From remotely‐sensed solar‐induced chlorophyll fluorescence to ecosystem structure, function, and service: Part II—Harnessing data - Ying Sun; Jiaming Wen; Lianhong Gu; Joanna Joiner; Christine Y. Chang; Christiaan van der Tol; Albert Porcar‐Castell; Troy Magney; Lixin Wang; Leiqiu Hu; Uwe Rascher; Pablo Zarco‐Tejada; et al. (Global Change Biology, 2023)
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.