Neural networks as a tool for constructing continuous NDVI time series from AVHRR and MODIS

被引：69

作者：

Brown, M. E. ^{[1
]}

Lary, D. J. ^{[2
]}

Vrieling, A. ^{[3
]}

Stathakis, D. ^{[3
]}

Mussa, H. ^{[4
]}

机构：

[1] NASA, Goddard Space Flight Ctr, Sci Syst & Applicat Inc, Greenbelt, MD 20771 USA

[2] NASA, Goddard Space Flight Ctr, UMBC GEST, Greenbelt, MD 20771 USA

[3] Commiss European Communities, Joint Res Ctr, I-21027 Ispra, VA, Italy

[4] Univ Cambridge, Dept Chem, Cambridge CBR 3QZ, England

来源：

INTERNATIONAL JOURNAL OF REMOTE SENSING | 2008年 / 29卷 / 24期

关键词：

D O I：

10.1080/01431160802238435

中图分类号：

TP7 [遥感技术];

学科分类号：

081102 ; 0816 ; 081602 ; 083002 ; 1404 ;

摘要：

The long term Advanced Very High Resolution Radiometer (AVHRR)-Normalized Difference Vegetation Index (NDVI) record provides a critical historical perspective on vegetation dynamics necessary for global change research. Despite the proliferation of new sources of global, moderate resolution vegetation datasets, the remote sensing community is still struggling to create datasets derived from multiple sensors that allow the simultaneous use of spectral vegetation for time series analysis. To overcome the non-stationary aspect of NDVI, we use an artificial neural network (ANN) to map the NDVI indices from AVHRR to those from MODIS using atmospheric, surface type and sensor-specific inputs to account for the differences between the sensors. The NDVI dynamics and range of MODIS NDVI data at 1 is matched and extended through the AVHRR record. Four years of overlap between the two sensors is used to train a neural network to remove atmospheric and sensor specific effects on the AVHRR NDVI. In this paper, we present the resulting continuous dataset, its relationship to MODIS data, and a validation of the product.

引用

页码：7141 / 7158

页数：18