Journal article
Bayesian MODIS NDVI back-prediction by intersensor calibration with AVHRR
Remote sensing of environment, Vol.186, pp.393-404
12/01/2016
DOI: 10.1016/j.rse.2016.09.002
Abstract
This article describes a Bayesian approach for ensuring the continuity of remote sensing records (1982–2014) by back-prediction of Moderate Resolution Imaging Spectroradiometer (MODIS) using historical Advanced Very High Resolution Radiometer (AVHRR) data. First, a historical 8km snow data was generated using MODIS snow Quality Assurance field and North American Regional Reanalysis snow depth and air temperature outputs. Second, the relationships between coarsened 8km MODIS and AVHRR Normalized Difference Vegetation Index (NDVI) were modeled pixel by pixel using spatial autocorrelation. Then the NDVI was back-predicted to 1982 from historical AVHRR observations and snow data. The back-predicted NDVI was validated against those data held-out from MODIS in 2001 and 2002 from four distinct biogeographic state regions (California, Kansas, Minnesota and Mississippi) in the continental United States. The results show consistency of the derived NDVI from two sensors, with a Root Mean Square Error within 0.05 over most land cover classes. This validation results suggest potential application of this approach for generating consistent long term multi-sensor NDVI data records for ecology and global climate change studies. •Bayesian modeling of variation in inter-sensor relation from snow and land cover•Two year out-sample validation of the method on four representative U.S. states•A validated and consistent 16-day 8km 30year NDVI record
Details
- Title: Subtitle
- Bayesian MODIS NDVI back-prediction by intersensor calibration with AVHRR
- Creators
- Dong Liang - Environmental Statistics Collaborative, Chesapeake Biological Laboratories, University of Maryland Center for Environmental Science, 146 Williams Street, Solomons, MD 20688, United StatesMary Kathryn Cowles - Department of Statistics and Actuarial Sciences, University of Iowa, 241 Schaeffer Hall, 20 East Washington Street, Iowa City, IA 52242, United StatesMarc Linderman - Department of Geographical and Sustainability Sciences, University of Iowa, 316 Jessup Hall, Iowa City, IA 52242, United States
- Resource Type
- Journal article
- Publication Details
- Remote sensing of environment, Vol.186, pp.393-404
- Publisher
- Elsevier Inc
- DOI
- 10.1016/j.rse.2016.09.002
- ISSN
- 0034-4257
- eISSN
- 1879-0704
- Grant note
- DOI: 10.13039/100008893, name: University of Iowa
- Language
- English
- Date published
- 12/01/2016
- Academic Unit
- Geographical and Sustainability Sciences; Epidemiology; Statistics and Actuarial Science; Biostatistics
- Record Identifier
- 9983985980902771
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