Crop Phenotyping of Sorghum bicolors Physiological Response to Salt-Affected Soils Using TLS and GPR Remote Sensing Technologies in Nevada Drylands

Saline and sodic soils are major abiotic stressors on the production of flood-irrigated crops in drylands. We conducted a crop phenotyping, remote sensing study on five genotypes of sorghum [Sorghum bicolor (L.) Moench], a drought and salt-tolerant crop, to assist in the molecular breeding of salt-tolerant cultivars. A control plot and a spatially heterogeneous saline-sodic plot (treatment plot) were established in collaboration with Dr. Yerka, Mr. Alfredo Delgado, Dr. Washington-Allen, the Nevada Agricultural Experiment Station (NAES) and the United States Department of Agriculture’s Plant Materials Center (USDA-PMC) in Fallon, Nevada. This location is representative of the variable salinity/sodicity conditions typical of Northern Nevada soils and associated belowground biomass dynamics in drylands. We generated pre- and post-harvest soil attribute maps of the treatment plot using spatial interpolation, we expected individual genotypes to be affected differently by the gradient of various soil constituents. We hypothesized that above- and belowground three-dimensional structural phenology of the five genotypes would be differently affected across the salinity gradient in the treatment plot relative to the control plot. Additionally, we hypothesized that the GPR signal return would vary with the salinity gradient. Finally, we expected an increase in belowground biomass, relative to the control plot, in response to salt-stress as an adaptation to drought. The phenology of coarse-root depth and three-dimensional structure from pre-planting to harvest was non-invasively measured 15 times using a real-time kinematic (RTK) GPS-mounted IDS GeoRadar dual channel (400MHz and 900MHz) ground penetrating radar (GPR) system. Plant height and three-dimensional structural phenology of the five varieties were mapped using a FARO Focus3D X 330 terrestrial laser scanner (TLS). We found differences in above- and belowground three-dimensional structural phenology across the five genotypes in response to the salinity and sodicity gradient. Of the five genotypes in this study, only four emerged in the treatment plot, where Richardson Seed’s Ultra-Early Hybrid performed best under the gradient of salinity and sodicity with the highest rate of emergence (68%), the highest rate of panicle production (4.1 panicles per row), and the greatest panicle volume (67.2%) relative to the control plot. Furthermore, we found that the GPR return signal was not able to detect root mass in the highly saline-sodic soil, however, I was able to detect root mass phenology in the control plot. GPR return signal was not linear in response to the salinity gradient, however, a signal pattern emerged from the different salinity ranges suggesting a gradient response. This study shows the efficacy of the use of these technologies in crop phenotyping and precision agriculture. Future work may improve TLS derived data processing efficiency by developing methods for automating the detection of phenotypic traits (e.g., panicles, leaf area index, number of individual plants). These methods likely will include machine learning algorithms, allometric equations for biomass calculations, and use of drone-mounted LiDAR to reduce occlusion. The use of GPR in the salt-affected soils of this study was not able to definitively identify root mass, however, its use in soil composition for salts and other constituents is indeed promising. Further testing of GPR’s non-detect threshold in salt-affected soils and its ability to quantify individual soil constituents has potential to be highly valuable to the field of soil science and precision agriculture. Furthermore, this study was able to detect a root mass response using GPR, future work may focus on differentiating genotypic variation in root phenology

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

Quantifying the aboveground biomass stock changes associated with oil palm expansion on tropical peatlands using plot-based methods and L-band radar

The recent rapid expansion of oil palm (OP, Elaeis guineensis) plantations into tropical forest peatlands has resulted in net ecosystem carbon emissions. However, quantifications of the net carbon flux from biomass changes require accurate estimates of the above ground biomass (AGB) accumulation rate of OP on peat in working plantations. Current efforts that aim to reduce the emissions from OP expansion would also benefit from the development of economically viable remote sensing approaches that enable the detection of OP plantation expansion and monitoring of AGB stocks across at a fine spatial and temporal resolution. Here, destructive harvest and non-destructive plot inventories are conducted across a chronosequence of OP planting blocks (3 to 12 years after planting (YAP)) in plantations on drained peat in Sarawak, Malaysia. The effectiveness of using a timeseries of L-band synthetic aperture radar (SAR) scenes (ALOS PALSAR-1/2) and a novel ‘biomass matching’ approach to detect, quantify and map the AGB stock changes associated with OP establishment and growth was then assessed. Peat specific allometric equations for palm (9 palms, R2 = 0.92) and frond biomass are developed and upscaled to estimate AGB at the plantation block-level (902 palms). Aboveground biomass stocks on peat accumulated at ~6.39 ± 1.12 Mg ha-1 per year in the first 12 years after planting. However, high inter-palm and inter-block AGB variability was observed in mature classes as a result of variations in palm leaning and mortality. The ‘biomass matching’ approach detected statistically significant deforestation associated with OP establishment. OP growth was well estimated between 4 and 10 YAP, however sensitivity to increases in AGB was lost at ~ 45 - 60 Mg ha. Validation of the allometric equations defined and expansion of non-destructive inventories across alternative plantations and age classes on peat would further strengthen our understanding of OP AGB accumulation rates. With further investigation into the relationship between OP structural characteristics and L-band radar cross section (RCS) in the HV and HH polarisations, ‘biomass matching’ could be a feasible tool for monitoring AGB stock changes to inform carbon emission mitigation strategies

How Universal is the Relationship Between Remotely Sensed Vegetation Indices and Crop Leaf Area Index? A Global Assessment

Leaf Area Index (LAI) is a key variable that bridges remote sensing observations to the quantification of agroecosystem processes. In this study, we assessed the universality of the relationships between crop LAI and remotely sensed Vegetation Indices (VIs). We first compiled a global dataset of 1459 in situ quality-controlled crop LAI measurements and collected Landsat satellite images to derive five different VIs including Simple Ratio (SR), Normalized Difference Vegetation Index (NDVI), two versions of the Enhanced Vegetation Index (EVI and EVI2), and Green Chlorophyll Index (CI(sub Green)). Based on this dataset, we developed global LAI-VI relationships for each crop type and VI using symbolic regression and Theil-Sen (TS) robust estimator. Results suggest that the global LAI-VI relationships are statistically significant, crop-specific, and mostly non-linear. These relationships explain more than half of the total variance in ground LAI observations (R2 greater than 0.5), and provide LAI estimates with RMSE below 1.2 m2/m2. Among the five VIs, EVI/EVI2 are the most effective, and the crop-specific LAI-EVI and LAI-EVI2 relationships constructed by TS, are robust when tested by three independent validation datasets of varied spatial scales. While the heterogeneity of agricultural landscapes leads to a diverse set of local LAI-VI relationships, the relationships provided here represent global universality on an average basis, allowing the generation of large-scale spatial-explicit LAI maps. This study contributes to the operationalization of large-area crop modeling and, by extension, has relevance to both fundamental and applied agroecosystem research

Investigation into the bio-physical constraints on farmer turn-out-date decisions using remote sensing and meteorological data.

ThesisDoctoral thesisAccepted versionGrass is the most common landcover in Ireland and covers a bigger percentage (52%) of the country than any other in Europe. Grass as fodder is Ireland’s most important crop and is the foundation of its most important indigenous industry, agriculture. Yet knowledge of its distribution, performance and yield is scant. How grass is nationally, on a farm by farm, year by year basis managed is not known. In this thesis the gaps in knowledge about grassland performance across Ireland are presented along with arguments on why these knowledge gaps should be closed. As an example the need for high spatial resolution animal stocking rate data in European temperate grassland systems is shown. The effect of high stocking density on grass management is most apparent early in the growing season, and a 250m scale characterization of early spring vegetation growth from 2003-2012, based on MODIS NDVI time series products, is constructed. The average rate of growth is determined as a simple linear model for each pixel, using only the highest quality data for the period. These decadal spring growth model coefficients, start of season cover and growth rate, are regressed against log of stocking rate (r2 19 = 0.75, p<0.001). This model stocking rate is used to create a map of grassland use intensity in Ireland, which, when tested against an independent set of stocking data, is shown to be successful with an RMSE of 0.13 Livestock Unit/ha for a range of stocking densities from 0.1 to 3.3 Livestock Unit/ha. This model provides the first validated high resolution approach to mapping stocking rates in intensively managed European grassland systems. There is a demonstrated a need for a system to estimate current growing conditions. Using the spring growth model constructed for estimating stocking density a new style of grass growth progress anomaly map in the time-domain was developed. Using the developed satellite dataset 1 and 12 years of ground climate station data in Ireland, NDVI was modelled against time as a proxy for grass growth This model is the reference for estimating current seasonal progress of grass growth against a ten year average. The model is developed to estimate Seasonal Progress Anomalies in the Time domain (SPAT), giving a result in terms of “days behind” and “days ahead” of the norm. SPAT estimates for 2012 and 2013 are compared to ground based estimates from 30 climate stations and have a correlation coefficient of 0.897 and RMSE of 15days. The method can successfully map current grass growth trends compared to the average and present this information to the farmer in simple everyday language. This is understood by the author to be the first validated growth anomaly service, and the first for intensive European grasslands. The decisions on when to turn out cattle (the turn out date (TOD)) from winter housing to spring grazing is an important one on Irish dairy farms which has significant impacts on operating costs on the farm. To examine the relationship of TOD to conditions, the National Farm Survey (NFS) of Ireland database was geocoded and the data on turn out dates from 199 farms across Ireland over five years was used. A fixed effects linear panel data model was employed to explore the association between TOD and conditions, as it allows for unobserved variation between farmers to be ignored in favour of modelling the variance year on year. The environmental variables used in the analysis account for 38% of the variance in the turn out dates on farms nationwide. National seasonal conditions dominate over local variation, and for every week earlier grass grows in spring, farmers gain 3.7 days in grazing season but ignore 3.3 days of growth that could have been used. Every 100mm extra rain in spring means TOD is a day later and every dry day leads to turn out being half a day earlier. A well-drained soil makes TOD 2.5 days earlier compared to a poorly drained soil and TOD gets a day later for every 16km north form the south coast. This work demonstrates that precision agriculture 1 driven by optical and radar satellite data is closer to being a reality in Europe driven by enormous amounts of free imagery from NASA and the ESA Sentinel programs coupled with open source meteorological data and models and new developments in data analytics

Maanpäällisten hiilivarastojen kvantifiointi : vertaileva kirjallisuuskatsaus

Ihmisen toiminta on vaikuttanut hiilen kiertokulkuun. Maalla sijaitsevien hiilivarastojen koko on pienentynyt, kun samaan aikaan ilmakehän hiilidioksidipitoisuus on kasvanut aiheuttaen ilmaston lämpenemistä. Pariisin ilmastosopimuksen tavoite on rajoittaa lämpeneminen alle 1,5 asteeseen ja tähän tavoitteeseen pääsemiseksi tarvitaan useita eri keinoja. Yksi mahdollinen keino on hiilensidonta maaperään ja kasvillisuuteen. Jotta luonnolliset hiilivarastot saadaan tehokkaasti mukaan EU -politiikkaan, tulee varastoihin sitoutuneen hiilen määrä pystyä kvantifioimaan. Kehittyviä hiilimarkkinoita varten menetelmän tulee olla tarkka, käytännöllinen ja taloudellisesti mahdollinen. Tämän tutkimuksen tutkimusmenetelmä oli vertaileva kirjallisuuskatsaus ja tavoitteena oli kerätä ja vertailla tietoa tällä hetkellä käytössä olevista menetelmistä hiilen varastojen koon määrittämiseen. Maaperän hiilivarastojen koko pystytään kvantifioimaan maaperänäytteillä, erilaisten maaperäantureiden ja mallinnuksen avulla. Kasvillisuuteen sitoutuneen hiilen määrää voidaan kvantifioida inventaarioperusteisesti tai kaukokartoituksen avulla. Koko ekosysteemin hiilibudjetti saadaan määritettyä kaasuvuo mittauksilla. Maaperän ja kasvillisuuden hiilivarastojen koon ja varaston muutoksen määrittäminen ei ole yksinkertainen tehtävä. Varastojen välillä on runsaasti vaihtelua ja varastoissa tapahtuvat muutokset ovat osittain erittäin hitaita. Varastojen koon määrittämisen kustannukset vaihtelevat tarkkuuden, varaston koon ja toimenpiteiden mukaan. Menetelmät ovat myös riippuvaisia hyvälaatuisesta tiedosta, minkä vuoksi erilaiset tutkimukset hiiliensidontaan vaikuttavista tekijöistä ovat välttämättömiä. Tällä hetkellä hiilimarkkinoita ajatellen erilaiset mallinukseen pohjautuvat menetelmät ovat kustannustehokkaita, läpinäkyviä ja toistettavia, minkä vuoksi niitä voitaisiin hyödyntää jo nyt. Ei ole kuitenkaan olemassa täydellistä mallia, eikä yhtä vaihtoehtoa kaikkiin tilanteisiin, minkä vuoksi eri mallien toiminta tulee olla todennettua. Tämän lähestymistavan myötä voisi kuitenkin olla mahdollista saada enemmän pieniä toimijoita hiilensidonnan markkinoille ja siten tehostaa ilmastotoimia. Eri menetelmät tuovat erilaista tietoa ja myös mallinnukseen pohjautuva lähestymistapa on riippuvainen empiriasta, minkä vuoksi olisi tärkeää, että kaikki informaatio saadaan hyödynnettyä menetelmien kehittämisessä ja tarkkuuksien parantamisessa.The natural carbon cycle is affected by human activity. Terrestrial carbon stocks have been decreasing as at the same time carbon dioxide concentration in the atmosphere has increased causing climate change. The Paris Agreement sets the target to limit climate change to 1.5°C and to reach that goal, all possible mitigation practises should be included into global framework to avoid the most serious consequences of warming. Carbon sequestration into natural soil and biomass could be one mitigation practice. To enhance carbon sequestration activities and to include natural carbon stocks into to the EU climate policy, it would be necessary to quantify stock sizes and changes in those stocks. For developing carbon trading markets, the quantification methods should provide accurate results and at the same time be practical and financially achievable. Used research method in this thesis was comparatively literature survey and aim was to gather and compere information about currently used carbon stock quantification methods against developing carbon trading markets. Soil carbon stocks can be quantified with direct soil sampling, spectroscopic sensing methods or by mathematical models. Biomass carbon stocks can be quantified with inventory-based field measurements and modelling and by remote sensing. The full carbon budget on the ecosystem level can be achieved with carbon flux measurements. Quantification of different terrestrial carbon stocks and their changes is not a simple task. There is a lot of variation between different stocks and in some cases, the stock changes occur slow. Cost of carbon stock quantification depends on the accuracy, size of the area under focus and frequency of the measures. Methods for terrestrial carbon stock quantification are dependent on high quality data and there is demand for research considering carbon sequestration. For carbon offsetting purposes of developing carbon markets, the modelling approach is achievable, cost efficient, repeatable and transparent. There is no perfect model or one universal model that would fit to every situation and thus the differences must be known. At this stage, this approach could be one possibility to include small scale projects and enhance climate actions. Different quantification methods provide information which can be used to different method developments and to increase accuracies. It’s important to know, how all information can be effectively utilized

Remote Sensing of Pasture Quality

Worldwide, farming systems are undergoing significant changes due to economic, environmental and social drivers. Agribusinesses must increasingly deliver products specified in terms of safety, health and quality. Increasing constraints are being placed on them by the market, the community and by government to achieve a financial benefit within social and environmental limits (Dynes et al. 2003). In order to meet these goals, producers must know the quantity and quality of the inputs into their feeding systems, be able to reliably predict the products and by-products being generated, and have the skills to be able to manage their business accordingly. Easy access to accurate and objective evaluation of forage is the first key component to meeting these objectives in livestock systems (Dynes et al. 2003) and remote sensing has considerable potential to be informative and cost-effective (Pullanagari et al. 2012b)