The Sensitivity of Satellite Solar‐Induced Chlorophyll Fluorescence to Meteorological Drought

Abstract

Solar‐induced chlorophyll fluorescence (SIF) could provide information on plant physiological response to water stress (e.g., drought). There are growing interests to study the effect of drought on SIF. However, to what extent SIF responds to drought and how the responses vary under different precipitation, temperature, and potential evapotranspiration conditions are not clear. In this regard, we evaluated the relationship between satellite‐based SIF product and four commonly used meteorological drought indices (Standardized Precipitation‐Evapotranspiration Index, Standardized Precipitation Index, Temperature Condition Index, and Palmer Drought Severity Index) under diverse climate regions in the continental United States. The four drought indices were used because they estimate meteorological drought conditions based on either single or combined meteorological factors such as precipitation, temperature, and potential evapotranspiration, representing different perspectives of drought. The relationship between SIF and meteorological drought varied spatially and differed for different ecosystem types. The high sensitivity occurred in dry areas characterized by a high mean annual growing season temperature and low vegetation productivity. Through random forest regression analyses, we found that temperature, gross primary production, precipitation, and land cover are the major factors affecting the relationships between SIF and meteorological drought indices. Taken together, satellite SIF is highly sensitive to meteorological drought, but the high sensitivity is constrained to dry regions.

Monitoring and Assessment of Agricultural Drought Based on Solar-Induced Chlorophyll Fluorescence During Growing Season in North China Plain

“…SIF is closely related to the photosynthesis of vegetation and can be used to monitor the physiological state of vegetation and water stress status [64]. When drought occurs, water stress will cause changes in the physiological state of vegetation, and this change will inevitably lead to changes in SIF [29], [33], [65]. However, there are still some limitations.…”

Estimating site-specific optimum air temperature and assessing its effect on the photosynthesis of grasslands in mid- to high-latitudes

“…Previous studies based on three different global GPP products reported that the impact of drought on terrestrial primary production was underestimated by satellite-based LUE GPP models (Turner et al 2005;Mu et al 2007;Sims et al 2008). The reason for the underestimation is that these GPP models did not simulate the water balance, or did not account for the direct effects of soil moisture in addition to VPD and changes in greenness (Jiao et al 2019a;Stocker et al 2019). Our study found that GPP VPM computed with T opt−s for the years with higher precipitation showed a greater improvement than for the years with lower precipitation (figure S5).…”

“…Global warming and climatic extremes (e.g. heatwaves and cold spills) have large impacts on vegetation production across space and time (Mu et al 2011;Jiao et al 2019a;Ryu et al 2019). Accurately quantifying the effects of air temperature on the GPP of vegetation at local, regional, and global scales is critical to improving the modeling of GPP and terrestrial carbon cycles.…”

Revisiting global vegetation controls using multi-layer soil moisture

“…Most previous studies did not consider more than three variables, thereby somewhat circumventing this problem while ignoring potentially important variables (Seddon et al, 2016;Garonna et al, 2018;Claessen et al, 2019;Li & Xiao, 2020). Machine learning methods such as random forests have no assumptions on the input data characteristics, and are designed to process large amounts of diverse input data (Breiman 2001;Forkel et al 2019;Jiao et al 2019). Though they are also challenged by the collinearity in the input data, they are better placed to deal with this than traditional statistical methods.…”

“…Jung et al 2020), reliable observation-based global photosynthesis proxies are only available for recent years through satellite-derived suninduced fluorescence (SIF, Baker et al, 2008;Frankenberg et al, 2011;Joiner et al,2013). SIF data is increasingly used to study the relationships between global vegetation productivity and hydro-meteorological drivers (Yang et al, 2015;Ying et al, 2015;Wagle et al, 2016;Zuromski et al, 2018;Jiao et al, 2019;Walther et al, 2019;Li & Xiao, 2020). Besides, spectral vegetation indices and biophysical parameters from multi-spectral satellite instruments such as the Moderate Resolution Imaging Spectroradiometer (MODIS) are widely used to study drivers of vegetation phenology and productivity (Forkel et al 2015;Seddon et al, 2016;Buermann et al, 2018).…”