Methods and Applications of 3D Ground Crop Analysis Using LiDAR Technology: A Survey

Light Detection and Ranging (LiDAR) technology is positioning itself as one of the most effective non-destructive methods to collect accurate information on ground crop fields, as the analysis of the three-dimensional models that can be generated with it allows for quickly measuring several key parameters (such as yield estimations, aboveground biomass, vegetation indexes estimation, perform plant phenotyping, and automatic control of agriculture robots or machinery, among others). In this survey, we systematically analyze 53 research papers published between 2005 and 2022 that involve significant use of the LiDAR technology applied to the three-dimensional analysis of ground crops. Different dimensions are identified for classifying the surveyed papers (including application areas, crop species under study, LiDAR scanner technologies, mounting platform technologies, and the use of additional instrumentation and software tools). From our survey, we draw relevant conclusions about the use of LiDAR technologies, such as identifying a hierarchy of different scanning platforms and their frequency of use as well as establishing the trade-off between the economic costs of deploying LiDAR and the agronomically relevant information that effectively can be acquired. We also conclude that none of the approaches under analysis tackles the problem associated with working with multiple species with the same setup and configuration, which shows the need for instrument calibration and algorithmic fine tuning for an effective application of this technology.Fil: Micheletto, Matías Javier. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro de Investigaciones y Transferencia Golfo San Jorge. Centro de Investigaciones y Transferencia Golfo San Jorge: Sede Caleta Olivia - Santa Cruz | Universidad Nacional de la Patagonia Austral. Centro de Investigaciones y Transferencia Golfo San Jorge. Centro de Investigaciones y Transferencia Golfo San Jorge: Sede Caleta Olivia - Santa Cruz | Universidad Nacional de la Patagonia "san Juan Bosco". Centro de Investigaciones y Transferencia Golfo San Jorge. Centro de Investigaciones y Transferencia Golfo San Jorge: Sede Caleta Olivia - Santa Cruz; ArgentinaFil: Chesñevar, Carlos Iván. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; ArgentinaFil: Santos, Rodrigo Martin. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentina. Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras; Argentin

A Clustering Framework for Monitoring Circadian Rhythm in Structural Dynamics in Plants From Terrestrial Laser Scanning Time Series

Terrestrial Laser Scanning (TLS) can be used to monitor plant dynamics with a frequency of several times per hour and with sub-centimeter accuracy, regardless of external lighting conditions. TLS point cloud time series measured at short intervals produce large quantities of data requiring fast processing techniques. These must be robust to the noise inherent in point clouds. This study presents a general framework for monitoring circadian rhythm in plant movements from TLS time series. Framework performance was evaluated using TLS time series collected from two Norway maples (Acer platanoides) and a control target, a lamppost. The results showed that the processing framework presented can capture a plant's circadian rhythm in crown and branches down to a spatial resolution of 1 cm. The largest movements in both Norway maples were observed before sunrise and at their crowns' outer edges. The individual cluster movements were up to 0.17 m (99th percentile) for the taller Norway maple and up to 0.11 m (99th percentile) for the smaller tree from their initial positions before sunset

ASSESSING THE USE OF LIDAR AND UAV TECHNOLOGY FOR MONITORING GROWING ALFALFA

Alfalfa is a popularly grown crop because of its value as a nutritious feed source for livestock. The efficient production of an alfalfa crop relies on the monitoring of certain parameters, like height, quality, and yield. Traditionally, producers have used manual measurements of alfalfa plant height to estimate the nutritive quality and yield of a growing alfalfa crop. Manual measurements of plant height are often labor intensive and provide low resolution data that is not acceptable for full field scale assessment of growing alfalfa. The two studies presented in this thesis offer detailed insight into the rapid and accurate monitoring of alfalfa with LiDAR and UAV technologies. The first study explores the use of a simple single beam LiDAR sensor to accurately estimate the average canopy height and yield of an alfalfa crop. Predictive models of alfalfa canopy height were developed and evaluated to find the optimal LiDAR derived measurements to use. The resulting measurements were then used to build predictive models of yield, and the best yield model was determined. The best models of canopy height and yield both incorporated the 95th percentile of LiDAR derived canopy height as a single explanatory variable. The second study assesses the field conditions, flight parameters, and general statistical descriptors that should be considered for the stable collection and application of UAV derived canopy height information. Data taken from different alfalfa fields at different flight parameters with different statistical processing were all compared. General canopy height distribution statistics from UAV flights flown at or below 50 m with nadir and oblique camera angles over thick stands of alfalfa were determined to be reliable for the detection and application of the alfalfa canopy surface. Using these determined methods, predictive models of canopy height and yield were generated and compared. The best model of average canopy height used the 50th percentile of UAV derived canopy height from an UAV flight at 30 m in a nadir imaging configuration. The best model of yield used the 95th percentile from an UAV flight at 50 m in an oblique imaging configuration

New sensing methods for scheduling variable rate irrigation to improve water use efficiency and reduce the environmental footprint : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Soil Science at Massey University, Palmerston North, New Zealand

Figures are re-used under an Attribution 4.0 International (CC BY 4.0) license, or are not copyrighted.Irrigation is the largest user of allocated freshwater, so conservation of water use should begin with improving the efficiency of crop irrigation. Improved irrigation management is necessary for humid areas such as New Zealand in order to produce greater yields, overcome excessive irrigation and eliminate nitrogen losses due to accelerated leaching and/or denitrification. The impact of two different climatic regimes (Hawkes Bay, Manawatū) and soils (free and imperfect drainage) on irrigated pea (Pisum sativum., cv. ‘Ashton’) and barley (Hordeum vulgare., cv. ‘Carfields CKS1’) production was investigated. These experiments were conducted to determine whether variable-rate irrigation (VRI) was warranted. The results showed that both weather conditions and within-field soil variability had a significant effect on the irrigated pea and barley crops (pea yield - 4.15 and 1.75 t/ha; barley yield - 4.0 and 10.3 t/ha for freely and imperfectly drained soils, respectively). Given these results, soil spatial variability was characterised at precision scales using proximal sensor survey systems: to inform precision irrigation practice. Apparent soil electrical conductivity (ECa) data were collected by a Dualem-421S electromagnetic (EM) survey, and the data were kriged into a map and modelled to predict ECa to depth. The ECa depth models were related to soil moisture (θv), and the intrinsic soil differences. The method was used to guide the placement of soil moisture sensors. After quantifying precision irrigation management zones using EM technology, dynamic irrigation scheduling for a VRI system was used to efficiently irrigate a pea crop (Pisum sativum., cv. ‘Massey’) and a French bean crop (Phaseolus vulgaris., cv. ‘Contender’) over one season at the Manawatū site. The effects of two VRI scheduling methods using (i) a soil water balance model and (ii) sensors, were compared. The sensor-based technique irrigated 23–45% less water because the model-based approach overestimated drainage for the slower draining soil. There were no significant crop growth and yield differences between the two approaches, and water use efficiency (WUE) was higher under the scheduling regime based on sensors. ii To further investigate the use of sensor-based scheduling, a new method was developed to assess crop height and biomass for pea, bean and barley crops at high field resolution (0.01 m) using ground-based LiDAR (Light Detection and Ranging) data. The LiDAR multi-temporal, crop height maps can usefully improve crop coefficient estimates in soil water balance models. The results were validated against manually measured plant parameters. A critical component of soil water balance models, and of major importance for irrigation scheduling, is the estimation of crop evapotranspiration (ETc) which traditionally relies on regional climate data and default crop factors based on the day of planting. Therefore, the potential of a simpler, site-specific method for estimation of ETc using in-field crop sensors was investigated. Crop indices (NDVI, and canopy surface temperature, Tc) together with site-specific climate data were used to estimate daily crop water use at the Manawatū and Hawkes Bay sites (2017-2019). These site-specific estimates of daily crop water use were then used to evaluate a calibrated FAO-56 Penman-Monteith algorithm to estimate ETc from barley, pea and bean crops. The modified ETc–model showed a high linear correlation between measured and modelled daily ETc for barley, pea, and bean crops. This indicates the potential value of in-field crop sensing for estimating site-specific values of ETc. A model-based, decision support software system (VRI–DSS) that automates irrigation scheduling to variable soils and multiple crops was then tested at both the Manawatū and Hawkes Bay farm sites. The results showed that the virtual climate forecast models used for this study provided an adequate prediction of evapotranspiration but over predicted rainfall. However, when local data was used with the VRI–DSS system to simulate results, the soil moisture deficit showed good agreement with weekly neutron probe readings. The use of model system-based irrigation scheduling allowed two-thirds of the irrigation water to be saved for the high available water content (AWC) soil. During the season 2018 – 2019, the VRI–DSS was again used to evaluate the level of available soil water (threshold) at which irrigation should be applied to increase WUE and crop water productivity (WP) for spring wheat (Triticum aestivum L., cv. ‘Sensas’) on the sandy loam and silt loam soil zones at the Manawatū site. Two irrigation thresholds (40% and 60% AWC), were investigated in each soil zone along with a rainfed control. Soil water uptake pattern was affected mainly by the soil type rather than irrigation. The soil iii water uptake decreased with soil depth for the sandy loam whereas water was taken up uniformly from all depths of the silt loam. The 60% AWC treatments had greater irrigation water use efficiency (IWUE) than the 40% AWC treatments, indicating that irrigation scheduling using a 60% AWC trigger could be recommended for this soil-crop scenario. Overall, in this study, we have developed new sensor-based methods that can support improved spatial irrigation water management. The findings from this study led to a more beneficial use of agricultural water

The acquisition of Hyperspectral Digital Surface Models of crops from UAV snapshot cameras

This thesis develops a new approach to capture information about agricultural crops by utilizing advances in the field of robotics, sensor technology, computer vision and photogrammetry: Hyperspectral digital surface models (HS DSMs) generated with UAV snapshot cameras are a representation of a surface in 3D space linked with hyperspectral information emitted and reflected by the objects covered by that surface. The overall research aim of this thesis is to evaluate if HS DSMs are suited for supporting a site-specific crop management. Based on six research studies, three research objectives are discussed for this evaluation. Firstly the influences of environmental effects, the sensing system and data processing of the spectral data within HS DSMs are discussed. Secondly, the comparability of HS DSMs to data from other remote sensing methods is investigated and thirdly their potential to support site-specific crop management is evaluated. Most data within this thesis was acquired at a plant experimental-plot experiment in Klein-Altendorf, Germany, with six different barley varieties and two different fertilizer treatments in the growing seasons of 2013 and 2014. In total, 22 measurement campaigns were carried out in the context of this thesis. HS DSMs acquired with the hyperspectral snapshot cameras Cubert UHD 185-Firefly show great potential for practical applications. The combination of UAVs and the UHD allowed data to be captured at a high spatial, spectral and temporal resolution. The spatial resolution allowed detection of small-scale heterogeneities within the plant population. Additionally, with the spectral and 3D information contained in HS DSMs, plant parameters such as chlorophyll, biomass and plant height could be estimated within individual, and across different growing stages. The techniques developed in this thesis therefore offer a significant contribution towards increasing cropping efficiency through the support of site-specific management

UAVs for the Environmental Sciences

This book gives an overview of the usage of UAVs in environmental sciences covering technical basics, data acquisition with different sensors, data processing schemes and illustrating various examples of application

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