Publications

Shekhar, A; Buchmann, N; Gharun, M (2022). How well do recently reconstructed solar-induced fluorescence datasets model gross primary productivity?. REMOTE SENSING OF ENVIRONMENT, 283, 113282.

Abstract
The collection of various long-term reconstructed solar-induced fluorescence (SIF) datasets derived at a range of spatio-temporal scales provides new opportunities for modelling vegetation dynamics, in particular, gross pri-mary productivity (GPP). Information about the proximity of the reconstructed SIF (SIFr) datasets to GPP across land cover types and climatic conditions provides important support for a better application of these products for modelling applications. We conducted a multiscale analysis of four different long-term (12 years, 2007-2018) high-resolution global SIFr datasets (0.05 degrees x 0.05 degrees), namely - CSIF (Contiguous SIF), GOSIF (Global OCO-2 SIF), LUE-SIF (Light Use Efficiency SIF), and HSIF (Harmonized SIF) -at 4-day, 8-day, and monthly time scales and found that for the majority of sites, the SIFr is linearly related to ground-based GPP measurements with the eddy covariance method. While the relationship between SIFr and GPP (i.e., the slope -GPP/SIFr) varied significantly across the SIFr datasets, sites, and land cover types, all four SIFr datasets were unequivocally a better predictor of GPP compared to remotely sensed vegetation indices - NDVI (normalized difference vegetation index) and EVI (enhanced vegetation index), sensed by the MODIS satellite. Furthermore, we also analyzed SIF-GPP relation-ships during drought vs non-drought conditions and found that for about 30% of the sites, comprising mostly non-forests site, the SIF-GPP relationship became weaker (decreased R2) with a lower slope during drought conditions compared to non-drought conditions. Among the four different products, the CSIF (at 4-day timescale) and GOSIF (at 8-day timescale) predicted GPP better compared to LUE-SIF and HSIF across all land cover types. Owing to their long-term availability (since 2000 for CSIF and GOSIF), these SIFr datasets combined with proxies of ecosystem properties can be used to appropriately capture vegetation dynamics and the interannual vari-abilities across a wide range of climatic conditions.

DOI:
10.1016/j.rse.2022.113282

ISSN:
1879-0704