Capturing the Impact of the 2018 European Drought and Heat across Different Vegetation Types Using OCO-2 Solar-Induced Fluorescence

Abstract

The European heatwave of 2018 led to record-breaking temperatures and extremely dry conditions in many parts of the continent, resulting in widespread decrease in agricultural yield, early tree-leaf senescence, and increase in forest fires in Northern Europe. Our study aims to capture the impact of the 2018 European heatwave on the terrestrial ecosystem through the lens of a high-resolution solar-induced fluorescence (SIF) data acquired from the Orbiting Carbon Observatory-2 (OCO-2) satellite. SIF is proposed to be a direct proxy for gross primary productivity (GPP) and thus can be used to draw inferences about changes in photosynthetic activity in vegetation due to extreme events. We explore spatial and temporal SIF variation and anomaly in the spring and summer months across different vegetation types (agriculture, broadleaved forest, coniferous forest, and mixed forest) during the European heatwave of 2018 and compare it to non-drought conditions (most of Southern Europe). About one-third of Europe’s land area experienced a consecutive spring and summer drought in 2018. Comparing 2018 to mean conditions (i.e., those in 2015–2017), we found a change in the intra-spring season SIF dynamics for all vegetation types, with lower SIF during the start of spring, followed by an increase in fluorescence from mid-April. Summer, however, showed a significant decrease in SIF. Our results show that particularly agricultural areas were severely affected by the hotter drought of 2018. Furthermore, the intense heat wave in Central Europe showed about a 31% decrease in SIF values during July and August as compared to the mean over the previous three years. Furthermore, our MODIS (Moderate Resolution Imaging Spectroradiometer) and OCO-2 comparative results indicate that especially for coniferous and mixed forests, OCO-2 SIF has a quicker response and a possible higher sensitivity to drought in comparison to MODIS’s fPAR (fraction of absorbed photosynthetically active radiation) and the Normalized Difference Vegetation Index (NDVI) when considering shorter reference periods, which highlights the added value of remotely sensed solar-induced fluorescence for studying the impact of drought on vegetation.

The European Forest Condition Monitor: Using Remotely Sensed Forest Greenness to Identify Hot Spots of Forest Decline

“…The overall development over the monitoring period of 2001–2020 (section Q and PDM From 2001 to 2020) and more specific case studies (sections Land Surface Phenology, Drought, Late-Frost, Water Stress, Ice Storm, and Windthrow) support the use of NDVI quantiles Q and proportional deviations from the median PDM for monitoring forest condition. Across Europe, the temporal development of Q and PDM indicates an increasing forest decline over the last two decades which is in line with the general observation that forests are becoming increasingly stressed in course of climate change (Allen et al, 2010 , 2015 ; Zang et al, 2014 ; Cailleret et al, 2017 ; Buras et al, 2018 , 2020 ; Bose et al, 2020 ; Senf et al, 2020 ; Shekhar et al, 2020 ). In comparing clusters of low Q and PDM ( Figure 1 , Supplementary Figure 1 ) with water availability indices, such as the standardized climatic water balance ( Supplementary Figure 2 ), clear similarities of spatial patterns were observed.…”

“…For the future, we intend to extend the product portfolio of the EFCM by additional vegetation indices such as EVI and SIF to provide a more complete picture of European forest condition. In this context, the next step forward would be to implement early-warning indicators which require integrating information over several time-steps based on statistical properties of NDVI time-series (Liu et al, 2019) is in line with the general observation that forests are becoming increasingly stressed in course of climate change (Allen et al, 2010(Allen et al, , 2015Zang et al, 2014;Cailleret et al, 2017;Buras et al, 2018Buras et al, , 2020Bose et al, 2020;Senf et al, 2020;Shekhar et al, 2020). In comparing clusters of low Q and PDM (Figure 1, Supplementary Figure 1) with water availability indices, such as the standardized climatic water balance (Supplementary Figure 2), clear similarities of spatial patterns were observed.…”

“…Among candidate vegetation indices are the solar induced chlorophyll fluorescence (SIF) and the enhanced vegetation index (EVI). While SIF was shown to provide a more precise proxy of GPP in comparison to NDVI (Liu et al, 2018 ; Shekhar et al, 2020 ), it suffers from relatively coarse spatiotemporal resolution. SIF observations are usually obtained at irregular frequencies with relatively long return periods for a given location and at the same time typically feature relatively coarse spatial resolutions of more than 1 km 2 which would not allow for forest monitoring on the aspired scale of individual forest stands.…”

“…For instance, so-called masting events of beech, which result in reduced NDVI due to a high share of beechnuts in the canopy, may lower NDVI while high GPP is maintained. Again, it, therefore, seems mandatory to always interpret EFCM products in the context of other environmental constellations and consider additional measures of productivity such as eddy covariance flux measurements (e.g., Bascietto et al, 2018 ; Pastorello et al, 2020 ) and solar induced fluorescence (Liu et al, 2018 ; Shekhar et al, 2020 ). In addition, when interpreting Q and PDM in the context of tree mortality, care needs to be taken.…”

“…Satellite-borne remote sensing renders a means to obtain such information on a large scale. That is, satellites provide information on vegetation surface reflectance which can be translated into vegetation indices and/or products that serve as proxies for tree condition and productivity (Kogan, 1995 ; Anyamba and Tucker, 2012 ; Misra et al, 2016 ; Rogers et al, 2018 ; Liu et al, 2019 ; Spruce et al, 2019 ; Brun et al, 2020 ; Buras et al, 2020 ; Rita et al, 2020 ; Shekhar et al, 2020 ). If corresponding satellite imagery is regularly updated and obtained at fairly high spatiotemporal resolution (i.e., high enough to resolve single forest stands at weekly intervals) over decades to provide sufficiently long referencing periods, such data can be used to generate products that allow for a near-real time monitoring of forest condition (but see section Limitations for limitations of this approach).…”