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An operational software program is now available for deriving 'scaled surface reflectances' from spectral data collected by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). The program simulates both the atmospheric scattering and absorption effects. Brief descriptions of the algorithm, inputs, outputs, the limitations of the software, and procedures for obtaining the software are given.
Modern Earth science is beginning to examine interactions among the different terrestrial components at all temporal and spatial scales. Such a global perspective requires an integrated remote-sensing program, the Earth Observing System (EOS), which uses instruments throughout the electromagnetic spectrum to collect data about the Earth's surface, oceans and atmosphere over a range of selected scales. At the finest scales, we will require instruments capable of detailed sampling both spatially and spectrally. We have designed the High-Resoulution Imaging Spectrometer (HIRIS) to acquire simultaneous images in 192 spectral bands in the dominant wavelengths of the solar spectrum, 0.4 to 2.5 micrometers, at a spectral sampling interval of 10 nm. The ground instantaneous field of view (GIFOV) will be 30 m over a 24 km swath. A pointing capability will allow image acquisition up to +52 deg/-30 deg down track and +/-45 deg or more cross-track. Thus we will be able to study surface spectral bidirectional reflectance properties and variations in atmospheric attenuation with viewing angle. The cross-track pointing will also allow multiple viewing opportunities during one 16-day orbital revisit cycle, so that any part of the Earth may be imaged in a two-day period.
The Clouds and the Earth's Radiant Energy System (CERES) spacecraft scanning thermistor bolometer sensors measure earth radiances in the broadband shortwave solar (O.3 - 5.0 micron and total (0.3 to 100 microns) spectral bands as well as in the 8-12 microns water vapor window spectral band. On November 27, 1997, the launch of the Tropical Rainfall Measuring Mission (TRMM) spacecraft placed the first set of CERES sensors into orbit, and 30 days later, the sensors initiated operational measurements of the earth radiance fields. In 1998, the Earth Observing System morning (EOS-AM1) spacecraft will place the second and third sensor sets into orbit. The prelaunch CERES sensors' count conversion coefficients (gains and zero-radiance offsets) were determined in vacuum ground facilities. The gains were tied radiometrically to the International Temperature Scale of 1990 (ITS-90). The gain determinations included the spectral properties (reflectance, transmittance, emittance, etc.) of both the sources and sensors as well as the in-field-of-view (FOV) and out-of-FOV sensor responses. The resulting prelaunch coefficients for the TRMM and EOS-AM1 sensors are presented. Inflight calibration systems and on-orbit calibration approaches are described, which are being used to determine the temporal stabilities of the sensors' gains and offsets from prelaunch calibrations through on-orbit measurements. Analyses of the TRMM prelaunch and on-orbit calibration results indicate that the sensors have retained their ties to ITS-90 at accuracy levels better than /- 0.3% between the 1995 prelaunch and 1997 on-orbit calibrations.
During summer of 2001 NASA's Cloud Absorption Radiometer (CAR) obtained measurement of ocean angular distribution of reflected radiation or BRDF (bidirectional reflectance distribution function) aboard the University of Washington Convair CV-580 research aircraft under cloud-free conditions. The measurements took place aver the Atlantic Ocean off the eastern seaboard of the U.S. in the vicinity of the Chesapeake Light Tower and at nearby National Oceanic and Atmospheric Administration (NOAA) Buoy Stations. The measurements were in support of CLAMS, Chesapeake Lighthouse and Aircraft Measurements for Satellites, field campaign that was primarily designed to validate and improve NASA's Earth Observing System (EOS) satellite data products being derived from three sensors: MODIS (MODerate Resolution Imaging Spectro-Radiometer), MISR (Multi-angle Imaging Spectro-Radiometer) and CERES (Clouds and Earth s Radiant Energy System). Because of the high resolution of the CAR measurements and its high sensitivity to detect weak ocean signals against a noisy background, results of radiance field above the ocean are seen in unprecedented detail. The study also attempts to validate the widely used Cox-Munk model for predicting reflectance from a rough ocean surface.
An extensive verification of cloud property retrievals has been conducted for two algorithms using zenith radiances measured by the Atmospheric Radiation Measurement (ARM) Program ground-based passive two-channel (673 and 870 nm) Narrow Field-Of-View Radiometer. The underlying principle of these algorithms is that clouds have nearly identical optical properties at these wavelengths, but corresponding spectral surface reflectances (for vegetated surfaces) differ significantly. The first algorithm, the RED vs. NIR, works for a fully three-dimensional cloud situation. It retrieves not only cloud optical depth, but also an effective radiative cloud fraction. Importantly, due to one-second time resolution of radiance measurements, we are able, for the first time, to capture detailed changes in cloud structure at the natural time scale of cloud evolution. The cloud optical depths tau retrieved by this algorithm are comparable to those inferred from both downward fluxes in overcast situations and microwave brightness temperatures for broken clouds. Moreover, it can retrieve tau for thin patchy clouds, where flux and microwave observations fail to detect them. The second algorithm, referred to as COUPLED, couples zenith radiances with simultaneous fluxes to infer 2. In general, the COUPLED and RED vs. NIR algorithms retrieve consistent values of tau. However, the COUPLED algorithm is more sensitive to the accuracies of measured radiance, flux, and surface reflectance than the RED vs. NIR algorithm. This is especially true for thick overcast clouds where it may substantially overestimate z.
The MODerate resolution Imaging Spectroradiometer (MODIS) aerosol algorithms have been working steadily since early 2000 to transform the MODIS-measured spectral solar reflectance from the Earth's surface and atmosphere into a variety of aerosol products. In this lecture I will proceed through a survey of these products, answering the following questions as I proceed. What are the products? How do they compare with ground truth? How do we use these products to describe the global aerosol system? Are aerosols increasing or decreasing? How do aerosols affect climate and clouds?
As more and more satellite observations become available to the science and user community, their on-orbit calibration accuracy and consistency over time continue to be an important and challenge issue, especially in the reflective solar spectral regions. In recent years, many sensor calibration inter-comparison methodologies have been developed by different groups and applied to a range of satellite observations, aiming to the improvement of satellite instrument calibration accuracy and data quality. This paper provides an overview of different methodologies developed for inter-comparisons of A VHRR and MODIS observations, and extends their applications to the Visible-Infrared Imaging Radiometer Suite (VIIRS) instrument. The first VIIRS was launched on-board the NPP spacecraft on October 28, 2011. The VIIRS, designed with MODIS heritage, collects data in 22 spectral bands from visible (VIS) to long-wave infrared (LWIR). Like both Terra and Aqua MODIS, the VIIRS on-orbit calibration is performed using a set of on-board calibrators (OBC), Methodologies discussed in this paper include the use of well-characterized ground reference targets, near simultaneous nadir overpasses (SNO), lunar observations, and deep convective clouds (DeC). Results from long-term A VHRR and MODIS observations and initial assessment of VIIRS on-orbit calibration are presented. Current uncertainties of different methodologies and potential improvements are also discussed in this paper.
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A radiative-transfer model for the bidirectional reflectance-distribution function (BRDF) shows that snow is moderately anisotropic in the NIR wavelengths. Although the directional-hemispherical albedo of snow decreases as the grains become larger, the forward scattering also increases, with the result that the illumination and viewing geometry must be considered when interpreting physical properties of the surface layer of the snow pack from remote sensing data. Measurements of the BRDF and the transmittance for a variety of snow conditions were made throughout the winter and spring seasons with an SE-590 spectroradiometer, for wavelengths from 0.38 to 1.11 microns. Coincident with these, the surface grain properties were analyzed by stereological methods. A sphere with the same surface-to-volume ratio as the ice grains is used as the equivalent sphere.
This grant was awarded to Dr. C. Megan Urry of the Space Telescope Science Institute in response to two successful ADP proposals to use archival Ginga and Rosat X-ray data for 'Testing the Pairs-Reflection model with X-Ray Spectral Variability' (in collaboration with Paola Grandi, now at the University of Rome) and 'X-Ray Properties of Complete Samples of Radio-Selected BL Lacertae Objects' (in collaboration with then-graduate student Rita Sambruna, now a post-doc at Goddard Space Flight Center). In addition, post-docs Joseph Pesce and Elena Pian, and graduate student Matthew O'Dowd, have worked on several aspects of these projects. The grant was originally awarded on 3/01/94; this report covers the full period, through May 1997. We have completed our project on the X-ray properties of radio-selected BL Lacs.
Emissivity and reflectivity in the thermal infrared spectral region (8-13 microns) may be used to discriminate among rocks and minerals. Although considerable success has been achieved in remote sensing classification of rock types based on emissivity measurements made with NASA's Thermal Infreared Multispectral Scanner (TIMS), classification based on reflectivity offers several advantages: much narrower bandwidths are used, higher signal to noise ratios are possible, and measurements are little affected by surface temperature. As a demonstration, an airborne CO2 laser instrument was flown along the margin of Death Valley, California. Measurements of spectral reflectance collected with this device were compared with emissivity measurements made with the TIMS. Data from either instrument provided the means for recognizing boundaries between geologic units including different rock types and fan surfaces of different ages.
The statistical characteristics of the spectral structure of the brightness field of the ocean/atmosphere system are determined from the spectra of incident radiation and the radiation reflected from the ocean, obtained from aircraft (Conveyor 990) at heights of 0.3 and 10 km above the Atlantic Ocean. Analysis of the spectral structure reveals a weak correlation between atmospheric brightness variations in the 0.4 to 0.5 micron and 0.55 to 0.70 micron regions of the spectrum. This is attributed to the possible influence of variations of the scattering coefficient or optical thickness on the brightness variations (whose sign depends on the predominance of damping or multiple scattering in a given spectral region).
Six spectral plots, each summarizing single-pixel reflectance for 128 channels of Airborne Imaging Spectrometer (AIS) data, were examined. The six sample pixels were located along a topographic/moisture gradient from lake surface to dune top in the Nebraska Sandhills. AIS spectra for various moisture regimes/vegetative zones appear quite logical, with a general positive relationship between increasing elevation (i.e., decreasing access of plant roots to water) and increasing reflectance in the spectral regions diagnostic of leaf-water content (i.e., bands centered on 1.65 and 2.20 microns).
The spectral signature of porphyrin compounds, considered to be biomarkers of depositional environment and thermal maturity, have been identified in reflectance spectra of oil shales. The key bands identified, in order of intensity, are the Soret (0.40 microns), alpha (0.57 microns), and beta (0.53 microns) bands. The observed bands represent the composite spectral signature of all porphyrin compounds present in the sample and, therefore, change position and intensity in accordance with changes in porphyrin chemistry.
The Climate Absolute Radiance and Refractivity Observatory (CLARREO) is a climate observation system designed to study Earth's climate variability with unprecedented absolute radiometric accuracy and SI traceability. Observation System Simulation Experiments (OSSEs) were developed using GCM output and MODTRAN to simulate CLARREO reflectance measurements during the 21st century as a design tool for the CLARREO hyperspectral shortwave imager. With OSSE simulations of hyperspectral reflectance, Feldman et al. [2011a,b] found that shortwave reflectance is able to detect changes in climate variables during the 21st century and improve time-to-detection compared to broadband measurements. The OSSE has been a powerful tool in the design of the CLARREO imager and for understanding the effect of climate change on the spectral variability of reflectance, but it is important to evaluate how well the OSSE simulates the Earth's present-day spectral variability. For this evaluation we have used hyperspectral reflectance measurements from the Scanning Imaging Absorption Spectrometer for Atmospheric Cartography (SCIAMACHY), a shortwave spectrometer that was operational between March 2002 and April 2012. To study the spectral variability of SCIAMACHY-measured and OSSE-simulated reflectance, we used principal component analysis (PCA), a spectral decomposition technique that identifies dominant modes of variability in a multivariate data set. Using quantitative comparisons of the OSSE and SCIAMACHY PCs, we have quantified how well the OSSE captures the spectral variability of Earth?s climate system at the beginning of the 21st century relative to SCIAMACHY measurements. These results showed that the OSSE and SCIAMACHY data sets share over 99% of their total variance in 2004. Using the PCs and the temporally distributed reflectance spectra projected onto the PCs (PC scores), we can study the temporal variability of the observed and simulated reflectance spectra. Multivariate time series analysis of the PC scores using techniques such as Singular Spectrum Analysis (SSA) and Multichannel SSA will provide information about the temporal variability of the dominant variables. Quantitative comparison techniques can evaluate how well the OSSE reproduces the temporal variability observed by SCIAMACHY spectral reflectance measurements during the first decade of the 21st century. PCA of OSSE-simulated reflectance can also be used to study how the dominant spectral variables change on centennial scales for forced and unforced climate change scenarios. To have confidence in OSSE predictions of the spectral variability of hyperspectral reflectance, it is first necessary for us to evaluate the degree to which the OSSE simulations are able to reproduce the Earth?s present-day spectral variability.
The spectral and broadband reflectance of naturally occurring desert sand, black lava, gypsum sand, and snow cover is measured from a twin engine Cessna 402-series aircraft. The measurement system is computer controlled and electrically isolated from the aircraft. It consists of upward and downward looking hemispheric diffusers, filters, a rotating 90 degree mirror, a focusing lens, and a double monochromator/PMT or a UV enhanced photodiode. Measurements are made at several altitudes enabling the empirical determination of the backscatter and attenuation effects on the reflectance. These reflectance results along with those reported earlier for a pine forest, green farmland, the open ocean, and brown farmland are represented analytically.
This paper describes an AERONET-based Surface Reflectance Validation Network (ASRVN) and its dataset of spectral surface bidirectional reflectance and albedo based on MODIS TERRA and AQUA data. The ASRVN is an operational data collection and processing system. It receives 50x50 square kilometer subsets of MODIS L1B data from MODAPS and AERONET aerosol and water vapor information. Then it performs an accurate atmospheric correction for about 100 AERONET sites based on accurate radiative transfer theory with high quality control of the input data. The ASRVN processing software consists of L1B data gridding algorithm, a new cloud mask algorithm based on a time series analysis, and an atmospheric correction algorithm. The atmospheric correction is achieved by fitting the MODIS top of atmosphere measurements, accumulated for 16-day interval, with theoretical reflectance parameterized in terms of coefficients of the LSRT BRF model. The ASRVN takes several steps to ensure high quality of results: 1) cloud mask algorithm filters opaque clouds; 2) an aerosol filter has been developed to filter residual semi-transparent and sub-pixel clouds, as well as cases with high inhomogeneity of aerosols in the processing area; 3) imposing requirement of consistency of the new solution with previously retrieved BRF and albedo; 4) rapid adjustment of the 16-day retrieval to the surface changes using the last day of measurements; and 5) development of seasonal back-up spectral BRF database to increase data coverage. The ASRVN provides a gapless or near-gapless coverage for the processing area. The gaps, caused by clouds, are filled most naturally with the latest solution for a given pixels. The ASRVN products include three parameters of LSRT model (k(sup L), k(sup G), k(sup V)), surface albedo, NBRF (a normalized BRF computed for a standard viewing geometry, VZA=0 deg., SZA=45 deg.), and IBRF (instantaneous, or one angle, BRF value derived from the last day of MODIS measurement for specific viewing geometry) for MODIS 500m bands 1-7. The results are produced daily at resolution of 1 km in gridded format. We also provide cloud mask, quality flag and a browse bitmap image. The new dataset can be used for a wide range of applications including validation analysis and science research.
Identification of solids via infrared reflection spectroscopy requires a spectral library of all solids likely to be encountered. A confounding factor in populating such a spectral library is that the reflectance spectra of solids vary with their form, including particle size, film thickness, and substrate. To reduce the efforts of experimentally constructing such a library, an alternate strategy is to use the wavelength-dependent optical constants, n and k, of a solid to calculate a series of reflectance spectra corresponding to each scenario or morphology. Because most n/k measurements are best performed on mm-sized crystals, however, the challenge of determining the optical constants increases when a solid is only readily available as a powder, as is often the case. Some organic solids, such as caffeine, are both unavailable in large crystals and difficult to press into pellets. In this study, the infrared optical constants, or complex refractive indices, of caffeine were determined using three different methods: single-angle reflectance, infrared spectroscopic ellipsometry, and quantitative absorbance measurements of KBr pellets. The n and k values derived through each method were used to model the hyperspectral imaging reflectance spectrum of a caffeine film on a steel planchet. Over 1,110 – 870 cm-1, the single-angle reflectance-derived n and k had the best correlation with the experimental spectrum. These results suggest different organic solids may require different methods to determine the most accurate infrared complex refractive indices for synthetic spectral libraries.