M. A. Bull
Jet Propulsion Laboratory
Network
Latest external collaboration on country level. Dive into details by clicking on the dots.
Publication
Featured researches published by M. A. Bull.
IEEE Transactions on Geoscience and Remote Sensing | 2009
Ralph A. Kahn; D. L. Nelson; Michael J. Garay; Robert C. Levy; M. A. Bull; David J. Diner; John V. Martonchik; Susan R. Paradise; Earl G. Hansen; Lorraine A. Remer
In this paper, Multi-angle Imaging SpectroRadiometer (MISR) aerosol product attributes are described, including geometry and algorithm performance flags. Actual retrieval coverage is mapped and explained in detail using representative global monthly data. Statistical comparisons are made with coincident aerosol optical depth (AOD) and Angstrom exponent (ANG) retrieval results from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument. The relationship between these results and the ones previously obtained for MISR and MODIS individually, based on comparisons with coincident ground-truth observations, is established. For the data examined, MISR and MODIS each obtain successful aerosol retrievals about 15% of the time, and coincident MISR-MODIS aerosol retrievals are obtained for about 6%-7% of the total overlap region. Cloud avoidance, glint and oblique-Sun exclusions, and other algorithm physical limitations account for these results. For both MISR and MODIS, successful retrievals are obtained for over 75% of locations where attempts are made. Where coincident AOD retrievals are obtained over ocean, the MISR-MODIS correlation coefficient is about 0.9; over land, the correlation coefficient is about 0.7. Differences are traced to specific known algorithm issues or conditions. Over-ocean ANG comparisons yield a correlation of 0.67, showing consistency in distinguishing aerosol air masses dominated by coarse-mode versus fine-mode particles. Sampling considerations imply that care must be taken when assessing monthly global aerosol direct radiative forcing and AOD trends with these products, but they can be used directly for many other applications, such as regional AOD gradient and aerosol air mass type mapping and aerosol transport model validation. Users are urged to take seriously the published product data-quality statements.
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing | 2012
Mark J. Chopping; Malcolm P. North; Jiquan Chen; Crystal B. Schaaf; J. B. Blair; John V. Martonchik; M. A. Bull
This study addresses the retrieval of spatially contiguous canopy cover and height estimates in southwestern US forests via inversion of a geometric-optical (GO) model against surface bidirectional reflectance factor (BRF) estimates from the Multi-angle Imaging SpectroRadiometer (MISR). Model inversion can provide such maps if good estimates of the background bidirectional reflectance distribution function (BRDF) are avail- able. The study area is in the Sierra National Forest in the Sierra Nevada of California. Tree number density, mean crown radius, and fractional cover reference estimates were obtained via analysis of QuickBird 0.6 m spatial resolution panchromatic imagery using the CANopy Analysis with Panchromatic Imagery (CANAPI) algorithm, while RH50, RH75 and RH100 (50%, 75%, and 100% energy return) height data were obtained from the NASA Laser Vegetation Imaging Sensor (LVIS), a full waveform light detection and ranging (lidar) instrument. These canopy parameters were used to drive a modified version of the simple GO model (SGM), accurately reproducing patterns of MISR 672 nm band surface reflectance (mean RMSE = 0.011, mean R2 = 0.82, N = 1048). Cover and height maps were obtained through model inversion against MISR 672 nm reflectance estimates on a 250 m grid. The free parameters were tree number density and mean crown radius. RMSE values with respect to reference data for the cover and height retrievals were 0.05 and 6.65 m, respectively, with R2 of 0.54 and 0.49. MISR can thus provide maps of forest cover and height in areas of topographic variation although refinements are required to improve retrieval precision.
Proceedings of SPIE | 2013
David J. Diner; Michael J. Garay; Olga V. Kalashnikova; Brian E. Rheingans; Sven Geier; M. A. Bull; Veljko M. Jovanovic; Feng Xu; Carol J. Bruegge; Ab Davis; Karlton Crabtree; Russell A. Chipman
The Airborne Multiangle SpectroPolarimetric Imager (AirMSPI) is an ultraviolet/visible/near-infrared pushbroom camera mounted on a single-axis gimbal to acquire multiangle imagery over a ±67° along-track range. The instrument flies aboard NASA’s high-altitude ER-2 aircraft, and acquires Earth imagery with ~10 m spatial resolution across an 11- km wide swath. Radiance data are obtained in eight spectral bands (355, 380, 445, 470, 555, 660, 865, 935 nm). Dual photoelastic modulators (PEMs), achromatic quarter-wave plates, and wire-grid polarizers also enable imagery of the linear polarization Stokes components Q and U at 470, 660, and 865 nm. During January-February 2013, AirMSPI data were acquired over California as part of NASA’s Polarimeter Definition Experiment (PODEX), a field campaign designed to refine requirements for the future Aerosol-Cloud-Ecosystem (ACE) satellite mission. Observations of aerosols, low- and mid-level cloud fields, cirrus, aircraft contrails, and clear skies were obtained over the San Joaquin Valley and the Pacific Ocean during PODEX. Example radiance and polarization images are presented to illustrate some of the instrument’s capabilities.
international geoscience and remote sensing symposium | 2010
Mark J. Chopping; Sawahiko Shimada; M. A. Bull; John V. Martonchik
Red band reflectance factor data from NASAs Multiangle Imaging SpectroRadiometer (MISR) were used to create maps of woody plant canopy cover, fractional height, and aboveground biomass for the southwestern United States, via inversion of a geometric-optical (GO) model provided with reflectance magnitude and anisotropy via a Li-Ross bidirectional reflectance distribution model. Crown cover, canopy height, and biomass distributions are compatible with those seen in other data sets, although there are anomalies associated with the use of the same set of background prediction coefficients over the 10-year period
Remote Sensing of Environment | 2011
Mark J. Chopping; Crystal B. Schaaf; Feng Zhao; Zhuosen Wang; Anne W. Nolin; Gretchen G. Moisen; John V. Martonchik; M. A. Bull
Remote Sensing of Environment | 2009
Mark J. Chopping; Anne W. Nolin; Gretchen G. Moisen; John V. Martonchik; M. A. Bull
Remote Sensing of Environment | 2007
Jiannan Hu; Yin Su; Bin Tan; Dong Huang; Wenze Yang; Mitchell A. Schull; M. A. Bull; John V. Martonchik; David J. Diner; Yuri Knyazikhin; Ranga B. Myneni
Archive | 2010
John V. Martonchik; M. A. Bull; David J. Diner; Barbara J. Gaitley; Michael J. Garay; Eric James Hansen; Ralph A. Kahn; Olga V. Kalashnikova; Douglas L. Nelson; Matteo Pellegrini Todd Yeates
Archive | 2009
John V. Martonchik; Barbara J. Gaitley; M. A. Bull
Archive | 2008
Mark J. Chopping; John V. Martonchik; M. A. Bull; Albert Rango; Crystal B. Schaaf; Fang-Jie Zhao; Zheng Wang