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|Title:||Neural Networks as a Tool for Constructing Continuous NDVI Time Series from AVHRR and MODIS|
|Authors:||BROWN Molly E.; LARY David J.; VRIELING ANTON; STATHAKIS Demetris; MUSSA Hamse|
|Citation:||INTERNATIONAL JOURNAL OF REMOTE SENSING vol. 29 no. 24 p. 7141-7158|
|Publisher:||TAYLOR & FRANCIS LTD|
|Type:||Articles in periodicals and books|
|Abstract:||The long term AVHRR-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 one degree 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.|
|JRC Directorate:||Sustainable Resources|
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