2004-10-03 Co-retrieval of Aerosol and Surface Reflectance: Analysis of Daily US SeaWiFS Data for 2000-2002
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The document discusses a method for co-retrieving aerosol and surface reflectance from daily SeaWiFS satellite data from 2000-2002. It describes preprocessing the raw satellite data, identifying preliminary clear days using a time series analysis approach, and further refining the surface reflectance estimates and removing residual haze through spectral analysis. Results show seasonal surface reflectance maps for the eastern and western US and an 8-month animation of the retrieved data.
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Measuring Solar Spectral Energy
The document discusses the use of full-range spectroradiometers for measuring solar spectral energy. ASD spectroradiometers are portable instruments that measure wavelengths from 350-2500 nm, allowing for calculation of parameters like aerosol optical depth. They are optimally designed for faster field measurements and calibrated for radiometric collection. ASD instruments offer a solution for analyzing and measuring solar spectral energy with accessories like diffuse fore optics for measuring full sky irradiance.
Solar Radiation Estimation based on Digital Image Processing
This document discusses a method for estimating solar radiation using digital image processing of sky camera images. Sky camera images are divided into three areas and pixel values from clear and cloudy day images are analyzed. Correlations between pixel values and solar altitude are used to create databases for clear and cloudy conditions. Beam, diffuse, and global radiation values are estimated based on averages of pixel values from the three areas and compared to measured values, achieving reasonably low error rates. The method provides information that can improve solar plant performance and operation based on meteorological conditions.
Geoinformatics(nce 402)
This document provides an introduction to aerial photography and remote sensing. It discusses key topics such as:
- The basic concepts and terminology of aerial photography, including flight height, focal length, scale, and camera geometry.
- The different types of aerial photographs, including vertical, low-oblique, and high-oblique views.
- Factors that influence photo quality, such as weather conditions, camera equipment, and processing methods.
- The principles of photogrammetry and stereo viewing, including parallax and stereoscopes.
- Common image interpretation elements like shape, size, tone, texture, pattern, site, association, and shadows.
- An overview of remote sensing concepts like
Rs
Remote sensing platforms can be ground-based, airplane-based, or satellite-based. Satellite platforms can be in sun-synchronous polar orbits for global coverage, non-sun-synchronous orbits for variable coverage, or geostationary orbits for continuous regional coverage. Remote sensing can be passive using sunlight or active using its own energy source like radar or lidar. Spatial, spectral, radiometric, and temporal resolutions provide information on a sensor's ability to distinguish locations, wavelengths, brightness values, and revisit times. Raster data formats represent imagery as a grid of pixels organized into rows, columns, and bands.
Envi sat satellite
Envisat was an Earth observation satellite launched by the European Space Agency in 2002. It carried 9 instruments to monitor the atmosphere, oceans, land, and ice. Envisat collected data over a 10-year period to help study the environment and climate change. Its instruments included MERIS, AATSR, RA-2, MWR, DORIS, SCIAMACHY, GOMOS, MIPAS, and ASAR, which observed phenomena like sea surface temperature, ocean topography, trace gases, and surface changes with precision.
Exact Normalized Lw
The document discusses several key concepts in ocean color remote sensing:
1) It defines normalized water-leaving radiance and describes its importance for comparing field and satellite measurements.
2) It outlines the steps to calculate normalized water-leaving radiance from radiance and irradiance measurements in the field and from satellite sensors.
3) It explains how atmospheric corrections and other factors are used to transfer measurements between field and satellite contexts.
Remote sensing for
This document discusses remote sensing and meteorology. It defines remote sensing as obtaining information about physical objects through non-contact sensors. Meteorology is the study of atmospheric phenomena like weather. Meteorological satellites and weather radars are important tools for monitoring weather. Satellites provide global coverage of cloud patterns and weather systems from space. They capture visible, infrared, and water vapor images to study cloud formations, temperatures, and moisture in the atmosphere. Radar emits microwaves that bounce off water droplets in clouds to measure precipitation and cloud locations. Satellite weather monitoring improves forecasts, especially over oceans with sparse weather station data.
Unit iv remote sensing
This document discusses remote sensing and its applications in civil engineering. It begins by defining remote sensing as acquiring information about Earth's surface without physical contact using sensors to detect electromagnetic energy. It then outlines the key elements of remote sensing systems including the energy source, atmosphere interactions, sensor recording, data transmission and processing, analysis and applications. The rest of the document discusses these elements in further detail, covering topics like passive and active systems, the electromagnetic spectrum, atmospheric effects, ground interactions, spectral concepts, sensor platforms and resolutions. It also provides an overview of the Indian Remote Sensing satellite program.
Application of remote sensing in agriculture
The document discusses the concepts and applications of remote sensing, GIS, and GPS in agriculture. It defines remote sensing as sensing things from a distance using electromagnetic radiation and describes the different platforms (ground, air, satellite) used. It explains key remote sensing concepts including spectral signatures, spectral reflectance curves, spatial/spectral/radiometric/temporal resolutions, and indices like NDVI. Interpretation of remote sensing imagery involves analyzing tone, shape, size, pattern, texture, shadow, and association. Spectral signatures can provide information about vegetation, soil moisture, organic matter, iron content, and other properties. Remote sensing allows monitoring and analyzing agriculture from a distance.
Madagascar2011 - 07 - OTB radiometry processing
The document discusses radiometric corrections for remote sensing images. It describes how digital numbers are converted to top-of-atmosphere reflectance values using calibration coefficients and solar irradiance normalization. Atmospheric corrections are needed to estimate top-of-canopy reflectance and account for effects of gas absorption, scattering, and emission using a radiative transfer model like 6S. Parameters for the 6S model include viewing geometry, atmospheric properties, and spectral filter functions. Aerosol optical thickness can be obtained from Aeronet ground stations. Radiometric calibration is needed using reference reflectance panels.
Similar to 2004-10-03 Co-retrieval of Aerosol and Surface Reflectance: Analysis of Daily US SeaWiFS Data for 2000-2002 (20)
2004-10-14 AIR-257: Satellite Detection of Aerosols Concepts and Theory
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2005-06-06 Satellite Data Us in PM Management: A Retrospective Assessment
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APPLICATION OF REMOTE SENSING AND GIS IN AGRICULTURE
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More from Rudolf Husar
100528 satellite obs_china_husar
This document discusses the challenges of characterizing air pollution using remote sensing observations over China. It describes the seven dimensions of data - spatial, height, time, particle size, composition, shape, and mixing - needed to fully characterize air pollution. While each individual observation method or data set has limitations, together they can provide consistent global-scale observations. There remain significant challenges to integrating data from multiple sensors to accurately measure air pollution. International collaboration combining global satellite data with detailed local observations in China may help advance progress in addressing this issue.
2013-04-30 EE DSS Approach and Demo
The document describes the Exceptional Event Decision Support System (EE DSS), a tool to help states and EPA regions implement the EPA's Exceptional Events Rule. The EE DSS uses air quality, meteorological, and other data to screen for exceedances and flag those likely caused by exceptional events like dust storms, wildfires, or July 4th fireworks. It aims to minimize the technical hurdles of the EE rule and provide a uniform, transparent methodology. The document outlines the EE DSS's data sources and modeling, screening approach, tools for visualizing events, and provides an example demo of the system in action.
Exceptional Event Decision Support System Description
This slideshow is a brief description of the Exceptional Event Decision Support System Description (EE DSS).
130205 epa exc_event_seminar
This document summarizes Rudolf Husar's presentation on exceptional event analysis and decision support systems. It discusses using diverse data like satellites, models, and real-time monitoring to evaluate exceptional events like wildfires, dust storms, and their impact on air quality measurements. Specific examples are presented of exceptional events from dust from Asia and Africa impacting North America, as well as wildfires in Georgia impacting ozone and PM2.5 levels. Tools like the Navy Aerosol Analysis and Prediction System model and satellite data are highlighted for their ability to analyze the transport and impact of these aerosol plumes to support regulatory decisions. The goal of reconciliation of emissions, observations, and models is discussed to improve the evaluation of exceptional events
130205 epa ee_presentation_subm
Rudolf B. Husar presented at the EPA on exceptional smoke and dust events. He discussed using diverse data like satellites, models, and real-time data in a decision support system to evaluate these events. The NAAPS aerosol model assimilates satellite data to provide the 3D structure of smoke, dust, and other aerosols. Long-term NAAPS data from 2006 to present show the vertical distribution of different aerosols. Satellite data help reduce biases between surface PM measurements and air quality models.
111018 geo sif_aq_interop
The document discusses the Air Quality Community of Practice (AQ CoP) which facilitates interoperability and data networking for air quality and health applications. The AQ CoP has developed an open-source Air Quality Data Network (ADN) consisting of 7 interoperable air quality data servers that provide access to diverse observational and model datasets using international standards. The ADN demonstrates GEO principles and infrastructure but requires further development to support real applications. The main role of the AQ CoP is to connect different initiatives and enable the ADN network.
110823 solta11 intro
The workshop will bring together practitioners from Europe and North America to discuss progress and challenges in realizing an interoperable air quality data network. Participants will assess the current state of the pilot network, address key technical issues around data standards, server implementation and maintenance, and catalog design. The goal is to advance the network from a virtual concept to an operational reality, facilitating improved access, integration and reuse of air quality observation and model data.
110823 data fed_solta11
The document describes DataFed, a federated data system that provides non-intrusive integration of diverse environmental datasets using open standards. DataFed allows users to find and access datasets through a catalog and flexible tools for processing and visualizing the data. It facilitates publishing, finding, and accessing geospatial and environmental data through loose coupling of autonomous nodes and OGC web service protocols.
110510 aq co_p_network
This document discusses the emerging pattern in the air quality information ecosystem. It notes that individual data providers, scientists, and decision supporters are being replaced by groups that facilitate access, sharing, and integration. These include data portals, science teams, and decision support systems. The ecosystem involves multiple stages from observations to decisions, with value added at each stage through activities like data aggregation, scientific collaboration, and predictive analysis. This new structure is more efficient and supports the goals of initiatives like GEOSS.
110509 aq co_p_solta
The document discusses a workshop on networking air quality observations and models to support decision making. The workshop aims to (1) introduce participants and identify shared data and applications, (2) exchange best practices for interoperability, and (3) address technical and collaboration issues. The preliminary agenda covers assessing the current state of air quality interoperability and the technical requirements for improved data sharing and integration to support applications and decision support systems.
110421 exploration of_pm_networks_and_data_over_the_us-_aqs_and_views
The document summarizes the exploration of PM networks and data over the US using two datasets: AQS and VIEWS. It presents information on the coverage and frequency of EPA monitoring data, as well as data from the VIEWS network. It also describes the user interface for the Datafed browser and schemes for processing and aggregating raw monitoring data spatially and temporally. Finally, it analyzes the spatial and temporal variation of PM levels and the correlation between continuous and EPA monitoring data in different regions of the US.
110410 aq user_req_methodology_sydney_subm
This document proposes a methodology to determine user requirements for Earth observations related to air quality management. The methodology is a bottom-up approach that (1) defines the major workflow steps of air quality management, (2) identifies the value-adding activities within each step, (3) determines the participants ("users") for each activity, and (4) establishes the Earth observation needs of each user. The methodology is intended to facilitate ongoing feedback to optimize the value of Earth observations for air quality management and reduce gaps. It provides a systematic way to account for user needs based on the specific activities and users involved in the air quality management process.
110408 aq co_p_uic_sydney_husar
This document provides a 2011 progress report for the GEOSS Air Quality Community of Practice (AQ CoP). It summarizes activities undertaken in 2011, including developing an air quality data server software to make data more accessible and interoperable, creating a user requirements registry to identify needed observations and models, and matching user needs with available data through a community catalog. It outlines ongoing projects and plans to further expand the air quality data network through coordination and workshops in 2011. The overall goal is to integrate air quality initiatives and make relevant data more findable, accessible and interoperable to support applications in air quality and health.
110105 htap pilot_aqco_p_esip_dc
The document describes the HTAP Data Network, which demonstrates a service-oriented approach to sharing atmospheric model outputs and air quality observations between various data servers using open standards. The main output is open-source WCS data server software and tools that allow different organizations to publish, find, and access distributed air quality data holdings in a interoperable way as part of the GEO Task DA-09-02d: Atmospheric Model Evaluation Network. The network aims to connect air quality data providers and users to enable effective air quality science and management.
100615 htap network_brussels
The REASoN Project will link NASA's air quality data, modeling, and systems to users in research, education, and applications. It aims to address hurdles users face in finding, accessing, evaluating, and merging relevant data. The project will utilize service orientation and interoperability standards to build an adaptable information infrastructure. This will include becoming a node on the air quality network, implementing standards for sharing data and tools, and participating in the GEOSS Architecture Implementation Pilot.
121117 eedss briefing_nasa_epa
This document summarizes the Exceptional Event Decision Support System (EE DSS) which uses NASA satellite data and the Navy Aerosol Analysis and Prediction System (NAAPS) model to help with air quality management decisions regarding exceptional events like smoke and dust events. The EE DSS has been developed since 2005 with NASA support and is now ready to serve air quality management at the federal, regional, and state levels. It can automatically detect and analyze events, display relevant data through interactive maps and cross-sections, and its tools have helped explain declines in exceptional event flags and PM2.5 concentrations from 2006-2012. Coordination is proposed with NASA and EPA for continued application of the EE DSS to smoke and dust events in
120910 nasa satellite_outline
This document discusses the usefulness of satellite observations for air quality applications and regulatory requirements. It outlines six key air quality requirements that satellites can help address, such as determining compliance with air quality standards and identifying long-range pollution transport events. The document also notes how satellites can help improve emissions estimates, characterize long-range transport of pollution, and increase interaction between air quality and remote sensing scientists. However, it cautions that relating satellite aerosol optical depth measurements directly to ground-level PM concentrations currently has too much uncertainty for regulatory or public health applications.
120612 geia closure_ofeo_ms_soa_subm
The document discusses tools for closing the gap between emissions, observations, and models of air quality. It proposes a service oriented architecture and network to integrate multiple datasets from observations, emissions, and models. This would allow iterative evaluation and improvement of models by comparing them to observations and adjusting emissions estimates to reduce biases. The end goal is to provide the best available composition of the atmosphere by integrating the best observations, emissions estimates, and models.
110414 extreme dustsmokesulfate
This proposal outlines a study on the influence of weather and climate events on air quality issues like dust, smoke, and sulfate events. The study would examine these events at both the continental/hemispherical scale and regional scale. At the continental scale, the analysis would demonstrate the role of global climate and emissions and identify tipping points for air quality regulations. At the regional scale, the study would analyze the effects of regional emissions, climate, and precipitation on air quality. The proposal describes tools and methods for conducting continental and regional air quality-climate analysis, including models, datasets, and satellite data. The goals are to support air quality management and identify implications for policy.
Aq Gci Infrastructure
The document discusses various applications of air quality data including regulatory exceptions, hemispheric transport projects, and atmospheric composition portals. It also describes the Air Quality Community of Practice's contributions to the GEOSS Common Infrastructure through developing an air quality community catalog and data finder to help users discover and access air quality data and metadata registered in the GEOSS clearinghouse and registry.
More from Rudolf Husar (20)
Exceptional Event Decision Support System Description
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2004-10-03 Co-retrieval of Aerosol and Surface Reflectance: Analysis of Daily US SeaWiFS Data for 2000-2002
- 1. Co-retrieval of Aerosol and Surface Reflectance: Analysis of Daily US SeaWiFS Data for 2000-2002 Sean Raffuse, Erin Robinson and Rudolf B. Husar CAPITA, Washington University Presented at A&WMA’s 97 th Annual Conference and Exhibition June 22-27, Indianapolis, IN
- 4. Aerosol effects on surface color and Surface effects on aerosol color The image was synthesized from the blue (0.412 μm), green (0.555 μm), and red (0.67 μm) channels of the 8 channel SeaWiFS sensor. Air scattering has been removed to highlight the haze and surface reflectance.
- 13. Results – Seasonal surface reflectance, Eastern US April 29, 2000, Day 120 July 18, 2000, Day 200 October 16, 2000, Day 290
- 14. Results – Seasonal surface reflectance, Western US April 29, 2000, Day 120 July 18, 2000, Day 200 October 16, 2000, Day 290
- 15. Results – Eight month animation
- 18. July 10, 2002 July 9, 2002 July 8, 2002 July 7, 2002 July 6, 22002 July 5, 2002