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Reflected spectral radiance measured by the Multiangle Imaging SpectroRadiometer (MISR) on the Terra satellite has been analyzed to determine the fraction of global cloudiness that appears to be spatially homogeneous over regions of various sizes.
Shortwave spectral radiance models for use in the spectral correction algorithms for the ERBE Scanner Instrument are provided. The required data base was delivered to the ERBe Data Reduction Group in October 1984. It consisted of two sets of data files: (1) the spectral bidirectional angular models and (2) the spectral flux modes. The bidirectional models employ the angular characteristics of reflection by the Earth-atmosphere system and were derived from detailed radiance calculations using a finite difference model of the radiative transfer process. The spectral flux models were created through the use of a delta-Eddington model to economically simulate the effects of atmospheric variability. By combining these data sets, a wide range of radiances may be approximated for a number of scene types.
Attention is given to an experiment in which spectral radiance data collected in three spectral regions are related to corn canopy variables. The study extends the work of Tucker et al. (1979) in that more detailed measurements of corn canopy variables were made using quantitative techniques. Wet and dry green leaf biomass is considered along with the green leaf area index, chlorotic leaf biomass, chlorotic leaf area, and leaf water content. In addition, spectral data were collected with a hand-held radiometer having Landsat-D Thematic Mapper (TM) bands TM3 (0.63-0.69 micrometers), TM4 (0.76-0.90 micrometers), and TM5 (1.55-1.75 micrometers). TM3, TM4, and TM5 seem to be well situated spectrally for making remotely sensed measurements related to chlorophyll concentration, leaf density, and leaf water content.
Imaging spectrometry data must be spectrally, radiometrically and geometrically calibrated in order to: 1) derive physical parameters from measured spectral radiance, 2) compare data acquired from different regions and at different times, 3) compare and analyze the imaging spectrometry data with data acquired from other calibrated sensors, and 4) compare and analyze data with results from computer models. The calibration of AVIRIS data is the process by which laboratory characterization data are applied to raw instrument data (digitized number versus spectral channels) to produce quantitative spectra (radiance versus wavelength) for each image pixel in units of spectral radiance. The AVIRIS sensor and calibration process are described by Vane and the application of the calibration data to the raw digital data is described by Green. This calibration process is validated for in-flight performance of the sensor using a rigorous ground-truth campaign. This workshop paper reviews the laboratory characterization data set that is used in the AVIRIS calibration process. The laboratory measurements used to acquire the calibration data are divided into three classes: 1) spectral calibration, 2) radiometric calibration, and 3) spatial calibration.
On 7 Mar. 1991, an in-flight calibration experiment was held at the Ivanpah Playa in southeastern California for the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) imaging spectrometer. Five AVIRIS overflights were acquired of a calibration target designated on the Ivanpah Playa surface. At the time of the overflights, the reflectance of the calibration target was measured with a field spectrometer. In addition, the atmospheric optical depths and water vapor abundance were measured from a radiometer station adjacent to the calibration target. These in-situ measurements were used to constrain the MODTRAN radiative transfer code to model the upwelling spectral radiance incident to the sensor aperture during the overflights. Analyses of this modeled radiance in conjunction with the laboratory-calibrated radiance were used to determine the spectral and radiometric calibration of AVIRIS while in flight.
Because of their radiative interactions, the vegetation canopy and the atmosphere are coupled together; each serves as a boundary condition to the radative transfer equations in the adjacent medium. To better understand radiative processes in these media we need an accurate description of their interactions. This presentation outlines a technique needed to describe interactions between vegetation and clouds and exploits it to retrieve cloud optical depth from ground-based radiance measurements. measurements of zenith radiance in the RED and NIR spectral regions. In addition to an algebraic combinations of spectral radiances such as Normalized Difference Cloud Index (NDCI) that can result in poor retrievals due to its insensitivity to cloud fraction, both RED and NIR radiances as points on the "RED vs. NIR" plane are proposed to be used for retrieval. The proposed method is applied to a multi-channel sunphotometer (as a part of AERONET) measurements at the ARM site in Oklahoma. In addition to cloud optical depth, the new method also infers a "radiatively effective" cloud fraction.
We have constructed, calibrated, and tested a cryogenic low-background infrared (IR) radiometer for both spectral radiance and irradiance measurements over the 4- to 20-μm wavelength range. The primary purpose of the Missile Defense Transfer Radiometer (MDXR) is to measure absolute irradiance or radiance from cryogenic IR test chamber sources using a photoconductive Si:As blocked-impurity band (BIB) detector and a set of spectral filters. The MDXR also includes an absolute cryogenic radiometer (ACR) and a Fourier-transform spectrometer (FTS). For irradiance measurements, the ACR is used to provide the primary power scale for the BIB detector in conjunction with spectral filters, whereas the FTS/BIB configuration derives its scale from an internal blackbody source. The two measurement scales show agreement for the irradiance of highly collimated (<1 mrad) IR beams from 10 -13 to 10 -8 W / μm / cm 2 within the combined relative uncertainties of 2.6% (coverage factor k = 1.) We have also calibrated the radiometer for radiance measurements using a large cavity fluid bath blackbody that overfills the spatial and angular extent of the radiometer entrance pupil. The radiometric calibration uncertainty analysis of the radiometer and its maintenance and stability are discussed.
Biomass burning is an important process on the Earth at the local, regional and global scales. To investigate issues related to biomass burning, a range of remotely acquired data were measured as part of the NASA Smoke Cloud Aerosol and Radiation experiment in Brazil, 1995. As part of this experiment, images of calibrated spectral radiance from 400 to 2500 nm at 10 nm intervals were acquired by AVIRIS. To investigate the expression of biomass fires in AVIRIS spectra, a model of the upwelling radiance from a burning fire was developed. This spectral model accounts for four components in the 20 by 20 m AVIRIS spatial resolution element. These are: (1) the atmospheric path radiance, (2) the solar reflected radiance from unburnt vegetation and soil, (3) the apparent temperature and area of a primary fire, and (4) the apparent temperature and area of a secondary fire. A nonlinear least squares spectral fitting algorithm was developed to invert this model for the AVIRIS spectra. The derived biomass burning parameters from this algorithm are presented for AVIRIS spectral images acquired over Cuiaba, Brazil on 25 August 1995.
The topographic effect is measured by the difference between the global radiance from inclined surfaces as a function of their orientation relative to the sensor position and light source. The short wave radiant energy incident on a surface is composed of direct sunlight, scattered skylight, and light reflected from surrounding terrain. The latter two components are commonly known as the diffuse component. The contribution of the diffuse light component to the topographic effect was examined and the significance of this diffuse component with respect to two direct radiance models was assessed. Diffuse and global spectral radiances were measured for a series of slopes and aspects of a uniform and surface in the red and photographic infrared parts of the spectrum, using a nadir pointing two channel handheld radiometer. The diffuse light was found to produce a topographic effect which varied from the topographic effect for direct light. The topographic effect caused by diffuse light was found to increase slightly with solar elevation and wavelength for the channels examined. The correlations between data derived from two simple direct radiance simulation models and the field data were not significantly affected when the diffuse component was removed from the radiances. Radiances from a 60 percent reflective surface, assuming no atmospheric path radiance, the diffuse light topographic effect contributed a maximum range of 3 pixel values in simulated LANDSAT data from all aspects with slopes up to 30 degrees.
A ground-based, hand-held radiometer, configured to measure red and photographic infrared spectral radiances, was successfully used to collect in situ temporal spectral measurements of corn and soybean crops. Significant relationships were found between the radiance data and the biomass, plant height, percentage crop cover, percentage crop chlorosis, and percentage leaf loss. The results of this experiment show conclusively that hand-held radiometers can be used to collect spectral data that are highly correlated to several agronomic variables. These findings suggest approaches for agronomic research, and confirm the value of remote sensing of agricultural targets.
Distribution and flow of water masses at four depth intervals were determined by analyzing ERTS imagery through the use of optical models of classes of vertical oceanographic profiles. Data used for these models was obtained from shipboard measurements including surface spectral radiance, and optical and more conventional oceanographic depth profiles. The spectral models obtained were applied to radiance-contoured ERTS imagery in band 4, 5, 6, and 7. Features mapped by direct photointerpretation of ERTS imagery include submerged shoals, current streamlines, and location of possible upwellings, downwellings and submarine springs.
Spectral reflectance characteristics of water pollutants and water bodies were compiled using the existing literature. Radiance calculations were performed at satellite altitude for selected illumination angles and atmospheric conditions. The work described in this report was limited to the reflective portion of the spectrum between 0.40 micrometer to 1.0 micrometer.
Satellite based measurements of top-of-atmosphere (TOA) spectral radiances in the infrared (IR) region have been in existence for almost two decades and are expected to be continued in the following decades. The data from multiple hyper-spectral IR sounders can therefore be combined to build a long-term data record to further global scale climate trend research. Challenges associated with the fusion of data from different sensors come from the stability and consistency requirement on the climate record. The direct radiance observations from different sounders need to be homogenized by reconciling the differences in calibration, spectral response function (SRF), and spatial-temporal sampling. When geophysical variables derived from radiances measured by different sounders are combined to form long-term climate records, the impacts of any inconsistencies between overlapping measurements on the retrieval must be carefully assessed in order to estimate the uncertainty of the corresponding climate anomalies/trends derived. This paper presents a novel climate fingerprinting methodology and establishes a rigorously-defined inverse relationship that allows us to efficiently evaluate the change in essential climate variables from the change in spectral radiances measured in prescribed spatial and temporal averaging scales. The inverse spectral fingerprinting relationship is constructed based on a unified spectral kernel scheme, providing a direct means for quantifying the potential discontinuity in the derived climate anomalies due to inconsistencies between overlapping measurements. We show in this paper a sample application of using the spectral fingerprinting scheme to derive long-term, global-scale surface temperatures from the Climate Hyperspectral Infrared Radiance Product (CHIRP) and quantify the inter-satellite biases.
Using the O2 118 GHz spectral radiance measurements obtained by the Microwave Limb Sounder instrument on board the Aura spacecraft, we demonstrate that the Zeeman effect can be used to remotely measure the magnetic field perturbations produced by the auroral electrojet near the Hall current closure altitudes. Our derived current-induced magnetic field perturbations are found to be highly correlated with those coincidently obtained by ground magnetometers. These perturbations are also found to be linearly correlated with auroral electrojet strength. The statistically derived polar maps of our measured magnetic field perturbation reveal a spatial-temporal morphology consistent with that produced by the Hall current during substorms and storms. With today's technology, a constellation of compact, low-power, high spectral-resolution cubesats would have the capability to provide high precision and spatiotemporal magnetic field samplings needed for auroral electrojet measurements to gain insights into the spatiotemporal behavior of the auroral electrojet system.
A semianalytical radiance model is developed which predicts the upwelled spectral radiance at the sea surface as a function of the phytoplankton pigment concentration for Morel Case 1 waters. The model is in good agreement with experimental measurements carried out in waters which were not included in the data base used to derive it. It suggests that the observed variability in the radiance is due to variations in the backscattering of plankton and the associated detrital material. The model is extended to include other material in the water, such as dissolved organic material, referred to as yellow substances, and detached coccoliths from coccolithophorids, e.g., Emiliana huxleyi. Potential applications include an improved biooptical algorithm for the retrieval of pigment concentrations from satellite imagery in the presence of interference from detached coccoliths and an improved atmospheric correction for satellite imagery. The model also serves to identify and to interpret deviations from Case 1 waters.
The most common approach for retrieving cloud optical depth from ground-based observations uses downwelling fluxes measured by pyranometers and Multi-Filter Rotating Shadowband Radiometers (MFRSR). The key element in both retrieval techniques is the one-to-one mapping of the "observed" fluxes into cloud optical depth through plane-parallel radiative transfer. Both methods are expected to work well only for completely overcast clouds giving an effective optical depth for the whole sky. To infer cloud optical depth locally one can assume to use a narrow-field-of view radiometer that measures radiances instead of fluxes. However, lack of one-to-one relationships between radiance and cloud optical depth, prevents the direct use of radiances either. Here we discuss a new method for retrieving cloud optical depth from ground-based measurements of zenith radiance in the RED and near infrared (NIR) spectral regions. In addition to an algebraic combinations of spectral radiances such as Normalized Difference Cloud Index (NDCI) that largely removes nonuniqueness and the radiative effects of cloud inhomogeneity but can result in poor retrievals due to its insensitivity to cloud fraction, both RED and NIR radiances as points on the "RED vs. NIR" plane are proposed to be used for retrieval. The proposed retrieval method is applied to a multi- channel sunphotometer (as a part of AERONET) measurements at the ARM site in Oklahoma. The results of retrieval are compared with the ones from Microwave Radiometer (MWR) and MFRSR. In addition to cloud optical depth, the new retrieval method also infers a "radiatively effective" cloud fraction.
The Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) measures spatial images of the total upwelling spectral radiance from 400 to 2500 nm through 10 nm spectral channels. Quantitative research and application objectives for surface investigations require inversion of the measured radiance of surface reflectance or surface leaving radiance. To calculate apparent surface reflectance, estimates of atmospheric water vapor abundance, cirrus cloud effects, surface pressure elevation, and aerosol optical depth are required. Algorithms for the estimation of these atmospheric parameters from the AVIRIS data themselves are described. From these atmospheric parameters we show an example of the calculation of apparent surface reflectance from the AVIRIS-measured radiance using a radiative transfer code.
Field measurements of spectral radiances are used to determine precise values of the spectral emissivity of grass-thatch-soil complexes to facilitate remote temperature determinations. The emissivity variation with wavelength is very small, emissivity is close to unity, and emissivity is fairly constant in terms of emission angle, land practice, and season. The prairie surface is therefore similar to a grey body and a quasiideal emitter, although determinations of the kinetic temperature are required to confirm the results.