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RSS FeedsRemote Sensing, Vol. 11, Pages 927: Sun-Induced Chlorophyll Fluorescence II: Review of Passive Measurement Setups, Protocols, and Their Application at the Leaf to Canopy Level (Remote Sensing)

 
 

16 april 2019 18:00:07

 
Remote Sensing, Vol. 11, Pages 927: Sun-Induced Chlorophyll Fluorescence II: Review of Passive Measurement Setups, Protocols, and Their Application at the Leaf to Canopy Level (Remote Sensing)
 


Imaging and non-imaging spectroscopy employed in the field and from aircraft is frequently used to assess biochemical, structural, and functional plant traits, as well as their dynamics in an environmental matrix. With the increasing availability of high-resolution spectroradiometers, it has become feasible to measure fine spectral features, such as those needed to estimate sun-induced chlorophyll fluorescence (F), which is a signal related to the photosynthetic process of plants. The measurement of F requires highly accurate and precise radiance measurements in combination with very sophisticated measurement protocols. Additionally, because F has a highly dynamic nature (compared with other vegetation information derived from spectral data) and low signal intensity, several environmental, physiological, and experimental aspects have to be considered during signal acquisition and are key for its reliable interpretation. The European Cooperation in Science and Technology (COST) Action ES1309 OPTIMISE has produced three articles addressing the main challenges in the field of F measurements. In this paper, which is the second of three, we review approaches that are available to measure F from the leaf to the canopy scale using ground-based and airborne platforms. We put specific emphasis on instrumental aspects, measurement setups, protocols, quality checks, and data processing strategies. Furthermore, we review existing techniques that account for atmospheric influences on F retrieval, address spatial scaling effects, and assess quality checks and the metadata and ancillary data required to reliably interpret retrieved F signals.


 
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Remote Sensing, Vol. 11, Pages 922: Exploring Weighted Dual Graph Regularized Non-Negative Matrix Tri-Factorization Based Collaborative Filtering Framework for Multi-Label Annotation of Remote Sensing Images (Remote Sensing)
Remote Sensing, Vol. 11, Pages 926: Change Analysis in Urban Areas Based on Statistical Features and Temporal Clustering Using TerraSAR-X Time-Series Images (Remote Sensing)
 
 
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