Articles | Volume 15, issue 19
Research article
02 Oct 2018
Research article |  | 02 Oct 2018

A global spatially contiguous solar-induced fluorescence (CSIF) dataset using neural networks

Yao Zhang, Joanna Joiner, Seyed Hamed Alemohammad, Sha Zhou, and Pierre Gentine

Data sets

CSIF Y. Zhang, J. Joiner, S. H. Alemohammad, S. Zhou, and P. Gentine

MODIS-derived global land products of shortwave radiation and diffuse and total photosynthetically active radiation at 5 km resolution from 2000 Y. Ryu, C. Jiang, H. Kobayashi and M. Detto

First operational BRDF, albedo nadir reflectance products from MODIS, Remote Sensing of Environment C. B. Schaaf, F. Gao, A. H. Strahler, W. Lucht, X. Li, T. Tsang, N. C. Strugnell, X. Zhang, Y. Jin, J.-P. Muller, P. Lewis, M. Barnsley, P. Hobson, M. Disney, G. Roberts, M. Dunderdale, C. Doll, R. P. d'Entremont, B. Hu, S. Liang, J. L. Privette and D. Roy

Model code and software

Contiguous SIF Y. Zhang, J. Joiner, S. H. Alemohammad, S. Zhou, and P. Gentine

Short summary
Using satellite reflectance measurements and a machine learning algorithm, we generated a new solar-induced chlorophyll fluorescence (SIF) dataset that is closely linked to plant photosynthesis. This new dataset has higher spatial and temporal resolutions, and lower uncertainty compared to the existing satellite retrievals. We also demonstrated its application in monitoring drought and improving the understanding of the SIF–photosynthesis relationship.
Final-revised paper