Downscaled GOME2 SIF

Version as described in the publication Duveiller et al. (2020)

First downscaled SIF dataset based on the Joiner et al. (2013) v25 GOME-2 SIF data, and described in Duveiller & Cescatti (2016). It has now been superseded by version 2 as described in Duveiller et al. 2020.

Sun-induced chlorophyll fluorescence (SIF) retrieved from satellite spectrometers can be a highly valuable proxy for photosynthesis. The SIF signal is very small and notoriously difficult to measure, requiring sub-nanometer spectral resolution measurements, which to-date are only available from atmospheric spectrometers sampling at coarse spatial resolution. For example, the widely used SIF dataset derived from the GOME-2 mission is typically provided in 0.5 degree composites. This paper presents a new SIF dataset based on GOME-2 satellite observations with an enhanced spatial resolution of 0.05 degree and an 8-day time step covering the period 2007-2018. It leverages on a proven methodology that relies on using a light use efficiency (LUE) modelling approach to establishing a semi-empirical relationship between SIF and various explanatory variables derived from remote sensing at finer spatial resolution. An optimal set of explanatory variables is selected based on an independent validation with OCO-2 SIF observations, which are only sparsely available but have a high accuracy and spatial resolution. After a bias-correction, the resulting downscaled SIF data shows high spatio-temporal agreement with the first SIF retrievals from the new TROPOMI mission, opening the path towards establishing a surrogate archive for this promising new dataset. We foresee that this new SIF dataset should be a valuable asset for Earth System Science in general, and for monitoring vegetation productivity in particular.

The dataset is available at https://doi.org/10.2905/21935FFC-B797-4BEE-94DA-8FEC85B3F9E1 (Duveiller et al., 2019).

Sun-induced chlorophyll fluorescence (SIF) is known to relate directly to leaf and canopy scale photosynthesis. Retrieving SIF from space can thus provide an indication on the temporal and spatial patterns of the terrestrial gross primary productivity (GPP). Recent studies have successfully demonstrated the serendipitous retrieval of SIF from satellite remote sensing instruments originally destined to atmospheric studies. However, the finest spatial resolution achieved by these products is 0.5°, which remains too coarse for many applications, including the early detection of drought impacts on vegetation and the integration with ground GPP measurements from flux-towers. This paper proposes a methodology to spatially disaggregate the information contained within each coarse SIF pixels by using a non-linear model based on the concept of light use efficiency (LUE). The strategy involves the aggregation of high-resolution (0.05°) remote sensing biophysical variables to calibrate the downscaling model locally and independently at each time step, which can then be applied to non-aggregated data to create a new layer, denoted SIF*, with a spatial resolution of 0.05°. A global SIF* dataset is generated by applying this methodology globally to 7 years of monthly GOME-2 SIF data. SIF* is shown to be a better proxy for GPP than the original coarse spatial resolution product according to flux-tower eddy covariance measurements. Its performance is comparable to dedicated GPP products despite that (unlike SIF*) these are calibrated based on the same flux towers, driven by meteorological data and not hampered by the large noise caused by the SIF retrieval. To further illustrate the added-value of the global SIF* product, this paper also presents: (1) an ecosystem level assessment showing a considerable reduction of noise with respect to the original SIF; (2) a spatio-temporal inter-comparison with existing GPP products; and (3) estimations of global terrestrial productivity per selected vegetation types based on SIF*.