Although satellite-based variables have for long been expected to be key components to a unified and global biodiversity monitoring strategy, a definitive and agreed list of these variables still remains elusive. The growth of interest in biodiversity variables observable from space has been partly underpinned by the development of the essential biodiversity variable (EBV) framework by the Group on Earth Observations -Biodiversity Observation Network, which itself was guided by the process of identifying essential climate variables. This contribution aims to advance the development of a global biodiversity monitoring strategy by updating the previously published definition of EBV, providing a definition of satellite remote sensing (SRS) EBVs and introducing a set of principles that are believed to be necessary if ecologists and space agencies are to agree on a list of EBVs that can be routinely monitored from space. Progress toward the identification of SRS-EBVs will require a clear understanding of what makes a biodiversity variable essential, as well as agreement on who the users of the SRS-EBVs are. Technological and algorithmic developments are rapidly expanding the set of opportunities for SRS in monitoring biodiversity, and so the list of SRS-EBVs is likely to evolve over time. This means that a clear and common platform for data providers, ecologists, environmental managers, policy makers and remote sensing experts to interact and share ideas needs to be identified to support long-term coordinated actions.

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With the advent of Sentinel-2, it is now possible to generate large-scale chlorophyll content maps with unprecedented spatial and temporal resolution, suitable for monitoring ecological processes such as vegetative stress and/or decline. However methodological gaps exist for adapting this technology to heterogeneous natural vegetation and for transferring it among vegetation species or plan functional types. In this study, we investigated the use of Sentinel-2A imagery for estimating needle chlorophyll (C a+b ) in a sparse pine forest undergoing significant needle loss and tree mortality. Sentinel-2A scenes were acquired under two extreme viewing geometries (June vs. December 2016) coincident with the acquisition of high-spatial resolution hyperspectral imagery, and field measurements of needle chlorophyll content and crown leaf area index. Using the high-resolution hyperspectral scenes acquired over 61 validation sites we found the CI chlorophyll index R 750 /R 710 and Macc index (which uses spectral bands centered at 680 nm, 710 nm and 780 nm) had the strongest relationship with needle chlorophyll content from individual tree crowns (r 2 = 0.61 and r 2 = 0.59, respectively; p < 0.001), while TCARI and TCARI/OSAVI, originally designed for uniform agricultural canopies, did not perform as well (r 2 = 0.21 and r 2 = 0.01, respectively). Using lower-resolution Sentinel-2A data validated against hyperspectral estimates and ground truth needle chlorophyll content, the red-edge index CI and the Sentinel-specific chlorophyll indices CI-Gitelson, NDRE1 and NDRE2 had the highest accuracy (with r 2 values >0.7 for June and >0.4 for December; p < 0.001). The retrieval of needle chlorophyll content from the entire Sentinel-2A bandset using the radiative transfer model INFORM yielded r 2 = 0.71 (RMSE = 8.1 μg/cm 2 ) for June, r 2 = 0.42 (RMSE = 12.2 μg/cm 2 ) for December, and r 2 = 0.6 (RMSE = 10.5 μg/cm 2 ) as overall performance using the June and December datasets together. This study demonstrates the retrieval of leaf C a+b with Sentinel-2A imagery by red-edge indices and by an inversion method based on a hybrid canopy reflectance model that accounts for tree density, background and shadow components common in sparse forest canopies.