Quantifying Vegetation Biophysical Variables from Imaging Spectroscopy Data: A Review on Retrieval Methods

An unprecedented spectroscopic data stream will soon become available with forthcoming Earth-observing satellite missions equipped with imaging spectroradiometers. This data stream will open up a vast array of opportunities to quantify a diversity of biochemical and structural vegetation properties. The processing requirements for such large data streams require reliable retrieval techniques enabling the spatiotemporally explicit quantification of biophysical variables. With the aim of preparing for this new era of Earth observation, this review summarizes the state-of-the-art retrieval methods that have been applied in experimental imaging spectroscopy studies inferring all kinds of vegetation biophysical variables. Identified retrieval methods are categorized into: (1) parametric regression, including vegetation indices, shape indices and spectral transformations; (2) nonparametric regression, including linear and nonlinear machine learning regression algorithms; (3) physically based, including inversion of radiative transfer models (RTMs) using numerical optimization and look-up table approaches; and (4) hybrid regression methods, which combine RTM simulations with machine learning regression methods. For each of these categories, an overview of widely applied methods with application to mapping vegetation properties is given. In view of processing imaging spectroscopy data, a critical aspect involves the challenge of dealing with spectral multicollinearity. The ability to provide robust estimates, retrieval uncertainties and acceptable retrieval processing speed are other important aspects in view of operational processing. Recommendations towards new-generation spectroscopy-based processing chains for operational production of biophysical variables are given

Divergent abiotic spectral pathways unravel pathogen stress signals across species

Plant pathogens pose increasing threats to global food security, causing yield losses that exceed 30% in food-deficit regions. Xylella fastidiosa (Xf) represents the major transboundary plant pest and one of the world’s most damaging pathogens in terms of socioeconomic impact. Spectral screening methods are critical to detect non-visual symptoms of early infection and prevent spread. However, the subtle pathogen-induced physiological alterations that are spectrally detectable are entangled with the dynamics of abiotic stresses. Here, using airborne spectroscopy and thermal scanning of areas covering more than one million trees of different species, infections and water stress levels, we reveal the existence of divergent pathogen- and host-specific spectral pathways that can disentangle biotic-induced symptoms. We demonstrate that uncoupling this biotic–abiotic spectral dynamics diminishes the uncertainty in the Xf detection to below 6% across different hosts. Assessing these deviating pathways against another harmful vascular pathogen that produces analogous symptoms, Verticillium dahliae, the divergent routes remained pathogen- and host-specific, revealing detection accuracies exceeding 92% across pathosystems. These urgently needed hyperspectral methods advance early detection of devastating pathogens to reduce the billions in crop losses worldwide.The study was partially funded by the European Union’s Horizon 2020 Research and Innovation Programme through grant agreements POnTE (635646) and XF-ACTORS (727987), as well as by projects AGL2009-13105 from the Spanish Ministry of Education and Science, P08-AGR-03528 from the Regional Government of Andalusia and the European Social Fund, project E-RTA2017-00004-02 from ‘Programa Estatal de I + D + I Orientada a los Retos de la Sociedad’ of Spain and FEDER, Intramural Project 201840E111 from CSIC, and Project ITS2017-095 Consejeria de Medio Ambiente, Agricultura y Pesca de las Islas Baleares, Spain. The views expressed are purely those of the writers and may not in any circumstance be regarded as stating an official position of the European Commission

Coupled canopy-atmosphere modelling for radiance-based estimation of vegetation properties

Vegetation is an important component of the Earth’s biosphere and therefore plays a crucial role in the carbon exchange of terrestrial ecosystems. Vegetation variables, such as leaf area index (LAI) and leaf chlorophyll content (Cab), can be monitored at global scale using remote sensing (RS). There are two main categories of approaches for estimating the vegetation variables from RS data: empirical and physically-based approaches. Physically-based approaches are more widely applicable because they rely on radiative transfer (RT) models, which can be adapted to the observation conditions and to the observed vegetation. For estimating the vegetation variables, however, the RT model has to be inverted, and this inversion is usually an ill-posed and under-determined problem. Several regularization methods have been proposed to allow finding stable and unique solutions: model coupling, using multi-angular data, using a priori information, as well as applying spatial or temporal constraints. Traditionally, radiance data measured at top-of the atmosphere (TOA) are pre-processed to top-of-canopy (TOC) reflectances. Corrections for atmospheric effects, and, if needed, for adjacency, directional, or topographic effects are usually applied sequentially and independently. Physically, however, these effects are inter-related, and each correction introduces errors. These errors propagate to the TOC reflectance data, which are used to invert the canopy RT model. The performance of the TOC approach is therefore limited by the errors introduced in the data during the pre-processing steps. This thesis proposes to minimize these errors by directly using measured TOA radiance data. In such a TOA approach, the atmospheric RT model, which is normally inverted to perform the atmospheric correction, is coupled to the canopy RT model. The coupled canopy-atmosphere model is inverted directly using the measured radiance data. Adjacency, directional and topographic effects can then be included in the coupled RT model. The same regularization methods as used for TOC approaches can be applied to obtain stable and unique estimates. The TOA approach was tested using four case studies based on mono-temporal data. A) The performance of the TOA approach was compared to a TOC approach for three Norway spruce stands in the Czech Republic, using near-nadir Compact High Resolution Imaging Spectrometer (CHRIS) data. The coupled model included canopy directional effects and simulated the CHRIS radiance data with similar accuracy as the canopy model simulated the atmospherically-corrected CHRIS data. Local sensitivity analyses showed that the atmospheric parameters had much less influence on the simulations than the vegetation parameters, and that the sensitivity profiles of the latter were very similar for both TOC and TOA approaches. The dimensionality of the estimation problem was evaluated to be 3 for both approaches. Canopy cover (Cv), fraction of bark material (fB), Cab, and leaf dry matter content (Cdm) were estimated using look-up tables (LUT) with similar accuracy with both approaches. B) Regularization using multi-angular data was tested for the TOA approach, using four angular CHRIS datasets, for the same three stands as used in A). The coupled model provided good simulations for all angles. The dimensionality increased from 3 to 6 when using all four angles. Two LUTs were built for each stand: a 4-variable LUT with fB, Cv, Cdm, and Cab, and a 7-variable LUT where leaf brown pigment concentration (Cs), dissociation factor (D), and tree shape factor (Zeta) were added. The results did not fully match the expectation that the more angles used, the more accurate the estimates become. Although their exploitation remains challenging, multi-angular data have higher potential than mono-angular data at TOA level. C) A Bayesian object-based approach was developed and tested on at-sensor Airborne Prism Experiment (APEX) radiance data for an agricultural area in Switzerland. This approach consists of two steps. First, up to six variables were estimated for each crop field object using a Bayesian optimization algorithm, using a priori information. Second, a LUT was built for each object with only LAI and Cab as free variables, thus spatially constraining the values of all other variables to the values obtained in the first step. The Bayesian object-based approach estimated LAI more accurately than a LUT with a Bayesian cost function approach. This case study relied on extensive field data allowing defining the objects and a priori data. D) The Bayesian object-based approach proposed in C) was applied to a simulated TOA Sentinel-2 scene, covering the area around Zurich, Switzerland. The simulated scene was mosaicked using seven APEX flight lines, which allowed including all spatial and spectral characteristics of Sentinel-2. Automatic multi-resolution segmentation and classification of the vegetated objects in four levels of brightness in the visible domain enabled defining the objects and a priori data without field data, allowing successful implementation of the Bayesian object-based approach. The research conducted in this thesis contributes to the improvement of the use of regularization methods in ill-posed RT model inversions. Three major areas were identified for further research: 1) inclusion of adjacency and topography effects in the coupled model, 2) addition of temporal constraints in the inversion, and 3) better inclusion of observation and model uncertainties in the cost function. The TOA approach proposed here will facilitate the exploitation of multi-angular, multi-temporal and multi-sensor data, leading to more accurate RS vegetation products. These higher quality products will support many vegetation-related applications.</p

Unlocking the benefits of spaceborne imaging spectroscopy for sustainable agriculture

With the Environmental Mapping and Analysis Program (EnMAP) mission, launched on April 1st 2022, new opportunities unfold for precision farming and agricultural monitoring. The recurring acquisition of spectrometric imagery from space, contiguously resolving the electromagnetic spectrum in the optical domain (400—2500 nm) within close narrow bands, provides unprecedented data about the interaction of radiation with biophysical and biochemical crop constituents. These interactions manifest in spectral reflectance, carrying important information about crop status and health. This information may be incorporated in agricultural management systems to support necessary efforts to maximize yields against the backdrop of an increased food demand by a growing world population. At the same time, it enables the effective optimization of fertilization and pest control to minimize environmental impacts of agriculture. Deriving biophysical and biochemical crop traits from hyperspectral reflectance thereby always relies on a model. These models are categorized into (1) parametric, (2) nonparametric, (3) physically-based, and (4) hybrid retrieval schemes. Parametric methods define an explicit parameterized expression, relating a number of spectral bands or derivates thereof with a crop trait of interest. Nonparametric methods comprise linear techniques, such as principal component analysis (PCA) which addresses collinearity issues between adjacent bands and enables compression of full spectral information into dimensionality reduced, maximal informative principal components (PCs). Nonparametric nonlinear methods, i.e., machine learning (ML) algorithms apply nonlinear transformations to imaging spectroscopy data and are therefore capable of capturing nonlinear relationships within the contained spectral features. Physically-based methods represent an umbrella term for radiative transfer models (RTMs) and related retrieval schemes, such as look-up-table (LUT) inversion. A simple, easily invertible and specific RTM is the Beer-Lambert law which may be used to directly infer plant water content. The most widely used general and invertible RTM is the one-dimensional canopy RTM PROSAIL, which is coupling the Leaf Optical Properties Spectra model PROSPECT and the canopy reflectance model 4SAIL: Scattering by Arbitrarily Inclined Leaves. Hybrid methods make use of synthetic data sets created by RTMs to calibrate parametric methods or to train nonparametric ML algorithms. Due to the ill-posed nature of RTM inversion, potentially unrealistic and redundant samples in a LUT need to be removed by either implementing physiological constraints or by applying active learning (AL) heuristics. This cumulative thesis presents three different hybrid approaches, demonstrated within three scientific research papers, to derive agricultural relevant crop traits from spectrometric imagery. In paper I the Beer-Lambert law is applied to directly infer the thickness of the optically active water layer (i.e., EWT) from the liquid water absorption feature at 970 nm. The model is calibrated with 50,000 PROSPECT spectra and validated over in situ data. Due to separate water content measurements of leaves, stalks, and fruits during the Munich-North-Isar (MNI) campaigns, findings indicate that depending on the crop type and its structure, different parts of the canopy are observed with optical sensors. For winter wheat, correlation between measured and modelled water content was most promising for ears and leaves, reaching coefficients of determination (R2) up to 0.72 and relative RMSE (rRMSE) of 26%, and in the case of corn for the leaf fraction only (R2 = 0.86, rRMSE = 23%). These results led to the general recommendation to collect destructive area-based plant organ specific EWT measurements instead of the common practice to upscale leaf-based EWT measurements to canopy water content (CWC) by multiplication of the leaf area index (LAI). The developed and calibrated plant water retrieval (PWR) model proved to be transferable in space and time and is ready to be applied to upcoming EnMAP data and any other hyperspectral imagery. In paper II the parametric concept of spectral integral ratios (SIR) is introduced to retrieve leaf chlorophyll a and b content (Cab), leaf carotenoid content (Ccx) and leaf water content (Cw) simultaneously from imaging spectroscopy data in the wavelength range 460—1100 nm. The SIR concept is based on automatic separation of respective absorption features through local peak and intercept analysis between log-transformed reflectance and convex hulls. The approach was validated over a physiologically constrained PROSAIL simulated database, considering natural Ccx-Cab relations and green peak locations. Validation on airborne spectrometric HyMAP data achieved satisfactory results for Cab (R2 = 0.84; RMSE = 9.06 µg cm-2) and CWC (R2 = 0.70; RMSE = 0.05 cm). Retrieved Ccx values were reasonable according to Cab-Ccx-dependence plausibility analysis. Mapping of the SIR results as multiband images (3-segment SIR) allows for an intuitive visualization of dominant absorptions with respect to the three considered biochemical variables. Hence, the presented SIR algorithm allows for computationally efficient and RTM supported robust retrievals of the two most important vegetation pigments as well as of water content and is applicable on satellite imaging spectroscopy data. In paper III a hybrid workflow is presented, combining RTM with ML for inferring crop carbon content (Carea) and aboveground dry and fresh biomass (AGBdry, AGBfresh). The concept involves the establishment of a PROSAIL training database, dimensionality reduction using PCA, optimization in the sampling domain using AL against the 4-year MNI campaign dataset, and training of Gaussian process regression (GPR) ML algorithms. Internal validation of the GPR-Carea and GPR-AGB models achieved R2 of 0.80 for Carea, and R2 of 0.80 and 0.71 for AGBdry and AGBfresh, respectively. Validation with an independent dataset, comprising airborne AVIRIS NG imagery (spectrally resampled to EnMAP) and in situ measurements, successfully demonstrated mapping capabilities for both bare and green fields and generated reliable estimates over winter wheat fields at low associated model uncertainties (< 40%). Overall, the proposed carbon and biomass models demonstrate a promising path toward the inference of these crucial variables over cultivated areas from upcoming spaceborne hyperspectral acquisitions, such as from EnMAP. As conclusions, the following important findings arise regarding parametric and nonparametric hybrid methods as well as in view of the importance of in situ data collection. (1) Uncertainties within the RTM PROSAIL should always be considered. A possible reduction of these uncertainties is thereby opposed to the invertibility of the model and its intended simplicity. (2) Both physiological constraints and AL heuristics should be applied to reduce unrealistic parameter combinations in a PROSAIL calibration or training database. (3) State-of-the-art hybrid ML approaches with the ability to provide uncertainty intervals are anticipated as most promising approach for solving inference problems from hyperspectral Earth observation data due to their synergistic use of RTMs and the high flexibility, accuracy and consistency of nonlinear nonparametric methods. (4) Parametric hybrid approaches, due to their algorithmic transparency, enable deeper insights into fundamental physical limitations of optical remote sensing as compared to ML approaches. (5) Integration-based indices that make full use of available hyperspectral information may serve as physics-aware dimensionality reduced input for ML algorithms to either improve estimations or to serve as endmember for crop type discrimination when additional time series information is available. (6) The validation of quantitative model-based estimations is crucial to evaluate and improve their performance in terms of the underlying assumptions, model parameterizations, and input data. (7) In the face of soon-to-be-available EnMAP data, collection of in situ data for validation of retrieval methods should aim at high variability of measured crop types, high temporal variability over the whole growing season, as well as include area- and biomass-based destructive measurements instead of LAI-upscaled leaf measurements. Provided the perfect functionality of the payload instruments, the success of the EnMAP mission and the here presented methods depend critically on a low-noise, accurate atmospherically corrected reflectance product. High-level outputs of the retrieval methods presented in this thesis may be incorporated into agricultural decision support systems for fertilization and irrigation planning, yield estimation, or estimation of the soil carbon sequestration potential to enable a sustainable intensive agriculture in the future.Mit der am 1. April 2022 gestarteten Satellitenmission Environmental Mapping and Analysis Program (EnMAP) eröffnen sich neue Möglichkeiten für die Präzisionslandwirtschaft und das landwirtschaftliche Monitoring. Die wiederkehrende Erfassung spektrometrischer Bilder aus dem Weltraum, welche das elektromagnetische Spektrum im optischen Bereich (400—2500 nm) innerhalb von engen, schmalen Bändern zusammenhängend auflösen, liefert nie dagewesene Daten über die Interaktionen von Strahlung und biophysikalischen und biochemischen Pflanzenbestandteilen. Diese Wechselwirkungen manifestieren sich in der spektralen Reflektanz, die wichtige Informationen über den Zustand und die Gesundheit der Pflanzen enthält. Vor dem Hintergrund einer steigenden Nachfrage nach Nahrungsmitteln durch eine wachsende Weltbevölkerung können diese Informationen in landwirtschaftliche Managementsysteme einfließen, um eine notwendige Ertragsmaximierung zu unterstützen. Gleichzeitig können sie eine effiziente Optimierung der Düngung und Schädlingsbekämpfung ermöglichen, um die Umweltauswirkungen der Landwirtschaft zu minimieren. Die Ableitung biophysikalischer und biochemischer Pflanzeneigenschaften aus hyperspektralen Reflektanzdaten ist dabei immer von einem Modell abhängig. Diese Modelle werden in (1) parametrische, (2) nichtparametrische, (3) physikalisch basierte und (4) hybride Ableitungsmethoden kategorisiert. Parametrische Methoden definieren einen expliziten parametrisierten Ausdruck, der eine Reihe von Spektralkanälen oder deren Ableitungen mit einem Pflanzenmerkmal von Interesse in Beziehung setzt. Nichtparametrische Methoden umfassen lineare Techniken wie die Hauptkomponentenanalyse (PCA). Diese adressieren Kollinearitätsprobleme zwischen benachbarten Kanälen und komprimieren die gesamte Spektralinformation in dimensionsreduzierte, maximal informative Hauptkomponenten (PCs). Nichtparametrische nichtlineare Methoden, d. h. Algorithmen des maschinellen Lernens (ML), wenden nichtlineare Transformationen auf bildgebende Spektroskopiedaten an und sind daher in der Lage, nichtlineare Beziehungen innerhalb der enthaltenen spektralen Merkmale zu erfassen. Physikalisch basierte Methoden sind ein Oberbegriff für Strahlungstransfermodelle (RTM) und damit verbundene Ableitungsschemata, d. h. Invertierungsverfahren wie z. B. die Invertierung mittels Look-up-Table (LUT). Ein einfaches, leicht invertierbares und spezifisches RTM stellt das Lambert-Beer'sche Gesetz dar, das zur direkten Ableitung des Wassergehalts von Pflanzen verwendet werden kann. Das am weitesten verbreitete, allgemeine und invertierbare RTM ist das eindimensionale Bestandsmodell PROSAIL, eine Kopplung des Blattmodells Leaf Optical Properties Spectra (PROSPECT) mit dem Bestandsreflexionsmodell 4SAIL (Scattering by Arbitrarily Inclined Leaves). Bei hybriden Methoden werden von RTMs generierte, synthetische Datenbanken entweder zur Kalibrierung parametrischer Methoden oder zum Training nichtparametrischer ML-Algorithmen verwendet. Aufgrund der Äquifinalitätsproblematik bei der RTM-Invertierung, müssen potenziell unrealistische und redundante Simulationen in einer solchen Datenbank durch die Implementierung natürlicher physiologischer Beschränkungen oder durch die Anwendung von Active Learning (AL) Heuristiken entfernt werden. In dieser kumulativen Dissertation werden drei verschiedene hybride Ansätze zur Ableitung landwirtschaftlich relevanter Pflanzenmerkmale aus spektrometrischen Bilddaten vorgestellt, die anhand von drei wissenschaftlichen Publikationen demonstriert werden. In Paper I wird das Lambert-Beer'sche Gesetz angewandt, um die Dicke der optisch aktiven Wasserschicht (bzw. EWT) direkt aus dem Absorptionsmerkmal von flüssigem Wasser bei 970 nm abzuleiten. Das Modell wird mit 50.000 PROSPECT-Spektren kalibriert und anhand von In-situ-Daten validiert. Aufgrund separater Messungen des Wassergehalts von Blättern, Stängeln und Früchten während der München-Nord-Isar (MNI)-Kampagnen, zeigen die Ergebnisse, dass je nach Kulturart und -struktur, unterschiedliche Teile des Bestandes mit optischen Sensoren beobachtet werden können. Bei Winterweizen wurde die höchste Korrelation zwischen gemessenem und modelliertem Wassergehalt für Ähren und Blätter erzielt und sie erreichte Bestimmtheitsmaße (R2) von bis zu 0,72 bei einem relativen RMSE (rRMSE) von 26%, bei Mais entsprechend nur für die Blattfraktion (R2 = 0,86, rRMSE = 23%). Diese Ergebnisse führten zu der allgemeinen Empfehlung, Kompartiment-spezifische EWT-Bestandsmessungen zu erheben, anstatt der üblichen Praxis, blattbasierte EWT-Messungen durch Multiplikation mit dem Blattflächenindex (LAI) auf den Bestandswassergehalt (CWC) hochzurechnen. Das entwickelte und kalibrierte Modell zur Ableitung des Pflanzenwassergehalts (PWR) erwies sich als räumlich und zeitlich übertragbar und kann auf bald verfügbare EnMAP-Daten und andere hyperspektrale Bilddaten angewendet werden. In Paper II wird das parametrische Konzept der spektralen Integralratios (SIR) eingeführt, um den Chlorophyll a- und b-Gehalt (Cab), den Karotinoidgehalt (Ccx) und den Wassergehalt (Cw) simultan aus bildgebenden Spektroskopiedaten im Wellenlängenbereich 460-1100 nm zu ermitteln. Das SIR-Konzept basiert auf der automatischen Separierung der jeweiligen Absorptionsmerkmale durch lokale Maxima- und Schnittpunkt-Analyse zwischen log-transformierter Reflektanz und konvexen Hüllen. Der Ansatz wurde anhand einer physiologisch eingeschränkten PROSAIL-Datenbank unter Berücksichtigung natürlicher Ccx-Cab-Beziehungen und Positionen der Maxima im grünen Wellenlängenbereich validiert. Die Validierung mit flugzeuggestützten spektrometrischen HyMAP-Daten ergab zufriedenstellende Ergebnisse für Cab (R2 = 0,84; RMSE = 9,06 µg cm-2) und CWC (R2 = 0,70; RMSE = 0,05 cm). Die ermittelten Ccx-Werte wurden anhand einer Plausibilitätsanalyse entsprechend der Cab-Ccx-Abhängigkeit als sinnvoll bewertet. Die Darstellung der SIR-Ergebnisse als mehrkanalige Bilder (3 segment SIR) ermöglicht zudem eine auf die drei betrachteten biochemischen Variablen bezogene, intuitive Visualisierung der dominanten Absorptionen. Der vorgestellte SIR-Algorithmus ermöglicht somit wenig rechenintensive und RTM-gestützte robuste Ableitungen der beiden wichtigsten Pigmente sowie des Wassergehalts und kann in auf jegliche zukünftig verfügbare Hyperspektraldaten angewendet werden. In Paper III wird ein hybrider Ansatz vorgestellt, der RTM mit ML kombiniert, um den Kohlenstoffgehalt (Carea) sowie die oberirdische trockene und frische Biomasse (AGBdry, AGBfresh) abzuschätzen. Das Konzept umfasst die Erstellung einer PROSAIL-Trainingsdatenbank, die Dimensionsreduzierung mittels PCA, die Reduzierung der Stichprobenanzahl mittels AL anhand des vier Jahre umspannenden MNI-Kampagnendatensatzes und das Training von Gaussian Process Regression (GPR) ML-Algorithmen. Die interne Validierung der GPR-Carea und GPR-AGB-Modelle ergab einen R2 von 0,80 für Carea und einen R2 von 0,80 bzw. 0,71 für AGBdry und AGBfresh. Die Validierung auf einem unabhängigen Datensatz, der flugzeuggestützte AVIRIS-NG-Bilder (spektral auf EnMAP umgerechnet) und In-situ-Messungen umfasste, zeigte erfolgreich die Kartierungsfähigkeiten sowohl für offene Böden als auch für grüne Felder und führte zu zuverlässigen Schätzungen auf Winterweizenfeldern bei geringen Modellunsicherheiten (< 40%). Insgesamt zeigen die vorgeschlagenen Kohlenstoff- und Biomassemodelle einen vielversprechenden Ansatz auf, der zur Ableitung dieser wichtigen Variablen über Anbauflächen aus künftigen weltraumgestützten Hyperspektralaufnahmen wie jenen von EnMAP genutzt werden kann. Als Schlussfolgerungen ergeben sich die folgenden wichtigen Erkenntnisse in Bezug auf parametrische und nichtparametrische Hybridmethoden sowie bezogen auf die Bedeutung der In-situ-Datenerfassung. (1) Unsicherheiten innerhalb des RTM PROSAIL sollten immer berücksichtigt werden. Eine mögliche Verringerung dieser Unsicherheiten steht dabei der Invertierbarkeit des Modells und dessen beabsichtigter Einfachheit entgegen. (2) Sowohl physiologische Einschränkungen als auch AL-Heuristiken sollten angewendet werden, um unrealistische Parameterkombinationen in einer PROSAIL-Kalibrierungs- oder Trainingsdatenbank zu reduzieren. (3) Modernste ML-Ansätze mit der Fähigkeit, Unsicherheitsintervalle bereitzustellen, werden als vielversprechendster Ansatz für die Lösung von Inferenzproblemen aus hyperspektralen Erdbeobachtungsdaten aufgrund ihrer synergetischen Nutzung von RTMs und der hohen Flexibilität, Genauigkeit und Konsistenz nichtlinearer nichtparametrischer Methoden angesehen. (4) Parametrische hybride Ansätze ermöglichen aufgrund ihrer algorithmischen Transparenz im Vergleich zu ML-Ansätzen tiefere Einblicke in die grundlegenden physikalischen Grenzen der optischen Fernerkundung. (5) Integralbasierte Indizes, die die verfügbare hyperspektrale Information voll ausschöpfen, können als physikalisch-basierte dimensionsreduzierte Inputs für ML-Algorithmen dienen, um entweder Schätzungen zu verbessern oder um als Eingangsdaten die verbesserte Unterscheidung von Kulturpflanzen zu ermöglichen, sobald zusätzliche Zeitreiheninformationen verfügbar sind. (6) Die Validierung quantitativer modellbasierter Schätzungen ist von entscheidender Bedeutung für die Bewertung und Verbesserung ihrer Leistungsfähigkeit in Bezug auf die zugrunde liegenden Annahmen, Modellparametrisierungen und Eingabedaten. (7) Angesichts der bald verfügbaren EnMAP-Daten sollte die Erhebung von In-situ-Daten zur Validierung von Ableitungsmethoden auf eine hohe Variabilität der gemessenen Pflanzentypen und eine hohe zeitliche Variabilität über die gesamte Vegetationsperiode abzielen sowie flächen- und biomassebasierte destruktive Messungen anstelle von LAI-skalierten Blattmessungen umfassen. Unter der Voraussetzung, dass die Messinstrumente perfekt funktionieren, hängt der Erfolg der EnMAP-Mission und der hier vorgestellten Methoden entscheidend von einem rauscharmen, präzise atmosphärisch korrigierten Reflektanzprodukt ab. Die Ergebnisse der in dieser Arbeit vorgestellten Methoden können in landwirtschaftliche Entscheidungsunterstützungssysteme für die Dünge- oder Bewässerungsplanung, die Ertragsabschätzung oder die Schätzung des Potenzials der Kohlenstoffbindung im Boden integriert werden, um eine nachhaltige Intensivlandwirtschaft in der Zukunft zu ermöglichen

Optimized and automated estimation of vegetation properties: Opportunities for Sentinel-2

La Biosfera es uno de los principales sistemas que conforman la Tierra. Su estudio permite comprender la relación entre la vegetación y el ciclo del carbono y cómo éste puede ser afectado por los cambios en los niveles de CO2 y los usos de suelo. Para el estudio de estas dinámicas a escala global y local, han sido desarrollados diversos modelos que son representaciones de la realidad en una escala y complejidad más simple. Parte de las variables de entrada de estos modelos son obtenidas mediante medidas de teledetección gracias al Global Climate Observing System (GCOS), que ha determinado un conjunto de 50 variables climáticas esenciales que contribuyen a los estudios de cambio climático que lidera la Convención Marco de las Naciones Unidas y el Panel Intergubernamental del Cambio Climático. En esta lista está incluido el índice de área foliar (LAI).El contenido de clorofila en hoja (LCC) es otro parámetro biofísico clave para los estudios de biosfera. El estudio de las propiedades de la vegetación desde el espacio requiere: (1) Métodos óptimos para el procesamiento y la estimación de la información y, (2) Disponibilidad de datos espaciales. Los métodos de procesado y estimación de parámetros biofísicos son necesarios ya que el sensor solo mide los flujos de energía reflejados por las cubiertas vegetales distribuidos espacialmente. Por ello, han sido desarrollados diversos modelos, que van desde complejos modelos con base física hasta modelos estadísticos o la combinación de los anteriores. En el desarrollo de esta tesis se ha reunido una amplia variedad de ellos. la Agencia Espacial Europea (ESA) ha desarrollado la misión Sentinel-2 que está especialmente diseñada para el monitoreo de las propiedades de la vegetación, con las capacidades operativas que cumplen los requerimientos espectrales, espaciales y temporales. Los datos que proporcionará la misión Sentinel-2 permitirán garantizar la continuidad de las misiones Spot y Landsat, aportando un tiempo de revisita menor, mejora de la amplitud de barrido, mayor resolución espectral y una mejor calibración y calidad de imagen. Para el procesamiento y la extracción de información de parámetros biofísicos han sido desarrollados diferentes paquetes computacionales por diversos grupos de investigación. Esta tesis pretende suministrar un conjunto de herramientas computacionales, dinámicas y flexibles que permitan automatizar y evaluar el potencial de los diferentes métodos que en la actualidad han sido publicados y están disponibles para su libre uso. Presenta los resultados científicos de la evaluación del impacto de diferentes parámetros de ajuste en los principales métodos de estimación de parámetros biofísicos, centrándonos en datos simulados del satélite Sentinel-2, previsto para ser lanzado en 2015. Para dicho trabajo se han reunido los principales métodos de estimación que van desde las simples relaciones espectrales hasta los complejos modelos de transferencia radiativa (RTM). Para esto, hemos implementado un conjunto de herramientas informáticas que permiten el diseño y evaluación de diversas estrategias de regularización como son la normalización de los datos, la sinergia entre datos simulados por RTM y datos de campañas de campo o de laboratorio, adición de modelos de ruido a los datos simulados y un amplio conjunto de métodos de regresión tanto paramétricos como no paramétricos. Este trabajo constituye la continuación de mi trabajo Final del Máster de Teledetección, donde he desarrolló una herramienta informática llamado ARTMO (por sus siglas en inglés Automated Radiative Transfer Models Operator) que reunió los RTM de la familia Prospect, SAIL y FLIGTH. Se implementó el método de estimación por tablas de búsqueda (LUT). Esta tesis presenta la evolución de ARTMO que pasa de ser una herramienta informática rígida que no permitía de manera sencilla la ampliación de sus funciones, a un flexible marco de desarrollo (framework software), donde ARTMO se convierte en una plataforma de soporte de diversos módulos implementados de manera independiente. Esta nueva versión de ARTMO permite a cualquier grupo de investigación desarrollar y compartir nuevas funciones, algoritmos y métodos de estimación de parámetros biofísicos. Además, hemos establecido las bases para la creación de una red tanto de usuarios como de desarrolladores en torno al estudio de las propiedades de la vegetación, sirviendo de apoyo para el estudio de nuevos algoritmos de estimación, diseño de nuevos sensores ópticos o para su uso en el campo de la educación.The biosphere is one of the main components of the Earth’s system since it regulates exchanges of energy and mass fluxes at the soil, vegetation and atmosphere level. To know the links between vegetation and the terrestrial energy, water and carbon cycles, and how these might change due to eco-physiological responses to elevated CO2 and changes in land use is of vital importance for the study of the biosphere. To study these exchanges, several kinds of models (scale and target) have been developed. In view of these models, the Global Climate Observing System (GCOS) aims to provide comprehensive information on the total climate system, involving a multidisciplinary range of physical, chemical and biological properties, and atmospheric, oceanic, hydrological, cryospheric and terrestrial processes. Fifty GCOS Essential Climate Variables (ECVs) are required to support the work of the United Nations Framework Convention on Climate Change (UNFCCC) and the Intergovernmental Panel on Climate Change. In support of these terrestrial models, but also in support of monitoring local-to-global vegetation dynamics, this Thesis focuses on improved estimation of vegetation properties from optical RS data, and more specifically leaf area index (LAI) and leaf chlorophyll content (LCC). Although LCC is currently not considered as an ECV due to the lack of a globally applicable retrieval algorithm, it is a key variable in vegetation studies. Monitoring the distribution and changes of LAI and LCC is important for assessing growth and vigour of vegetation on the planet. The quantification of these essential vegetation properties are fundamentally important in land-atmosphere processes and parametrization in climate models. LAI variable represents the amount of leaf material in ecosystems and controls the links between biosphere and atmosphere through various processes such as photosynthesis, respiration, transpiration and rain interception. LCC provides important information about the physiological status of plants and photosynthetic activity, therefore is related to the nitrogen content, water stress and yield forecasting The European Space Agency (ESA)’s forthcoming Sentinel-2 mission is particularly tailored to the monitoring vegetation properties mapping, with operational monitoring capabilities that goes beyond any existing operational mission. A pair of Sentinel-2 polar-orbiting satellites will provide systematic global acquisitions of high-resolution multispectral imagery (10-60 m) with a high revisit frequency on a free and open data policy basis. With the pair of satellites in operation it has a revisit time of five days at the equator (under cloud-free conditions) and 2–3 days at mid-latitudes. Sentinel-2 images will be used to derive the highly prioritized time series of ECVs such as LAI. Sentinel-2 images will also be used provide various experimental variables, e.g. biochemical variables such as LCC. This Thesis is dedicated to tackle the stated recommendation and turn it into consolidated guidelines. The undertaken road map was to work on both generating scientific outputs, as well on developing software to automate the retrieval routines. All essential tools to deliver a prototype retrieval approach that could be embedded into an operational Sentinel-2 processing scheme have been prepared into a scientific software package called ARTMO (Automated Radiative Transfer Models Operator). Physically-based approaches but also latest statisticallybased methods have been implemented into the software package and systematically evaluated. The retrieval methods have been applied to the estimation of LAI and LCC from simulated Sentinel-2 data, but the majority of investigated methods can essentially be applied to derive any detectable vegetation biochemical or biophysical variable. The fundamentals of ARTMO has been laid during J.P. Rivera’s MSc thesis project and has been further developed during the course of my PhD Thesis. The toolbox is built on a suite of radiative transfer models and image processing modules in a modular graphical user interface (GUI) environment. ARTMO has been mainly developed and tested for processing (simulated) Sentinel-2 data in a semiautomatic way, but in principle data from any optical sensor can be processed