Biomass burning releases a significant amount of trace gases and aerosol emissions into the
atmosphere. If unaccounted for in the modeling of climate, carbon cycle, and air quality, it leads
to large uncertainties. The amount of biomass burning emissions depends significantly on burned
areas. This study estimates near-real time burned areas from multiple satellite-based active fires
in Hazard Mapping System (HMS) developed in NOAA, which capitalizes automated fire
detections from Geostationary Operational Environmental Satellite (GOES) Imager, Advanced
Very High Resolution Radiometer (AVHRR), Moderate Resolution Imaging Spectroradiometer
(MODIS). The HMS fire counts are compared with a set of Landsat ETM+ burn scars for various
ecosystems to investigate the rate of burned area in a fire count. The fire size and fire duration
derived from multiple satellites are then used to calculate burned area every half hour. The
estimated burned areas are evaluated using national inventory of burned area across the United
States for 2005.
Various methods to generate satellite-based biomass burning emission estimates
have recently been developed for their use in air quality models. Each method has different
assumptions, data sources, and algorithms. This paper compares three different satellitebased
biomass burning emission estimates against a control case of no biomass burning and
ground-based biomass estimate in an air quality model. We have chosen August 2002 for
comparison, since all data sets were readily available. In addition, there was significant
wildfire activity during this month. Our results suggest that there is large uncertainty in the
emission estimates which results in both under-prediction and over-prediction of PM2.5
concentration fields.
We compare biomass burning emissions estimates from four different techniques that use satellite based fire products to determine area burned over regional to global domains. Three of the techniques use active fire detections from polar-orbiting MODIS sensors and one uses detections and instantaneous fire size estimates from geostationary GOES sensors. Each technique uses a different approach for estimating trace gas and particulate emissions from active fires. Here we evaluate monthly area burned and CO emission estimates for most of 2006 over the contiguous United States domain common to all four techniques. Two techniques provide global estimates and these are also compared. Overall we find consistency in temporal evolution and spatial patterns but differences in these monthly estimates can be as large as a factor of 10. One set of emission estimates is evaluated by comparing model CO predictions with satellite observations over regions where biomass burning is significant. These emissions are consistent with observations over the US but have a high bias in three out of four regions of large tropical burning. The large-scale evaluations of the magnitudes and characteristics of the differences presented here are a necessary first step toward an ultimate goal of reducing the large uncertainties in biomass burning emission estimates, thereby enhancing environmental monitoring and prediction capabilities.
Land surface vegetation phenology is an important process for the real-time monitoring and detecting inter-annual
variability in terrestrial ecosystem carbon exchange and climate-biosphere interactions. Crop phenology is an important
factor that influences crop growth and yield estimation models. Since the mid-1980s, coarse-resolution,
temporally-composited satellite data have been used to study vegetation phenology. View-angle corrected nadir
reflectances from the 16-day, 1km operational MODIS BRDF/Albedo product are currently used to monitor global land
cover dynamics. In this paper, we developed an improved methodology for using the new 500-m MODIS BRDF/Albedo
Version 005 product to monitor global vegetation phenology by utilizing time series of the Normalized Difference
Vegetation Index (NDVI). The method adopts a rolling strategy for the continuous updating of the underlying anisotropy
(or BRDF shape), so that the latest land surface BRDF information can be used as prior-knowledge for next retrieval.
Using this approach, transition dates for vegetation phenology in time series of NDVI can be determined from MODIS
data at finer temporal and spatial resolution. Preliminary results based on monitoring crops in northern China
demonstrate the effectiveness of our rolling retrievals coupled with the improved spatial resolution of the new MODIS
product.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.