A global spatially contiguous solar-induced fluorescence (CSIF) dataset using neural networks

By Yao Zhang, Joanna Joiner, S. H. Alemohammad, Sha Zhou, Pierre Gentine. Biogeosciences, 2018

447 citations recorded by the source corpus.

Abstract. 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 retrievals, limit the applications of SIF. In addition, inconsistency in measurement footprints also hinders the direct comparison between gross primary production (GPP) from eddy covariance (EC) flux towers and satellite-retrieved SIF. In this study, by training a neural network (NN) with surface reflectance from the MODerate-resolution Imaging Spectroradiometer (MODIS) and SIF from Orbiting Carbon Observatory-2 (OCO-2), we generated two global spatially contiguous SIF (CSIF) datasets at moderate spatiotemporal (0.05∘ 4-day) resolutions during the MODIS era, one for clear-sky conditions (2000–2017) and the other one in all-sky conditions (2000–2016). The clear-sky instantaneous CSIF (CSIFclear-inst) shows high accuracy against the clear-sky OCO-2 SIF and little bias across biome types. The all-sky daily average CSIF (CSIFall-daily) dataset exhibits strong spatial, seasonal and interannual dynamics that are consistent with daily SIF from OCO-2 and the Global Ozone Monitoring Experiment-2 (GOME-2). An increasing trend (0.39 %) of annual average CSIFall-daily is also found, confirming the greening of Earth in most regions. Since the difference between satellite-observed SIF and CSIF is mostly caused by the environmental down-regulation on SIFyield, the ratio between OCO-2 SIF and CSIFclear-inst can be an effective indicator of drought stress that is more sensitive than the normalized difference vegetation index and enhanced vegetation index. By comparing CSIFall-daily with GPP estimates from 40 EC flux towers across the globe, we find a large cross-site variation (c.v. = 0.36) of the GPP–SIF relationship with the highest regression slopes for evergreen needleleaf forest. However, the cross-biome variation is relatively limited (c.v. = 0.15). These two contiguous SIF datasets and the derived GPP–SIF relationship enable a better understanding of the spatial and temporal variations of the GPP across biomes and climate.

Subfields: Ecology, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/3803a6a68c93a11d6c5b862772ebe75d4ce3315f

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Citations in the corpus

Mapping Photosynthesis Solely from Solar-Induced Chlorophyll Fluorescence: A Global, Fine-Resolution Dataset of Gross Primary Production Derived from OCO-2 (2019, methodology): “Our estimate of global annual GPP (135.5 ± 8.8 Pg C yr−1) is between the 75th percentile and 95th percentile of the estimates from non-process based methods and higher than the estimate (~110 Pg C yr−1) from the MODIS GPP product [49] and the estimates from data-driven methods (119 - 123 Pg C yr−1) ”
Coupling between the terrestrial carbon and water cycles—a review (2019, background): “…the way though, such as estimating changes in light use efficiency with increased [CO2] from remote sensing platforms (Gentine and Alemohammad 2018, Zhang et al 2018b), as well as partitioning transpiration from total ET, which remains highly uncertain (Jasechko et al 2013, Michel et al 2016,…”
Remote sensing of solar-induced chlorophyll fluorescence (SIF) in vegetation: 50 years of progress. (2019, methodology): “In comparison, Zhang et al. (2018e) cautioned that identifying a near-universal linear relationship between OCO-2 SIF and GPP may be complicated by sun-sensor angle considerations related to OCO-2's three observation modes (nadir, glint, and target) and their different geometries.”