A Spatiotemporal Constrained Machine Learning Method for OCO-2 Solar-Induced Chlorophyll Fluorescence (SIF) Reconstruction

Solar-induced chlorophyll fluorescence (SIF) is an intuitive and accurate way to measure vegetation photosynthesis. Orbiting Carbon Observatory-2 (OCO-2)-retrieved SIF has shown great potential in estimating terrestrial gross primary production (GPP), but the discontinuous spatial coverage limits it...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	IEEE transactions on geoscience and remote sensing 2022, Vol.60, p.1-17
Hauptverfasser:	Shen, Huanfeng, Wang, Yuchen, Guan, Xiaobin, Huang, Wenli, Chen, Jiajia, Lin, Dekun, Gan, Wenxia
Format:	Artikel
Sprache:	eng
Schlagworte:	Accuracy Biological system modeling Carbon Chlorophyll Chlorophylls Data models Fluorescence Instruments Learning algorithms Machine learning Methods Missing data Monitoring instruments Orbiting Carbon Observatory-2 (OCO-2) Photosynthesis Primary production Reconstruction Satellites Sensitivity analysis solar-induced chlorophyll fluorescence (SIF) Spatial distribution Spatial resolution spatiotemporal constraint Temporal distribution Temporal variations Vegetation Vegetation mapping
Online-Zugang:	Volltext bestellen
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!