Help Guide Disclaimer Contact us Login
  Advanced SearchBrowse




Journal Article

Identifying multiple spatiotemporal patterns: A refined view on terrestrial photosynthetic activity


Mahecha,  M. D.
Research Group Biogeochemical Model-data Integration, Dr. M. Reichstein, Max Planck Institute for Biogeochemistry, Max Planck Society;

There are no locators available
Fulltext (public)
There are no public fulltexts available
Supplementary Material (public)
There is no public supplementary material available

Mahecha, M. D., Fürst, L. M., Gobron, N., & Lange, H. (2010). Identifying multiple spatiotemporal patterns: A refined view on terrestrial photosynthetic activity. Pattern Recognition Letters, 31(14), 2309-2317. doi:10.1016/j.patrec.2010.06.021.

Cite as:
Information retrieval from spatiotemporal data cubes is key to earth system sciences. Respective analyses need to consider two fundamental issues: First, natural phenomena fluctuate on different time scales. Second, these characteristic temporal patterns induce multiple geographical gradients. Here we propose an integrated approach of subsignal extraction and dimensionality reduction to extract geographical gradients on multiple time scales. The approach is exemplified using global remote sensing estimates of photosynthetic activity. A wide range of partly well interpretable gradients is retrieved. For instance, well known climate-induced anomalies in FAPAR over Africa and South America during the last severe ENSO event are identified. Also, the precise geographical patterns of the annual-seasonal cycle and its phasing are isolated. Other features lead to new questions on the underlying environmental dynamics. Our method can provide benchmarks for comparisons of data cubes, model runs, and thus be used as a basis for sophisticated model performance evaluations. (C) 2010 Elsevier B.V. All rights reserved.