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

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 inva...

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Main Author: Barreto-Munoz, Armando
Other Authors: Yitayew, Muluneh
Language:en
Published: The University of Arizona. 2013
Subjects:
Online Access:http://hdl.handle.net/10150/301661
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spelling ndltd-arizona.edu-oai-arizona.openrepository.com-10150-3016612015-10-23T05:25:27Z 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 Barreto-Munoz, Armando Yitayew, Muluneh Didan, Kamel Slack, Donald Hawkins, Richard Yitayew, Muluneh long term modis NDVI phenology seamless Agricultural & Biosystems Engineering data quality 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. 2013 text Electronic Dissertation http://hdl.handle.net/10150/301661 en 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. The University of Arizona.
collection NDLTD
language en
sources NDLTD
topic long term
modis
NDVI
phenology
seamless
Agricultural & Biosystems Engineering
data quality
spellingShingle long term
modis
NDVI
phenology
seamless
Agricultural & Biosystems Engineering
data quality
Barreto-Munoz, Armando
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
description 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.
author2 Yitayew, Muluneh
author_facet Yitayew, Muluneh
Barreto-Munoz, Armando
author Barreto-Munoz, Armando
author_sort Barreto-Munoz, Armando
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
title_short 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
title_full 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
title_fullStr 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
title_full_unstemmed 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
title_sort 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
publisher The University of Arizona.
publishDate 2013
url http://hdl.handle.net/10150/301661
work_keys_str_mv AT barretomunozarmando multisensorvegetationindexandlandsurfacephenologyearthsciencedatarecordsinsupportofglobalchangestudiesdataqualitychallengesanddataexplorersystem
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