Integrating SIF and Clearness Index to Improve Maize GPP Estimation Using Continuous Tower-Based Observations.

Area of Science:

• Earth and Environmental Sciences
• Plant Biology
• Remote Sensing

Background:

• Solar-induced chlorophyll fluorescence (SIF) correlates with vegetation photosynthesis and gross primary production (GPP).
• Previous studies show SIF-GPP correlation, but estimation at various temporal scales needs further exploration.
• Far-red SIF (SIF760) is a potential indicator for GPP.

Purpose of the Study:

• To assess the quality of GPP estimates using SIF760 from tower-based observations in a maize field.
• To investigate the influence of meteorological factors (PAR, CI, AT, VPD) on the SIF760-GPP relationship.
• To improve SIF-based GPP estimation models by incorporating the clearness index (CI).

Main Methods:

• Continuous tower-based observations of SIF760 and GPP in a maize field over two years (2017-2018).
• Analysis of SIF760 and GPP responses to photosynthetically active radiation (PAR), clearness index (CI), air temperature (AT), and vapor pressure deficit (VPD).
• Development and validation of SIF760-based GPP models, with and without CI, using 70% training and 30% validation data.

Main Results:

• SIF760 tracked GPP well at diurnal and seasonal scales, showing a hyperbolic relationship.
• The SIF760-GPP relationship was influenced by environmental conditions, with CI being the dominant factor.
• Models incorporating CI significantly improved daily GPP estimates (R² increased from 0.71 to 0.82 for linear, 0.82 to 0.87 for non-linear).

Conclusions:

• SIF760 is a reliable proxy for estimating maize GPP.
• Integrating the clearness index (CI) substantially enhances the accuracy of SIF760-based GPP estimation models.
• These findings support the use of SIF760 and CI for improved remote sensing of vegetation GPP.