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Huete, Alfredo

Publications and source records attributed to Huete, Alfredo.

12 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2017

GEONEX: Land Monitoring From a New Generation of Geostationary Satellite Sensors

The latest generation of geostationary satellites carry sensors such as ABI (Advanced Baseline Imager on GOES-16) and the AHI (Advanced Himawari Imager on Himawari) that closely mimic the spatial and spectral characteristics of Earth Observing System flagship MODIS for monitoring land surface conditions. More importantly they provide observations at 5-15 minute intervals. Such high frequency data offer exciting possibilities for producing robust estimates of land surface conditions by overcoming cloud cover, enabling studies of diurnally varying local-to-regional biosphere-atmosphere interactions, and operational decision-making in agriculture, forestry and disaster management. But the data come with challenges that need special attention. For instance, geostationary data feature changing sun angle at constant view for each pixel, which is reciprocal to sun-synchronous observations, and thus require careful adaptation of EOS algorithms. Our goal is to produce a set of land surface products from geostationary sensors by leveraging NASA's investments in EOS algorithms and in the data/compute facility NEX. The land surface variables of interest include atmospherically corrected surface reflectances, snow cover, vegetation indices and leaf area index (LAI)/fraction of photosynthetically absorbed radiation (FPAR), as well as land surface temperature and fires. In order to get ready to produce operational products over the US from GOES-16 starting 2018, we have utilized 18 months of data from Himawari AHI over Australia to test the production pipeline and the performance of various algorithms for our initial tests. The end-to-end processing pipeline consists of a suite of modules to (a) perform calibration and automatic georeference correction of the AHI L1b data, (b) adopt the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm to produce surface spectral reflectances along with compositing schemes and QA, and (c) modify relevant EOS retrieval algorithms (e.g., LAI and FPAR, GPP, etc.) for subsequent science product generation. Initial evaluation of Himawari AHI products against standard MODIS products indicate general agreement, suggesting that data from geostationary sensors can augment low earth orbit (LEO) satellite observations.

geostationary↗
NASA NTRS2016

Drought Rapidly Diminishes the Large Net CO2 Uptake in 2011 Over Semi-Arid Australia

Each year, terrestrial ecosystems absorb more than a quarter of the anthropogenic carbon emissions, termed as land carbon sink. An exceptionally large land carbon sink anomaly was recorded in 2011, of which more than half was attributed to Australia. However, the persistence and spatially attribution of this carbon sink remain largely unknown. Here we conducted an observation-based study to characterize the Australian land carbon sink through the novel coupling of satellite retrievals of atmospheric CO2 and photosynthesis and in-situ flux tower measures. We show the 2010-11 carbon sink was primarily ascribed to savannas and grasslands. When all biomes were normalized by rainfall, shrublands however, were most efficient in absorbing carbon. We found the 2010-11 net CO2 uptake was highly transient with rapid dissipation through drought. The size of the 2010-11 carbon sink over Australia (0.97 Pg) was reduced to 0.48 Pg in 2011-12, and was nearly eliminated in 2012-13 (0.08 Pg). We further report evidence of an earlier 2000-01 large net CO2 uptake, demonstrating a repetitive nature of this land carbon sink. Given a significant increasing trend in extreme wet year precipitation over Australia, we suggest that carbon sink episodes will exert greater future impacts on global carbon cycle.

fluorescence↗
NASA NTRS2013

Researcher-driven Campaigns Engage Nature's Notebook Participants in Scientific Data Collection

One of the many benefits of citizen science projects is the capacity they hold for facilitating data collection on a grand scale and thereby enabling scientists to answer questions they would otherwise not been able to address. Nature's Notebook, the plant and animal phenology observing program of the USA National Phenology Network (USA-NPN) suitable for scientists and non-scientists alike, offers scientifically-vetted data collection protocols and infrastructure and mechanisms to quickly reach out to hundreds to thousands of potential contributors. The USA-NPN has recently partnered with several research teams to engage participants in contributing to specific studies. In one example, a team of scientists from NASA, the New Mexico Department of Health, and universities in Arizona, New Mexico, Oklahoma, and California are using juniper phenology observations submitted by Nature's Notebookparticipants to improve predictions of pollen release and inform asthma and allergy alerts. In a second effort, researchers from the University of Maryland Center for Environmental Science are engaging Nature's Notebookparticipants in tracking leafing phenophases of poplars across the U.S. These observations will be compared to information acquired via satellite imagery and used to determine geographic areas where the tree species are most and least adapted to predicted climate change. Researchers in these partnerships receive benefits primarily in the form of ground observations. Launched in 2010, the juniper pollen effort has engaged participants in several western states and has yielded thousands of observations that can play a role in model ground validation. Periodic evaluation of these observations has prompted the team to improve and enhance the materials that participants receive, in an effort to boost data quality. The poplar project is formally launching in spring of 2013 and will run for three years; preliminary findings from 2013 will be presented. Participants in these special campaigns benefit through direct engagement in science. This form of researcher partnership has now been successfully pilot-tested and implemented in several instances, and provides a template for future research project campaigns.

Crimmins, Theresa M.↗
NASA NTRS2011

Use of MODIS Satellite Images and an Atmospheric Dust Transport Model To Evaluate Juniperus spp. Pollen Phenology and Dispersal

Pollen can be transported great distances. Van de Water et. al., 2003 reported Juniperus spp. pollen was transported 200-600 km. Hence local observations of plant phenology may not be consistent with the timing and source of pollen collected by pollen sampling instruments. The DREAM (Dust REgional Atmospheric Model, Nickovic et al. 2001) is a verified model for atmospheric dust transport modeling using MODIS data products to identify source regions and quantities of dust. We are modifying the DREAM model to incorporate pollen transport. Pollen release will be estimated based on MODIS derived phenology of Juniperus spp. communities. Ground based observational records of pollen release timing and quantities will be used as verification. This information will be used to support the Centers for Disease Control and Prevention's National Environmental Public Health Tracking Program and the State of New Mexico environmental public health decision support for asthma and allergies alerts.

Luvall, J. C.↗
NASA NTRS2011

Current NASA Earth Remote Sensing Observations

This slide presentation reviews current NASA Earth Remote Sensing observations in specific reference to improving public health information in view of pollen sensing. While pollen sampling has instrumentation, there are limitations, such as lack of stations, and reporting lag time. Therefore it is desirable use remote sensing to act as early warning system for public health reasons. The use of Juniper Pollen was chosen to test the possibility of using MODIS data and a dust transport model, Dust REgional Atmospheric Model (DREAM) to act as an early warning system.

Luvall, Jeffrey C.↗
NASA NTRS2011

Integration of Airborne Aerosol Prediction Systems and Vegetation Phenology to Track Pollen for Asthma Alerts in Public Health Decision Support Systems

This slide presentation reviews the study that used a model to forecast pollen to assist in warning for asthma populations. Using MODIS daily reflectances to input to a model, PREAM, adapted from the Dust REgional Atmospheric Modeling (DREAM) system, a product of predicted pollen is produced. Using the pollen from Juniper the PREAM model was shown to be an assist in alerting the public of pollen bursts, and reduce the health impact on asthma populations.

Luvall, Jeffrey C.↗
NASA NTRS2009

Integration of Dust Prediction Systems and Vegetation Phenology to Track Pollen for Asthma Alerts in Public Health

Initial efforts to develop a deterministic model for predicting and simulating pollen release and downwind concentration to study dependencies of phenology on meteorology will be discussed. The development of a real-time, rapid response pollen release and transport system as a component of the New Mexico Environmental Public Health Tracking System (EPHTS), is based on meteorological models, NASA Earth science results (ESR), and an in-situ network of phenology cameras. The plan is to detect pollen release verified using ground based atmospheric pollen sampling within a few hours using daily MODIS daa in nearly real-time from Direct Broadcast, similar to the MODIS Rapid Response System for fire detection. As MODIS winds down, the NPOESS-VIIRS sensor will assume daily vegetation monitoring tasks. Also, advancements in geostationary satellites will allow 1km vegetation indices at 15-30 minute intervals. The pollen module in EPHTS will be used to: (1) support public health decisions for asthma and allergy alerts in New Mexico, Texas and Oklahoma; (2) augment the Centers for Disease Control and Prevention (CDC)'s Environmental Public Health Tracking Network (EPHTN); and (3) extend surveillance services to local healthcare providers subscribing to the Syndrome Reporting Information System (SYRIS). Previous studies in NASA's public health applications portfolios provide the infrastructure for this effort. The team is confident that NASA and NOAA ESR data, combined into a verified and validated dust model will yield groundbreaking results using the modified dust model to transport pollen. The growing ESR/health infrastructure is based on results from a rapid prototype scoping effort for pollen detection and simulation carried out by the principal investigators.

Luvall, Jeffrey↗
NASA NTRS2009

Integration of Airborne Aerosol Prediction Systems and Vegetation Phenology to Track Pollen for Asthma Alerts in Public Health Decision Support Systems

The residual signal indicates that the pollen event may influence the seasonal signal to an extent that would allow detection, given accurate QA filtering and BRDF corrections. MODIS daily reflectances increased during the pollen season. The DREAM model (PREAM) was successfully modified for use with pollen and may provide 24-36 hour running pollen forecasts. Publicly available pollen forecasts are linked to general weather patterns and roughly-known species phenologies. These are too coarse for timely health interventions. PREAM addresses this key data gap so that targeting intervention measures can be determined temporally and geospatially. The New Mexico Department of Health (NMDOH) as part of its Environmental Public Health Tracking Network (EPHTN) would use PREAM a tool for alerting the public in advance of pollen bursts to intervene and reduce the health impact on asthma populations at risk.

Luvall, Jeffrey C.↗
NASA NTRS2009

Environmental Factors Affecting Asthma and Allergies: Predicting and Simulating Downwind Exposure to Airborne Pollen

This slide presentation reviews the environmental factors that affect asthma and allergies and work to predict and simulate the downwind exposure to airborne pollen. Using a modification of Dust REgional Atmosphere Model (DREAM) that incorporates phenology (i.e. PREAM) the aim was to predict concentrations of pollen in time and space. The strategy for using the model to simulate downwind pollen dispersal, and evaluate the results. Using MODerate-resolution Imaging Spectroradiometer (MODIS), to get seasonal sampling of Juniper, the pollen chosen for the study, land cover on a near daily basis. The results of the model are reviewed.

Luvall, Jeffrey↗
NASA NTRS2004

Use of EO-1 Hyperion Data for Inter-Sensor Calibration of Vegetation Indices

Numerous satellite sensor systems useful in terrestrial Earth observation and monitoring have recently been launched and their derived products are increasingly being used in regional and global vegetation studies. The increasing availability of multiple sensors offer much opportunity for vegetation studies aimed at understanding the terrestrial carbon cycle, climate change, and land cover conversions. Potential applications include improved multiresolution characterization of the surface (scaling); improved optical-geometric characterization of vegetation canopies; improved assessments of surface phenology and ecosystem seasonal dynamics; and improved maintenance of long-term, inter-annual, time series data records. The Landsat series of sensors represent one group of sensors that have produced a long-term, archived data set of the Earth s surface, at fine resolution and since 1972, capable of being processed into useful information for global change studies (Hall et al., 1991).

Huete, Alfredo↗