Reply on RC2

Abstract

Sun-induced chlorophyll fluorescence (SIF) retrieved from satellites has shown potential as a remote sensing proxy for gross primary productivity (GPP). However, to fully exploit the potential of this signal, the robustness and stability of the SIF-GPP relationship across vegetation types and climates must be assessed. For this purpose, current studies have been limited by the availability of SIF datasets with sufficient spatial resolution to disentangle the signal between different vegetation cover types. To overcome this limitation our analysis uses GOME-2 SIF retrievals, downscaled to a resolution of 0.05° (∼5 km) to explore the relationship between SIF and FLUXCOM GPP (GPPFX), a data-driven dataset of primary productivity obtained by upscaling flux-tower measurements. The high resolution of the downscaled SIF (SIFDS) dataset allows the relationships to be broken down by vegetation cover for separate climate zones, thus enabling a confrontation between GPP and SIF at fine granularity,and the comparison and categorisation of vegetation covers based on the SIFDS-GPPFX response. This analysis first investigates the spatial and temporal relationships between FLUXCOM GPP and downscaled SIF at a global scale. Divergences and convergences between the two datasets are explored to see where high-resolution SIF can enhance our understanding of GPP. A reasonably strong linear relationship is generally observed between SIFDS and GPPFX in all vegetation categories, and an analysis of covariance (ANCOVA) shows that the spatial response is similar between certain plant traits, with some exceptions, such as equatorial broadleaf forests, and continental needleleaf forests, and distinction between woody and herbaceous vegetation. The temporal relationship between SIFDS and GPPFX, within a growing season, is shown to be the strongest, followed by the spatial relationship and finally the SIFDS-GPPFX trend between growing seasons. Whilst a linear spatial relationship is generally observed between SIFDS and GPPFX in all vegetation categories, geographical regions of non-linearity suggest where SIFDS could potentially provide information about ecosystem dynamics that are not represented in the FLUXCOM GPP dataset. With the demonstration of downscaled SIF as a proxy for GPP, the response of SIFDS to short-term fluctuations in several meteorological variables is analysed, and the utility of SIFDS as a measure of environmental stress explored. For a broad range of land cover categories, the most significant environmental driving and limiting meteorological variables are determined and vegetation groupings of similar SIF-meteo response reinforce the vegetation categorisations suggested by the SIF-GPP ANCOVA. This comparative exploration of two of the most recent products in carbon productivity estimation shows the value in downscaling SIF data, provides an independent probe of the FLUXCOM GPP model, enhances our understanding of the global SIF-GPP spatio-temporal relationship with a particular focus on the role of vegetation cover, and demonstrates the utility of SIF as a measure of environmental stress.