Assessment of maize nitrogen uptake from PRISMA hyperspectral data through hybrid modelling

The spaceborne imaging spectroscopy mission PRecursore IperSpettrale della Missione Applicativa (PRISMA), launched on 22 March 2019 by the Italian Space Agency, opens new opportunities in many scientific domains, including precision farming and sustainable agriculture. This new Earth Observation (EO) data stream requires new-generation approaches for the estimation of important biophysical crop variables (BVs). In this framework, this study evaluated a hybrid approach, combining the radiative transfer model PROSAIL-PRO and several machine learning (ML) regression algorithms, for the retrieval of canopy chlorophyll content (CCC) and canopy nitrogen content (CNC) from synthetic PRISMA data. PRISMA-like data were simulated from two images acquired by the airborne sensor HyPlant, during a campaign performed in Grosseto (Italy) in 2018. CCC and CNC estimations, assessed from the best performing ML algorithms, were used to define two relations with plant nitrogen uptake (PNU). CNC proved to be slightly more correlated to PNU than CCC (R-2 = 0.82 and R-2 = 0.80, respectively). The CNC-PNU model was then applied to actual PRISMA images acquired in 2020. The results showed that the estimated PNU values are within the expected ranges, and the temporal trends are compatible with plant phenology stages

Seasonal mapping of irrigated winter wheat traits in Argentina with a hybrid retrieval workflow using sentinel-2 imagery

Earth observation offers an unprecedented opportunity to monitor intensively cultivated areas providing key support to assess fertilizer needs and crop water uptake. Routinely, vegetation traits mapping can help farmers to monitor plant development along the crop’s phenological cycle, which is particularly relevant for irrigated agricultural areas. The high spatial and temporal resolution of the Sentinel-2 (S2) multispectral instrument leverages the possibility to estimate leaf area index (LAI), canopy chlorophyll content (CCC), and vegetation water content (VWC) from space. Therefore, our study presents a hybrid retrieval workflow combining a physically-based strategy with a machine learning regression algorithm, i.e., Gaussian processes regression, and an active learning technique to estimate LAI, CCC and VWC of irrigated winter wheat. The established hybrid models of the three traits were validated against in-situ data of a wheat campaign in the Bonaerense valley, South of the Buenos Aires Province, Argentina, in the year 2020. We obtained good to highly accurate validation results with LAI: R2 = 0.92, RMSE = 0.43 m2 m−2, CCC: R2 = 0.80, RMSE = 0.27 g m−2 and VWC: R2 = 0.75, RMSE = 416 g m−2. The retrieval models were also applied to a series of S2 images, producing time series along the seasonal cycle, which reflected the effects of fertilizer and irrigation on crop growth. The associated uncertainties along with the obtained maps underlined the robustness of the hybrid retrieval workflow. We conclude that processing S2 imagery with optimised hybrid models allows accurate space-based crop traits mapping over large irrigated areas and thus can support agricultural management decisions.Fil: Caballero, Gabriel. Technological University of Uruguay (UTEC). Agri-Environmental Engineering; Uruguay. University of Valencia. Image Processing Laboratory (IPL); EspañaFil: Pezzola, Alejandro. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Hilario Ascasubi; ArgentinaFil: Winschel, Cristina Ines. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Hilario Ascasubi; ArgentinaFil: Casella, Alejandra. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; ArgentinaFil: Sanchez Angonova, Paolo Andres. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Hilario Ascasubi; ArgentinaFil: Rivera Caicedo, Juan Pablo. CONACYT-UAN. Secretary of Research and Graduate Studies; MéxicoFil: Berger, Katja. University of Valencia. Image Processing Laboratory (IPL); España. Mantle Labs GmbH; AustriaFil: Verrelst, Jochem. University of Valencia. Image Processing Laboratory (IPL); EspañaFil: Delegido, Jesús. Universidad de Valencia. Image Processing Laboratory (IPL); Españ

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

Farming and earth observation: sentinel-2 data to estimate within-field wheat grain yield

Wheat grain yield (GY) is a crop feature of central importance affecting agricultural, environmental, and socioeconomic sustainability worldwide. Hence, the estimation of within-field variability of GY is pivotal for the agricultural management, especially in the current global change context. In this sense, Earth Observation Systems (EOS) are key technologies that use satellite data to monitor crop yield, which can guide the application of precision farming. Yet, novel research is required to improve the multiplatform integration of data, including data processing, and the application of this discipline in agricultural management. This article provides a novel methodological analysis and assessment of its applications in precision farming. It presents an integration of wheat GY, Global Positioning Systems (GPS), combine harvester data, and EOS Sentinel-2 multispectral bands. Moreover, it compares several indices and machine learning (ML) approaches to map within-field wheat GY. It also analyses the importance of multi-date remote sensing imagery and explores its potential applications in precision agriculture. The study was conducted in Spain, a major European wheat producer. Within-field GY data was obtained from a GPS combine harvester machine for 8 fields over three seasons (2017-2019) and consecutively processed to match Sentinel-2 10 m pixel size. Seven vegetation indices (NDVI, GNDVI, EVI, RVI, TGI, CVI and NGRDI) as well as the biophysical parameter LAI (leaf area index) retrieved with radiative transfer models (RTM) were calculated from Sentinel-2 bands. Sentinel-2 10 m resolution bands alone were also used as variables. Random forest, support vector machine and boosted regressions were used as modelling approaches, and multilinear regression was calculated as baseline. Different combinations of dates of measurement were tested to find the most suitable model feeding data. LAI retrieved from RTM had a slightly improved performance in estimating within-field GY in comparison with vegetation indices or Sentinel-2 bands alone. At validation, the use of multi-date Sentinel-2 data was found to be the most suitable in comparison with single date images. Thus, the model developed with random forest regression (e.g. R-2 = 0.89, and RSME = 0.74 t/ha when using LAI) outperformed support vector machine (R-2 = 0.84 and RSME = 0.92 t/ha), boosting regression (R-2 = 0.85 and RSME = 0.88 t/ha) and multilinear regression (R-2 = 0.69 and RSME = 1.29 t/ha). However, single date images at specific phenological stages (e.g. R-2 = 0.84, and RSME = 0.88 t/ha using random forest at stem elongation) also posed relatively high R-2 and low RMSE, with potential for precision farming management before harvest.A & nbsp;We acknowledge the support of the project PID2019-106650RB-C21 from the Ministerio de Ciencia e Innovacion, Spain. J.S. is a recipient of a FPI doctoral fellowship from the same institution (grant: PRE2020-091907) . J.L.A. acknowledges support from the Institucio Catalana de Recerca i Estudis Avancats (ICREA) , Generalitat de Catalunya, Spain) . S. C.K. is supported by the Ramon y Cajal RYC-2019-027818-I research fellowship from the Ministerio de Ciencia e Innovacion, Spain. We acknowledge the support of Cerealto Siro Group, together with Cristina de Diego and Javier Velasco, technical staff from the company, by providing the wheat yield data. This research was also supported by the COST Action CA17134 SENSECO (Optical synergies for spatiotemporal sensing of scalable ecophysiological traits) funded by COST (European Cooperation in Science and Technology, www.cost.eu)

A Mixed Data-Based Deep Neural Network to Estimate Leaf Area Index in Wheat Breeding Trials

Remote and non-destructive estimation of leaf area index (LAI) has been a challenge in the last few decades as the direct and indirect methods available are laborious and time-consuming. The recent emergence of high-throughput plant phenotyping platforms has increased the need to develop new phenotyping tools for better decision-making by breeders. In this paper, a novel model based on artificial intelligence algorithms and nadir-view red green blue (RGB) images taken from a terrestrial high throughput phenotyping platform is presented. The model mixes numerical data collected in a wheat breeding field and visual features extracted from the images to make rapid and accurate LAI estimations. Model-based LAI estimations were validated against LAI measurements determined non-destructively using an allometric relationship obtained in this study. The model performance was also compared with LAI estimates obtained by other classical indirect methods based on bottom-up hemispherical images and gaps fraction theory. Model-based LAI estimations were highly correlated with ground-truth LAI. The model performance was slightly better than that of the hemispherical image-based method, which tended to underestimate LAI. These results show the great potential of the developed model for near real-time LAI estimation, which can be further improved in the future by increasing the dataset used to train the model

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

Remote Sensing of Biophysical Parameters

Vegetation plays an essential role in the study of the environment through plant respiration and photosynthesis. Therefore, the assessment of the current vegetation status is critical to modeling terrestrial ecosystems and energy cycles. Canopy structure (LAI, fCover, plant height, biomass, leaf angle distribution) and biochemical parameters (leaf pigmentation and water content) have been employed to assess vegetation status and its dynamics at scales ranging from kilometric to decametric spatial resolutions thanks to methods based on remote sensing (RS) data.Optical RS retrieval methods are based on the radiative transfer processes of sunlight in vegetation, determining the amount of radiation that is measured by passive sensors in the visible and infrared channels. The increased availability of active RS (radar and LiDAR) data has fostered their use in many applications for the analysis of land surface properties and processes, thanks to their insensitivity to weather conditions and the ability to exploit rich structural and texture information. Optical and radar data fusion and multi-sensor integration approaches are pressing topics, which could fully exploit the information conveyed by both the optical and microwave parts of the electromagnetic spectrum.This Special Issue reprint reviews the state of the art in biophysical parameters retrieval and its usage in a wide variety of applications (e.g., ecology, carbon cycle, agriculture, forestry and food security)

Non-Parametric Statistical Approaches for Leaf Area Index Estimation from Sentinel-2 Data: A Multi-Crop Assessment

The leaf area index (LAI) is a key biophysical variable for agroecosystem monitoring, as well as a relevant state variable in crop modelling. For this reason, temporal and spatial determination of LAI are required to improve the understanding of several land surface processes related to vegetation dynamics and crop growth. Despite the large number of retrieved LAI products and the efforts to develop new and updated algorithms for LAI estimation, the available products are not yet capable of capturing site-specific variability, as requested in many agricultural applications. The objective of this study was to evaluate the potential of non-parametric approaches for multi-temporal LAI retrieval by Sentinel-2 multispectral data, in comparison with a VI-based parametric approach. For this purpose, we built a large database combining a multispectral satellite data set and ground LAI measurements collected over two growing seasons (2018 and 2019), including three crops (i.e., winter wheat, maize, and alfalfa) characterized by different growing cycles and canopy structures, and considering different agronomic conditions (i.e., at three farms in three different sites). The accuracy of parametric and non-parametric methods for LAI estimation was assessed by cross-validation (CV) at both the pixel and field levels over mixed-crop (MC) and crop-specific (CS) data sets. Overall, the non-parametric approach showed a higher accuracy of prediction at pixel level than parametric methods, and it was also observed that Gaussian Process Regression (GPR) did not provide any significant difference (p-value > 0.05) between the predicted values of LAI in the MC and CS data sets, regardless of the crop. Indeed, GPR at the field level showed a cross-validated coefficient of determination (R2CV) higher than 0.80 for all three crops