Data from: Comparison of solar-induced chlorophyll fluorescence, light-use efficiency, and process-based GPP models in maize

Accurately quantifying cropland gross primary production (GPP) is of great
importance to monitor cropland status and carbon budgets. Satellite-based
light-use efficiency (LUE) models and process-based terrestrial biosphere
models (TBMs) have been widely used to quantify cropland GPP at different
scales in past decades. However, model estimates of GPP are still subject
to large uncertainties, especially for croplands. More recently,
space-borne solar-induced chlorophyll fluorescence (SIF) has shown the
ability to monitor photosynthesis from space, providing new insights into
actual photosynthesis monitoring. In this study, we examined the potential
of SIF data to describe maize phenology and evaluated three GPP modeling
approaches (space-borne SIF retrievals, a LUE-based Vegetation
Photosynthesis Model (VPM), and a process-based Soil Canopy Observation of
Photochemistry and Energy flux (SCOPE) model constrained by SIF) at a
maize (Zea mays L.) site in Mead, Nebraska, USA. The result shows that SIF
captured the seasonal variations (particularly during the early and late
growing season) of tower-derived GPP (GPP_EC) much better than did
satellite-based vegetation indices (enhanced vegetation index, EVI and
land surface water index, LSWI). Consequently, SIF was strongly correlated
with GPP_EC than were EVI and LSWI. Evaluation of GPP estimates against
GPP_EC during the growing season demonstrated that all three modeling
approaches provided reasonable estimates of maize GPP, with Pearson's
correlation coefficients (r) of 0.97, 0.94, and 0.93 for the SCOPE, VPM,
and SIF models, respectively. The SCOPE model provided the best simulation
of maize GPP when SIF observations were incorporated through optimizing
the key parameter of maximum carboxylation capacity (Vcmax). Our results
illustrate the potential of SIF data to offer an additional way to
investigate the seasonality of photosynthetic activity, to constrain
process-based models for improving GPP estimates, and to reasonably
estimate GPP by integrating SIF and GPP_EC data without dependency on
climate inputs and satellite-based vegetation indices.