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Due to climate change, increases in frequency, intensity and duration of heat waves and droughts have impacts on crops’ productivity, reducing yields of staple crops such as maize and soybean. Moreover, a foreseen increase of irrigation demands in agriculture requires an efficient management of already scarce water resources. Quantitative and real time information about crop water status can help stakeholders’ decision making from farm to the watershed levels. As the carbon uptake or photosynthesis, determining crop yields, is closely coupled to crop transpiration by the regulation of leaf stomata, joint management of water and carbon fluxes is essential. However, plants’ mechanisms to cope with water and temperature stresses are complex and not totally understood, which emphasizes the need to investigate energy balance components and photosynthesis interactions at both leaf and canopy levels. Under temperature and soil water content variations, different physiological, morphological, and biochemical adjustments can occur on crops depending on crop type, variety, structure, life history, growth stage and environmental conditions. Remote sensing information in the optical and thermal domains provides reflectance and emission at leaf and canopy levels. This can be used in data-driven or physically based models, to detect changes in plant physiology more efficiently and over larger regions compared to in situ field sensors. A promising avenue of research comes from the use of miniaturized hyperspectral and thermal sensors on unmanned aerial systems (UASs). Compared to satellites, UASs can provide information at very high spatial resolutions, flexible times and under cloudy conditions. The aim of this PhD thesis is to provide methodologies and improve our understanding of crop responses to reductions in soil water content and increases in air temperature, including statistical extremes, using information from sensors in the optical and thermal domains at leaf, canopy, and field scales. Specifically, this thesis addresses these questions: 1. Ecophysiology: a. What are the drought coping strategies of soybean (C3) and maize (C4) under controlled conditions? (Paper I) b. How does maize regulate leaf temperature in response to average and extreme diurnal oscillations (hot events) in air temperatures during a growing season in a near-field growth chamber experiment? (Paper II) 2. Remote sensing synergies and modelling: a. What are the benefits of machine learning partial least squares regression (PLS-R) model to predict leaf ecophysiology compared to narrowband indices? (Paper I) b. Can models relying on information from remote sensing synergies (e.g. reflectance, thermal and crop height estimates) provide good estimates of carbon and water fluxes at leaf, canopy, and field scales compared to in situ sensors? (Paper II and III) To achieve the objective, hyperspectral and thermal information from miniaturized remote sensors and in-situ measurements was taken under controlled environments and in the field. To assess maize and soybean responses to water and temperature changes, we conducted two experiments in the growth chambers at the Risø Environmental Risk Assessment Facility (RERAF) phytotron located in Denmark and at the Water Trans-formation Dynamical Processes Experimental Device (WATDPED) in China. To assess carbon and water fluxes Under field conditions, eight flight campaigns with an UAS were carried over the Danish willow bioenergy plantation eddy covariance site (DK-RCW). The thesis results include two main parts to answer the specific questions stated above. 1. Ecophysiology: (1.a.) Under constant air temperature and relative humidity in a controlled experiment, we found that soybean and maize reduced canopy transpiration about 35% and 23%, respectively due to water stress. However, the two crops undertook different physiological and morphological adjustments to cope with drought. Under 60% soil water content reduction, soybean reacted rapidly by closing leaf stomata, reducing transpiration and photosynthesis, while maize showed reductions in structural traits such as canopy height and leaf area index at the end of the experiment (Paper I). (1.b.) Under near-field environmental conditions, maize behaved as a limited homeotherm, maintaining leaf temperature in a narrower range than the range of air temperature. We found thermal optimums between 33 and 38⁰C for the control soil moisture scenario. Reduction of 60% soil water content showed an increase of about 2⁰C in thermal optimums, helping to cope not only with water stress but also with thermal extremes. Leaf thermoregulation was explained through higher energy dissipated as heat from non-photochemical quenching by photosystem II and less cooling. Sub daily temperature variation showed that leaf temperature responds fast to a change in air temperature (10 minutes after). Under extreme hot events, we found more drastic responses of stomatal conductance, controlled by changes in vapor pressure deficit, and transpiration, mainly driven by the difference in leaf-air temperature. In addition, only for hot days, we observed reductions on the maximum quantum yield of photosystem II photochemistry with higher Tair shocks (Paper II). 2. Remote sensing synergies and modelling: (2.a.) Under a controlled environment, we demonstrated for the first time the ability of PLS-R combining hyperspectral and thermal imaging, and canopy height to estimate leaf stomatal conductance, transpiration, and photosynthesis. In addition, this model was able to capture the distinct drought coping strategies for soybean (C3) and maize (C4), showing that thermal and structural information can notably improve predictions of physiological changes to water stress in soybean and maize, respectively. Compared to narrowband indices, we found that the most important wavelengths to predict physiological changes to water stress in soybean and maize were not necessarily centred in the same bands as known vegetation indices, varying among the two crops and highlighting the benefits of using hyperspectral information (Paper I). (2.b.) Models relying on combined optical and thermal information provided accurate estimates of carbon and water fluxes. Under controlled conditions, leaf energy modelling approach was able to estimate leaf stomatal conductance and transpiration. Modelled leaf transpiration upscaled to canopy level and compared to measured evapotranspiration showed adequate correlation (R2>0.7 and RMSD<15 Wm-2) (Paper II). Under field conditions, the results from eight UAS campaigns demonstrated the joint power of combining thermal and optical data to simulate canopy-level evapotranspiration and photosynthesis using a big leaf canopy model (joint “top down” PT-JPL ET and LUE GPP model). Results were validated with in situ measurements and eddy covariance information, with R2 = 0.85 and RMSD = 39.37Wm-2 for ET and R2 = 0.83 and RMSD = 5µmol m-2 s-1 for GPP (Paper III). The results of this thesis highlight the relevance of crop information potentially obtained from a multi-sensor approach, using thermal, hyperspectral, active, or passive fluorescence, or light detection and ranging (LiDAR) applications. In addition, we also demonstrated the need to develop methods that can capture distinct crop responses associated with different photosynthetic routes or hydraulic strategies (iso/anisohydric). Some of our findings related with responses to drought and heat stress could be used to improve current modelling frameworks after upscaling from leaf to canopy levels and testing them in outdoor conditions. Finally, farmers and scientific communities focused on remote sensing, ecophysiology, hydrology, agronomy, and phenotyping could benefit from a better understanding of crop responses towards changing environmental factors.
|Place of Publication||Kgs. Lyngby|
|Publisher||Technical University of Denmark|
|Number of pages||48|
|Publication status||Published - 2021|