Spectroradiometer

About: Spectroradiometer is a research topic. Over the lifetime, 1358 publications have been published within this topic receiving 30713 citations.

The MODIS Aerosol Algorithm, Products and Validation

Abstract: The Moderate Resolution Imaging Spectroradiometer (MODIS) aboard both NASA’s Terra and Aqua satellites is making near-global daily observations of the earth in a wide spectral range (0.41–15 m). These measurements are used to derive spectral aerosol optical thickness and aerosol size parameters over both land and ocean. The aerosol products available over land include aerosol optical thickness at three visible wavelengths, a measure of the fraction of aerosol optical thickness attributed to the fine mode, and several derived parameters including reflected spectral solar flux at the top of the atmosphere. Over the ocean, the aerosol optical thickness is provided in seven wavelengths from 0.47 to 2.13 m. In addition, quantitative aerosol size information includes effective radius of the aerosol and quantitative fraction of optical thickness attributed to the fine mode. Spectral irradiance contributed by the aerosol, mass concentration, and number of cloud condensation nuclei round out the list of available aerosol products over the ocean. The spectral optical thickness and effective radius of the aerosol over the ocean are validated by comparison with two years of Aerosol Robotic Network (AERONET) data gleaned from 132 AERONET stations. Eight thousand MODIS aerosol retrievals collocated with AERONET measurements confirm that one standard deviation of MODIS optical thickness retrievals fall within the predicted uncertainty of 0.03 0.05 over ocean and 0.05 0.15 over land. Two hundred and seventy-one MODIS aerosol retrievals collocated with AERONET inversions at island and coastal sites suggest that one standard deviation of MODIS effective radius retrievals falls within reff 0.11 m. The accuracy of the MODIS retrievals suggests that the product can be used to help narrow the uncertainties associated with aerosol radiative forcing of global climate.

Sparse Unmixing of Hyperspectral Data

Abstract: Linear spectral unmixing is a popular tool in remotely sensed hyperspectral data interpretation. It aims at estimating the fractional abundances of pure spectral signatures (also called as endmembers) in each mixed pixel collected by an imaging spectrometer. In many situations, the identification of the end-member signatures in the original data set may be challenging due to insufficient spatial resolution, mixtures happening at different scales, and unavailability of completely pure spectral signatures in the scene. However, the unmixing problem can also be approached in semisupervised fashion, i.e., by assuming that the observed image signatures can be expressed in the form of linear combinations of a number of pure spectral signatures known in advance (e.g., spectra collected on the ground by a field spectroradiometer). Unmixing then amounts to finding the optimal subset of signatures in a (potentially very large) spectral library that can best model each mixed pixel in the scene. In practice, this is a combinatorial problem which calls for efficient linear sparse regression (SR) techniques based on sparsity-inducing regularizers, since the number of endmembers participating in a mixed pixel is usually very small compared with the (ever-growing) dimensionality (and availability) of spectral libraries. Linear SR is an area of very active research, with strong links to compressed sensing, basis pursuit (BP), BP denoising, and matching pursuit. In this paper, we study the linear spectral unmixing problem under the light of recent theoretical results published in those referred to areas. Furthermore, we provide a comparison of several available and new linear SR algorithms, with the ultimate goal of analyzing their potential in solving the spectral unmixing problem by resorting to available spectral libraries. Our experimental results, conducted using both simulated and real hyperspectral data sets collected by the NASA Jet Propulsion Laboratory's Airborne Visible Infrared Imaging Spectrometer and spectral libraries publicly available from the U.S. Geological Survey, indicate the potential of SR techniques in the task of accurately characterizing the mixed pixels using the library spectra. This opens new perspectives for spectral unmixing, since the abundance estimation process no longer depends on the availability of pure spectral signatures in the input data nor on the capacity of a certain endmember extraction algorithm to identify such pure signatures.

Multi-angle Imaging SpectroRadiometer (MISR) instrument description and experiment overview

Abstract: The Multi-angle Imaging SpectroRadiometer (MISR) instrument is scheduled for launch aboard the first of the Earth Observing System (EOS) spacecraft, EOS-AM1. MISR will provide global, radiometrically calibrated, georectified, and spatially coregistered imagery at nine discrete viewing angles and four visible/near-infrared spectral bands. Algorithms specifically developed to capitalize on this measurement strategy will be used to retrieve geophysical products for studies of clouds, aerosols, and surface radiation. This paper provides an overview of the as-built instrument characteristics and the application of MISR to remote sensing of the Earth.

Validation of MODIS aerosol optical depth retrieval over land

Abstract: Aerosol optical depths are derived operationally for the first time over land in the visible wavelengths by MODIS (Moderate Resolution Imaging Spectroradiometer) onboard the EOSTerra spacecraft. More than 300 Sun photometer data points from more than 30 AERONET (Aerosol Robotic Network) sites globally were used in validating the aerosol optical depths obtained during July - September 2000. Excellent agreement is found with retrieval errors within (Delta)tau=+/- 0.05 +/- 0.20 tau, as predicted, over (partially) vegetated surfaces, consistent with pre-launch theoretical analysis and aircraft field experiments. In coastal and semi-arid regions larger errors are caused predominantly by the uncertainty in evaluating the surface reflectance. The excellent fit was achieved despite the ongoing improvements in instrument characterization and calibration. This results show that MODIS-derived aerosol optical depths can be used quantitatively in many applications with cautions for residual clouds, snow/ice, and water contamination.

Synergistic algorithm for estimating vegetation canopy leaf area index and fraction of absorbed photosynthetically active radiation from MODIS and MISR data

Abstract: A synergistic algorithm for producing global leaf area index and fraction of absorbed photosynthetically active radiation fields from canopy reflectance data measured by MODIS (moderate resolution imaging spectroradiometer) and MISR (multiangle imaging spectroradiometer) instruments aboard the EOS-AM 1 platform is described here. The proposed algorithm is based on a three-dimensional formulation of the radiative transfer process in vegetation canopies. It allows the use of information provided by MODIS (single angle and up to 7 shortwave spectral bands) and MISR (nine angles and four shortwave spectral bands) instruments within one algorithm. By accounting features specific to the problem of radiative transfer in plant canopies, powerful techniques developed in reactor theory and atmospheric physics are adapted to split a complicated three-dimensional radiative transfer problem into two independent, simpler subproblems, the solutions of which are stored in the form of a look-up table. The theoretical background required for the design of the synergistic algorithm is discussed. Large-scale ecosystem modeling is used to simulate a range of ecological responses to changes in climate and chemical composition of the atmosphere, including changes in the distribution of terrestrial plant communities across the globe in response to climate changes. Leaf area index (LAI) is a state parameter in all models describing the exchange of fluxes of energy, mass (e.g., water and CO 2), and momentum between the surface and the planetary boundary layer. Analyses of global carbon budget indicate a large terrestrial middle- to high-latitude sink, without which the accumulation of carbon in the atmosphere would be higher than the present rate. The problem of accurately evaluating the exchange of carbon between the atmosphere and the terrestrial vegetation therefore requires special attention. In this context the fraction of photosynthetically active radiation (FPAR) absorbed by global vegetation is a key state variable in most ecosystem productivity models and in global models of climate, hydrology, biogeochemestry, and ecology (Sellers et al., 1997). Therefore these variables that describe vegetation canopy structure and its energy absorption capacity are required by many of the EOS Interdisciplinary Projects (Myneni et al., 1997a). In order to quantitatively and accurately model global dynamics of these processes, differentiate short-term from long-term trends, as well as to distinguish regional from global phenomena, these two