Y. Knyazikhin

Boston University, Boston, Massachusetts, United States

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Publications (108)144.89 Total impact

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    ABSTRACT: The ground-based Atmospheric Radiation Measurement Program (ARM) and NASA Aerosol Robotic Network (AERONET) routinely monitor clouds using zenith radiances at visible and near-infrared wavelengths. Using the transmittance calculated from such measurements, we have developed a new retrieval method for cloud effective droplet size and conducted extensive tests for non-precipitating liquid water clouds. The underlying principle is to combine a liquid-water-absorbing wavelength (i.e., 1640 nm) with a non-water-absorbing wavelength for acquiring information on cloud droplet size and optical depth. For simulated stratocumulus clouds with liquid water path less than 300 g m-2 and horizontal resolution of 201 m, the retrieval method underestimates the mean effective radius by 0.8 μm, with a root-mean-squared error of 1.7 μm and a relative deviation of 13%. For actual observations with a liquid water path less than 450 g m-2 at the ARM Oklahoma site during 2007-2008, our 1.5-min-averaged retrievals are generally larger by around 1 μm than those from combined ground-based cloud radar and microwave radiometer at a 5-min temporal resolution. We also compared our retrievals to those from combined shortwave flux and microwave observations for relatively homogeneous clouds, showing that the bias between these two retrieval sets is negligible, but the error of 2.6 μm and the relative deviation of 22% are larger than those found in our simulation case. Finally, the transmittance-based cloud effective droplet radii agree to better than 11% with satellite observations and have a negative bias of 1 μm. Overall, the retrieval method provides reasonable cloud effective radius estimates, which can enhance the cloud products of both ARM and AERONET.
    ATMOSPHERIC CHEMISTRY AND PHYSICS 11/2012; 12(21):10313-10329. · 5.51 Impact Factor
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    ABSTRACT: The ground-based Atmospheric Radiation Measurement Program (ARM) and NASA Aerosol Robotic Network (AERONET) routinely monitor clouds using zenith radiances at visible and near-infrared wavelengths. Using the transmittance calculated from such measurements, we have developed a new retrieval method for cloud effective droplet size and conducted extensive tests for non-precipitating liquid water clouds. The underlying principle is to combine a water-absorbing wavelength (i.e. 1640 nm) with a non-water-absorbing wavelength for acquiring information on cloud droplet size and optical depth. For simulated stratocumulus clouds with liquid water path less than 300 g m-2 and horizontal resolution of 201 m, the retrieval method underestimates the mean effective radius by 0.8 μm, with a root-mean-squared error of 1.7 μm and a relative deviation of 13%. For actual observations with a liquid water path less than 450 g m-2 at the ARM Oklahoma site during 2007-2008, our 1.5 min-averaged retrievals are generally larger by around 1 μm than those from combined ground-based cloud radar and microwave radiometer at a 5 min temporal resolution. We also compared our retrievals to those from combined shortwave flux and microwave observations for relatively homogeneous clouds, showing that the bias between these two retrieval sets is negligible, but the error of 2.6 μm and the relative deviation of 22% are larger than those found in our simulation case. Finally, the transmittance-based cloud effective droplet radii agree to better than 11% with satellite observations and have a negative bias of 1 μm. Overall, the retrieval method provides reasonable cloud effective radius estimates, which can enhance the cloud products of both ARM and AERONET.
    Atmospheric Chemistry and Physics 08/2012; 12(8):19163-19208. · 4.88 Impact Factor
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    ABSTRACT: Certain algebraic combinations of single scattering albedo and solar radiation reflected from, or transmitted through, vegetation canopies do not vary with wavelength. These ''spectrally invariant relationships'' are the consequence of wavelength independence of the extinction coefficient and scattering phase function in veg-etation. In general, this wavelength independence does not hold in the atmosphere, but in cloud-dominated atmospheres the total extinction and total scattering phase function vary only weakly with wavelength. This paper identifies the atmospheric conditions under which the spectrally invariant approximation can accu-rately describe the extinction and scattering properties of cloudy atmospheres. The validity of the as-sumptions and the accuracy of the approximation are tested with 1D radiative transfer calculations using publicly available radiative transfer models: Discrete Ordinate Radiative Transfer (DISORT) and Santa Barbara DISORT Atmospheric Radiative Transfer (SBDART). It is shown for cloudy atmospheres with cloud optical depth above 3, and for spectral intervals that exclude strong water vapor absorption, that the spectrally invariant relationships found in vegetation canopy radiative transfer are valid to better than 5%. The physics behind this phenomenon, its mathematical basis, and possible applications to remote sensing and climate are discussed.
    Journal of the Atmospheric Sciences 12/2011; 68(12):3094-3111. · 2.67 Impact Factor
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    ABSTRACT: The Earth sensors on the Deep Space Climate Observatory (DSCOVR) platform will view the full sunlit disk of the Earth and provide surface reflectance in near retro-solar direction. In the RED spectral band due to strong absorption by leaves the single scattering dominates. The backscatters therefore are directly related to the sunlit fraction of the leaf area, a variable required by many global models of climate, hydrology, biogeochemistry, and ecology. In NIR spectral region a leaf absorbs little radiation. This gives rise to photon multiple interactions. The backscatters become dependent on both the total leaf area and its sunlit fraction due to the strong effect of multiple scattering. This feature allows us to retrieve the total leaf area index (LAI) and its sunlit fraction from DSCOVR data. The operational NASA MODIS LAI algorithm ingests up to seven atmosphere-corrected surface spectral bi-directional reflectance factors and their uncertainties and outputs the most probable values for pixel LAI and their respective dispersions. The MODIS retrieval technique is applicable to any optical sensor, however, it requires selection of sensor-specific values of configurable parameters to adjust the LUT for sensor spectral band characteristics, data resolution and observation uncertainties. Here we will demonstrate the feasibility of adjusting the NASA operational algorithm to retrieve the total LAI and its sunlit fraction from synthetic DSCOVR data with an emphasis on achieving consistency and complementarity between DSCOVR and existing NASA land surface products.
    AGU Fall Meeting Abstracts. 12/2011;
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    ABSTRACT: The three-dimensional structure of a forest - its composition, density, height, crown geometry, within-crown foliage distribution and properties of individual leaves - has a direct impact on the lidar waveform. The pair-correlation function defined as the probability of finding simultaneously phytoelements at two points is the most natural and physically meaningful descriptor of the canopy structure over wide range of scales. The stochastic radiative transfer equations naturally admit this measure and thus provide a powerful means to investigate 3D canopy from space. NASA's Airborne Laser Vegetation Imaging Sensor (LVIS) and ground based data on canopy structure acquired over 5 sites in New England, California and La Selva (Costa Rica) tropical forest were analyzed to assess the impact of 3D canopy structure on lidar waveform and the ability of stochastic radiative transfer equations to simulate the 3D effects. Our results suggest the pair correlation function is sensitive to horizontal and vertical clumping, crown geometry and spatial distribution of trees. Its use in the stochastic radiative transfer equation allows us to accurately simulate the effects of 3D canopy structure on the lidar waveform. Specifically, we found that (1) attenuation of the waveform occurs at a slower rate than 1D models predict; this may result in an underestimation of foliage profile if 3D effects are ignored; (2) 1D model is unable to match simulated waveform and measured surface reflectance, i.e., an unrealistic high value of surface reflectance needs to be used to simulate ground return of sparse vegetation; (3) spatial distribution of trees has a strong impact on the lidar waveform. Simple analytical models of the pair-correlation function will also be discussed.
    AGU Fall Meeting Abstracts. 12/2011;
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    ABSTRACT: The importance of nitrogen for terrestrial ecosystem carbon dynamics and its climate feedback has been well recognized by the ecological community. Interaction between carbon and nitrogen at leaf level is among the fundamental mechanisms that directly control the dynamics of terrestrial vegetation carbon. This process influences absorption and scattering of solar radiation by foliage, which in turn impacts radiation reflected by the vegetation and measured by satellite sensors. NASA's Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and ground based data on canopy structure and foliage nitrogen concentration acquired over six sites in Maine, New England, Florida, North Carolina and Washington were analyzed to assess the role of canopy structure, leaf optics and its biochemical constituents in the spectral variation of radiation reflected by the forest. The study sites represent closed canopy forests (LAI~5). Our results suggest: 1. Impact of canopy structure is so strong that it can significantly suppress the sensitivity of hyperspectral data to leaf optics. 2. Forest reflectance spectra in the interval [710, 790 nm] are required to obtain the fraction of the total leaf area that a "sensor sees" in a given direction. For closed canopy forests its retrieval does not require canopy reflectance models, suggesting that canopy reflectance spectra in this interval provide a direct estimate of the leaf area fraction. 3. The leaf area fraction fully explains variation in measured reflectance spectra due to variation in canopy structure. This variable is used to estimate the mean leaf scattering over foliage that the "sensor sees." For example the nadir-viewing AVIRIS sensor accumulates foliage optical properties over 25% of the total foliage area in needle leaf forest and about 50% in broadleaf forest. 4. Leaf surface properties have an impact on forest reflectivity, lowering its sensitivity to leaf absorbing pigments. 5. Variation in foliar nitrogen concentration can explain up to 55% of variation in AVIRIS spectra in the interval between 400 and 900 nm. The remaining factors could be due to (a) impact of leaf surface properties and/or (b) under-sampling of leaf optical properties due to the single view of the AVIRIS sensor. The theory of canopy spectral invariants underlies the separation of leaf scattering from the total canopy reflectance spectrum.
    AGU Fall Meeting Abstracts. 12/2011;
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    ABSTRACT: In this study we use the 500 m Moderate Resolution Imaging Spectroradiometer (MODIS) Bidirectional Reflectance Distribution Function (BRDF) product to develop multivariate linear regression models that estimate canopy heights over study sites at Howland Forest, Maine, Harvard Forest, Massachusetts and La Selva Forest, Costa Rica using (1) directional escape probabilities that are spectrally independent and (2) the directional spectral reflectances used to derive the directional escape probabilities. These measures of canopy architecture are compared with canopy height information retrieved from the airborne Laser Vegetation Imaging Sensor (LVIS). Both the escape probability and the directional reflectance approaches achieve good results, with correlation coefficients in the range 0.54-0.82, although escape probability results are usually slightly better. This suggests that MODIS 500 m BRDF data can be used to extrapolate canopy heights observed by widely-spaced satellite LIDAR swaths to larger areas, thus providing wide-area coverage of canopy height. © 2011 Elsevier Inc.
    Remote Sensing of Environment. 01/2011; 115(6):1595-1601.
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    ABSTRACT: Many studies have been conducted to demonstrate the ability of hyperspectral data to discriminate plant dominant species. Most of them have employed the use of empirically based techniques, which are site specific, requires some initial training based on characteristics of known leaf and/or canopy spectra and therefore may not be extendable to operational use or adapted to changing or unknown land cover. In this paper we propose a physically based approach for separation of dominant forest type using hyperspectral data. The radiative transfer theory of canopy spectral invariants underlies the approach, which facilitates parameterization of the canopy reflectance in terms of the leaf spectral scattering and two spectrally invariant and structurally varying variables—recollision and directional escape probabilities. The methodology is based on the idea of retrieving spectrally invariant parameters from hyperspectral data first, and then relating their values to structural characteristics of three-dimensional canopy structure. Theoretical and empirical analyses of ground and airborne data acquired by Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) over two sites in New England, USA, suggest that the canopy spectral invariants convey information about canopy structure at both the macro- and micro-scales. The total escape probability (one minus recollision probability) varies as a power function with the exponent related to the number of nested hierarchical levels present in the pixel. Its base is a geometrical mean of the local total escape probabilities and accounts for the cumulative effect of canopy structure over a wide range of scales. The ratio of the directional to the total escape probability becomes independent of the number of hierarchical levels and is a function of the canopy structure at the macro-scale such as tree spatial distribution, crown shape and size, within-crown foliage density and ground cover. These properties allow for the natural separation of dominant forest classes based on the location of points on the total escape probability vs the ratio log–log plane.
    Journal of Quantitative Spectroscopy and Radiative Transfer 01/2011; · 2.38 Impact Factor
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    ABSTRACT: The concept of canopy spectral invariants expresses the observation that simple algebraic combinations of leaf and canopy spectral reflectance become wavelength independent and determine two canopy structure specific variables - the recollision and escape probabilities. These variables specify an accurate relationship between the spectral response of a vegetation canopy to incident solar radiation at the leaf and the canopy scale. They are sensitive to important structural features of the canopy such as forest cover, tree density, leaf area index, crown geometry, forest type and stand age. The canopy spectral invariant behavior is a very strong effect clearly seen in optical remote sensing data. The relative simplicity of retrieving the spectral invariants however is accompanied by considerable difficulties in their interpretations due to the lack of models for these parameters. We use the stochastic radiative transfer equation to relate the spectral invariants to the 3D canopy structure. Stochastic radiative transfer model treats the vegetation canopy as a stochastic medium. It expresses the 3D spatial correlation with the use of the pair correlation function, which plays a key role in measuring the spatial correlation of the 3D canopy structure over a wide range of scales. Data analysis from a simulated single bush to the comprehensive forest canopy is presented for both passive and active (lidar) remote sensing domain.
    AGU Fall Meeting Abstracts. 12/2010;
  • A. Marshak, C. Chiu, Y. Knyazikhin, W. J. Wiscombe
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    ABSTRACT: Clouds with lateral boundaries are inescapably 3D radiative transfer situations. Though clouds seem to have distinct lateral boundaries, remote sensing measurements have great difficulty distinguishing cloudy from cloud free air; thus, research on this "transition zone" is exploding. The transition zone is often not precisely clear nor precisely cloudy according to standard definitions. We have studied the zone using ARM Shortwave Spectrometer (SWS) measurements, ancillary ARM observations, and 3D radiative transfer models. The SWS looks at zenith and, every second, measures downward solar radiation at 418 wavelengths between 350 and 2200 nm. Analysis of high spectral and temporal resolution SWS measurements led to the surprising discovery of a wavelength-independent function that characterizes the transition zone. Using this function we demonstrated that the shortwave spectra within the transition zone are a linear combination of zenith radiance spectra of purely cloudy and purely clear regions. We confirm the spectral-invariant behavior found in the SWS observations with radiative transfer calculations and some theoretical considerations. The results also provide insights for understanding changes of cloud drop size within the cloud-clear transition zone.
    AGU Fall Meeting Abstracts. 12/2010;
  • Liang Xu, M.A. Schull, R.B. Myneni, Y. Knyazikhin
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    ABSTRACT: The spectral invariants of vegetation canopy convey a lot of information on the canopy structure at hierarchical levels. Recent findings of the wavelength independent and scale independent variable - the ratio between the directional escape and the total escape probability - show that it does dependent on the selection of reference leaf albedo in getting correct reflectance values and can be treated as the identifier of macro scale canopy structure (foliage density, aspect ratio, ground cover, tree shape). In order to better utilize this variable in the retrieval algorithm for 3D canopy structure. Model simulation based on the stochastic radiative transfer equation is used to test the sensitivity of this variable to the structural parameters. Hyperspectral and multi-angle data are simulated and analyzed.
    Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2010 2nd Workshop on; 07/2010
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    ABSTRACT: The Siberian tundra is one of the key permafrost regions in the Arctic because of its large spatial extent and carbon-rich yedoma soils. Changes in permafrost thaw and concomitant carbon losses to the atmosphere can have large impacts on the global climate. Permafrost thaw is believed to strongly increase this century as a result of predicted increasing air temperature. At the same time, Arctic vegetation growth and composition is predicted to respond to future climate change. Deciduous shrubs are expected to benefit most from climate warming by increasing growth and expanding their range to higher latitudes. Evidence for recent increases in deciduous shrub cover in the Arctic region is limited thus far to small areas in Alaska. We examined if deciduous shrubs at our research site in the Indigirka lowlands, Northeastern Siberia, show a growth response to the main climate variables, temperature and precipitation. We constructed tree-ring width chronologies for two key Arctic deciduous shrub species, Betula nana and Salix pulchra, dating back roughly 60 years. The ring widths records are compared to summer-warmth index and summer-precipitation data from the closest climate station, approximately 30 km from our site in order to detect the climate factor that mainly determines shrub growth. On a larger scale, recent increases in Arctic productivity, measured as Arctic greenness (Normalized Difference Vegetation Index, NDVI), suggest that shrubs may have expanded during the 80ies and 90ies of the last century. Spectral reflectance data of varying vegetation composition measured at the tundra site were reduced to NDVI to link up with long-term NDVI data. We used a multiple regression analysis to estimate how variation in NDVI is explained by plant fractional cover of different plant functional types (graminoids, deciduous shrubs, evergreen shrubs, forbs, mosses and lichens). Deciduous shrub cover was the only significant explanatory parameter in the model after parameter deletion and explained most of the variation in NDVI, with NDVI increasing with deciduous shrub cover. We used a monthly 25-year long AVHRR satellite leaf area index (LAI) record to analyze how the vegetation in our research area responded to changes in summer warmth index and summer precipitation. Both climate variables were found to have a significant positive effect on LAI. Finally, we present experimental evidence from a shrub removal experiment that shrub cover is negatively correlated with active layer thickness through shading of the soil surface and significantly reduces ground heat fluxes. These results show that an expansion of deciduous shrubs may stabilize permafrost at least on the short term, even under increasing air temperature.
    05/2010;
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    ABSTRACT: The sensitivity of Amazon rainforests to dry-season droughts is still poorly understood, with reports of enhanced tree mortality and forest fires on one hand, and excessive forest greening on the other. Here, we report that the previous results of large-scale greening of the Amazon, obtained from an earlier version of satellite-derived vegetation greenness data - Collection 4 (C4) Enhanced Vegetation Index (EVI), are irreproducible, with both this earlier version as well as the improved, current version (C5), owing to inclusion of atmosphere-corrupted data in those results. We find no evidence of large-scale greening of intact Amazon forests during the 2005 drought - approximately 11%-12% of these drought-stricken forests display greening, while, 28%-29% show browning or no-change, and for the rest, the data are not of sufficient quality to characterize any changes. These changes are also not unique - approximately similar changes are observed in non-drought years as well. Changes in surface solar irradiance are contrary to the speculation in the previously published report of enhanced sunlight availability during the 2005 drought. There was no co-relation between drought severity and greenness changes, which is contrary to the idea of drought-induced greening. Thus, we conclude that Amazon forests did not green-up during the 2005 drought. Citation: Samanta, A., S. Ganguly, H. Hashimoto, S. Devadiga, E. Vermote, Y. Knyazikhin, R. R. Nemani, and R. B. Myneni (2010), Amazon forests did not green-up during the 2005 drought, Geophys. Res. Lett., 37, L05401, doi:10.1029/2009GL042154.
    Geophysical Research Letters 03/2010; 37. · 3.98 Impact Factor
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    ABSTRACT: In a previous paper, we discovered a surprising spectrally-invariant relationship in shortwave spectrometer observations taken by the Atmospheric Radiation Measurement (ARM) program. The relationship suggests that the shortwave spectrum near cloud edges can be determined by a linear combination of zenith radiance spectra of the cloudy and clear regions. Here, using radiative transfer simulations, we study the sensitivity of this relationship to the properties of aerosols and clouds, to the underlying surface type, and to the finite field-of-view (FOV) of the spectrometer. Overall, the relationship is mostly sensitive to cloud properties and has little sensitivity to other factors. At visible wavelengths, the relationship primarily depends on cloud optical depth regardless of cloud phase function, thermodynamic phase and drop size. At water-absorbing wavelengths, the slope of the relationship depends primarily on cloud optical depth; the intercept, by contrast, depends primarily on cloud absorbing and scattering properties, suggesting a new retrieval method for cloud drop effective radius. These results suggest that the spectrally-invariant relationship can be used to infer cloud properties near cloud edges even with insufficient or no knowledge about spectral surface albedo and aerosol properties.
    ATMOSPHERIC CHEMISTRY AND PHYSICS 01/2010; · 5.51 Impact Factor
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    A. Marshak, Y. Knyazikhin, C. Chiu, W. Wiscombe
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    ABSTRACT: The Atmospheric Radiation Measurement Program's (ARM) new Shortwave Spectrometer (SWS) looks straight up and measures zenith radiance at 418 wavelengths between 350 and 2200 nm. Because of its 1-sec sampling resolution, the SWS provides a unique capability to study the transition zone between cloudy and clear sky areas. A surprising spectral invariant behavior is found between ratios of zenith radiance spectra during the transition from cloudy to cloud-free atmosphere. This behavior suggests that the spectral signature of the transition zone is a linear mixture between the two extremes (definitely cloudy and definitely clear). The weighting function of the linear mixture is found to be a wavelength-independent characteristic of the transition zone. It is shown that the transition zone spectrum is fully determined by this function and zenith radiance spectra of clear and cloudy regions. This new finding may help us to better understand and quantify such physical phenomena as humidification of aerosols in the relatively moist cloud environment and evaporation and activation of cloud droplets.
    01/2010;
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    ABSTRACT: The Multi-angle Imaging SpectroRadiometer (MISR) instrument has been collecting global Earth data from NASA's Terra satellite since February 2000. With its nine along-track view angles, four visible/near-infrared spectral bands, intrinsic spatial resolution of 275 m, and stable radiometric and geometric calibration, no instrument that combines MISR's attributes has previously flown in space. The more than 10-year (and counting) MISR data record provides unprecedented opportunities for characterizing long-term trends in aerosol, cloud, and surface properties, and includes 3-D textural information conventionally thought to be accessible only to active sensors.
    Geoscience and Remote Sensing Symposium (IGARSS), 2010 IEEE International; 01/2010
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    ABSTRACT: The evaluation of a new global monthly leaf area index (LAI) data set for the period July 1981 to December 2006 derived from AVHRR Normalized Difference Vegetation Index (NDVI) data is described. The theoretical principle in retrieving LAI from NDVI is based on a physical algorithm rooted on the radiative transfer theory of canopy spectral invariants. Establishing the consistency and validity of the long-term coarse resolution LAI product is a challenging task. First, a detailed implementation of the LAI retrieval algorithm from AVHRR NDVI in a global scale is performed. Second, we evaluate the consistency of our dataset with respect to the MODIS LAI product, which is taken as a benchmark for tuning the performance of our retrieval algorithm. The results indicate qualitative agreement of the AVHRR LAI dataset with the MODIS Collection 5 (C5) LAI data set for the overlapping time period, 2000 - 2003, covering a global- to a regional- and to a pixel-scale. On a global scale, considering all biomes (land cover types), the AVHRR LAI values can explain 97.5 % (R2=0.975) variability in MODIS C5 LAI and, on average the AVHRR LAI values will be in error (RMSE = 0.187) in their estimation by 0.18 LAI, compared to the MODIS C5 LAI. The regional and local scale comparison also testifies to a high degree of correspondence between the AVHRR and MODIS LAI within an error of 0.5 LAI units. The LAI dataset is also compared with CYCLOPES LAI for the period 2000 - 2003. The inter-comparison indicates reasonable agreement in most of the biomes, with a systematic underestimation in the AVHRR LAI (
    AGU Fall Meeting Abstracts. 12/2009;
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    ABSTRACT: Cloud optical depth is the most fundamental intrinsic cloud property determining the Earth's radiative energy balance. However, cloud optical depth is one of the most poorly observed climate variables and is difficult to remotely sense from the surface using traditional methods. We have developed a new ``cloud mode'' in the Aerosol Robotic Network (AERONET) that inexpensively yet dramatically increases cloud optical depth observations in both number and accuracy. We will demonstrate cloud mode retrievals tested at the Atmospheric Radiation Measurement program's Oklahoma site for a variety of situations ranging from broken clouds to overcast. Our retrievals will be compared with those from the satellite-based Moderate-resolution Imaging Spectroradiometer (MODIS) and from ground-based shortwave flux, microwave, and cloud radar instruments. This advance allows AERONET to acquire cloud and aerosol properties using a single instrument, and it greatly enhances current ground-based cloud observations by making possible an instant expansion to global scale.
    AGU Fall Meeting Abstracts. 12/2009;
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    ABSTRACT: On February 24, 2000 ``first light'' on the MISR instrument ushered in a new era of terrestrial remote sensing. With 9 view angles ranging from 70 degrees backward to forward along Terra's flight track, near-simultaneous multiangle and multispectral images are acquired with moderately high spatial resolution, accurate and stable radiometric and geometric calibration, and global coverage. Novel data processing algorithms have been developed to mine the information content of angular reflectance anisotropies and to perform multi-camera stereophotogrammetry, opening new avenues for inferring 3-D structure and dynamics of the atmosphere and surface in support of climate and environmental research. The nearly 10-year (and counting) MISR data record provides unprecedented opportunities for characterizing long-term trends in parameters such as aerosol optical depth, airmass type, and spectral top-of-atmosphere and surface albedo, as well as 3-D information conventionally thought to be accessible only to active sensors, such as height-resolved cloud fractions and atmospheric motion vectors, smoke injection height distributions, and vegetation canopy heights. Technology development efforts are underway to build upon the MISR experience and extend future observational methodologies to broader spectral range (ultraviolet to thermal infrared), wider spatial swaths (enabling more rapid global coverage), and accurate polarimetric imaging. Data processing approaches that take advantage of continually increasing computer speeds are also being explored to facilitate algorithm advances that were not operationally practical 10 years ago.
    AGU Fall Meeting Abstracts. 12/2009;
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    ABSTRACT: Many studies have been conducted to demonstrate the ability of multi-angle spectral data to discriminate plant dominant species. Most have employed the use of empirically based techniques, which are site specific, requires some initial training based on characteristics of known leaf and/or canopy spectra and therefore may not be extendable to operational use or adapted to changing/unknown land cover. An ancillary objective of the MISR LAI/FPAR algorithm is classification of global vegetation into biome types. The algorithm is based on the 3D radiative transfer equation. Its performance suggests that is has valid LAI retrievals and correct biome identification in about 20% of the pixels. However with a probability of about 70%, uncertainties in LAI retrievals due to biome misclassification do not exceed uncertainties in the observations. In this poster we present an approach to improve reliability of the distribution of biomes and dominant species from multi angle spectral data. The radiative transfer theory of canopy spectral invariants underlies the approach, which facilitates parameterization of the canopy bidirectional reflectance factor in terms of the leaf spectrum and two spectrally invariant and structurally varying variables - recollision and directional escape probabilities. Theoretical and empirical analyses of ground and airborne data acquired by AVIRIS, AirMISR over two sites in New England and CHRIS/PROBA over BARAX site in Spain suggest that the canopy spectral invariants convey information about canopy structure at both the macro and micro scales. These properties allow for the natural separation of biome classes based on the location of points on the total escape probability vs the proportional escape ratio log-log plane.
    AGU Fall Meeting Abstracts. 12/2009;

Publication Stats

3k Citations
144.89 Total Impact Points

Institutions

  • 1998–2011
    • Boston University
      • Department of Geography and Environment
      Boston, Massachusetts, United States
  • 2003
    • University of Zurich
      • Department of Geography
      Zürich, Zurich, Switzerland
  • 2001
    • Universitätsmedizin Göttingen
      Göttingen, Lower Saxony, Germany
  • 1994
    • Georg-August-Universität Göttingen
      • Department of Bioclimatology
      Göttingen, Lower Saxony, Germany