287 research outputs found
A Direct Algorithm for Estimating Land Surface Broadband Albedos From MODIS Imagery
Land surface albedo is a critical variable needed in
land surface modeling. The conventional methods for estimating
broadband albedos rely on a series of steps in the processing chain,
including atmospheric correction, surface angular modeling, and
narrowband-to-broadband albedo conversions. Unfortunately,
errors associated with each procedure may be accumulated and
significantly impact the accuracy of the final albedo products.
In an earlier study, we developed a new direct procedure that
links the top-of-atmosphere spectral albedos with land surface
broadband albedos without performing atmospheric correction
and other processes. In this paper, this method is further improved
in several aspects and implemented using actual Moderate Resolution
Imaging Spectroradiometer (MODIS) imagery. Several
case studies indicated that this new method can predict land
surface broadband albedos very accurately and eliminate aerosol
effects effectively. It is very promising for global applications and
is particularly suitable for nonvegetated land surfaces. Note that
a Lambertian surface has been assumed in the radiative transfer
simulation in this paper as a first-order approximation; this
assumption can be easily removed as long as a global bidirectional
reflectance distribution function climatology is available,This work
was supported in part by the National Aeronautics and Space Administration
(NASA) under Grant NAG5-6459
An Optimization Algorithm for Separating Land Surface Temperature and Emissivity from Multispectral Thermal Infrared Imagery
Land surface temperature (LST) and emissivity are
important components of land surface modeling and applications.
The only practical means of obtaining LST at spatial and temporal
resolutions appropriate for most modeling applications is through
remote sensing. While the popular split-window method has
been widely used to estimate LST, it requires known emissivity
values. Multispectral thermal infrared imagery provides us with
an excellent opportunity to estimate both LST and emissivity
simultaneously, but the difficulty is that a single multispectral
thermal measurement with bands presents equations in
+ 1 unknowns ( spectral emissivities and LST). In this study,
we developed a general algorithm that can separate land surface
emissivity and LST from any multispectral thermal imagery, such
as moderate-resolution imaging spectroradiometer (MODIS) and
advanced spaceborne thermal emission and reflection radiometer
(ASTER). The central idea was to establish empirical constraints,
and regularization methods were used to estimate both emissivity
and LST through an optimization algorithm. It allows us to
incorporate any prior knowledge in a formal way. The numerical
experiments showed that this algorithm is very effective (more
than 43.4% inversion results differed from the actual LST within
0.5 , 70.2% within 1 and 84% within 1.5 ), although improvements
are still needed
An Improved Atmospheric Correction Algorithm for Hyperspectral Remotely Sensed Imagery
There is an increased trend toward quantitative
estimation of land surface variables from hyperspectral remote
sensing. One challenging issue is retrieving surface reflectance
spectra from observed radiance through atmospheric correction,
most methods for which are intended to correct water vapor and
other absorbing gases. In this letter, methods for correcting both
aerosols and water vapor are explored. We first apply the cluster
matching technique developed earlier for Landsat-7 ETM+
imagery to Airborne Visible/Infrared Imaging Spectrometer
(AVIRIS) data, then improve its aerosol estimation and incorporate
a new method for estimating column water vapor content
using the neural network technique. The improved algorithm
is then used to correct Hyperion imagery. Case studies using
AVIRIS and Hyperion images demonstrate that both the original
and improved methods are very effective to remove heterogeneous
atmospheric effects and recover surface reflectance spectra.This work was
supported in part by the National Aeronautics and Space Administration under
EO1 Grant NCC5462
Retrieving Leaf Area Index With a Neural Network Method: Simulation and Validation
Leaf area index () is a crucial biophysical parameter
that is indispensable for many biophysical and climatic models.
A neural network algorithm in conjunction with extensive canopy
and atmospheric radiative transfer simulations is presented in this
paper to estimateLAIfromLandsat-7 Enhanced ThematicMapper
Plus data. Two schemes were explored; the first was based on surface
reflectance, and the second on top-of-atmosphere (TOA) radiance.
The implication of the second scheme is that atmospheric
corrections are not needed for estimating the surface LAI. A soil
reflectance index (SRI) was proposed to account for variable soil
background reflectances. Ground-measured LAI data acquired at
Beltsville, MD were used to validate both schemes. The results indicate
that both methods can be used to estimate LAI accurately.
The experiments also showed that the use of SRI is very critical.This work was
supported in part by the U.S. National Aeronautics and Space Administration
(NASA) under Grant NAG5-6459 and Grant NCC5462
Evaluation of five satellite top-of-atmosphere albedo products over land
Five satellite top-of-atmosphere (TOA) albedo products over land were evaluated in this study including global products from the Advanced Very High Resolution Radiometer (AVHRR) (TAL-AVHRR), Moderate Resolution Imaging Spectroradiometer (MODIS) (TAL-MODIS), and Clouds and the Earth’s Radiant Energy System (CERES); one regional product from the Climate Monitoring Satellite Application Facility (CM SAF); and one harmonized product termed Diagnosing Earth’s Energy Pathways in the Climate system (DEEP-C). Results showed that overall, there is good consistency among these five products, particularly after the year 2000. The differences among these products in the high-latitude regions were relatively larger. The percentage differences among TAL-AVHRR, TAL-MODIS, and CERES were generally less than 20%, while the differences between TAL-AVHRR and DEEP-C before 2000 were much larger. Except for the obvious decrease in the differences after 2000, the differences did not show significant changes over time, but varied among different regions. The differences between TAL-AVHRR and the other products were relatively large in the high-latitude regions of North America, Asia, and the Maritime Continent, while the differences between DEEP-C and CM SAF in Europe and Africa were smaller. Interannual variability was consistent between products after 2000, before which the differences among the three products were much larger
Atmospheric Correction of Landsat ETM+ Land Surface Imagery—Part I: Methods
To extract quantitative information from the Enhanced
Thematic Mapper-Plus (ETM+) imagery accurately,
atmospheric correction is a necessary step. After reviewing historical
development of atmospheric correction of Landsat thematic
mapper (TM) imagery, we present a new algorithm that can effectively
estimate the spatial distribution of atmospheric aerosols and
retrieve surface reflectance from ETM+ imagery under general
atmospheric and surface conditions. This algorithm is therefore
suitable for operational applications. A new formula that accounts
for adjacency effects is also presented. Several examples are given
to demonstrate that this new algorithm works very well under a
variety of atmospheric and surface conditions. The companion
paper will validate this method using ground measurements,
and illustrate the improvements of several applications due to
atmospheric correction.This work was supported in part by the U.S. National Aeronautics and Space Administration
(NASA), Washington, DC, under Grant NAG5-6459
Spatio-Temporal Patterns and Climate Variables Controlling of Biomass Carbon Stock of Global Grassland Ecosystems from 1982 to 2006
Grassland ecosystems play an important role in subsistence agriculture and the global carbon cycle. However, the global spatio-temporal patterns and environmental controls of grassland biomass are not well quantified and understood. The goal of this study was to estimate the spatial and temporal patterns of the global grassland biomass and analyze their driving forces using field measurements, Normalized Difference Vegetation Index (NDVI) time series from satellite data, climate reanalysis data, and a satellite-based statistical model. Results showed that the NDVI-based biomass carbon model developed from this study explained 60% of the variance across 38 sites globally. The global carbon stock in grassland aboveground live biomass was 1.05 Pg·C, averaged from 1982 to 2006, and increased at a rate of 2.43 Tg·C·y−1 during this period. Temporal change of the global biomass was significantly and positively correlated with temperature and precipitation. The distribution of biomass carbon density followed the precipitation gradient. The dynamics of regional grassland biomass showed various trends largely determined by regional climate variability, disturbances, and management practices (such as grazing for meat production). The methods and results from this study can be used to monitor the dynamics of grassland aboveground biomass and evaluate grassland susceptibility to climate variability and change, disturbances, and management
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