A Global Spatially Contiguous Solar-Induced Fluorescence (CSIF) Dataset Using Neural Networks

• Published in: Biogeosciences Vol. 15; no. 19; pp. 5779 - 5800
• Main Authors: Zhang, Yao, Joiner, Joanna, Alemohammad, Seyed Hamed, Zhou, Sha, Gentine, Pierre
• Format: Journal Article
• Language: English
• Published: Goddard Space Flight Center Biogeosciences 02.10.2018; Copernicus GmbH; Copernicus Publications
• Subjects: Artificial neural networks; Chlorophyll; Chlorophylls; Covariance; Datasets; Drought; Dynamics; Earth; Ecosystems; Eddy covariance; Fluorescence; Geosciences (General); Global ozone; Greening; Imaging techniques; MODIS; Neural networks; Normalized difference vegetative index; Observations; Ozone; Ozone monitoring; Photosynthesis; Primary production; Reflectance; Resolution; Satellite observation; Satellites; Slope; Spectroradiometers; Temporal resolution; Temporal variations; Terrestrial ecosystems; Towers; Training; Vegetation; Vegetation index

Satellite-retrieved solar-induced chlorophyll fluorescence (SIF) has shown great potential to monitor the photosynthetic activity of terrestrial ecosystems. However, several issues, including low spatial and temporal resolution of the gridded datasets and high uncertainty of the individual retrieval...