TY  - JOUR
AU  - Sun, Ying
AU  - Wen, Jiaming
AU  - Gu, Lianhong
AU  - Joiner, Joanna
AU  - Chang, Christine Y.
AU  - van der Tol, Christiaan
AU  - Porcar-Castell, Albert
AU  - Magney, Troy
AU  - Wang, Lixin
AU  - Hu, Leiqiu
AU  - Rascher, Uwe
AU  - Zarco-Tejada, Pablo
AU  - Barrett, Christopher B.
AU  - Lai, Jiameng
AU  - Han, Jimei
AU  - Luo, Zhenqi
TI  - From remotely‐sensed solar‐induced chlorophyll fluorescence to ecosystem structure, function, and service: Part II—Harnessing data
JO  - Global change biology
VL  - 29
IS  - 11
SN  - 1354-1013
CY  - Oxford [u.a.]
PB  - Wiley-Blackwell
M1  - FZJ-2023-04490
SP  - 2893 - 2925
PY  - 2023
AB  - Although our observing capabilities of solar-induced chlorophyll fluorescence (SIF) have been growing rapidly, the quality and consistency of SIF datasets are still in an active stage of research and development. As a result, there are considerable inconsistencies among diverse SIF datasets at all scales and the widespread applications of them have led to contradictory findings. The present review is the second of the two companion reviews, and data oriented. It aims to (1) synthesize the variety, scale, and uncertainty of existing SIF datasets, (2) synthesize the diverse applications in the sector of ecology, agriculture, hydrology, climate, and socioeconomics, and (3) clarify how such data inconsistency superimposed with the theoretical complexities laid out in (Sun et al., 2023) may impact process interpretation of various applications and contribute to inconsistent findings. We emphasize that accurate interpretation of the functional relationships between SIF and other ecological indicators is contingent upon complete understanding of SIF data quality and uncertainty. Biases and uncertainties in SIF observations can significantly confound interpretation of their relationships and how such relationships respond to environmental variations. Built upon our syntheses, we summarize existing gaps and uncertainties in current SIF observations. Further, we offer our perspectives on innovations needed to help improve informing ecosystem structure, function, and service under climate change, including enhancing in-situ SIF observing capability especially in "data desert" regions, improving cross-instrument data standardization and network coordination, and advancing applications by fully harnessing theory and data.
LB  - PUB:(DE-HGF)16
C6  - 36802124
UR  - <Go to ISI:>//WOS:000950362600001
DO  - DOI:10.1111/gcb.16646
UR  - https://juser.fz-juelich.de/record/1018027
ER  -