Leveraging Image Analysis for High-Throughput Plant Phenotyping

The complex interaction between a genotype and its environment controls the biophysical properties of a plant, manifested in observable traits, i.e., plant’s phenome, which influences resources acquisition, performance, and yield. High-throughput automated image-based plant phenotyping refers to the sensing and quantifying plant traits non-destructively by analyzing images captured at regular intervals and with precision. While phenomic research has drawn significant attention in the last decade, extracting meaningful and reliable numerical phenotypes from plant images especially by considering its individual components, e.g., leaves, stem, fruit, and flower, remains a critical bottleneck to the translation of advances of phenotyping technology into genetic insights due to various challenges including lighting variations, plant rotations, and self-occlusions. The paper provides (1) a framework for plant phenotyping in a multimodal, multi-view, time-lapsed, high-throughput imaging system; (2) a taxonomy of phenotypes that may be derived by image analysis for better understanding of morphological structure and functional processes in plants; (3) a brief discussion on publicly available datasets to encourage algorithm development and uniform comparison with the state-of-the-art methods; (4) an overview of the state-of-the-art image-based high-throughput plant phenotyping methods; and (5) open problems for the advancement of this research field

High-throughput estimation of crop traits: A review of ground and aerial phenotyping platforms

Crop yields need to be improved in a sustainable manner to meet the expected worldwide increase in population over the coming decades as well as the effects of anticipated climate change. Recently, genomics-assisted breeding has become a popular approach to food security; in this regard, the crop breeding community must better link the relationships between the phenotype and the genotype. While high-throughput genotyping is feasible at a low cost, highthroughput crop phenotyping methods and data analytical capacities need to be improved. High-throughput phenotyping offers a powerful way to assess particular phenotypes in large-scale experiments, using high-tech sensors, advanced robotics, and imageprocessing systems to monitor and quantify plants in breeding nurseries and field experiments at multiple scales. In addition, new bioinformatics platforms are able to embrace large-scale, multidimensional phenotypic datasets. Through the combined analysis of phenotyping and genotyping data, environmental responses and gene functions can now be dissected at unprecedented resolution. This will aid in finding solutions to currently limited and incremental improvements in crop yields

Optimized Matrix Feature Analysis – Convolutional Neural Network (OMFA-CNN) Model for Potato Leaf Diseases Detection System

One of the most often grown crops is the potato. As a main food, potatoes are prioritised for cultivation worldwide. Because potatoes are such a rich source of vitamins and minerals, we can create a robust system for food security. However, a number of illnesses delay the growth of agriculture and harm potato output. Consequently, early disease identification can offer a better answer for effective crop production. In this research work aim is to classify and detect the potato leave (PL) diseases using OMFA-CNN deep learning model. An optimized matrix feature analysis-CNN deep learning model for PL disease detection is implemented. In the first phase, the PLs features are extracted from the potato leave images using K-means clustering image segmentation method. At the last phase, a new OMFA-CNN model are proposed using CNN to classify virus, and bacterial diseases of PLs, The PL disease dataset consists 2351 images gathered in real-time and from the Kaggle (PlantVillage) dataset. The implemented OMFA-CNN model attained 99.3 % precision and 99 % recall on potato disease detection. The implemented method is also compared with MASK RCNN,SVM and other models and attained significantly high precision and recall

Image Analysis and Machine Learning in Agricultural Research

Agricultural research has been a focus for academia and industry to improve human well-being. Given the challenges in water scarcity, global warming, and increased prices of fertilizer, and fossil fuel, improving the efficiency of agricultural research has become even more critical. Data collection by humans presents several challenges including: 1) the subjectiveness and reproducibility when doing the visual evaluation, 2) safety when dealing with high toxicity chemicals or severe weather events, 3) mistakes cannot be avoided, and 4) low efficiency and speed. Image analysis and machine learning are more versatile and advantageous in evaluating different plant characteristics, and this could help with agricultural data collection. In the first chapter, information related to different types of imaging (e.g., RGB, multi/hyperspectral, and thermal imaging) was explored in detail for its advantages in different agriculture applications. The process of image analysis demonstrated how target features were extracted for analysis including shape, edge, texture, and color. After acquiring features information, machine learning can be used to automatically detect or predict features of interest such as disease severity. In the second chapter, case studies of different agricultural applications were demonstrated including: 1) leaf damage symptoms, 2) stress evaluation, 3) plant growth evaluation, 4) stand/insect counting, and 5) evaluation for produce quality. Case studies showed that the use of image analysis is often more advantageous than visual rating. Advantages of image analysis include increased objectivity, speed, and more reproducibly reliable results. In the third chapter, machine learning was explored using romaine lettuce images from RD4AG to automatically grade for bolting and compactness (two of the important parameters for lettuce quality). Although the accuracy is at 68.4 and 66.6% respectively, a much larger data base and many improvements are needed to increase the model accuracy and reliability. With the advancement in cameras, computers with high computing power, and the development of different algorithms, image analysis and machine learning have the potential to replace part of the labor and improve the current data collection procedure in agricultural research. Advisor: Gary L. Hei

Hyperspectral Imaging from Ground Based Mobile Platforms and Applications in Precision Agriculture

This thesis focuses on the use of line scanning hyperspectral sensors on mobile ground based platforms and applying them to agricultural applications. First this work deals with the geometric and radiometric calibration and correction of acquired hyperspectral data. When operating at low altitudes, changing lighting conditions are common and inevitable, complicating the retrieval of a surface's reflectance, which is solely a function of its physical structure and chemical composition. Therefore, this thesis contributes the evaluation of an approach to compensate for changes in illumination and obtain reflectance that is less labour intensive than traditional empirical methods. Convenient field protocols are produced that only require a representative set of illumination and reflectance spectral samples. In addition, a method for determining a line scanning camera's rigid 6 degree of freedom (DOF) offset and uncertainty with respect to a navigation system is developed, enabling accurate georegistration and sensor fusion. The thesis then applies the data captured from the platform to two different agricultural applications. The first is a self-supervised weed detection framework that allows training of a per-pixel classifier using hyperspectral data without manual labelling. The experiments support the effectiveness of the framework, rivalling classifiers trained on hand labelled training data. Then the thesis demonstrates the mapping of mango maturity using hyperspectral data on an orchard wide scale using efficient image scanning techniques, which is a world first result. A novel classification, regression and mapping pipeline is proposed to generate per tree mango maturity averages. The results confirm that maturity prediction in mango orchards is possible in natural daylight using a hyperspectral camera, despite complex micro-illumination-climates under the canopy

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

On the Use of Imaging Spectroscopy from Unmanned Aerial Systems (UAS) to Model Yield and Assess Growth Stages of a Broadacre Crop

Snap bean production was valued at $363 million in 2018. Moreover, the increasing need in food production, caused by the exponential increase in population, makes this crop vitally important to study. Traditionally, harvest time determination and yield prediction are performed by collecting limited number of samples. While this approach could work, it is inaccurate, labor-intensive, and based on a small sample size. The ambiguous nature of this approach furthermore leaves the grower with under-ripe and over-mature plants, decreasing the final net profit and the overall quality of the product. A more cost-effective method would be a site-specific approach that would save time and labor for farmers and growers, while providing them with exact detail to when and where to harvest and how much is to be harvested (while forecasting yield). In this study we used hyperspectral (i.e., point-based and image-based), as well as biophysical data, to identify spectral signatures and biophysical attributes that could schedule harvest and forecast yield prior to harvest. Over the past two decades, there have been immense advances in the field of yield and harvest modeling using remote sensing data. Nevertheless, there still exists a wide gap in the literature covering yield and harvest assessment as a function of time using both ground-based and unmanned aerial systems. There is a need for a study focusing on crop-specific yield and harvest assessment using a rapid, affordable system. We hypothesize that a down-sampled multispectral system, tuned with spectral features identified from hyperspectral data, could address the mentioned gaps. Moreover, we hypothesize that the airborne data will contain noise that could negatively impact the performance and the reliability of the utilized models. Thus, We address these knowledge gaps with three objectives as below: 1. Assess yield prediction of snap bean crop using spectral and biophysical data and identify discriminating spectral features via statistical and machine learning approaches. 2. Evaluate snap bean harvest maturity at both the plant growth stage and pod maturity level, by means of spectral and biophysical indicators, and identify the corresponding discriminating spectral features. 3. Assess the feasibility of using a deep learning architecture for reducing noise in the hyperspectral data. In the light of the mentioned objectives, we carried out a greenhouse study in the winter and spring of 2019, where we studied temporal change in spectra and physical attributes of snap-bean crop, from Huntington cultivar, using a handheld spectrometer in the visible- to shortwave-infrared domain (400-2500 nm). Chapter 3 of this dissertation focuses on yield assessment of the greenhouse study. Findings from this best-case scenario yield study showed that the best time to study yield is approximately 20-25 days prior to harvest that would give out the most accurate yield predictions. The proposed approach was able to explain variability as high as R2 = 0.72, with spectral features residing in absorption regions for chlorophyll, protein, lignin, and nitrogen, among others. The captured data from this study contained minimal noise, even in the detector fall-off regions. Moving the focus to harvest maturity assessment, Chapter 4 presents findings from this objective in the greenhouse environment. Our findings showed that four stages of maturity, namely vegetative growth, budding, flowering, and pod formation, are distinguishable with 79% and 78% accuracy, respectively, via the two introduced vegetation indices, as snap-bean growth index (SGI) and normalized difference snap-bean growth index (NDSI), respectively. Moreover, pod-level maturity classification showed that ready-to-harvest and not-ready-to-harvest pods can be separated with 78% accuracy with identified wavelengths residing in green, red edge, and shortwave-infrared regions. Moreover, Chapters 5 and 6 focus on transitioning the learned concepts from the mentioned greenhouse scenario to UAS domain. We transitioned from a handheld spectrometer in the visible to short-wave infrared domain (400-2500 nm) to a UAS-mounted hyperspectral imager in the visible-to-near-infrared region (400-1000 nm). Two years worth of data, at two different geographical locations, were collected in upstate New York and examined for yield modeling and harvest scheduling objectives. For analysis of the collected data, we introduced a feature selection library in Python, named “Jostar”, to identify the most discriminating wavelengths. The findings from the yield modeling UAS study show that pod weight and seed length, as two different yield indicators, can be explained with R2 as high as 0.93 and 0.98, respectively. Identified wavelengths resided in blue, green, red, and red edge regions, and 44-55 days after planting (DAP) showed to be the optimal time for yield assessment. Chapter 6, on the other hand, evaluates maturity assessment, in terms of pod classification, from the UAS perspective. Results from this study showed that the identified features resided in blue, green, red, and red-edge regions, contributing to F1 score as high as 0.91 for differentiating between ready-to-harvest vs. not ready-to-harvest. The identified features from this study is in line with those detected from the UAS yield assessment study. In order to have a parallel comparison of the greenhouse study against the UAS study, we adopted the methodology employed for UAS studies and applied it to the greenhouse studies, in Chapter 7. Since the greenhouse data were captured in the visible-to-shortwave-infrared (400-2500 nm) domain, and the UAS study data were captured in the VNIR (400-1000 nm) domain, we truncated the spectral range of the collected data from the greenhouse study to the VNIR domain. The comparison experiment between the greenhouse study and the UAS studies for yield assessment, at two harvest stages early and late, showed that spectral features in 450-470, 500-520, 650, 700-730 nm regions were repeated on days with highest coefficient of determination. Moreover, 46-48 DAP with high coefficient of determination for yield prediction were repeated in five out of six data sets (two early stages, each three data sets). On the other hand, the harvest maturity comparison between the greenhouse study and the UAS data sets showed that similar identified wavelengths reside in ∼450, ∼530, ∼715, and ∼760 nm regions, with performance metric (F1 score) of 0.78, 0.84, and 0.9 for greenhouse, 2019 UAS, and 2020 UAS data, respectively. However, the incorporated noise in the captured data from the UAS study, along with the high computational cost of the classical mathematical approach employed for denoising hyperspectral data, have inspired us to leverage the computational performance of hyperspectral denoising by assessing the feasibility of transferring the learned concepts to deep learning models. In Chapter 8, we approached hyperspectral denoising in spectral domain (1D fashion) for two types of noise, integrated noise and non-independent and non-identically distributed (non-i.i.d.) noise. We utilized Memory Networks due to their power in image denoising for hyperspectral denoising, introduced a new loss and benchmarked it against several data sets and models. The proposed model, HypeMemNet, ranked first - up to 40% in terms of signal-to-noise ratio (SNR) for resolving integrated noise, and first or second, by a small margin for resolving non-i.i.d. noise. Our findings showed that a proper receptive field and a suitable number of filters are crucial for denoising integrated noise, while parameter size was shown to be of the highest importance for non-i.i.d. noise. Results from the conducted studies provide a comprehensive understanding encompassing yield modeling, harvest scheduling, and hyperspectral denoising. Our findings bode well for transitioning from an expensive hyperspectral imager to a multispectral imager, tuned with the identified bands, as well as employing a rapid deep learning model for hyperspectral denoising

The EnMAP Managed Vegetation Scientific Processor

Nach jahrelanger wissenschaftlicher und technischer Vorbereitungszeit wird voraussichtlich Ende des Jahres 2020 der Start der orbitalen Phase einer unbemannten deutschen Weltraum-Mission initiiert. Das Environmental Mapping and Analysis Program (EnMAP) wird an Bord des gleichnamigen Satelliten einen hyperspektralen Sensor zur Erfassung terrestrischer Oberflächen tragen. In den Umweltdisziplinen zur Erforschung von Ökosystemen, landwirtschaftlicher, forstwirtschaftlicher und urbaner Flächen, im Bereich der Küsten- und Inlandsgewässer sowie der Geologie und Bodenkunde bereitete man sich im Vorfeld des Starts auf die kommenden Daten vor. Zwar existiert bereits eine Vielzahl an Algorithmen zur wissenschaftlichen Analyse von spektralen Daten, allerdings ergeben sich auch neue Herausforderungen, da die EnMAP-Mission bislang im weltweiten Kontext der Fernerkundung einzigartig ist. Die Abdeckung des vollen optischen Spektrums (420 nm – 2450 nm) in Verbindung mit einer moderaten räumlichen Auflösung von 30 m und einem hohen Signal-Rausch-Verhältnis von mindestens 180 im kurzwelligen Infrarot und über 400 im sichtbaren Spektrum, ermöglichen eine Aufnahmequalität, die bislang nur von flugzeuggestützten Systemen erreicht werden konnte. Die Bemühungen in dieser Dissertation umfassen Aktivitäten in der wissenschaftlichen Vorbereitungsphase zu agrargeographischen Fragestellungen. Algorithmen und Tools zur Analyse der hyperspektralen Daten werden kostenlos im QGIS-Plugin EnMAP-Box 3 zur Verfügung gestellt. Die drängenden Fragen im Agrarsektor drehen sich hierbei um die Ableitung biochemischer und biophysikalischer Parameter aus Fernerkundungsdaten, weshalb die übergeordnete Problemstellung des Promotionsvorhabens die Entwicklung eines wissenschaftsbasierten EnMAP-Tools für bewirtschaftete Vegetationsflächen (EnMAP Managed Vegetation Scientific Processor) darstellt. Zu Beginn wurde eine umfassende Feldkampagne geplant, welche ab April 2014 umgesetzt wurde. Neben der spektralen Erfassung von Blatt-, Bestands- und Bodensignaturen in einem Winterweizen- und einem Maisfeld erfolgte auch die Messung wesentlicher Pflanzenparameter an den exakt gleichen Positionen. Hierzu zählt die non-destruktive Ableitung des Blattflächenindex (LAI), des Blattchlorophyllgehalts (Ccab), des Blattwassergehalts (EWT oder Cw), des relativen Blatttrockengewichts (LMA oder Cm), des mittleren Blattneigungswinkels im Bestand (ALIA) sowie weiterer sekundärer Parameter wie Wuchshöhe, das phänologisches Stadium und der Sonnenvektor. Um die Fähigkeit des späteren EnMAP-Satelliten sich um bis zu 30° orthogonal zur Flugrichtung zu kippen nachzustellen, wurden die spektralen Aufnahmen aus verschiedenen Betrachtungswinkeln erstellt, die dieser Aufnahme-Geometrien nachempfunden sind. Ein gängiges Verfahren zur Ableitung der relevanten Pflanzenparameter ist die Verwendung des Strahlungstransfermodells PROSAIL, welches das spektrale Signal einer Vegetationsfläche auf Basis der zugrundeliegenden biophysikalischen und biochemischen Parameter simuliert. Bei der Umkehr dieses Prozesses können ebendiese Variablen von gemessenen spektralen Daten abgeleitet werden. Hierzu wurde eine Datenbank (Look-Up-Table, LUT) aus PROSAIL-Modellläufen aufgebaut und die in den Feldkampagnen gemessenen Spektren mit dieser abgeglichen. Mit dieser Methode der LUT-Invertierung aus unterschiedlichen Aufnahmewinkeln konnten Genauigkeiten bei der LAI-Schätzung von 18 % und bei Blattchlorophyll von 20 % erzielt werden. Eine starke Anisotropie, also eine Reflexionsabhängigkeit von der Beleuchtungs- und Aufnahmerichtung, wurde bei Winterweizen vor allem für frühe Entwicklungsstadien festgestellt. Bei einer anschließenden Studie zur Unsicherheitsanalyse des Spektralmodells wurden PROSAIL-Ergebnisse, bei denen real gemessene Pflanzenparameter als Input dienten, den zugehörigen Reflektanzspektren gegenübergestellt. Es zeigten sich hierbei mitunter starke Abweichungen zwischen gemessenen und modellierten Spektren, die im Falle des Winterweizens einen saisonalen Verlauf zeichneten. Vor allem während frühen Wachstumsstadien tendierte das Modell dazu die Reflektanz im nahen Infrarot zu überschätzen, während es gegen Ende der Wachstumsperiode eher eine Unterschätzung aufwies. Als Unsicherheitsfaktor wurde die Parametrisierung des Modells ausgemacht, wenn der ALIA-Parameter als echter physikalische Blattwinkel interpretiert wird. Es wurde geschlussfolgert, dass eine Separierung von LAI und ALIA bei der Invertierung von PROSAIL eine korrekte Abschätzung der weniger sensitiven Parameter behindert. Die Erstellung des Vegetations-Prozessors erforderte die Verwendung von Regressions-Algorithmen des maschinellen Lernens (MLRA), da eine Verteilung von großen LUTs an die User nicht praktikabel wäre. Die MLRAs wurden an synthetischen Datensätzen trainiert, wobei zunächst die Optimierung der Hyperparameter im Vordergrund stand, bevor die Anwendung an echten Spektraldaten unternommen wurde. Es konnten dabei erst aussagekräftige Ergebnisse produziert werden, als die Trainingsdaten mit einem künstlichen Rauschen belegt wurden, da die Algorithmen unter einer Überanpassung an die Modellumgebung litten. Mithilfe des Prozessors konnten schließlich LAI, ALIA, Ccab und Cw aus hyperspektralen Daten abgeleitet werden. Künstliche neuronale Netze dienen dabei als Blackbox-Modelle, die in kurzer Zeit große Datenmengen verarbeiten können und somit einen entscheidenden Beitrag zur modernen angewandten Fernerkundung für eine breite User-Community leisten.After years of scientific and technical preparation, the launch of an unmanned German space-mission is planned to be initiated in 2020. The Environmental Mapping and Analysis Program (EnMAP) is going to provide an equally named hyperspectral imager to map land surfaces. Scientists of environmental disciplines of monitoring of ecosystems, agricultural, forestry and urban areas as well as coastal and inland waters, geology and soils prepared themselves for the upcoming data prior to the actual launch. Although there already exists a variety of useful algorithms for a profound analysis of spectral data, new challenges will arise given the uniqueness of the EnMAP-mission in the global context of remote sensing; i.e. coverage of the full range of the optical spectrum (420 nm – 2450 nm) in combination with a moderate spatial resolution of 30 m and a high signal-to-noise ratio of at least 180 in the shortwave infrared and above 400 in the visible spectrum. This enables an imaging quality which to this date has only been reached by airborne systems. The efforts of this dissertation comprise activities in the scientific preparation phase for agro-geographical tasks. Algorithms and tools for an analysis of hyperspectral data are being provided for free in the QGIS-plugin EnMAP-Box 3. Urgent questions in the agricultural sector revolve around the derivation of biochemical and biophysical parameters from remote sensing data. For this reason, the overarching objective of this promotion is the development of a scientific EnMAP-tool for managed areas of vegetation (EnMAP Managed Vegetation Scientific Processor). At first, an extensive field campaign was planned and then started in April, 2014. Apart from spectral observations of leaves, canopies and soils in a winter wheat and a maize field, also relevant plant parameters were acquired at the exact same spots. Namely, they are the Leaf Area Index (LAI), leaf chlorophyll content (Ccab), leaf water content (EWT or Cw), relative dry leaf weight (LMA or Cm), Average Leaf Inclination Angle (ALIA) as well as other secondary parameters like canopy height, phenological stage and the solar vector. Spectral measurements were captured from different observation angles to match ground data with the sensing geometry of the future EnMAP-satellite, which can be tilted up to 30° orthogonal to its direction of flight. A common procedure to derive relevant crop parameters is to make use of the radiative transfer model PROSAIL, which simulates the spectral signal of a vegetated surface based on biophysical and biochemical input parameters. If this process is reverted, said parameters can be derived from measured spectral data. To do so, a Look-Up-Table (LUT) is built containing model runs of PROSAIL and then subsequently compared against spectra from the field campaigns. With this approach of LUT-inversions from different observation angles, an accuracy of 18 % could be achieved for LAI and 20 % for Ccab. Strong anisotropic effects, i.e. dependence on illumination geometry and sensor orientation, were identified for winter wheat mainly in the early stages of plant development. In a consecutive study about uncertainties of the spectral model, PROSAIL results fed with in situ measured crop parameters as input, were opposed to their associated reflectance signatures. A strong deviation between measured and modelled spectra was observed, which – in the case of winter wheat – showed a seasonal behavior. The model tended to overestimate reflectances in the near infrared for early phenological stages and to underestimate them at end of the growing period. The parametrization of the model was identified as an uncertainty factor if the ALIA parameter is interpreted as true physical leaf inclinations. It was concluded that a separation of LAI and ALIA at inversion of PROSAIL prevents an adequate estimation of the less sensitive parameters. The development of the vegetation processor required the use of Machine Learning Regression Algorithms (MLRA), since distribution of large LUTs to the user would be impracticable. The MLRAs were trained with synthetic datasets with primary importance to optimize their hyperparameters, before attempting to apply the algorithms to real spectral data. Significant results could not be obtained until training data were altered with artificial noise, because algorithms suffered from overfitting to the model environment. Executing the processor allowed to derive LAI, ALIA, Ccab and Cw from hyperspectral data. Artificial neural networks served as black box models, which digest great amount of data in a short period of time and thus make a decisive contribution to modern applied remote sensing with relevance for a broad user-community

The EnMAP Managed Vegetation Scientific Processor

Nach jahrelanger wissenschaftlicher und technischer Vorbereitungszeit wird voraussichtlich Ende des Jahres 2020 der Start der orbitalen Phase einer unbemannten deutschen Weltraum-Mission initiiert. Das Environmental Mapping and Analysis Program (EnMAP) wird an Bord des gleichnamigen Satelliten einen hyperspektralen Sensor zur Erfassung terrestrischer Oberflächen tragen. In den Umweltdisziplinen zur Erforschung von Ökosystemen, landwirtschaftlicher, forstwirtschaftlicher und urbaner Flächen, im Bereich der Küsten- und Inlandsgewässer sowie der Geologie und Bodenkunde bereitete man sich im Vorfeld des Starts auf die kommenden Daten vor. Zwar existiert bereits eine Vielzahl an Algorithmen zur wissenschaftlichen Analyse von spektralen Daten, allerdings ergeben sich auch neue Herausforderungen, da die EnMAP-Mission bislang im weltweiten Kontext der Fernerkundung einzigartig ist. Die Abdeckung des vollen optischen Spektrums (420 nm – 2450 nm) in Verbindung mit einer moderaten räumlichen Auflösung von 30 m und einem hohen Signal-Rausch-Verhältnis von mindestens 180 im kurzwelligen Infrarot und über 400 im sichtbaren Spektrum, ermöglichen eine Aufnahmequalität, die bislang nur von flugzeuggestützten Systemen erreicht werden konnte. Die Bemühungen in dieser Dissertation umfassen Aktivitäten in der wissenschaftlichen Vorbereitungsphase zu agrargeographischen Fragestellungen. Algorithmen und Tools zur Analyse der hyperspektralen Daten werden kostenlos im QGIS-Plugin EnMAP-Box 3 zur Verfügung gestellt. Die drängenden Fragen im Agrarsektor drehen sich hierbei um die Ableitung biochemischer und biophysikalischer Parameter aus Fernerkundungsdaten, weshalb die übergeordnete Problemstellung des Promotionsvorhabens die Entwicklung eines wissenschaftsbasierten EnMAP-Tools für bewirtschaftete Vegetationsflächen (EnMAP Managed Vegetation Scientific Processor) darstellt. Zu Beginn wurde eine umfassende Feldkampagne geplant, welche ab April 2014 umgesetzt wurde. Neben der spektralen Erfassung von Blatt-, Bestands- und Bodensignaturen in einem Winterweizen- und einem Maisfeld erfolgte auch die Messung wesentlicher Pflanzenparameter an den exakt gleichen Positionen. Hierzu zählt die non-destruktive Ableitung des Blattflächenindex (LAI), des Blattchlorophyllgehalts (Ccab), des Blattwassergehalts (EWT oder Cw), des relativen Blatttrockengewichts (LMA oder Cm), des mittleren Blattneigungswinkels im Bestand (ALIA) sowie weiterer sekundärer Parameter wie Wuchshöhe, das phänologisches Stadium und der Sonnenvektor. Um die Fähigkeit des späteren EnMAP-Satelliten sich um bis zu 30° orthogonal zur Flugrichtung zu kippen nachzustellen, wurden die spektralen Aufnahmen aus verschiedenen Betrachtungswinkeln erstellt, die dieser Aufnahme-Geometrien nachempfunden sind. Ein gängiges Verfahren zur Ableitung der relevanten Pflanzenparameter ist die Verwendung des Strahlungstransfermodells PROSAIL, welches das spektrale Signal einer Vegetationsfläche auf Basis der zugrundeliegenden biophysikalischen und biochemischen Parameter simuliert. Bei der Umkehr dieses Prozesses können ebendiese Variablen von gemessenen spektralen Daten abgeleitet werden. Hierzu wurde eine Datenbank (Look-Up-Table, LUT) aus PROSAIL-Modellläufen aufgebaut und die in den Feldkampagnen gemessenen Spektren mit dieser abgeglichen. Mit dieser Methode der LUT-Invertierung aus unterschiedlichen Aufnahmewinkeln konnten Genauigkeiten bei der LAI-Schätzung von 18 % und bei Blattchlorophyll von 20 % erzielt werden. Eine starke Anisotropie, also eine Reflexionsabhängigkeit von der Beleuchtungs- und Aufnahmerichtung, wurde bei Winterweizen vor allem für frühe Entwicklungsstadien festgestellt. Bei einer anschließenden Studie zur Unsicherheitsanalyse des Spektralmodells wurden PROSAIL-Ergebnisse, bei denen real gemessene Pflanzenparameter als Input dienten, den zugehörigen Reflektanzspektren gegenübergestellt. Es zeigten sich hierbei mitunter starke Abweichungen zwischen gemessenen und modellierten Spektren, die im Falle des Winterweizens einen saisonalen Verlauf zeichneten. Vor allem während frühen Wachstumsstadien tendierte das Modell dazu die Reflektanz im nahen Infrarot zu überschätzen, während es gegen Ende der Wachstumsperiode eher eine Unterschätzung aufwies. Als Unsicherheitsfaktor wurde die Parametrisierung des Modells ausgemacht, wenn der ALIA-Parameter als echter physikalische Blattwinkel interpretiert wird. Es wurde geschlussfolgert, dass eine Separierung von LAI und ALIA bei der Invertierung von PROSAIL eine korrekte Abschätzung der weniger sensitiven Parameter behindert. Die Erstellung des Vegetations-Prozessors erforderte die Verwendung von Regressions-Algorithmen des maschinellen Lernens (MLRA), da eine Verteilung von großen LUTs an die User nicht praktikabel wäre. Die MLRAs wurden an synthetischen Datensätzen trainiert, wobei zunächst die Optimierung der Hyperparameter im Vordergrund stand, bevor die Anwendung an echten Spektraldaten unternommen wurde. Es konnten dabei erst aussagekräftige Ergebnisse produziert werden, als die Trainingsdaten mit einem künstlichen Rauschen belegt wurden, da die Algorithmen unter einer Überanpassung an die Modellumgebung litten. Mithilfe des Prozessors konnten schließlich LAI, ALIA, Ccab und Cw aus hyperspektralen Daten abgeleitet werden. Künstliche neuronale Netze dienen dabei als Blackbox-Modelle, die in kurzer Zeit große Datenmengen verarbeiten können und somit einen entscheidenden Beitrag zur modernen angewandten Fernerkundung für eine breite User-Community leisten.After years of scientific and technical preparation, the launch of an unmanned German space-mission is planned to be initiated in 2020. The Environmental Mapping and Analysis Program (EnMAP) is going to provide an equally named hyperspectral imager to map land surfaces. Scientists of environmental disciplines of monitoring of ecosystems, agricultural, forestry and urban areas as well as coastal and inland waters, geology and soils prepared themselves for the upcoming data prior to the actual launch. Although there already exists a variety of useful algorithms for a profound analysis of spectral data, new challenges will arise given the uniqueness of the EnMAP-mission in the global context of remote sensing; i.e. coverage of the full range of the optical spectrum (420 nm – 2450 nm) in combination with a moderate spatial resolution of 30 m and a high signal-to-noise ratio of at least 180 in the shortwave infrared and above 400 in the visible spectrum. This enables an imaging quality which to this date has only been reached by airborne systems. The efforts of this dissertation comprise activities in the scientific preparation phase for agro-geographical tasks. Algorithms and tools for an analysis of hyperspectral data are being provided for free in the QGIS-plugin EnMAP-Box 3. Urgent questions in the agricultural sector revolve around the derivation of biochemical and biophysical parameters from remote sensing data. For this reason, the overarching objective of this promotion is the development of a scientific EnMAP-tool for managed areas of vegetation (EnMAP Managed Vegetation Scientific Processor). At first, an extensive field campaign was planned and then started in April, 2014. Apart from spectral observations of leaves, canopies and soils in a winter wheat and a maize field, also relevant plant parameters were acquired at the exact same spots. Namely, they are the Leaf Area Index (LAI), leaf chlorophyll content (Ccab), leaf water content (EWT or Cw), relative dry leaf weight (LMA or Cm), Average Leaf Inclination Angle (ALIA) as well as other secondary parameters like canopy height, phenological stage and the solar vector. Spectral measurements were captured from different observation angles to match ground data with the sensing geometry of the future EnMAP-satellite, which can be tilted up to 30° orthogonal to its direction of flight. A common procedure to derive relevant crop parameters is to make use of the radiative transfer model PROSAIL, which simulates the spectral signal of a vegetated surface based on biophysical and biochemical input parameters. If this process is reverted, said parameters can be derived from measured spectral data. To do so, a Look-Up-Table (LUT) is built containing model runs of PROSAIL and then subsequently compared against spectra from the field campaigns. With this approach of LUT-inversions from different observation angles, an accuracy of 18 % could be achieved for LAI and 20 % for Ccab. Strong anisotropic effects, i.e. dependence on illumination geometry and sensor orientation, were identified for winter wheat mainly in the early stages of plant development. In a consecutive study about uncertainties of the spectral model, PROSAIL results fed with in situ measured crop parameters as input, were opposed to their associated reflectance signatures. A strong deviation between measured and modelled spectra was observed, which – in the case of winter wheat – showed a seasonal behavior. The model tended to overestimate reflectances in the near infrared for early phenological stages and to underestimate them at end of the growing period. The parametrization of the model was identified as an uncertainty factor if the ALIA parameter is interpreted as true physical leaf inclinations. It was concluded that a separation of LAI and ALIA at inversion of PROSAIL prevents an adequate estimation of the less sensitive parameters. The development of the vegetation processor required the use of Machine Learning Regression Algorithms (MLRA), since distribution of large LUTs to the user would be impracticable. The MLRAs were trained with synthetic datasets with primary importance to optimize their hyperparameters, before attempting to apply the algorithms to real spectral data. Significant results could not be obtained until training data were altered with artificial noise, because algorithms suffered from overfitting to the model environment. Executing the processor allowed to derive LAI, ALIA, Ccab and Cw from hyperspectral data. Artificial neural networks served as black box models, which digest great amount of data in a short period of time and thus make a decisive contribution to modern applied remote sensing with relevance for a broad user-community