Digital phenotyping and genotype-to-phenotype (G2P) models to predict complex traits in cereal crops

The revolution in digital phenotyping combined with the new layers of omics and envirotyping tools offers great promise to improve selection and accelerate genetic gains for crop improvement. This chapter examines the latest methods involving digital phenotyping tools to predict complex traits in cereals crops. The chapter has two parts. In the first part, entitled “Digital phenotyping as a tool to support breeding programs”, the secondary phenotypes measured by high-throughput plant phenotyping that are potentially useful for breeding are reviewed. In the second part, “Implementing complex G2P models in breeding programs”, the integration of data from digital phenotyping into genotype to phenotype (G2P) models to improve the prediction of complex traits using genomic information is discussed. The current status of statistical models to incorporate secondary traits in univariate and multivariate models, as well as how to better handle longitudinal (for example light interception, biomass accumulation, canopy height) traits, is reviewe

Application of proximal sensing techniques for epidemiological investigations of <em>Fusarium </em>head blight in wheat under field and controlled conditions

Sensors can provide valuable insight into studying the physiological disorder due to plant pathogens. Fusarium head blight (FHB) influences the optical properties of wheat (Triticum aestivum L.) at canopy and ear levels. This research aimed to investigate these complex disease situations under field as well as controlled conditions with the application of proximal sensing systems. Observations under field conditions revealed that the presence of foliar diseases is associated with higher Fusarium infection in the wheat canopy (cv. Tobak and Pamier), which might be attributed to reduced defence mechanisms. This was reflected in increased FHB incidence visually assessed at growth stage (GS) 83. Fungicides applied against foliar diseases before anthesis reduced FHB presence, which might be not only due to reducing the available inoculum in the canopy but also due to promoting defenses against Fusarium infection. Furthermore, prediction of FHB through spectral parameters such as blue-green index 2 (BGI2) and photochemical reflection index (PRI) proved to be very promising. At ear level, development of Fusarium infection is dependent on the primary infection site within ears and the prevailing environmental conditions after infection. Such a relationship was verified under controlled conditions after tip, centre and base inoculations, separately, by F. graminearum and F. culmorum of wheat ears (cv. Passat). Symptom dynamics (FHB index) were slower downwards within ears in comparison to the upward development. In contrast to the symptom appearance the infection of Fusarium species proved to be directed basipetally – a rare development of fungal infections. According to these observations it could be revealed that higher temperatures accelerated the ripening of ears and allowed these plants to escape the infection within ears. In contrast, at lower temperatures, higher disease severity was observed even for tip infection. Infrared thermography could predict this primary site of ear infection through temperature span within ears and enabled disease detection before symptoms became visible. The temperature difference between air and ear was negatively correlated to FHB index and allowed disease detection at early senescence stage. Combining the features of thermal measurements and chlorophyll fluorescence images proved to present a high potential in characterising FHB at spikelet level. Discriminating spikelets infected with F. graminearum from those infected with F. culmorum were enabled up to 100% accuracy by fusion of sensor data. This study demonstrated that FHB is influenced by foliar wheat diseases when at low severities of both. The control of leaf pathogens by fungicides can play an important part in integrated disease management – also against Fusarium infections. It could also be confirmed that primary infection sites within ears and the prevailing environmental conditions after infection are key factors which determine the later development of FHB. Sensors proved to be useful in monitoring and assessing FHB under field conditions – detailed investigations under controlled conditions provided more profound insights in this regard. The findings of this research contribute to more efficient control of FHB using the concepts of remote sensing to improve precision plant protection and may be applied in selection processes of breeding for FHB resistance as well.Geeignete Sensoren können einen wertvollen Einblick in die physiologischen Verhältnisse in Pflanzen bieten, wenn diese von pathogenen Organismen heimgesucht werden. Die Infektionen von Getreide durch Fusarium Arten (Fusarium Head Blight = FHB) verändern die optischen Eigenschaften von Wirtspflanzen – vor allem von Weizen (Triticum aestivum L.) – sowohl im Getreidestand als auch auf dem Niveau der einzelnen Ähren. Die vorliegenden Untersuchungen hatten zum Ziel, die näheren Gegebenheiten dieser komplexen Befallssituationen im Freiland und unter kontrollierten Bedingungen durch die Anwendung von zerstörungsfreien Messmethoden zu charakterisieren. Erhebungen im Feld machten deutlich, dass der Befall mit Fusarium Arten durch das Auftreten weiterer Blattkrankheiten im Weizenbestand gefördert wurde. Dies wurde beispielhaft an den Sorten „Tobak“ und „Pamier“ ermittelt und deutet auf eine geringere Widerstandsfähigkeit der Pflanzen gegenüber Fusariosen bei multiplem Befall hin. Dies konnte im Wachstumsstadium 83 (BBCH-Skala) auch makroskopisch festgestellt werden. Fungizidanwendungen, die vor der Blüte durchgeführt wurden, konnten das Auftreten der Fusarien am Weizen reduzieren. Dies war zweifellos auf die Reduktion des verfügbaren Inokulums der Fusarium-Arten zurückzuführen. Zugleich kann angenommen werden, dass durch die Gesunderhaltung der Blattfläche auch eine erhöhte Widerstandsfähigkeit der Pflanzen gegenüber Fusariosen hervorgerufen werden. Die Möglichkeit zur Vorhersage von FHB durch spektrale Parameter konnte bestätigt werden – vor allem an Hand des Blue- Green-Index 2 (BGI2) und des photochemischen Reflektionsindexes (PRI) erwiesen sich als besonders geeignet. An der Getreideähre ist die Entwicklung der Fusariosen in hohem Maße abhängig von dem primären Infektionsort und den nachfolgenden Umweltbedingungen im Anschluss an die Primärinfektion der Ähre. Dies konnte unter kontrollierten Bedingungen am Weizen der anfälligeren Sorte „Passat“ für Primärinokulationen an der an der Spitze, in der Mitte und an der Basis der Ähren nachgewiesen werden. Es zeigte sich, dass die Symptomentwicklung (FHB index) in der Ähre deutlich weniger nach unten gerichtet war, als in der Zone oberhalb des Inokulationspunktes. Dies galt sowohl für Infektionen durch F. graminearum als auch für F. culmorum. Im Gegensatz zur Symptomentwicklung entwickeln sich die Fusariosen vor allem abwärts in der Ähre – ein durchaus eher seltener Prozess für pflanzenpathogene Organismen. Erhöhte Temperaturen beschleunigen die Reifung der Ähren – obwohl günstig für das Auftreten von Fusariosen ermöglichen diese Bedingungen auch ein „disease escape“ gegenüber Fusarium-Arten. Bei niedrigeren Temperaturen führen die Infektionen zu deutlich höheren Infektionsraten, weil mehr Zeit zur Ausbreitung besteht – selbt in den Ährenspitzen. Mit Hilfe der Infrarot-Thermographie gelang es, die Primärinfektionen in der Ähre durch die Temperaturdifferenz zwischen Umwelt und den biologisch relevanten Zonen zu charakterisieren bevor bereits makroskopisch Symptome erkennbar wurden. Die Gewebetemperaturdifferenzen waren negativ korreliert mit dem FHB index – sie erlaubten aber auch eine Bestimmung des Reifestatus der Ähren. Wurden die Infrarotmessungen mit der Messung von Chlorophyllkorrelierten Messungen zerstörungsfrei kombiniert, lies sich damit eine hohe Korrelation identifizieren – insbesondere auf dem Niveau der einzelnen Ährchen. Wurden diese Beziehungen betrachtet, dann zeigte sich, dass sich sogar Unterschiede zwischen F. graminearum und F. culmorum erkennen ließen. Die vorliegenden Untersuchungen zeigen, dass das Auftreten von Fusariosen an Getreide auch in besonderem Umfang durch andere Blattkrankheiten gefördert wird – erkennbar allerdings nur bei geringen Befallsintensitäten. Die Förderung der Pflanzengesundheit – auch durch Fungizide – kann zu einer wichtigen Funktion im Integrierten Pflanzenschutz führen – sicher auch zum Schutz vor Ährenfusariosen. Es konnte nachgewiesen und durch Anwendung geeigneter Sensoren genutzt werden, dass die Fusarium-Infektionen eine besondere Rolle spielen. Vor allem die Primärinfektionsorte, die sehr umweltabhängig sind – haben großen Einfluss auf die Schadwirkung. Sensoren können offenbar sehr hilfreich bei dem Erkennen und der Befallsbestimmung – und das bereits unter Freilandbedingungen. Dies wurde durch Erhebungen unter Feldbedingungen bestätigt – ergänzt durch weitere Untersuchungen unter praktischen Bedingungen. Die vorliegenden Ergebnisse und Erkenntnisse können eine effizientere Unterdrückung von FHB ermöglichen. Dabei geht es darum, dass die Elemente des „sensing of diseases“ einerseits in den Integrierten Pflanzenschutz eingebunden werden können und zudem auch für Selektionsprozesse in der Züchtung zur Vermeidung von FHB genutzt werden kann

Predicting plant environmental exposure using remote sensing

Wheat is one of the most important crops globally with 776.4 million tonnes produced in 2019 alone. However, 10% of all wheat yield is predicted to be lost to Septoria Tritici Blotch (STB) caused by Zymoseptoria tritici (Z. tritici). Throughout Europe farmers spend £0.9 billion annually on preventative fungicide regimes to protect wheat against Z. tritici. A preventative fungicide regime is used as Z. tritici has a 9-16 day asymptomatic latent phase which makes it difficult to detect before symptoms develop, after which point fungicide intervention is ineffective. In the second chapter of my thesis I use hyperspectral sensing and imaging techniques, analysed with machine learning to detect and predict symptomatic Z. tritici infection in winter wheat, in UK based field trials, with high accuracy. This has the potential to improve detection and monitoring of symptomatic Z. tritici infection and could facilitate precision agriculture methods, to use in the subsequent growing season, that optimise fungicide use and increase yield. In the third chapter of my thesis, I develop a multispectral imaging system which can detect and utilise none visible shifts in plant leaf reflectance to distinguish plants based on the nitrogen source applied. Currently, plants are treated with nitrogen sources to increase growth and yield, the most common being calcium ammonium nitrate. However, some nitrogen sources are used in illicit activities. Ammonium nitrate is used in explosive manufacture and ammonium sulphate in the cultivation and extraction of the narcotic cocaine from Erythroxylum spp. In my third chapter I show that hyperspectral sensing, multispectral imaging, and machine learning image analysis can be used to visualise and differentiate plants exposed to different nefarious nitrogen sources. Metabolomic analysis of leaves from plants exposed to different nitrogen sources reveals shifts in colourful metabolites that may contribute to altered reflectance signatures. This suggests that different nitrogen feeding regimes alter plant secondary metabolism leading to changes in plant leaf reflectance detectable via machine learning of multispectral data but not the naked eye. These results could facilitate the development of technologies to monitor illegal activities involving various nitrogen sources and further inform nitrogen application requirements in agriculture. In my fourth chapter I implement and adapt the hyperspectral sensing, multispectral imaging and machine learning image analysis developed in the third chapter to detect asymptomatic (and symptomatic) Z. tritici infection in winter wheat, in UK based field trials, with high accuracy. This has the potential to improve detection and monitoring of all stages of Z. tritici infection and could facilitate precision agriculture methods to be used during the current growing season that optimise fungicide use and increase yield.Open Acces

Effects of fungicides on physiological parameters and yield formation of wheat assessed by non-invasive sensors

Apart from fungicidal effects, some fungicide classes have been reported to induce physiological changes in crops such as increased tolerance to abiotic stress, delayed senescence of the photosynthetic leaf area and modifications in the balance of plant growth regulators. The aim of this study was to investigate the effects of different fungicidal groups on physiological parameters of wheat through the use of non-invasive sensors and imaging techniques. Experiments were conducted under field and also under disease-free conditions in the greenhouse. Under field conditions, application of the azole, carboxamide and strobilurin compounds resulted in low disease incidence. All fungicide treatments delayed the senescence of the uppermost leaf layers; treatments with longer leaf life and lower disease incidence resulted in higher chlorophyll content. The effect of the fungicides on wheat senescence was positively correlated to grain yield and the thousand-kernel weight. However, under field conditions, the presence of the main foliar pathogens of wheat influenced the green leaf area duration as well as the yield, generating a disadvantage for the fungicide treatments with low disease control efficacy. Under disease-free conditions, an effect produced by the pyrazole carboxamide fungicide bixafen was observed. Bixafen delayed the senescence of leaves and ears resulting in a significantly extended green leaf area duration compared to untreated plants. In addition, an effect produced by this compound on morphogenesis was observed. The combination of the positive effects on physiology and morphogenesis of wheat resulted in a yield advantage of bixafen-treated plants. Furthermore, bixafen had a positive effect on plant tolerance to water stress conditions. Different non-invasive sensors and imaging techniques were used and compared to measure the effects of fungicidal compounds on wheat physiology. By using ground-based optical sensors it was possible to detect the influence of fungicidal compounds in crop physiology, i.e. degradation of photosynthetic pigments, photosynthetic activity, leaf reflectance, and transpiration of plant tissue earlier than with destructive and visual methods. Chlorophyll fluorescence of leaves was useful to measure differences in the effective quantum yield of photosystem II. Reflectance measurements of wheat leaves were highly sensitive to changes in plant vitality. The spectral vegetation indices were useful to determine differences between treatments in terms of leaf senescence, pigments and water content. Digital infrared images revealed significant differences between untreated and fungicide-treated plants at different growth stages. Moreover, thermography proved to be a suitable technique for distinguishing the beneficial effects of fungicides on plant senescence under different water supply conditions. Through the use of an image analysis software program, leaf senescence differences were successfully detected, thus allowing an early detection of the effect produced by the fungicide on the senescence status of flag leaves. Using hyperspectral imaging, it was possible to study differences in the senescence status of flag leaves. Furthermore, through the analysis of hyperspectral images it was achievable to study the pattern of the senescence process in flag leaves and to determine a delay of senescence of wheat produced by fungicides. The results of this study demonstrated that non-invasive sensors and imaging techniques are excellent alternatives to conventional screening methods for detecting the beneficial effects of fungicides on plant physiology. Furthermore, among this innovative group of sensors and techniques it was spectrometry, which proved to be the most sensitive and specific method with a high potential for large-scale fungicide screening. Sensors can be incorporated in automatic and reproducible screening of new active ingredients with high efficiency and accuracy. The recent development of hyperspectral imaging techniques will improve future studies to additionally explore plant physiology with high spatial and temporal resolution

A Cost-effective Multispectral Sensor System for Leaf-Level Physiological Traits

With the concern of the global population to reach 9 billion by 2050, ensuring global food security is a prime challenge for the research community. One potential way to tackle this challenge is sustainable intensification; making plant phenotyping a high throughput may go a long way in this respect. Among several other plant phenotyping schemes, leaf-level plant phenotyping needs to be implemented on a large scale using existing technologies. Leaf-level chemical traits, especially macronutrients and water content are important indicators to determine crop’s health. Leaf nitrogen (N) level, is one of the critical macronutrients that carries a lot of worthwhile nutrient information for classifying the plant’s health. Hence, the non-invasive leaf’s N measurement is an innovative technique for monitoring the plant’s health. Several techniques have tried to establish a correlation between the leaf’s chlorophyll content and the N level. However, a recent study showed that the correlation between chlorophyll content and leaf’s N level is profoundly affected by environmental factors. Moreover, it is also mentioned that when the N fertilization is high, chlorophyll becomes saturated. As a result, determining the high levels of N in plants becomes difficult. Moreover, plants need an optimum level of phosphorus (P) for their healthy growth. However, the existing leaf-level P status monitoring methods are expensive, limiting their deployment for the farmers of low resourceful countries. The aim of this thesis is to develop a low-cost, portable, lightweight, multifunctional, and quick-read multispectral sensor system to sense N, P, and water in leaves non-invasively. The proposed system has been developed based on two reflectance-based multispectral sensors (visible and near-infrared (NIR)). In addition, the proposed device can capture the reflectance data at 12 different wavelengths (six for each sensor). By deploying state of the art machine learning algorithms, the spectroscopic information is modeled and validated to predict that nutrient status. A total of five experiments were conducted including four on the greenhouse-controlled environment and one in the field. Within these five, three experiments were dedicated for N sensing, one for water estimation, and one for P status determination. In the first experiment, spectral data were collected from 87 leaves of canola plants, subjected to varying levels of N fertilization. The second experiment was performed on 1008 leaves from 42 canola cultivars, which were subjected to low and high N levels, used in the field experiment. The K-Nearest Neighbors (KNN) algorithm was employed to model the reflectance data. The trained model shows an average accuracy of 88.4% on the test set for the first experiment and 79.2% for the second experiment. In the third and fourth experiments, spectral data were collected from 121 leaves for N and 186 for water experiments respectively; and Rational Quadratic Gaussian Process Regression (GPR) algorithm is applied to correlate the reflectance data with actual N and water content. By performing 5-fold cross-validation, the N estimation shows a coefficient of determination (R^2) of 63.91% for canola, 80.05% for corn, 82.29% for soybean, and 63.21% for wheat. For water content estimation, canola shows an R^2 of 18.02%, corn of 68.41%, soybean of 46.38%, and wheat of 64.58%. Finally, the fifth experiment was conducted on 267 leaf samples subjected to four levels of P treatments, and KNN exhibits the best accuracy, on the test set, of about 71.2%, 73.5%, and 67.7% for corn, soybean, and wheat, respectively. Overall, the result concludes that the proposed cost-effective sensing system can be viable in determining leaf N and P status/content. However, further investigation is needed to improve the water estimation results using the proposed device. Moreover, the utility of the device to estimate other nutrients as well as other crops has great potential for future research

Remote Sensing as a Precision Farming Tool in the Nile Valley, Egypt

Detecting stress in plants resulting from different stressors including nitrogen deficiency, salinity, moisture, contamination and diseases, is crucial in crop production. In the Nile Valley, crop production is hindered perhaps more fundamentally by issues of water supply and salinity. Predicting stress in crops by conventional methods is tedious, laborious and costly and is perhaps unreliable in providing a spatial context of stress patterns. Accurate and quick monitoring techniques for crop status to detect stress in crops at early growth stages are needed to maximize crop productivity. In this context, remotely sensed data may provide a useful tool in precision farming. This research aims to evaluate the role of in situ hyperspectral and high spatial resolution satellite remote sensing data to detect stress in wheat and maize crops and assess whether moisture induced stress can be distinguished from salinity induced stress spectrally. A series of five greenhouse based experiments on wheat and maize were undertaken subjecting both crops to a range of salinity and moisture stress levels. Spectroradiometry measurements were collected at different growth stages of each crop to assess the relationship between crop biophysical and biochemical properties and reflectance measurements from plant canopies. Additionally, high spatial resolution satellite images including two QuickBird, one ASTER and two SPOT HRV were acquired in south-west Alexandria, Egypt to assess the potential of high spectral and spatial resolution satellite imagery to detect stress in wheat and maize at local and regional scales. Two field work visits were conducted in Egypt to collect ground reference data and coupled with Hyperion imagery acquisition, during winter and summer seasons of 2007 in March (8-30: wheat) and July (12-17: maize). Despite efforts, Hyperion imagery was not acquired due to factors out with the control of this research. Strong significant correlations between crop properties and different vegetation indices derived from both ground based and satellite platforms were observed. RDVI showed a sensitive index to different wheat properties (r > 0.90 with different biophysical properties). In maize, GNDVIbr and Cgreen had strong significant correlations with maize biophysical properties (r > 0.80). PCA showed the possibility to distinguish between moisture and salinity induced stress at the grain filling stages. The results further showed that a combined approach of high (2-5 m) and moderate (15-20) spatial resolution satellite imagery can provide a better mechanistic interpretation of the distribution and sources of stress, despite the typical small size of fields (20-50 m scale). QuickBird imagery successfully detects stress within field and local scales, whereas SPOT HRV imagery is useful in detecting stress at a regional scale, and therefore, can be a robust tool in identifying issues of crop management at a regional scale. Due to the limited spectral capabilities of high spatial resolution images, distinguishing different sources of stress is not directly possible, and therefore, hyperspectral satellite imagery (e.g. Hyperion or HyspIRI) is required to distinguish between moisture and salinity induced stress. It is evident from the results that remotely sensed data acquired by both in situ hyperspectral and high spatial resolution satellite remote sensing can be used as a useful tool in precision farming in the Nile Valley, Egypt. A combined approach of using reliable high spatial and spectral satellite remote sensing data could provide better insight about stress at local and regional scales. Using this technique as a precision farming and management tool will lead to improved crop productivity by limiting stress and consequently provide a valuable tool in combating issues of food supply at a time of rapid population growth

Monitoring of the Biophysical Status of Vegetation: Using Multi-angular, Hyperspectral Remote Sensing for the Optimization of a Physically-based SVAT Mode

Diese Arbeit ist das Ergebnis der letzten acht Jahre meines wissenschaftlichen Lebensweges und spiegelt die Schwerpunkte meiner Forschungsinteressen wider: Einen wesentlichen Schwerpunkt bildet das Thema Pflanzen, das nahezu unerschöpfliche Möglichkeiten der Forschung bietet. Der Großteil aller Austauschprozesse zwischen der Landoberfläche und der Atmosphäre werden durch Landpflanzen vermittelt (Schurr et al. 2006). Dabei stellt die Photosynthese den primären Energiewandlungsprozess dar, der die Sonnenenergie in chemisch nutzbare Energie überführt, der Biomasseproduktion und Wachstum treibt. Photosynthese, Stoffproduktion und Pflanzenwachstum sind dynamische, in hohem Maße geregelte Prozesse, die von den verschiedensten Umweltfaktoren beeinflusst werden und zur Ausbildung vielfältiger räumlicher und zeitlicher Muster – von der Ebene der einzelnen Zelle bis zum Ökosystem – führen. Das Verstehen der komplexen Prozesse und ihrer Interaktionen führt dabei über die Analyse ihrer raumzeitlichen Dynamik auf verschiedenste Ebenen. Die Zukunft vieler Themen der Menschheit ist eng mit dem Verständnis der raumzeitlichen Dynamik der Entwicklung und Funktion der Landpflanzen verbunden, wozu unter anderem die Sicherung der Ernährung und der Versorgung der Atmosphäre mit Sauerstoff gehört (Osmond et al. 2004). Die Spannbreite der relevanten Muster reicht dabei von der subzellulären Ebene bis hin zu raum-zeitlichen Prozessen, die sogar aus dem Weltraum beobachtet werden können. Dies verdeutlicht die vielfältigen Möglichkeiten, welche Pflanzen für einen Wissenschaftler bieten und vielleicht erklärt sich damit mein Interesse an diesem Themenkomplex. Dabei liegt mir die Einbeziehung der Pflanzenphysiologie in die klassische Vegetationsgeographie besonders am Herzen. Wer sich mit Vegetation beschäftigt, stößt bald auf Fragestellungen zum Pflanzenbau und zu modernen Methoden des Managements von Pflanzen im Rahmen derer ackerbaulichen Nutzung, die in den letzten Jahren aufgrund der geänderten Anforderungen des Landbaus an den Umweltschutz vermehrt auftauchten. Insbesondere im teilflächenspezifischen Anbau (precision farming) spielt die flächenhafte Untersuchung von Ackerkulturen eine wichtige Rolle, wobei hier eine besondere Rolle der Fernerkundung als Möglichkeit zur Beobachtung raumzeitlicher Prozesse zwischen und innerhalb von Pflanzenbeständen zukommt. Dabei stehen insbesondere hyperspektrale Instrumente im Zentrum des Interesses, da die Vielzahl der engbandigen Kanäle die Analyse von Pflanzeninhaltsstoffen, wie z. B. Chlorophyll, ermöglicht. Damit bietet sich eine Vielzahl von Möglichkeiten zur Beobachtung von pflanzenphysiologischen Vorgängen und deren raum-zeitlichen Mustern. Im Rahmen dieser Arbeit werden dabei C3 und C4 Pflanzen untersucht, welche die gängigsten Wege der Kohlenstoffassimilierung darstellen. Als Beispielpflanzen dienen Weizen (Triticum aestivum L.) und Mais (Zea mays L.), welche im Rahmen von Geländekampagnen in den Jahren 2004 und 2005 intensiv beprobt wurden und mit Hilfe von Fernerkundungssensoren im Laufe der Vegetationsperioden dieser beiden Jahre überflogen wurden, so oft es die örtlichen Wetterbedingungen erlaubten. Die Fernerkundungssensorik bestand aus dem satellitengestützten, Abbildenden Spektrometer CHRIS sowie dem flugzeuggetragenen Hyperspektralsensor AVIS. Die Analyse der Frage zur winkelabhängigen Beobachtung von Sonnen- und Schattenchlorophyll basiert auf regelmäßigen CHRIS Überflügen, welche die fernerkundliche Datengrundlage liefern. Räumlich hochaufgelöste, winkelabhängige Aufnahmen konnten im Jahr 2004 mit dem lehrstuhleigenen Sensor AVIS erhoben werden, dessen Daten als wertvolle Ergänzung dienen. Neben der Analyse von Pflanzenbeständen hinsichtlich ihres Chlorophyllgehaltes und dessen raum-zeitlicher Dynamik stellt die modellhafte Abbildung dieser Dynamik einen weiteren Schwerpunkt dieser Arbeit dar. Pflanzen reagieren aufgrund ihrer sessilen Lebensweise auf globale Klimaveränderungen und auf regionale Umwelteinflüsse sehr sensibel. Dies verdeutlicht das seit Jahren wachsende Interesse an der Abbildung des pflanzlichen Stoffwechsels und der Photosynthese im Rahmen von Modellen (von Caemmerer 2000). Dafür ist ein vertieftes Verständnis des Metabolismus von Pflanzen erforderlich sowie eben die raum-zeitliche Dynamik, welche mit Hilfe von Fernerkundungsdaten abgebildet werden kann. Daher sollen die fernerkundlich abgeleiteten Chlorophyllgehalte von Sonnen- und Schattenbereichen in das physikalisch-basierte SVAT Modell PROMET implementiert werden. In PROMET wird die Photosynthese von Pflanzenbeständen bereits in einen Sonnen- und Schattenbereich unterteilt vorgenommen. Die obere Bestandesschicht unterliegt dabei einem Strahlungsregime, welches hauptsächlich von direkter Strahlung dominiert wird. Die untere, beschattete Bestandesschicht unterliegt einem Strahlungsregime, das von der diffusen Strahlungskomponente dominiert wird