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Potential of Hyperion imagery for simulation of MODIS NDVI and AVHRR-consistent NDVI time series in a semi-arid region

Ghadiri, Masoumeh (2010) In Lunds universitets Naturgeografiska institution - Seminarieuppsatser
Dept of Physical Geography and Ecosystem Science
Abstract
Long time series of satellite remotely sensed Normalized Difference Vegetation Index
(NDVI) as an indicator of vegetation greenness can be used in a broad range of research
topics such as climate change, vegetation dynamics and desertification monitoring.
The first phase of this study tests a practical method using Hyperion imagery onboard on
Earth Observing-1 (EO1) spacecraft for simulation of MODerate resolution Imaging
Spectroradiometer (MODIS) NDVI and prediction of Advanced Very High Resolution Radiometer
(AVHRR) NDVI data series in a semi-arid region in New Mexico, USA in 2007. Bo-Cai
(2000) introduced the main concepts of this method. He used Airborne Visible/Infra-Red
Imaging Spectrometer (AVIRIS) dataset and an atmospheric... (More)
Long time series of satellite remotely sensed Normalized Difference Vegetation Index
(NDVI) as an indicator of vegetation greenness can be used in a broad range of research
topics such as climate change, vegetation dynamics and desertification monitoring.
The first phase of this study tests a practical method using Hyperion imagery onboard on
Earth Observing-1 (EO1) spacecraft for simulation of MODerate resolution Imaging
Spectroradiometer (MODIS) NDVI and prediction of Advanced Very High Resolution Radiometer
(AVHRR) NDVI data series in a semi-arid region in New Mexico, USA in 2007. Bo-Cai
(2000) introduced the main concepts of this method. He used Airborne Visible/Infra-Red
Imaging Spectrometer (AVIRIS) dataset and an atmospheric radiative transfer model based
on the look up table procedure for simulation of AVHRR-consistent NDVI. Here, Hyperion
data and MODTRAN Organizer software (MODO) as an interface of the MODTRAN
radiative transfer code for performing the atmospheric gaseous absorption corrections on the
AVHRR NIR channel are instead applied. The second part, evaluates the consistency of the
simulated MODIS NDVI and the predicted AVHRR NDVI time series (derived by the
practical method) with time series of the real MODIS/Terra and MODIS/Aqua NDVI (16-day
composites, 250 m) and the real AVHRR NDVI (biweekly, 1000 m) in two sites with
different land covers (shrubland and grassland) located in the study area, in 2007. Before
performing the consistency evaluation, both the simulated MODIS and predicted AVHRR
NDVI data sets (with 30 m resolution) are scaled down to the resolution of the corresponding
real data sets i.e., 250 m and 100 m, respectively.
The results of applying Hyperion data and MODO software in the tested method demonstrate
that the MODIS NDVI and AVHRR NDVI time series can be simulated at image level (with
30 m resolution) promisingly. For example, the results of employing one of the Hyperion
images over the study area (on 19 July 2007) into the method show that the bias in the
prediction of AVHRR NDVI is about 27% lower compared to the case that it is estimated by
MODIS NDVI. In addition, the results of the consistency evaluation based on the correlation
and linear regression analysis for the time series of simulated MODIS NDVI and predicted
AVHRR NDVI in a shrubland site and a grassland site in 2007, indicate that (1) the simulated
MODIS NDVI values are highly correlated (0.81-0.98) with the real MODIS NDVI values,
with Terra more than Aqua platforms and the root mean square error (RMSE) of the
simulated values is in the range of 0.13-0.20 ndvi unit and (2) the predicted AVHRR NDVI
time series is correlated with the real AVHRR NDVI, in the shrubland site higher than in the
grassland site (0.87 and 0.66, respectively) while RMSE in the grassland site (0.12-ndvi unit)
is lower than that in the shrubland site (0.18-ndvi unit).
However, two main limitations in the Hyperion data (disordered temporal resolution and not
globally coverage) make it useless in the generating of a reliable NDVI time series by the
tested method for large and long period applications. Optimistically, if some kind of hyper
spectral data with high temporal resolution and global coverage in future are applied in this
ii
method, results will have the capability of integration with real AVHRR NDVI time series for
making long time series for global application. (Less)
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author
Ghadiri, Masoumeh
supervisor
organization
year
type
H2 - Master's Degree (Two Years)
subject
keywords
Hyperion, NDVI, AVHRR, MODIS, time series, MODO
publication/series
Lunds universitets Naturgeografiska institution - Seminarieuppsatser
report number
197
language
English
id
2438778
date added to LUP
2012-04-12 17:46:41
date last changed
2012-04-12 17:46:41
@misc{2438778,
  abstract     = {Long time series of satellite remotely sensed Normalized Difference Vegetation Index
(NDVI) as an indicator of vegetation greenness can be used in a broad range of research
topics such as climate change, vegetation dynamics and desertification monitoring.
The first phase of this study tests a practical method using Hyperion imagery onboard on
Earth Observing-1 (EO1) spacecraft for simulation of MODerate resolution Imaging
Spectroradiometer (MODIS) NDVI and prediction of Advanced Very High Resolution Radiometer
(AVHRR) NDVI data series in a semi-arid region in New Mexico, USA in 2007. Bo-Cai
(2000) introduced the main concepts of this method. He used Airborne Visible/Infra-Red
Imaging Spectrometer (AVIRIS) dataset and an atmospheric radiative transfer model based
on the look up table procedure for simulation of AVHRR-consistent NDVI. Here, Hyperion
data and MODTRAN Organizer software (MODO) as an interface of the MODTRAN
radiative transfer code for performing the atmospheric gaseous absorption corrections on the
AVHRR NIR channel are instead applied. The second part, evaluates the consistency of the
simulated MODIS NDVI and the predicted AVHRR NDVI time series (derived by the
practical method) with time series of the real MODIS/Terra and MODIS/Aqua NDVI (16-day
composites, 250 m) and the real AVHRR NDVI (biweekly, 1000 m) in two sites with
different land covers (shrubland and grassland) located in the study area, in 2007. Before
performing the consistency evaluation, both the simulated MODIS and predicted AVHRR
NDVI data sets (with 30 m resolution) are scaled down to the resolution of the corresponding
real data sets i.e., 250 m and 100 m, respectively.
The results of applying Hyperion data and MODO software in the tested method demonstrate
that the MODIS NDVI and AVHRR NDVI time series can be simulated at image level (with
30 m resolution) promisingly. For example, the results of employing one of the Hyperion
images over the study area (on 19 July 2007) into the method show that the bias in the
prediction of AVHRR NDVI is about 27% lower compared to the case that it is estimated by
MODIS NDVI. In addition, the results of the consistency evaluation based on the correlation
and linear regression analysis for the time series of simulated MODIS NDVI and predicted
AVHRR NDVI in a shrubland site and a grassland site in 2007, indicate that (1) the simulated
MODIS NDVI values are highly correlated (0.81-0.98) with the real MODIS NDVI values,
with Terra more than Aqua platforms and the root mean square error (RMSE) of the
simulated values is in the range of 0.13-0.20 ndvi unit and (2) the predicted AVHRR NDVI
time series is correlated with the real AVHRR NDVI, in the shrubland site higher than in the
grassland site (0.87 and 0.66, respectively) while RMSE in the grassland site (0.12-ndvi unit)
is lower than that in the shrubland site (0.18-ndvi unit).
However, two main limitations in the Hyperion data (disordered temporal resolution and not
globally coverage) make it useless in the generating of a reliable NDVI time series by the
tested method for large and long period applications. Optimistically, if some kind of hyper
spectral data with high temporal resolution and global coverage in future are applied in this
ii
method, results will have the capability of integration with real AVHRR NDVI time series for
making long time series for global application.},
  author       = {Ghadiri, Masoumeh},
  keyword      = {Hyperion,NDVI,AVHRR,MODIS,time series,MODO},
  language     = {eng},
  note         = {Student Paper},
  series       = {Lunds universitets Naturgeografiska institution - Seminarieuppsatser},
  title        = {Potential of Hyperion imagery for simulation of MODIS NDVI and AVHRR-consistent NDVI time series in a semi-arid region},
  year         = {2010},
}