Application of a Testbed for Validating Remote Sensor Data and Retrieval Algorithms Alan Lipton∗, Jean-Luc Moncet, John Galantowicz, Haijun Hu, Richard Lynch, Sid-Ahmed Boukabara, David Hogan, Robert d’Entremont, Yuguang He Atmospheric and Environmental Research, Inc., 131 Hartwell Ave., Lexington, MA 02421-3126 ABSTRACT The AER algorithm testbed has been applied to instruments measuring in spectra from the ultraviolet to the microwave. The sensor simulation component starts with environmental data from numerical weather prediction models, surface property and terrain databases, and imagers, and simulates the detailed sensor spatial and spectral sampling processes, radiative transfer, polarization, and detector characteristics. This simulation is integrated with algorithm execution, to provide end-to-end capability. The tools allow for simulation of specific sensor errors, and tracing of their impact through the algorithm process to the quality of the retrieved environmental products. A critical component of the testbed is its radiative transfer models, which employ state-of-the-science programs for line-by-line optical properties, for radiative transfer in scattering atmospheres, and for a variety of surfaces. Fast and accurate computational methods are incorporated, such as Optimal Spectral Sampling (OSS). Application examples are shown, including characterization of AMSU sensor data errors and retrieval performance evaluation, in which retrievals from the microwave sounder data and infrared image data contribute to the interpretation of remote sensing phenomena in cloudy environments. Keywords: Sensor simulation, remote sensing, radiative transfer, satellite
1. INTRODUCTION What is a testbed? The answer depends on the context. In environmental remote sensing, a testbed is a computational system for analyzing a remote sensing system. The remote sensing system generally consists of several components, including sensor hardware and data processing algorithms and software. The testbed may evaluate the individual components or the entire system in an end-to-end sense. To analyze the sensor in detail, the testbed needs to be able to simulate the sensor data products. Environmental data are fed into a sensor model that computes the sensor output for the specific environmental conditions. To evaluate the usefulness of the sensor, the testbed needs to have algorithms and associated software to retrieve environmental parameters from the sensor data. Tools are needed to analyze the sensor data and the environmental retrievals in relation to the environmental inputs. The Atmospheric and Environmental Research (AER) testbed has two basic purposes: to simulate future remote sensing systems and to evaluate current systems. For future systems, we use the testbed in sensor design trade-off studies. Intelligent decision-making in the sensor design process requires information about the repercussions of each design option for the characteristics of the retrieved environmental products. This mode of application is illustrated in Figure 1, which shows feedback to the sensor design being provided by performance assessments. The radiative phenomenology is represented quantitatively in the radiative transfer model, and also has bearing on the measurement requirements. For example, radiative phenomenology indicates that it is appropriate for a satellite microwave system to be expected to retrieve soil moisture, because there are microwave frequencies at which the radiation to space depends on soil moisture, and there are other frequencies that are less sensitive to soil moisture that can be used to correct for other influences on the radiation, such as vegetative water. The specific requirements for soil moisture measurement error can be assessed with the radiative transfer model, the sensor model, and the retrieval algorithms.
∗
[email protected]; phone 1-781-761-2288; fax 1-781-761-2299; www.aer.com
Radiative P henomenology Sensor H a rdwa re
Meas u re men t Require me n ts Sensor Design
Radiative Tr a n sfer Modeling
Sensor/Environ men t Dat a Simulation
E nvironmen t al Remote Sensing Algorit h ms
E nd-to-End Performa nce Assess men t
Figure 1. Schematic diagram of testbed application to sensor design trade-off studies. Another function of a testbed is to assess the usefulness of a proposed new sensor system. The quality of simulated data products can be compared with what is available from other measurement systems, such as from prior-generation sensors or algorithms. A testbed can also be used to evaluate the benefit of adding a proposed sensor system to the existing suite of measurement systems. When this is done in the context of numerical weather prediction, such evaluations are referred to as observing system simulation experiments (OSSEs). The reliability of an OSSE depends heavily on the fidelity of the sensor model simulations of sensor errors.1 When we use the testbed to evaluate current systems, we commonly compare sensor products to data from other sources. One option is to compare at the level of environmental parameters retrieved by the algorithms, to analyze the quality of retrieved products. It is often most enlightening to start by comparing the sensor radiometric measurements with simulations of those measurements derived by applying ground-truth data to the sensor model. In this approach, the retrieval algorithm is removed from the chain of processing, which allows for assessing the sensor measurement error and diagnosing sources and magnitudes of sensor errors. Errors that affect only certain channels, or that occur only under certain conditions, can be identified and, in some cases, corrected. Sensor errors are much easier to recognize in analyses at this level of processing than in analyses of retrieved products, since the retrievals typically depend on data from several channels in a complicated manner. Once the sensor data have been validated or corrected, analyses of retrieved products provide a means to find conditions where algorithm improvements may be possible, and to test algorithm revisions. Retrievals can be made from simulated sensor data in parallel with retrievals from real measurements so that the simulations allow for tests of sensitivity of retrievals to specific variations of the environmental conditions or sensor errors.
2. AER TESTBED DESCRIPTION 2.1. Testbed structure The top-level structure of the AER testbed is in Figure 2 in generic form, while the specifics depend on the type of sensor and the relevant portion of electromagnetic spectrum. In simulation, the process starts with environmental data, which includes all parameters relevant to the radiative phenomenology associated with the sensor. The data may come from in situ measurements, such as radiosondes, numerical weather prediction models, other remote sensors, or (most commonly) a combination of sources. We extract from the databases a representation of the environmental conditions within the view path of the sensor. The view paths are obtained from a simulator of the satellite orbit and the sensor scan geometry. The radiative transfer component computes the top-of-atmosphere radiance (or brightness temperature in the microwave or reflectance in the visible) in the direction of the sensor. We use a radiative transfer model (Sec. 2.2) appropriate to the specific application. Rather than compute the radiance across the entire spectrum, the model incorporates the sensor spectral sampling characteristics to obtain the radiance in each channel. The radiances are input to a module that simulates the spatial sampling characteristics of the sensor, performing the appropriate averaging within each measurement field-of-view. The following module simulates the radiometric process, including the
radiometer transfer function and the radiometric and thermometric measurements of calibration targets. Within each of the modules leading up to this stage of processing, simulated sensor errors may be applied, such as pointing errors in the spatial sampling or noise in the radiometric process. The product at this stage is simulated raw sensor data. The subsequent algorithm processing may be applied either to the simulated data or data from an operating sensor. Figure 2 illustrates the two main points in the processing chain at which we perform analyses.
Environmental Data
Orbit and View-Path Simulation
Radiative Transfer Model
Top-ofAtmosphere Radiance
Sensor Spatial Sampling
Radiometry
Real Sensor Environmental Parameter Analysis Tool
Retrieved Environmental Parameters
Environmental Retrieval Algorithm
Radiometric Data Analysis Tool
Sensor Raw Data
Radiance Measurement
Calibration and Geolocation
Figure 2. Schematic top-level diagram of AER testbed
2.2. Testbed components and resources Some of the distinctive components and resources employed in the AER testbed are described briefly in this section. This discussion does not cover all the resources used, which are too numerous to cover in this paper. 2.2.1. Databases AER obtains data in real-time from the Air Force Weather Agency and the Air Force Research Laboratory, by means of Cooperative Research and Development Agreements. The data available by these means include satellite measurements, in situ measurements, and weather prediction products. We obtain real-time data from other data producers as well. On a continuous basis, we are currently obtaining and processing surface observations, radiosonde soundings, analyses and forecasts from the NOAA Global Forecast System (GFS), data from the NOAA-16 Advanced Microwave Sounding Unit (AMSU) and from the east and west Geostationary Operational Environmental Satellite (GOES) imagers. We have also been processing data from the NASA Earth Observing System (EOS) Aqua satellite on an intermittent basis, and will begin continuous processing of the data in the near-term. From Aqua, our processing includes radiances and retrieval products from the Atmospheric Infrared Sounder (AIRS), Advanced Microwave Scanning Radiometer -EOS (AMSR-E), and the Moderate Resolution Imaging Spectroradiometer (MODIS). These real-time data are used for long-term analyses of sensor products and algorithms. Our testbed also accesses a variety of archived databases. We use several diverse sets of radiosonde soundings for training and testing radiative transfer models and algorithms. We have archives of mesoscale model simulations at resolutions suitable for representing sub-field-of-view effects of some sensors (as fine as 1 km). For microwave applications, we use global monthly surface emissivity databases2. These datasets are being augmented in the course of our analyses of new data. The emissivity database, in particular, is being greatly expanded and improved under an ongoing project to analyze Aqua data. 2.2.2. Radiative transfer models Our testbed applications benefit from the extensive radiative transfer model development that has been conducted at AER. The primary reference line-by-line molecular absorption model is LBLRTM3,4, which is widely used in the
scientific community. In addition, MonoRTM is a new atmospheric absorption and radiative transfer model based on the same theory as the LBLRTM, with higher efficiency for monochromatic calculations, and is valid for all spectral regions (from microwave to UV). It benefits from the most recent spectroscopic HITRAN database and has been validated against Atmospheric Radiation Measurement (ARM) microwave data5. Line-by-line models are generally too slow for direct use in the testbed, so instead we use fast models based on line-by-line reference computations. The Optimal Spectral Sampling (OSS) method is a rapid and accurate technique developed at AER for the numerical modeling of narrow-band transmittances in media with non-homogeneous thermodynamic properties containing a mixture of absorbing gases with variable concentrations6. The method has been specifically designed for the modeling of radiances measured by Earth-orbiting down-looking microwave and infrared radiometers, but can be applied to any spectral domain and instrument viewing geometry in emitting and scattering atmospheres. The OSS method is particularly well suited for remote sensing applications because of its high computational speed and accuracy that can be made as close as needed to the reference line-by-line model. 2.2.3. Algorithms The range of algorithms available in the testbed is very large and diverse, covering parameters of land and ocean surfaces and clouds and other atmospheric characteristics. For imager data analysis, we have developed a suite of algorithms for retrieving spatial, radiative, and microphysical cloud and aerosol properties from multispectral sensor data7,8,9,10. For sounding in the infrared and microwave spectra, we have developed an iterative, non-linear, physical algorithm based on optimal estimation principles6. In this algorithm, regularization is achieved by eigenvector transformations of the profiles of temperature and atmospheric constituents (including water vapor) and, in the microwave, the multichannel surface emissivities. Algorithm convergence in highly non-linear problems is enhanced with a representation of linearization error. 2.3. Microwave configuration The configuration of the testbed for microwave applications is described here as an illustration of the treatment of significant issues in sensor simulation and performance analysis. This configuration is currently being applied to the development and testing of algorithms for the Conical-scanning Microwave Imager/Sounder (CMIS) on the forthcoming National Polar-orbiting Operational Environmental Satellite System (NPOESS) spacecraft. It is also being applied to data from AMSU, AMSR, and the newly operating Special Sensor Microwave Imager Sounder (SSMIS). The flow of the microwave testbed (Figure 3) has the same basic structure as the generic one (Figure 2), while more details are shown. For microwave applications, polarization and cross-polarization coupling must be treated. For CMIS, the analysis of those components and the associated algorithms are handled by Remote Sensing Systems, Inc. In addition, there are processes treating the imperfect coupling between the feedhorns and the main reflectors (spillover) and the calibration targets. The most elaborate segment of the testbed flow relates to spatial processing. The view-path (or slant path) depiction is coordinated with the spatial averaging of radiometric data through orbit simulation. Preparation of data for validating against algorithm products also involves spatial processing, with options to validate using data on the slant path of the satellite view or for data that have been averaged over vertically-oriented columns according to CMIS validation requirements. The algorithms for retrieving environmental parameters (EDRs) have a footprint matching function as a pre-processor. Individual measurements are averaged in an optimal way to align the various channels to common footprints and to reduce measurement noise. There are shortcuts that can be taken through the processing (represented by gray lines of data flow) to allow for isolating and analyzing certain processes. In case of the shortcut from spatial processing to EDR processing, radiometric noise can be applied without simulating the calibration process. One distinctive feature of the testbed is its use of sensor constants files. These files contain data regarding the sensor design and performance, and provide consistent information for simulation and algorithm operation. As an example, the antenna view parameter file contains the best estimates available of reflector spillover and other relevant parameters. In the simulation process (shown in the top branch of the diagram), errors in the antenna view parameters may be simulated, such as spillover errors within the range of spillover measurement uncertainty. The spillover correction
algorithm (bottom branch of the diagram) can correct only for the estimated spillover, so simulated spillover errors affect the algorithm products and the magnitude and character of the impacts can be assessed.
OSS Generation
Passbands (with errors applied)
Sensor Model Define slant path atmos+sfc state
RT Model
Spatial Averaging
Noise param
Apply radiom noise
Noise param
Orbit Simulation Orbit&Scan Parameter Perturbation
RAOB or NWP +Sfc Data
Apply radiom noise
Model Load Radiance and Radiom Transfer Fctn (for scene and cal loads) V H , VC , TH, TC
Polarization State Perturbation
Antenna Pattern Weights Anten Pattern Delta View Angles Scan Angle and Timing Params Orbit parameters
S/C Location Errors Pointing Errors Anten Pattern Errors
Model Radiation from Earth-scene+ Antenna+Space
Polarization Transformation
Anten View Perturb
Cal Param Perturbation
Polarization Errors
Noise Param RTF Error Load View Errors
Antenna View Errors
Location Orbit Simulation FOV Location Define vertical atmos+sfc state
Define slant path atmos+sfc state
Horiz Cell Pattern
Spatial Averaging
EDR Validation
EDR Product Formula Validation Data
Convert to RDR parameters RDR (post-PRT)
Footprint Pattern
Channel Polarization State (in standard basis)
Antenna View Parameters Load View Param
Passbands
Geolocation Algorithm
Location OSS Generation
Validation EDR Product
EDR Algorithms (includes footprint matching)
Note: Sensor Model parameters may be according to design or as-built (measured)
SDR
Cross-Polarization Correction
TA'+ Location
Spillover Correction
TDR (TA)
Calibration
RDR->SDR->EDR Process
Figure 3. Functional flow of microwave testbed for algorithm testing. The NPOESS acronym RDR (raw data record) corresponds to the sensor raw data in Figure 2. TDR (temperature data record) and SDR (sensor data record) represent stages of radiance measurements in terms of brightness temperature, and EDR (environmental data record) represents the retrieved environmental parameters.
3. APPLICATIONS A few examples of testbed applications are described here, to illustrate its functionality and demonstrate some of its uses. 3.1. Spatial processing and footprint matching of microwave data To test the fidelity of spatial sampling and footprint alignment errors requires that the environmental input data be of high spatial resolution in relation to the sensor footprint size. We have generated test data for this purpose using the NCAR/PSU MM5 mesoscale model, with nested grids that extend to a spacing of 1 km. It is not feasible to run a mesoscale model at that resolution for a domain as large a satellite swath. To cover larger areas, we take model data from a small domain and spread it over the required domain by applying mirror imaging and periodicity, as illustrated in
Figure 4. A band of data was set to 270 K to create a sharp spatial feature that provides a highly stressing test case for the simulation and algorithms. To test the footprint matching algorithm, identical brightness temperature data were sampled according to the 18 and 89-GHz antenna patterns, each was matched to a 50-km circular footprint, and the matched data were compared (Figure 5). The difference between Figure 4 and Figure 6 reflects the spatial sampling of the 89-GHz CMIS antenna pattern, as projected as footprints on the Earth. The differences between the footprint matched data from the two antenna patterns (Figure 7) shows the spatial processing errors that occur when these two channels are footprint matched before they are used in an environmental retrieval algorithm. The amount of error depends on the satellite scan and antenna patterns for the channels, the size and shape of the target matched footprint, and the footprint matching algorithm tuning. For this case, the differences are generally less than 0.1 K.
Band of data set to 270 K for testing
Original MM5 grid
Figure 4. Top-of-atmosphere brightness temperatures at 10 GHz computed from MM5 model data at 3-km grid spacing, where MM5 data from a small domain were expanded by tiling to cover a larger area for CMIS simulation. The small-scale structure is traceable to numerical artifacts in the MM5 surface skin temperatures.
INPUT: 3 km TOA TB field
89H TB
Footprint Matching
89H TB
Sensor Model
Compare 18H TB
Footprint Matching
Synthetic sensor TB sampling (not collocated)
18H TB
89H-18H TB difference
TEST: Composite TB difference should be small
Composite TB sampling (collocated)
Figure 5. Footprint matching test design.
Sate s llite c ubtra k
Figure 6. The top-of-atmosphere brightness temperature as sampled and weighted by the CMIS 89-GHz projected antenna pattern.
Figure 7. Brightness temperature differences due to differences in spatial sampling and footprint averaging of 18 and 89-GHz H-polarization CMIS channels, for 50-km circular matched footprints.
3.2. AMSU data analysis AER is processing AMSU data and SSMIS data, with an objective to monitor aspects of sensor performance that have bearing on the performance of CMIS sensor data and environmental retrievals. In addition, the algorithms under development for CMIS are being tested to analyze the performance of the algorithms on data from instruments that are somewhat similar to CMIS and to identify any environmental conditions where algorithm improvement may be needed. In the area of sensor monitoring, we are archiving AMSU data and analyzing sensor errors that arise from the scanning process and intrusions in the sensor field of view. For this purpose, matches of AMSU data with radiosonde observations are insufficient, because the network of radiosonde stations is does not have fine enough resolution to produce stable difference statistics for all scan positions in a short period of time. AMSU data can be analyzed independently, but the trends of averages as a function of scan position are affected by the variation in Earth incidence (zenith) angle across the scan, and the inclination of the satellite orbit causes one side of the scan to be consistently at higher latitudes than the other side, which can confound efforts to isolate the sensor errors. To avoid these limitations, we collect analysis and forecast data from the GFS and use those data in our sensor model to simulate the brightness temperature at the location of each AMSU observation. In this case, the sensor model does not perform orbit simulation, but uses the geolocation of the AMSU measurements to direct the sampling of the GFS data. We are computing biases between the AMSU measurements and the GFS-based simulations on various time scales to investigate the stability of the scan-dependent trends. The accuracy of bias-corrected data depends on the stability of the biases. As an example, we compared biases from a single day of data to biases from an average over a prior week (Figure 8). The primary trend in bias is an upward trend in the last several scan positions, which is consistent between the two computed values and with the static bias correction derived by NOAA. To the best of our knowledge, this trend has not been attributed to a specific source.
Figure 8. Bias in NOAA-16 AMSU channel 2 (31.4 GHz) brightness temperature showing the NOAA correction (green), the correction computed at AER from one week of data (black), the bias from a single day of data (cyan, with black bars showing the uncertainty of estimate), and the bias for that day after applying the AER correction (diamonds).
The ability of microwave sensors to measure in completely cloudy environments is a significant advantage, in relation to infrared sensors, for monitoring an atmosphere where clouds cover a substantial portion of the globe. This advantage may be particularly pronounced for water vapor monitoring, considering that humidity is highly correlated with cloud cover. A systematic omission of water vapor data in areas under clouds would cause systematic biases in water vapor analyses. Microwave systems are resistant, but not immune to cloud contamination.11,12 To a significant degree, even nonprecipitating clouds affect the microwave frequencies employed by AMSU for temperature and water vapor measurement, particularly at the higher frequencies used for detecting upper-tropospheric water vapor (Gasiewski, 1993).13 We are using the testbed to study this effect, toward improving algorithm performance and quality control monitoring in the presence of moderate cirrus clouds. An example of this application is in Figure 9. The image
analysis can help answer whether the upper-level moistening in the real-data retrieval is due to moisture missed by the radiosonde or contamination by clouds.
AMSU Footprint
Radiosonde
•Low clouds – Red 3.7 µìm •Thin Cirrus – Blue 10.8 µìm •Thick Cirrus – White 11.9 µìm
Figure 9. An example of displays from the AER testbed for a scene in the Indian Ocean, including a) a multispectral AVHRR image with markers of a selected pair of collocated soundings, b) a cirrus analysis retrieved from AVHRR data, and c) profiles from the collocated sites. The dashed curve is the profile retrieved from the real AMSU data. The dash-dot curve is the profile retrieved from AMSU data simulated by applying radiative transfer to the radiosonde without sensor noise, which is the profile that could be obtained with AMSU if the radiosonde truly represented the site of the AMSU measurements.
Radiosonde Retrieved Ssimulated Retrieved Real Data
3.3. Infrared sounding AER developed the algorithms for the NPOESS Cross-track Infrared/Microwave Sounding Suite (CrIMSS), which consists of an interferometric infrared sensor and a microwave sensor with a similar scan pattern. The baseline method within the CrIMSS algorithm for dealing with partly cloudy conditions is “cloud-clearing”14,15, which is essentially an extrapolation method that starts from the clearest, or warmest, observation within a cluster and, using the contrast between neighboring observations, builds an observation which represents the clear portions of the measurement. The
retrieval is performed on the cloud-cleared radiance. For algorithm test and validation, AER has been applying the algorithm to data from AIRS and AMSU. Retrieved profiles of temperature and water vapor are compared with data from radiosondes on a daily basis. The example in Figure 10 is for a case where an approximate space/time match was available between the AIRS data and a radiosonde sounding. Figure 10c shows a comparison of the radiosonde, microwave-only retrieval, and final retrieval product (AMSU+AIRS) for temperature and water vapor. The high level of agreement, particularly for the water vapor profile, is indicative of the information content of the AIRS data and the quality of the retrieval algorithm.
a
AIRS Radiances
b
Cloud-Cleared Radiances
Radiosonde site
c
Figure 10. AIRS retrieval validation example, with a) AIRS radiances at 11µm, b) AIRS radiances at 11µm after the cloud-clearing algorithm (note the change of scale between frames), and c) retrieval validation with the radiosonde from an island station. 3.4. Hyperspectral analysis The application of the testbed for hyperspectral data is illustrated in Figure 11. The scene characteristics were defined on the basis of data from the Moderate-Resolution Imaging Spectroradiometer (MODIS) and SeaWIFS. Hyperspectral top-of-atmosphere radiances for the spectral range 400 to 900 nm were generated with the MODTRAN4+. In this application, chlorophyll concentration was retrieved using an algorithm developed at AER for the NPOESS Visible/Infrared Imager/Radiometer Suite.
412 nm
443 nm
490 nm
530 nm
Hy p er s p e c tr al Ima g e Cub e
Chlor o p h yll Co n c e n tr atio n R e tri e v al
Figure 11. Hyperspectral simulation and retrieval of chlorophyll concentration. The spectrum runs from 400 to 900 nm from the top to the bottom of the image cube. The white rectangle corresponds to the area covered by the displayed image cube.
4. CONCLUSION The preceding examples illustrate a few of the applications that have been made with the AER testbed. Some of the distinctive features of this testbed are: o
The capability for routine processing and the validation benefits from real-time data access through our Cooperative Research and Development Agreements with the Air Force. The data obtained by this avenue include satellite data as well as global analyses and forecasts of atmospheric (clouds, water vapor, etc.) and surface data required for simulation and validation. Included among these analyses are products from numerical weather prediction centers, and also products from operational analysis systems such as the Cloud Depiction and Forecast System (CDFS-II), whose algorithms where developed by AER.
o
Radiative transfer is based on state-of-the-art reference models. A consistent treatment of radiative transfer is available throughout the electromagnetic spectrum. The Optimum Spectral Sampling (OSS) method, in particular, provides outstanding computational efficiency and precision. Because of its monochromatic core, it is highly adaptable to incorporation of whichever radiative processes may be relevant to a particular research and development requirement, including surface interactions, scattering, and molecular absorption.
o
A very wide range of tested algorithms is available for application to new datasets. The parameters retrieved range over vegetation and soil characteristics, sea ice properties, water vapor profiles, cloud geometric and microphysical parameters, and others. With the existing radiative transfer tools, we have developed multispectral and multisensor algorithms that enable exploring data fusion solutions to remote sensing challenges.
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