Long-term Continuous SIF-informed Photosynthesis Proxy reconstructed with calibrated AVHRR surface reflectance (LCSPP-AVHRR), 1982-2000

Usage Notes:
This is the updated LCSPP dataset (v3.2), reconstructed using the AVHRR record from 1982–2023. Due to Zenodo’s size constraints, LCSPP-AVHRR is divided into two separate repositories. Previously referred to as "LCSIF," the dataset was renamed to emphasize its role as a SIF-informed long-term photosynthesis proxy derived from surface reflectance and to avoid confusion with directly measured SIF signals.

Key updates in version 3.2 include:

• Improved Calibration: Enhanced consistency in calibration methods, addressing technical limitations in version 3.1 including applying more stringent quality filtering and snow masks. We also 
• Quality Flags: New quality flag layer enables users to identify whether a pixel is derived from observed surface reflectance (QA=0), high-quality gap-filled values (QA=1), lower-quality gap-filled based on the mean seasonal cycle (QA=2), or missing entirely (QA=3). We advice the user to rely only on observed and high-quality gap-filled values for their analyses.
• Extension to include observations from the year of 2023.

Other LCSPP repositories can be accessed via the following links:

• LCSPP-AVHRR v3.2 (2001-2023): 10.5281/zenodo.11906675
• LCSPP-MODIS v3.2(2001-2023): 10.5281/zenodo.11658088

The user can choose between LCSPP-AVHRR and LCSPP-MODIS for the overlapping period from 2001-2023. The two datasets are generally consistent during this overlapping period, although LCSPP-MODIS shows a stronger greening trend between 2001-2023. For studies exploring the long-term vegetation dynamics, the user can either use only LCSPP-AVHRR or use a blend dataset of LCSPP-AVHRR and LCSPP-MODIS as a sensitivity test. 

In addition, the updated long-term continuous reflectance datasets (LCREF), used for the production of LCSPP, can be accessed using the following links:

• LCREF-AVHRR v3.1 (1982-2023): 10.5281/zenodo.11905959
• LCREF-MODIS v3.1 (2001-2023): 10.5281/zenodo.11657458

A manuscript describing the technical details is available at https://arxiv.org/abs/2311.14987, while detailed the uses and limitations of the dataset. In particular, we note that LCSPP is a reconstruction of SIF-informed photosynthesis proxy and should not be treated as SIF measurements. Although LCSPP has demonstrated skill in tracking the dynamics of GPP and PAR absorbed by canopy chlorophyll (APARchl), it is not suitable for estimating fluorescence quantum yield.

All data outputs from this study are available at 0.05° spatial resolution and biweekly temporal resolution in NetCDF format. Each month is divided into two files, with the first file “a” representative of the 1^st day to the 15^th day of a month, and the second file “b” representative of the 16^th day to the last day of a month.

Abstract:

Satellite-observed solar-induced chlorophyll fluorescence (SIF) is a powerful proxy for the photosynthetic characteristics of terrestrial ecosystems. Direct SIF observations are primarily limited to the recent decade, impeding their application in detecting long-term dynamics of ecosystem function. In this study, we leverage two surface reflectance bands available both from Advanced Very High-Resolution Radiometer (AVHRR, 1982-2023) and MODerate-resolution Imaging Spectroradiometer (MODIS, 2001-2023). Importantly, we calibrate and orbit-correct the AVHRR bands against their MODIS counterparts during their overlapping period. Using the long-term bias-corrected reflectance data from AVHRR and MODIS, a neural network is trained to produce a Long-term Continuous SIF-informed Photosynthesis Proxy (LCSPP) by emulating Orbiting Carbon Observatory-2 SIF, mapping it globally over the 1982-2023 period. Compared with previous SIF-informed photosynthesis proxies, LCSPP has similar skill but can be advantageously extended to the AVHRR period. Further comparison with three widely used vegetation indices (NDVI, kNDVI, NIRv) shows a higher or comparable correlation of LCSPP with satellite SIF and site-level GPP estimates across vegetation types, ensuring a greater capacity for representing long-term photosynthetic activity.