Multi-Sensor Vegetation Index and Land Surface Phenology Earth Science Data Records in Support of Global Change Studies: Data Quality Challenges and Data Explorer System

Persistent Link:
http://hdl.handle.net/10150/301661
Title:
Multi-Sensor Vegetation Index and Land Surface Phenology Earth Science Data Records in Support of Global Change Studies: Data Quality Challenges and Data Explorer System
Author:
Barreto-Munoz, Armando
Issue Date:
2013
Publisher:
The University of Arizona.
Rights:
Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
Abstract:
Synoptic global remote sensing provides a multitude of land surface state variables. The continuous collection, for more than 30 years, of global observations has contributed to the creation of a unique and long term satellite imagery archive from different sensors. These records have become an invaluable source of data for many environmental and global change related studies. The problem, however, is that they are not readily available for use in research and application environment and require multiple preprocessing. Here, we looked at the daily global data records from the Advanced Very High Resolution Radiometer (AVHRR) and the Moderate Resolution Imaging Spectroradiometer (MODIS), two of the most widely available and used datasets, with the objective of assessing their quality and suitability to support studies dealing with global trends and changes at the land surface. Findings show that clouds are the major data quality inhibitors, and that the MODIS cloud masking algorithm performs better than the AVHRR. Results show that areas of high ecological importance, like the Amazon, are most prone to lack of data due to cloud cover and aerosols leading to extended periods of time with no useful data, sometimes months. While the standard approach to these challenges has been compositing of daily images to generate a representative map over a preset time periods, our results indicate that preset compositing is not the optimal solution and a hybrid location dependent method that preserves the high frequency of these observations over the areas where clouds are not as prevalent works better. Using this data quality information the Vegetation Index and Phenology (VIP) Laboratory at The University of Arizona produced over 30 years of seamless sensor independent record of vegetation indices and land surface phenology metrics. These data records consist of 0.05-degree resolution global images for daily, 7-days, 15-days and monthly temporal frequency. These sort of remote sensing based products are normally made available through the internet by large data centers, like the Land Processes Distributed Active Archive Center (LP DAAC), however, in this project an online tool, the VIP Data Explorer, was developed to support the visualization, exploration, and distribution of these Earth Science Data Records (ESDRs) keeping it closer to the data generation center which provides a more active data support and distribution model. This web application has made it possible for users to explore and evaluate the products suite before download and use.
Type:
text; Electronic Dissertation
Keywords:
long term; modis; NDVI; phenology; seamless; Agricultural & Biosystems Engineering; data quality
Degree Name:
Ph.D.
Degree Level:
doctoral
Degree Program:
Graduate College; Agricultural & Biosystems Engineering
Degree Grantor:
University of Arizona
Advisor:
Yitayew, Muluneh

Full metadata record

DC FieldValue Language
dc.language.isoenen_US
dc.titleMulti-Sensor Vegetation Index and Land Surface Phenology Earth Science Data Records in Support of Global Change Studies: Data Quality Challenges and Data Explorer Systemen_US
dc.creatorBarreto-Munoz, Armandoen_US
dc.contributor.authorBarreto-Munoz, Armandoen_US
dc.date.issued2013-
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en_US
dc.description.abstractSynoptic global remote sensing provides a multitude of land surface state variables. The continuous collection, for more than 30 years, of global observations has contributed to the creation of a unique and long term satellite imagery archive from different sensors. These records have become an invaluable source of data for many environmental and global change related studies. The problem, however, is that they are not readily available for use in research and application environment and require multiple preprocessing. Here, we looked at the daily global data records from the Advanced Very High Resolution Radiometer (AVHRR) and the Moderate Resolution Imaging Spectroradiometer (MODIS), two of the most widely available and used datasets, with the objective of assessing their quality and suitability to support studies dealing with global trends and changes at the land surface. Findings show that clouds are the major data quality inhibitors, and that the MODIS cloud masking algorithm performs better than the AVHRR. Results show that areas of high ecological importance, like the Amazon, are most prone to lack of data due to cloud cover and aerosols leading to extended periods of time with no useful data, sometimes months. While the standard approach to these challenges has been compositing of daily images to generate a representative map over a preset time periods, our results indicate that preset compositing is not the optimal solution and a hybrid location dependent method that preserves the high frequency of these observations over the areas where clouds are not as prevalent works better. Using this data quality information the Vegetation Index and Phenology (VIP) Laboratory at The University of Arizona produced over 30 years of seamless sensor independent record of vegetation indices and land surface phenology metrics. These data records consist of 0.05-degree resolution global images for daily, 7-days, 15-days and monthly temporal frequency. These sort of remote sensing based products are normally made available through the internet by large data centers, like the Land Processes Distributed Active Archive Center (LP DAAC), however, in this project an online tool, the VIP Data Explorer, was developed to support the visualization, exploration, and distribution of these Earth Science Data Records (ESDRs) keeping it closer to the data generation center which provides a more active data support and distribution model. This web application has made it possible for users to explore and evaluate the products suite before download and use.en_US
dc.typetexten_US
dc.typeElectronic Dissertationen_US
dc.subjectlong termen_US
dc.subjectmodisen_US
dc.subjectNDVIen_US
dc.subjectphenologyen_US
dc.subjectseamlessen_US
dc.subjectAgricultural & Biosystems Engineeringen_US
dc.subjectdata qualityen_US
thesis.degree.namePh.D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.disciplineAgricultural & Biosystems Engineeringen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.advisorYitayew, Mulunehen_US
dc.contributor.committeememberDidan, Kamelen_US
dc.contributor.committeememberSlack, Donalden_US
dc.contributor.committeememberHawkins, Richarden_US
dc.contributor.committeememberYitayew, Mulunehen_US
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