QUANTIFICATION OF ERROR IN AVHRR NDVI DATA
Several influential Earth system science studies in the last three decades were based on Normalized Difference Vegetation Index (NDVI) data from Advanced Very High Resolution Radiometer (AVHRR) series of instruments. Although AVHRR NDVI data are known to have significant uncertainties resulting from...
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ftunivmaryland:oai:drum.lib.umd.edu:1903/11619 2023-05-15T13:06:49+02:00 QUANTIFICATION OF ERROR IN AVHRR NDVI DATA Nagol, Jyoteshwar Reddy Prince, Stephen D Vermote, Eric F Digital Repository at the University of Maryland University of Maryland (College Park, Md.) Geography 2011 application/pdf http://hdl.handle.net/1903/11619 unknown http://hdl.handle.net/1903/11619 Remote Sensing Climate Change Geography AVHRR Global change LTDR NDVI Uncertainty Dissertation 2011 ftunivmaryland 2022-11-11T11:14:29Z Several influential Earth system science studies in the last three decades were based on Normalized Difference Vegetation Index (NDVI) data from Advanced Very High Resolution Radiometer (AVHRR) series of instruments. Although AVHRR NDVI data are known to have significant uncertainties resulting from incomplete atmospheric correction, orbital drift, sensor degradation, etc., none of these studies account for them. This is primarily because of unavailability of comprehensive and location-specific quantitative uncertainty estimates. The first part of this dissertation investigated the extent of uncertainty due to inadequate atmospheric correction in the widely used AVHRR NDVI datasets. This was accomplished by comparison with atmospherically corrected AVHRR data at AErosol RObotic NETwork (AERONET) sunphotometer sites in 1999. Of the datasets included in this study, Long Term Data Record (LTDR) was found to have least errors (precision=0.02 to 0.037 for clear and average atmospheric conditions) followed by Pathfinder AVHRR Land (PAL) (precision=0.0606 to 0.0418), and Top of Atmosphere (TOA) (precision=0.0613 to 0.0684). ` Although the use of field data is the most direct type of validation and is used extensively by the remote sensing community, it results in a single uncertainty estimate and does not account for spatial heterogeneity and the impact of spatial and temporal aggregation. These shortcomings were addressed by using Moderate Resolution Imaging Spectrometer (MODIS) data to estimate uncertainty in AVHRR NDVI data. However, before AVHRR data could be compared with MODIS data, the nonstationarity introduced by inter-annual variations in AVHRR NDVI data due to orbital drift had to be removed. This was accomplished by using a Bidirectional Reflectance Distribution Function (BRDF) correction technique originally developed for MODIS data. The results from the evaluation of AVHRR data using MODIS showed that in many regions minimal spatial aggregation will improve the precision of AVHRR NDVI data significantly. ... Doctoral or Postdoctoral Thesis Aerosol Robotic Network University of Maryland: Digital Repository (DRUM) |
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Open Polar |
collection |
University of Maryland: Digital Repository (DRUM) |
op_collection_id |
ftunivmaryland |
language |
unknown |
topic |
Remote Sensing Climate Change Geography AVHRR Global change LTDR NDVI Uncertainty |
spellingShingle |
Remote Sensing Climate Change Geography AVHRR Global change LTDR NDVI Uncertainty Nagol, Jyoteshwar Reddy QUANTIFICATION OF ERROR IN AVHRR NDVI DATA |
topic_facet |
Remote Sensing Climate Change Geography AVHRR Global change LTDR NDVI Uncertainty |
description |
Several influential Earth system science studies in the last three decades were based on Normalized Difference Vegetation Index (NDVI) data from Advanced Very High Resolution Radiometer (AVHRR) series of instruments. Although AVHRR NDVI data are known to have significant uncertainties resulting from incomplete atmospheric correction, orbital drift, sensor degradation, etc., none of these studies account for them. This is primarily because of unavailability of comprehensive and location-specific quantitative uncertainty estimates. The first part of this dissertation investigated the extent of uncertainty due to inadequate atmospheric correction in the widely used AVHRR NDVI datasets. This was accomplished by comparison with atmospherically corrected AVHRR data at AErosol RObotic NETwork (AERONET) sunphotometer sites in 1999. Of the datasets included in this study, Long Term Data Record (LTDR) was found to have least errors (precision=0.02 to 0.037 for clear and average atmospheric conditions) followed by Pathfinder AVHRR Land (PAL) (precision=0.0606 to 0.0418), and Top of Atmosphere (TOA) (precision=0.0613 to 0.0684). ` Although the use of field data is the most direct type of validation and is used extensively by the remote sensing community, it results in a single uncertainty estimate and does not account for spatial heterogeneity and the impact of spatial and temporal aggregation. These shortcomings were addressed by using Moderate Resolution Imaging Spectrometer (MODIS) data to estimate uncertainty in AVHRR NDVI data. However, before AVHRR data could be compared with MODIS data, the nonstationarity introduced by inter-annual variations in AVHRR NDVI data due to orbital drift had to be removed. This was accomplished by using a Bidirectional Reflectance Distribution Function (BRDF) correction technique originally developed for MODIS data. The results from the evaluation of AVHRR data using MODIS showed that in many regions minimal spatial aggregation will improve the precision of AVHRR NDVI data significantly. ... |
author2 |
Prince, Stephen D Vermote, Eric F Digital Repository at the University of Maryland University of Maryland (College Park, Md.) Geography |
format |
Doctoral or Postdoctoral Thesis |
author |
Nagol, Jyoteshwar Reddy |
author_facet |
Nagol, Jyoteshwar Reddy |
author_sort |
Nagol, Jyoteshwar Reddy |
title |
QUANTIFICATION OF ERROR IN AVHRR NDVI DATA |
title_short |
QUANTIFICATION OF ERROR IN AVHRR NDVI DATA |
title_full |
QUANTIFICATION OF ERROR IN AVHRR NDVI DATA |
title_fullStr |
QUANTIFICATION OF ERROR IN AVHRR NDVI DATA |
title_full_unstemmed |
QUANTIFICATION OF ERROR IN AVHRR NDVI DATA |
title_sort |
quantification of error in avhrr ndvi data |
publishDate |
2011 |
url |
http://hdl.handle.net/1903/11619 |
genre |
Aerosol Robotic Network |
genre_facet |
Aerosol Robotic Network |
op_relation |
http://hdl.handle.net/1903/11619 |
_version_ |
1766022207192956928 |