Vegetation Index-Based Models Without Meteorological Constraints Underestimate the Impact of Drought on Gross Primary Productivity

Recently developed solar-induced chlorophyll fluorescence-related vegetation indices (e.g., near infrared reflectance of vegetation (NIRv) and kernel normalized difference vegetation index (kNDVI)) have been reported to be appropriate proxies for vegetation photosynthesis. These vegetation indices can be used to estimate gross primary productivity (GPP) without considering meteorological constraints. However, it is not clear whether such a statement holds true under various environmental conditions. In this study, we explored whether these vegetation indices require meteorological constraints to better characterize GPP under extreme drought conditions using three extreme drought cases in Europe in 2003, 2010, and 2018. According to the long-term series of observations, vegetation indices (NIRv and kNDVI) alone explained 60% and 57%, respectively, of the weekly GPP variation across the 66 flux sites. The explained variation increased to 69% and 64%, respectively, for the models that take into account radiative effects (NIRv and kNDVI multiplied by radiation). However, without considering meteorological constraints, these vegetation index-based estimations severely underestimated negative GPP anomalies under drought stress, especially in models that incorporate radiative effects. After incorporating vapor pressure deficit (VPD)-based meteorological constraints, the GPP estimations exhibited more pronounced negative anomalies during drought periods while maintaining model accuracy (at 70% and 65%, respectively). In addition, the GPP models based on site observations were applied at the regional scale (Europe). Our results indicated that the models without meteorological constraints again underestimated the impact of drought on GPP. This study emphasizes the importance of meteorological constraints in the estimation of GPP, especially under extreme drought conditions. The modeling of photosynthesis usually requires temperature and moisture constraints. However, the recently developed vegetation index has been reported to be a viable alternative for estimating gross primary productivity (GPP) without the need to account for these constraints; this concept warrants further investigation. Although the vegetation index can partially capture the response of vegetation to climate change, it is insufficient for estimating GPP. This is particularly the case under extreme drought conditions, and the variation in GPP is directly affected by meteorological constraints. In this study, we used drought events in Europe during 2003, 2010, and 2018 as case studies to investigate whether the incorporation of meteorological constraints is necessary for GPP estimation using these vegetation indices. Through site observation and regional estimation, we confirmed that vegetation index-based models without meteorological constraints tend to underestimate the impact of drought on GPP. These findings provide a significant complement to the investigation of the relationship between remotely sensed vegetation indices and photosynthesis. Vegetation index-based models have high uncertainty in terms of model performance and response to extreme droughtModels considering only radiation and vegetation indices underestimate the impact of extreme drought on gross primary productivityIncorporating meteorological constraints improves estimates of gross primary productivity under extreme drought conditions