Calibration Sites (EPICS) and Their Validation Using Radiometric Calibration Network (RadCalNet)

This study introduces a global land cover clustering using an unsupervised algorithm, incorporating the novel step of filtering data to retain only temporally stable pixels before applying K-means clustering. Unlike previous approaches that did not assess the pixel-level temporal stability, this method provides more reliable clustering results. The K-means identified 160 distinct clusters, with Cluster 13 Global Temporally Stable (Cluster 13-GTS) showing significant improvements in temporal stability. Compared to Cluster 13 Global (Cluster 13-G) from earlier research, Cluster 13-GTS reduced the coefficient of variation by up to 1% and increased the number of calibration locations from 23 to over 50. This study also validated these clusters using TOA reflectance from ground-truth measurements collected at the Radiometric Calibration Network (RadCalNet) Gobabeb (RCN-GONA) site, incorporating data from Landsat 8, Landsat 9, Sentinel-2A, and Sentinel-2B. The GONA Extended Pseudo Invariant Calibration Sites (EPICS) GONA-EPICS cluster used for the validation provided statistically comparable mean TOA reflectance to RCN-GONA, with a reduced chi-square test indicating minimal differences within the cluster’s uncertainty range. Notably, the difference in reflectance between RCN-GONA and GONA-EPICS was less than 0.023 units across all the bands. Although GONA-EPICS exhibited slightly higher uncertainty (6.4% to 10.3%) compared to RCN-GONA site (<5%), it offered advantages such as 80 potential calibration points per Landsat cycle and reduced temporal instability, and it provided alternatives to reduce the reliance on single sites like traditional PICS or RCN-GONA, making it a valuable tool for calibration efforts. These findings highlight the potential of the newly developed EPICS for radiometric calibration and stability monitoring of optical satellite sensors. Distributed across diverse regions, these global targets increase the number of calibration points available for any sensor in any orbital cycle, reducing the reliance on traditional PICS and offering more robust targets for radiometric calibration efforts.

An Empirical Line approach for Agrowing Camera Aerial Images of inland waters based on exponential fit and in situ water measurements

Abstract. With the advancement of technology in the area of remote sensing, monitoring the Earth's surface using multispectral cameras attached to unmanned aerial vehicles (UAV) has become promising. However, in many Earth observation applications, it is needed to make compatible the spatial data of images captured by high spatial resolution multispectral sensors with the spectral response of targets on the Earth's surface. This relation is obtained through a radiometric calibration. The empirical line method is commonly used to calibrate the spectral bands of sensors. Thus, applications of this method using linear fit have retrieved negative values in water bodies. So that, attempting different adjustments, as well different reference targets, can solve this issue. In this study, the water quality of small bodies of water was analysed using a Agrowing multispectral camera, which derived negative values when applying linear fit. The aim of this study, therefore, was to fit a radiometric calibration based on empirical line method for Agrowing camera in inland water applications. Besides of standard reference targets, water samples also were attempted because showed a lower radiance response than the darkest (black) calibration target. Two empirical line methods were applied to convert the digital number (DN) from the Agrowing images into remote sensing reflectance (Rrs): linear and exponential. The exponential method showed to be more appropriate, with greater accuracy, unlike the linear method.