Abstract
A precise ecosystem evapotranspiration (ET) estimate is essential for understanding the complex relationship between plants' energy-water-carbon fluxes. Besides, robust ecosystem ET estimation under different water stresses can provide insight into plants' response to extreme weather and environmental conditions. However, for such very preciseness, we must accept the individual and comprehensive interlinked mechanistic relationships between ecosystem ET and its controlling variables for determining the response of ecosystem ET towards extreme climate events. Due to recent drought events in the European continent since the 2000s, many geographical hotspots are getting attention to understanding the complicated mechanistic relationship between ecosystem ET and its controlling variables under such extreme water stress conditions. Consequently, precise ecosystem ET estimation of the European continent's water-stressed ecosystems, i.e. agriculture, will give insight into the sustainability of Europe's agricultural production to ensure sufficient food for millions of people in future. The recent heatwaves and drought in 2018 impacted ecosystem ET substantially over the European continent, which may be a big concern for the European ecosystems' future water, energy, and CO2 balance. The research outcomes of the first research article (c.f. Ahmed et al., 2021) showed that the European continent had up to 50% reduced ecosystem ET compared to a 10-year reference period due to a combined heatwave and drought event in 2018. The results also showed extreme surface air temperature (Tsa) and precipitation (P) anomalies. Due to such extreme climatic phenomena, agricultural land, mixed natural vegetation, and the European continent's non-irrigated agricultural areas were mainly affected. In conclusion, the first research article explains the importance of modelling precise ecosystem ET in variable time and space. However, modelling and estimating precise ecosystem ET is still challenging, especially under extreme climates within continuous time and ample variable space. Remote sensing (RS) data based modelling approaches often encounter uncertainties due to complex parameterizations of different variables for ecosystem ET modelling schemes. Further, uncertainties may be introduced by different data types, data quality, multi-sensor systems, and spatio-temporal resolution of satellite images. The growing advancement of using RS based sun-induced chlorophyll fluorescence (SIF) for ecosystem studies has introduced SIF's use case for ecosystem ET estimates. However, previous studies have limitations due to applying specific ET models, only considering energy or water constraints and different strategies to add SIF in such specifically selected models. The second research article (c.f. Ahmed et al., 2023) investigated possible SIF integration in an advanced ecosystem ET modelling scheme. The research considers the mechanistic relationships between SIF and ecosystem ET and their abiotic and biotic drivers. The results concluded the best possible ways of empirically applying SIF for ecosystem ET estimates under water-limited and well-watered conditions under an experimental setup in maize crop fields in northern Italy. The research assesses the absolute and relative sensitivity of several SIF based ecosystem ET estimation strategies for evolving soil water limitation using extensive in-situ and airborne RS data acquired during the water limitation experiment. The study evaluated five strategies to integrate SIF in an ecosystem ET modelling framework based on the Penman Monteith (PM) and the Ball-Berry-Leuning (BBL) models. The results showed that replacing canopy conductance (including canopy resistance and leaf's net CO2 assimilation rate), leaf area index and net radiation with SIF significantly correspond with in-situ reference ecosystem ET (unit based conversion of measured sap flow) under evolving water limitation. Indeed, considering a single SIF as an indirect proxy for ecosystem ET with a one-to-one relationship showed inconsequential outcomes. In conclusion, the research's outcomes give insight into the importance and scientific advantage of applying SIF in a multi-sensors RS data based framework to increase the sensitivity of SIF based ecosystem ET estimates for evolving water limitations. Besides, the results highlighted the uses of SIF for the scientific advancement of ecosystem drought monitoring. Recent studies have proposed the usability of SIF to establish SIF-based drought indices (DIs) using comparatively coarse spatio-temporal resolution RS data. However, the temporal and spatial sensitivity of such newly proposed SIF-based DIs for growing crop water limitation with higher spatio-temporal resolution RS data must be determined. Therefore, the third Ph.D. research article (in review) conducted a temporal and spatial sensitivity analysis of SIF-based DI for gradually increasing soil and crop water limitation for different crop types. Temporal sensitivity analysis of the study showed that SIF based DI is sensitive throughout evolving soil water limitations, and traditional optical index (OI) based DI is only sensitive at extreme soil water limitations. However, both DIs showed their sensitivity towards the highest soil water limitation. Spatial sensitivity analysis reveals that SIF based DI is sensitive towards decreasing plant available water (PAW) zones and continues till the lowest PAW zones, and OI based DI is only sensitive in the lowest PAW zones. Furthermore, like the temporal analysis, from the spatial analysis, it is also visible that both DIs are sensitive towards the lowest PAW. The research concludes that both SIF based and traditional OI based DIs are sensitive to increasing soil and crop water limitation; however, the experimental setup was not sufficient to say that SIF based DI can be more beneficial for monitoring crop water limitation throughout drought events than OI based DI, instead, both DIs can be applied for monitoring evolving soil and crop water limitation within shorter spatio-temporal scales. Besides, SIF based DI can be applicable for predicting early crop water limitation and promoting incentive preparation for drought, but further studies within different ecosystems with different environmental conditions are needed. In contrast, resulting ecosystem ET values and SIF have been examined with their absolute, relative, temporal, and spatial sensitivities under different soil and crop water availability for monitoring and predicting early plants’ water limitation within different spatio-temporal scales in various spaces and times. Combining all three research articles gives a forward consideration towards the sensitivity of SIF for robust forward ecosystem ET modelling and SIF embedded drought monitoring application within an advanced multi-sensors RS data modelling approach.
Ahmed, Kazi Rifat