Subsetting
Each step comprises a number of discrete components, outlined in the Contents
The hierarchical definition of each component begins with overarching purposes applicable to all inter-calibrations and progresses to implementation specifics for distinct instrument pairs Initially, it is essential to articulate the purpose of each component within this generic data flow Subsequently, various implementation options should be explored to ensure adaptability Additionally, it is crucial to recommend standardized procedures for the inter-calibration class, such as GEO-LEO Finally, specific details must be provided for each instrument pair, exemplified by SEVIRI-IASI, to enhance clarity and effectiveness in the inter-calibration process.
Each component of the algorithm is defined independently and can have various versions The implementation should adhere to the overall logic, allowing for flexibility in execution; components do not have to be processed in a strict sequence For instance, certain tasks may be executed iteratively, or multiple components can be integrated within a single loop in the code.
GSICS aims to define a “baseline” algorithm by identifying one version of each component, against which the performance of other versions may be compared
Figure 1: Diagram of generic data flow for inter-calibration of monitored (MON) instrument with respect to reference (REF) instrument
Correction Coeffs Analysis Data Comparison Data Collocated Data Subset MON Data Subset REF Data
MON Lvl 1 Data Re-Cal Data
MON Level 1 Data REF Level 1 Data
The Algorithm Theoretical Basis Document (ATBD) outlines the PATMOS-x inter-calibration process for the visible channels 1 (red) and 2 (near infrared) of all AVHRRs from TIROS-N to NOAA-19 and Metop-A, in conjunction with MODIS on Aqua and Terra This calibration utilizes ground targets located in the Libyan Desert and Dome C, Antarctica The PATMOS-x AVHRR Calibration provides a set of calibration slopes for converting raw AVHRR counts to reflectance for channels 1 and 2, but is not designed to correct reflectances derived from other calibration methods.
The main documentation for this calibration is found in a pair of papers published in the International Journal of Remote Sensing The paper
In their 2010 article, "Calibrations for AVHRR channels 1 and 2: review and path toward consensus," Molling et al highlight the importance of calibrating AVHRR channels 1 and 2, outlining the conditions that necessitate this process The authors provide a comprehensive overview of previous calibration studies and propose a set of consensus data sets and methodologies that should be adopted for future calibration research on these channels This review serves as a critical resource for advancing the accuracy and reliability of remote sensing data.
HEIDINGER, A.K., STRAKA III, W.C., MOLLING, C.C., and SULLIVAN, J.T.,
In 2010, the article "Deriving an inter-sensor consistent calibration for the AVHRR solar reflectance data record," published in the International Journal of Remote Sensing, details the methodologies employed in the PATMOS-x Level2b calibration It also presents the calibration coefficients essential for ensuring consistent solar reflectance measurements across different sensors.
The ATDB features various versions of the PATMOS-x Level2b specific algorithm, each marked with a version number to indicate their implementation status The initial version, v1.0, was published in Heidinger et al., 2010, while subsequent modifications will be identified in later versions.
3.a Convert Radiances and/or Reflectances 13
5.b Regression of Most Recent Results 21
Annex A Any other Needed Documentation 30
Acquiring raw satellite data is essential for effective inter-calibration, which relies on comparing collocated observations To enhance the data acquisition process for satellite instrument inter-comparison, it is crucial to predict the timing and location of collocation events.
Figure 2: Step 1 of Generic Data Flow, showing inputs and outputs
MON refers to the monitored instrument REF refers to the reference instrument
We begin by conducting a rough cut to minimize data volume, focusing on relevant segments of the dataset, including channels, area, time, and viewing geometry This step aims to select data collected by two instruments that are likely to yield collocations or views of Regions of Interest (RoI) Since only a small fraction of measurements typically align with collocations or RoIs, excluding irrelevant measurements significantly reduces processing time.
Data selection occurs on a per-orbit or per-image basis, requiring an understanding of the characteristics of potential views at specific collocations This knowledge is essential for determining the most appropriate locations and dates for calibration.
To achieve optimal calibration, it is essential to identify data with suitable characteristics for the calibration method Utilizing observed orbital parameters, such as Two Line Elements (TLE), in conjunction with orbit prediction software like Simplified General Perturbations can enhance the accuracy of predictions.
1.a.iii.Solar LEO-LEO inter-satellite/inter-sensor Class
1.a.iii.0 As the sensors measure sun-sourced energy reflected back toward the sensor, only daytime data are selected Furthermore, imagery with high
Subset MON Data Subset REF Data
MON Level 1 Data and REF Level 1 Data satellites, when positioned in different orbits, will intersect multiple times each month This phenomenon, known as Simultaneous Nadir Overpasses (SNOs), primarily occurs in bands at high latitudes Consequently, data collection is focused on these high-latitude regions and specific selected ground targets.
1.a.iv.1 The monitored instruments (MON) are the Advanced Very High
Resolution Radiometers (AVHRR) on National Oceanographic and Atmospheric Administration (NOAA) Polar-orbiting Operational
Environmental Satellite (POES) and European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Metop spacecraft All Global Area Coverage (GAC) Level 1b files for AVHRRs from TIROS-N through NOAA-19 and Metop-A were downloaded from the Comprehensive Large Array-data Stewardship System (CLASS) archive (http://www.nsof.class.noaa.gov/saa/products/welcome) Files were stored locally on disk for processing Level1b GAC files for
AVHRR are nominally 4 km subsetted and averaged level 1b data with navigation and quality control appended.
The reference instruments (REF) are the Moderate Resolution Imaging Spectrometers (MODIS) on Aqua and Terra MYD02SSH and
MYD02SSH Collection 5 files were downloaded from Level 1 and Atmosphere Archive and Distributions System (LAADS Web) archive (http://ladsweb.nascom.nasa.gov/) Files were stored locally on disk for processing MYD02SSH files are nominally 5km subsetted level 1b data reprocessed to contain improved navigation, noise reduction, and cloud mask In some cases, an AVHRR on one spacecraft is used as a reference for a coeval AVHRR on a different spacecraft The reference AVHRR data types used are the same as for the monitored AVHRR.
Constant-reflectance ground targets play a crucial role in calibrating AVHRR, particularly during and prior to the MODIS era During the MODIS era, the benchmark reflectances of these ground targets are established exclusively using MODIS data, without the incorporation of any other independent data sources.
The authors downloaded the complete AVHRR archive for alternative uses, while selectively acquiring relevant MODIS imagery, specifically daytime scenes in high latitudes during peak sun angle periods (June 15 – August 15 for the northern hemisphere and December 15 – February 15 for the southern hemisphere) Additionally, they obtained imagery of the Libyan ROI throughout the entire year.
Monitoring
Each step comprises a number of discrete components, outlined in the Contents
The hierarchical structure of inter-calibration components begins with overarching purposes that apply universally, progressing to specific implementation details tailored for individual instrument pairs Each component's purpose within this generic data flow should be clearly defined, along with various implementation options available It is essential to recommend standardized procedures for specific inter-calibration classes, such as GEO-LEO, while also providing precise details pertinent to each instrument pair, exemplified by SEVIRI-IASI.
Each component of the algorithm is defined independently and can have multiple versions The implementation should adhere to the overall logic, allowing for non-sequential execution of components For instance, certain parts may be executed iteratively, or several components can be integrated within a single code loop.
GSICS aims to define a “baseline” algorithm by identifying one version of each component, against which the performance of other versions may be compared
Figure 1: Diagram of generic data flow for inter-calibration of monitored (MON) instrument with respect to reference (REF) instrument
Correction Coeffs Analysis Data Comparison Data Collocated Data Subset MON Data Subset REF Data
MON Lvl 1 Data Re-Cal Data
MON Level 1 Data REF Level 1 Data
The Algorithm Theoretical Basis Document (ATBD) outlines the PATMOS-x inter-calibration process for the visible channels 1 (red) and 2 (near infrared) of all AVHRRs from TIROS-N to NOAA-19 and Metop-A, in conjunction with MODIS on Aqua and Terra This calibration utilizes ground targets located in the Libyan Desert and Dome C, Antarctica The PATMOS-x AVHRR Calibration provides a set of calibration slopes for converting raw AVHRR counts into reflectance values for channels 1 and 2, but it is important to note that these slopes are not designed to correct reflectances derived from other calibration methods.
The main documentation for this calibration is found in a pair of papers published in the International Journal of Remote Sensing The paper
The article by Molling et al (2010) in the International Journal of Remote Sensing emphasizes the importance of calibrating AVHRR channels 1 and 2, detailing the context and need for such calibrations It reviews previous calibration studies and suggests a set of consensus data sets and methodologies that should be adopted in future calibration research for these channels.
HEIDINGER, A.K., STRAKA III, W.C., MOLLING, C.C., and SULLIVAN, J.T.,
In 2010, the article "Deriving an inter-sensor consistent calibration for the AVHRR solar reflectance data record," published in the International Journal of Remote Sensing, volume 31, pages 6493-6517, details the methodologies employed in the PATMOS-x Level2b calibration and presents the associated calibration coefficients.
The ATDB features various versions of the PATMOS-x Level2b specific algorithm, each marked with a version number to indicate their implementation status The initial version, v1.0, was published in Heidinger et al., 2010, while subsequent modifications will be clearly identified in later versions.
3.a Convert Radiances and/or Reflectances 13
5.b Regression of Most Recent Results 21
Annex A Any other Needed Documentation 30
Acquiring raw satellite data is a crucial initial step in the inter-calibration process that relies on comparing collocated observations To enhance the effectiveness of data collection for inter-comparing satellite instruments, it is essential to predict the timing and location of collocation events.
Figure 2: Step 1 of Generic Data Flow, showing inputs and outputs
MON refers to the monitored instrument REF refers to the reference instrument
Initially, we conduct a rough cut to minimize data volume by focusing on relevant sections of the dataset, including channels, area, time, and viewing geometry This approach aims to select data from the two instruments that are most likely to yield collocations or views of Regions of Interest (RoIs) Since only a small percentage of measurements typically correspond to collocated data or RoIs, this method significantly reduces processing time by eliminating irrelevant measurements.
Data selection occurs on a per-orbit or per-image basis, necessitating an understanding of the characteristics of views expected at specific collocations This knowledge is essential for determining the most appropriate locations and dates for calibration.
To achieve optimal calibration, it is essential to identify data with suitable characteristics Utilizing observed orbital parameters, like Two Line Elements (TLE), in conjunction with orbit prediction software such as Simplified General Perturbations can enhance the accuracy of the results.
1.a.iii.Solar LEO-LEO inter-satellite/inter-sensor Class
1.a.iii.0 As the sensors measure sun-sourced energy reflected back toward the sensor, only daytime data are selected Furthermore, imagery with high
Subset MON Data Subset REF Data
MON Level 1 Data and REF Level 1 Data satellites frequently cross paths multiple times each month when positioned in different orbits, a phenomenon known as Simultaneous Nadir Overpasses (SNOs) These occurrences predominantly take place in bands at high latitudes, necessitating the collection of data primarily from these regions and select ground targets.
1.a.iv.1 The monitored instruments (MON) are the Advanced Very High
Resolution Radiometers (AVHRR) on National Oceanographic and Atmospheric Administration (NOAA) Polar-orbiting Operational
Environmental Satellite (POES) and European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Metop spacecraft All Global Area Coverage (GAC) Level 1b files for AVHRRs from TIROS-N through NOAA-19 and Metop-A were downloaded from the Comprehensive Large Array-data Stewardship System (CLASS) archive (http://www.nsof.class.noaa.gov/saa/products/welcome) Files were stored locally on disk for processing Level1b GAC files for
AVHRR are nominally 4 km subsetted and averaged level 1b data with navigation and quality control appended.
The reference instruments (REF) are the Moderate Resolution Imaging Spectrometers (MODIS) on Aqua and Terra MYD02SSH and
MYD02SSH Collection 5 files were downloaded from Level 1 and Atmosphere Archive and Distributions System (LAADS Web) archive (http://ladsweb.nascom.nasa.gov/) Files were stored locally on disk for processing MYD02SSH files are nominally 5km subsetted level 1b data reprocessed to contain improved navigation, noise reduction, and cloud mask In some cases, an AVHRR on one spacecraft is used as a reference for a coeval AVHRR on a different spacecraft The reference AVHRR data types used are the same as for the monitored AVHRR.
Constant-reflectance ground targets play a crucial role in calibrating the Advanced Very High Resolution Radiometer (AVHRR) both during and prior to the MODIS era During the MODIS era, the benchmark reflectances for these ground targets are established using data from MODIS, with no reliance on other independent data sources.
The authors downloaded the complete AVHRR archive for alternative purposes, while selectively acquiring relevant MODIS imagery, specifically high-latitude daytime scenes during peak sun angle periods (June 15 – August 15 for the Northern Hemisphere and December 15 – February 15 for the Southern Hemisphere) Additionally, they obtained imagery of the Libyan Region of Interest (ROI) throughout the entire year.
GSICS Correction
The final step in the algorithm involves calculating the GSICS Correction, which adjusts the observed data from one instrument to align with that of a reference instrument This correction is defined offline and may vary by instrument Its application depends on the Correction Coefficients obtained from inter-comparisons conducted in earlier steps of the algorithm using Analysis Data.
Figure 9: Step 6 of Generic Data Flow, showing inputs and outputs, and illustrating schematically how the correction could be applied by users
To enhance the accuracy of the final GSICS Correction, it is essential to combine data from a time series of inter-comparison results, which can help mitigate the random component of uncertainty This process involves defining representative periods for data smoothing, ensuring that calibration drifts do not introduce bias during this timeframe By comparing the observed rate of change in inter-comparison results against a pre-established threshold, based on the desired accuracy levels, we can identify these periods While this analysis is typically conducted offline due to its complexity, involving a thorough examination of relative biases and potential influencing factors, it can also be adjusted in near real-time The following outlines the recommended approaches for implementation.
GSICS Correction e.g Look-Up Table, FORTRAN subroutine, New calibration coefficients, …
Time Series of Inter-calibration Regression Coefficients
In section 5.e.ii.1, the time series of radiance biases are analyzed through regression against the date as the independent variable This analysis provides an estimate of the rate at which the bias changes over time, represented as dt y dΔˆ REF.
, which can be compared to the threshold Δy max to determine the smoothing period, τs:
6.a.iii.Solar LEO-LEO inter-satellite/inter-sensor Class
6.a.iii.0 The above method is not necessarily useable, as the calibration sources, notably the SNOs, are not consistently available over time A visual inspection may be used instead.
6.a.iv.0 The calibration biases are inspected visually.
Combining data from a time series of inter-comparison results can effectively minimize the random uncertainty associated with the final GSICS Correction The resulting smoothed coefficients serve as the Correction Coefficients for GSICS Correction, utilizing the specified smoothing period outlined in section 6.a.
6.b.ii.0 Typically, calibration data during entire time period is used in the calculation, as the equation allows for non-linear trends.
6.b.iii.Solar LEO-LEO inter-satellite/inter-sensor Class
6.b.iii.0 To compensate for short time scale variability and uneven temporal availability of calibration data, monthly averages of the calibration slope can be computed during the calibration time period.
6.b.iv.0 Monthly averages of calibration slope are used to derive the parameters for the quadratic fit equation (5).
This component aims to produce revised sets of calibration coefficients for one instrument following its inter-calibration against a reference instrument using the
Step 4 analysis data enables users to adjust the monitored instrument's data to align with the reference instrument Additionally, it is essential to generate tables of recalibration coefficients for both near-real-time and archived data.
6.c.ii.0 The regression coefficients provided as the Analysis Data output from
In Step 4, new correction coefficients are generated along with estimates of their uncertainties as full covariances These coefficients are essential for converting the observations from the monitored instrument into radiances that align with the GSICS reference standard.
6.c.iii.Solar LEO-LEO inter-satellite/inter-sensor Class
6.c.iii.0 Equation (6) and the parameters for the sensor/channel are used to calculate the calibration slope valid at the time of the observation.
6.c.iv.0 Raw counts are converted to reflectance or radiance using the calibrated slope.
HEIDINGER, A.K., CAO, C and SULLIVAN, J.T., 2002, Using Moderate
Resolution Imaging Spectrometer (MODIS) to calibrate advanced very high resolution radiometer reflectance channels Journal of Geophysical Research, 107, Doi:10.1029/2001JD002035.
HEIDINGER, A.K., STRAKA III, W.C., MOLLING, C.C., and SULLIVAN, J.T.,
2010, Deriving an inter-sensor consistent calibration for the AVHRR solar reflectance data record International Journal of Remote Sensing, 31:6493 – 6517.
McCLATCHEY, R.A., FENN, R.W., SELBY, J.E.A., VOLZ, F.E and GARING, J.S., 1971, Optical properties of the atmosphere, ARCRL-71-0279, Air Force
MOLLING, C.C., HEIDINGER, A.K., STRAKA III, W.C and WU, X., 2010, Calibrations for AVHRR channels 1 and 2: review and path toward consensus International Journal of Remote Sensing, 31:6519 – 6540.
SULLIVAN, J.T., 1980, Accurate least-squares techniques using the orthogonal function approach NOAA Technical Report, EDIS 33, 104p.