ECE 583 Lecture 26 Imaging Visible and Infrared Radiometers Multi-Spectral Analysis Active/Passive
Ash Plume across the North Atlantic April 15, 2010 MODIS
Elevated MODIS Aerosol Optical Depth near Iceland volcanic eruption Giovanni AOD images and time-series show increase near location of eruption, coinciding with eruption onset
Aerosol retrieval from space- the MODIS aerosol algorithm Uses bi-modal, log-normal aerosol size distributions. 5 small - accumulation mode (.04-.5 μm) 6 large - coarse mode (>.5 μm) Look up table (LUT) approach 15 view angles (1.5-88 degrees by 6) 15 azimuth angles (0-180 degrees by 12) 7 solar zenith angles 5 aerosol optical depths (0, 0.2, 0.5, 1, 2) 7 modis spectral bands (in SW) Ocean retrievals compute I S and I L from LUT find ratio of small to large modes (η)and the aerosol model by minimizing n m c 1 I I ε = m n j = 1 I + 0.01 where c S L I = η I + (1 η ) I and I m is the measured radiance. then compute optical depth from aerosol model and mode ratio.
Example: reflection of sunlight from a plane parallel atmosphere r z/ μ r ξ μ, φ r r P( ξ ξ ) σsca, σext and are all constant 4π Unscattered component (Beers law, direct beam) r r 0 I (z', ξ ) = I (z, ξ )exp[ σ (z z')/ μ ] At an intermediate level z' r r 1 P( ξ ξ ) I (z', μ) = σsca I (z * 4π F r r 1 I (z', μ) = σ exp[ σ (z z')/ μ ]P( ξ ξ ) * sca ext t 4π Total accumlated radiation along the path z z 1 I (z, μ) = Substitute and integrate, z zt ϖ F r r 1 o I (z, μ) = P( ξ ξ )[ 1 exp( σ 4πμm where σ z 0 ext I 1 * z Ω (z', μ)exp[ σ t t = τ*, m ext = 1/ μ ext r, ξ dz' (z z')/ μ ] μ t t + 1/ μ )exp[ σ ext z t m)] ext t (z t z')/ μ r ]dω( ξ )
Multiple layers aerosol surface contrast The Problem: Cloud or aerosol Surface Optical depth is obtained through the relationship between reflected sunlight and optical depth To do so requires that reflections from surface be removed. This is difficult when there is little contrast between the surface and cloud or aerosol Low contrast conditions occur frequentlye.g thin layers of cloud or aerosol over land, cloud over snow/ice
Land retrievals Select dark pixels in near IR, assume it applies to red and blue bands. Using the continental aerosol model, derive optical depth from the red and blue bands (LUT approach including multiple scattering. Determine aerosol model using single-scattering relationship Adjust the optical depth according to the new aerosol model. The key to both ocean and land retrievals is that the surface reflection is small.
Measurement Requirements for Imaging Radiometers Spatial resolution (pixel size) Number and wavelength of channels Spectral width of wavelength channels Spatial alignment (registration) between wavelength channels Minimum signal measurement accuracy (%) Measurement accuracy of radiance (calibration) Basic Type of Image Scanning Radiometer
Types of Optical Scanning
Pushbroom Imaging Whiskbroom Imaging Grating Spectrometer Pushbroom Imaging
Instrument Requirements for Imaging Radiometers (Derived from measurement requirements) Instantaneous Field-of-View IFOV = d/r d Pixel resolution dimension requirement R Satellite nadir altitude Line Frequency (mirror RPM) f L = V/d V Satellite velocity Angular Rate α v = 2π / f L (radians / second) Sample Rate f c = α v / IFOV Detector Electronic Bandwidth f b = f c / 2 Nyquist frequency
Instrument Requirements for Imaging Radiometers (Derived from measurement requirements) Accuracy Requirement: Signal/Noise = 1 at minimum signal error or resolution (watts orδt brightness temperature ). Noise is the combination of signal shot noise and detector noise (as seen before). Signal S at detector in watts: S (λ) = I pix (λ) A [π(ifov) 2 /4] T s Δλ I pix (λ) Spectral Intensity of pixel T s System optical transmission A System effective aperture area Δλ - Spectral Band pass requirement Calculated Aperture Requirement : Area calculated from S min signal noise and I pix,min measurement requirement A r = S min /(I min A [π(ifov) 2 /4] T s Δλ) Higher spatial or spectral resolution requires larger apertures Higher spatial resolution also requires faster scan rate and signal bandwidth, increasing noise. But, multi element detectors increase through put by N the number of detectors, reducing the aperture requirement correspondingly.
ER-2 Cloud and Aerosol Observation Experiment Science Applications (1983-2000) Multi Sensor Observation Experiment VIS Multi Spectral Radiance IR Height Structure from Lidar
Spatial Resolution: 20 m
0.75392 μm 0.76045 μm 0.76346 μm 1.64 μm 2.16 μm 10.7 μm
Stephens, Remote Sensing of the Lower Atmosphere, Chapter 6
Thermal IR Cirrus Parameter Sensing Multispectral Analysis of Cloud Particles Lidar and IR Radiance Technique Active/Passive Sensing Temperature Backscatter Source Function of IR Radiance
Thermal IR Particle Size
Contrail Sensing Example MODIS Airborne Simulator Data SPINHIRNE ET AL.: CONTRAIL CIRRUS FROM AIRBORNE REMOTE SENSING, GRL 1998
Early Weather Imaging Radiometers HRIR High Resolution Imaging Radiometer Data Mining Forty-three years after the Nimbus II satellite collected these data, a team from NSIDC and NASA recovered a global image from September 23, 1966. In this view over Antarctica, overlaid on Google Earth, the Ross Ice Shelf appears clearly at left.
Some instruments: Early NASA Experimental Meteorological Satellite Nimbus I VII Launched (1964 1978) Data Operations through 1994 Nimbus II Medium Resolution Infrared Radiometer (MRIR) (4.6-6.9 micron, 10-11 micron, 14-16 micron, 5-30 micron, 0.2-4.0 micron) Nimbus II High Resolution Infrared Radiometer (HRIR) (3.5 to 4.1 micron) Nimbus III single-channel dual band-pass High Resolution Infrared Radiometer (HRIR) (3.4 4.2 micron at nighttime, 0.7-1.3 micron at daytime) Nimbus III Medium Resolution Infrared Radiometer (MRIR) (4.5-7.0 micron, 10-11 micron, 14.5-15.5 micron, 20-23 micron, 0.2-4.0 micron) Nimbus IV Temperature and Humidity Infrared Radiometer (THIR) at 11.5 micron channel Nimbus IV Temperature and Humidity Infrared Radiometer (THIR) at 6.7 micron channel HRIR The single-channel dual-band pass scanning radiometer uses a PBSe photoconductive detector cell and provides measurements of blackbody temperatures 210K 330K. The Scan mirror is inclined to 45 degrees with a scan rate of 44.7 revolutions per minute. The Instantaneous field of view is 8.8 milliradians and the scan line separation is 8.3 km. The ground resolution is 8 km at 1110 km. The Nimbus III HRIR was designed to allow nighttime and daytime cloud cover mapping by use of dual band-pass filter which transmits 0.7 to 1.3 micron, and 3.4 to 4.2 micron emitted radiation. The improvement of detector temperature control and electronics compensation has eliminated the multiple calibrations of previous instruments. Nimbus 3 Image of Australia (1969)
NOAA 14 19 1994 to 2009
NOAA-19 Characteristics Main body: 4.2m (13.75 ft) long, 1.88m (6.2 ft) diameter Solar array: 2.73m (8.96 ft) by 6.14m (20.16 ft) Weight at liftoff: 1419.8 kg (3130 pounds) including 4.1 kg of gaseous nitrogen Launch vehicle: Delta-II 7320-10 Space Launch Vehicle Launch date: February 06, 2009 Vandenburg Air Force Base, CA Orbital information: Type: sun synchronous Altitude: 870 km Period: 102.14 minutes Inclination: 98.730 degrees Sensors: Advanced Very High Resolution Radiometer(AVHRR/3) Advanced Microwave Sounding Unit-A (AMSU-A) Microwave Humidity Sounder (MHS) High Resolution Infrared Radiation Sounder (HIRS/4) Solar Backscatter Ultraviolet Spectral radiometer (SBUV/2) Space Environment Monitor (SEM/2) Search and Rescue (SAR) Repeater and Processor Advance Data Collection System (ADCS)
AVHRR Advanced Very High Resolution Radiometer
Imaging Radiometer Detectors
Visible and near IR Radiance Calibration Remote Sensing Group University of Arizona The Remote Sensing Group in the College of Optical Sciences at the University of Arizona is best known for its work on the in-flight, radiometric calibration of remote sensing imagers using ground-based measurements at desert test sites. Radiometric calibration in this context refers to the ability to take the data from a sensor and convert it to a standard energy scale. Such work allows for the comparison of data from an array of imagers (by last count more than 30 sensors). The methods of the group have been in use since the mid-1980s and currently provide absolute radiometric calibration to better than 2%, both in accuracy and precision in the mid-visible.
Terra Satellite
MODIS
VIRS on NPOESS follows MODIS Vincent V. Salomonson et al.
Earth Observing System (EOS) PM Formation Aqua EOS main platform with six imagers and sounders, UV microwave Aura EOS main stratospheric platform with 4 sounding instruments Parasol CNES polarization imager Cloudsat Cloud Radar Calipso Cloud Lidar OCO Orbiting Carbon Observatory GLORY- NASA Aerosol Polarization Imager
Reflection Example OCO: Aerosol retrieval from oxygen absorption A-band 1 nm Wavelength index The key is to make measurements at high spectral resolution (Δλ 0.01-0.1 nm). Actual aircraft data from O Brien et al (1998)
Optical Depth of Overlapping Layers Coded in A-band spectra is information about cloud and aerosol layering This is actual satellite data from MOS. With better resolution (such as PABSI), profiling of layers becomes even more capable
Optical Depth Under Low Contrast Conditions sensitivity to surface albedo wavenumber cm sensitivity to -1 optical depth Red wavelengths respond mostly to surface albedo changes (reflecting the capabilities of most existing instruments) Yellow wavelengths respond mostly to aerosol changes. Simulation of PABSI measurement for thin aerosol layer overlying land surface Key point: The ability to see into the absorption lines provides a way of discriminating surface from atmosphere. Thus surface reflection as well as optical depth is obtained from PABSI.
Retrieval simulation absolute error relative error Best case scenario (assume we know asymmetry parameter, single-scattering albedo, and location of aerosol layer). aerosol optical depth error surface albedo error Aerosol retrieval is difficult: small signature in observed spectrum. instrument noise. instrument convolution (smearing or averaging of observations). uncertainties in a priori data.