Estimating Photosynthetic Attributes from High-Throughput Canopy Hyperspectral Sensing in Sorghum

Sorghum, a genetically diverse C(4) cereal, is an ideal model to study natural variation in photosynthetic capacity. Specific leaf nitrogen (SLN) and leaf mass per leaf area (LMA), as well as, maximal rates of Rubisco carboxylation (V (cmax)), phosphoenolpyruvate (PEP) carboxylation (V (pmax)), and electron transport (J (max)), quantified using a C(4) photosynthesis model, were evaluated in two field-grown training sets (n = 169 plots including 124 genotypes) in 2019 and 2020. Partial least square regression (PLSR) was used to predict V (cmax) (R (2) = 0.83), V (pmax) (R (2) = 0.93), J (max) (R (2) = 0.76), SLN (R (2) = 0.82), and LMA (R (2) = 0.68) from tractor-based hyperspectral sensing. Further assessments of the capability of the PLSR models for V (cmax), V (pmax), J (max), SLN, and LMA were conducted by extrapolating these models to two trials of genome-wide association studies adjacent to the training sets in 2019 (n = 875 plots including 650 genotypes) and 2020 (n = 912 plots with 634 genotypes). The predicted traits showed medium to high heritability and genome-wide association studies using the predicted values identified four QTL for V (cmax) and two QTL for J (max). Candidate genes within 200 kb of the V (cmax) QTL were involved in nitrogen storage, which is closely associated with Rubisco, while not directly associated with Rubisco activity per se. J (max) QTL was enriched for candidate genes involved in electron transport. These outcomes suggest the methods here are of great promise to effectively screen large germplasm collections for enhanced photosynthetic capacity

The potential for using remote sensing to quantify stress in and predict yield of sugarcane (Saccharum spp. hybrid)

Thesis (Ph.D.)-University of KwaZulu-Natal, Pietermaritzburg, 2010

Remote Sensing for Precision Nitrogen Management

This book focuses on the fundamental and applied research of the non-destructive estimation and diagnosis of crop leaf and plant nitrogen status and in-season nitrogen management strategies based on leaf sensors, proximal canopy sensors, unmanned aerial vehicle remote sensing, manned aerial remote sensing and satellite remote sensing technologies. Statistical and machine learning methods are used to predict plant-nitrogen-related parameters with sensor data or sensor data together with soil, landscape, weather and/or management information. Different sensing technologies or different modelling approaches are compared and evaluated. Strategies are developed to use crop sensing data for in-season nitrogen recommendations to improve nitrogen use efficiency and protect the environment

Improving contrast for the detection of archaeological vegetation marks using optical remote sensing techniques.

Airborne archaeological prospection in arable crops relies on detecting features using contrasts in the growth of the overlying crop as a proxy. This is possible because thecomposition of the soil in the features differs from the unmodified subsoil, and this exerts influence on the state of the crop. This influence is expressed as changes in crop canopydensity, structure, and in periods of resource constraint, variations in vegetation stressand vigour. These contrasts are dynamic, and vary temporally with local weather, andspatially with variations in drift geology and land use. This means that the archaeologicalfeatures have no unique spectral signature usable for classification. Rather, contrast isexpressed as relative, local variation in the crop. The extent to which the features are detectable using a particular technique is dependanton the strength of the contrast and the ability of the sensor to resolve it. Current practicerelies heavily on photography in the visible spectrum, but other sensors and processingtechniques have the potential to improve our ability to resolve subtle contrasts. This isimportant, as it affords the opportunity to extend the detection temporally and in soiltypes not normally considered conducive to detection. This work uses multi-temporal spectro-radiometry and ground-based survey to studycontrast at two sites in southern England. From these measurements leaf area index, vegetationindices, the red-edge position, chlorophyll fluorescence and continuum removalof foliar absorption features were derived and compared to evaluate contrast. The knowledgegained from the ground-based surveys was used to inform the analysis of the airbornesurveys. This included the application of vegetation indices to RGB cameras, theuse of multi-temporal and full-waveform LiDAR to detect biomass variations, and the useof various techniques with hyper-spectral imaging spectroscopy. These methods providea demonstrable improvement in contrast, particularly in methods sensitve to chlorophyllfluorescence, which afford the opportunity to record transient and short term contraststhat are not resolved by other sensors

Remote Sensing of Plant Biodiversity

This Open Access volume aims to methodologically improve our understanding of biodiversity by linking disciplines that incorporate remote sensing, and uniting data and perspectives in the fields of biology, landscape ecology, and geography. The book provides a framework for how biodiversity can be detected and evaluated—focusing particularly on plants—using proximal and remotely sensed hyperspectral data and other tools such as LiDAR. The volume, whose chapters bring together a large cross-section of the biodiversity community engaged in these methods, attempts to establish a common language across disciplines for understanding and implementing remote sensing of biodiversity across scales. The first part of the book offers a potential basis for remote detection of biodiversity. An overview of the nature of biodiversity is described, along with ways for determining traits of plant biodiversity through spectral analyses across spatial scales and linking spectral data to the tree of life. The second part details what can be detected spectrally and remotely. Specific instrumentation and technologies are described, as well as the technical challenges of detection and data synthesis, collection and processing. The third part discusses spatial resolution and integration across scales and ends with a vision for developing a global biodiversity monitoring system. Topics include spectral and functional variation across habitats and biomes, biodiversity variables for global scale assessment, and the prospects and pitfalls in remote sensing of biodiversity at the global scale

Sequestration and Characterization of Soil Organic Carbon for Shelterbelt Agroforestry Systems in Saskatchewan

The increase in atmospheric concentration of carbon dioxide (CO₂) is contributing to global climate change. Agroforestry systems, such as shelterbelts, can contribute to the mitigation of increasing CO₂ levels, through carbon (C) sequestration in plant biomass and soils. However, little information is available on the storage and dynamics of soil organic carbon (SOC) for shelterbelt systems. The objective of this research was to examine the effect of shelterbelt plantings on the storage, physical stabilization and chemical composition of SOC for major shelterbelt species across Saskatchewan compared to adjacent agricultural fields. Soil and litter samples were collected for six major shelterbelt species including green ash (Fraxinus pennsylvanica), hybrid poplar (Populus spp.), Manitoba maple (Acer negundo), white spruce (Picea glauca), Scots pine (Pinus sylvestris) and caragana (Caragana arborescens) and the adjacent agricultural fields at 59 sampling sites across the agricultural region of Saskatchewan. Measurement of SOC concentration for soil samples was preceded by fumigation with concentrated HCl (12N), which was determined to be the efficient method for SOC determination in carbonate-rich soils. Physical stabilization of SOC was characterized by using the density fraction technique to separate SOC into uncomplexed, plant-derived debris (i.e. light fraction) and mineral-associated organic matter (i.e. heavy fraction). Changes in SOC composition due to shelterbelt plantation were studied using attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy and synchrotron based carbon K-edge X-ray absorption near edge structure (XANES) spectroscopy. Concentration of SOC for shelterbelts was significantly higher compared to agricultural fields throughout the soil profile (0-50 cm). Sequestration of SOC for shelterbelts varied from 6-38 Mg C ha⁻¹ under different shelterbelt species, along with 3-8 Mg C ha⁻¹ stored in the litter layer. Shelterbelts led to an increase in SOC content for both the labile light fraction and the mineral-associated heavy fraction. The increase in the heavy fraction was higher in coniferous shelterbelt species including white spruce and Scots pine, while the increase in the light fraction C was higher in hybrid poplar, Manitoba maple, green ash and caragana. These trends were attributed to differences in quality and decomposition rate of litter among shelterbelt species. Maximum amount of SOC was sequestered at the 10-30 cm soil depth, and the majority (70%) of it was in the stable mineral-associated form. Light fraction C was predominant in the surface layer (0-10 cm), where it accounted for 92% of the total sequestered C. Younger shelterbelts tended to lose SOC in the early years of shelterbelt establishment, but eventually resulted in net addition of C after about 20 years of age. SOC sequestration potential of shelterbelts was positively related to shelterbelt characteristics including stand age, tree height, diameter and crown width and density of litter layer. These variables together explained 56-67% of the inter-site variability in the amount of SOC sequestered. Analysis of molecular composition of SOC revealed shelterbelts had higher abundance of processed forms of C such as aromatic and conjugated carboxyl groups for hybrid poplar and white spruce shelterbelts and aromatic and aliphatic C moieties for Manitoba maple shelterbelts. In contrast, agricultural field soils were enriched in easily degradable C forms such as polysaccharides. These results revealed a strong effect of initial litter quality and extent of decomposition on SOC composition. Together, these findings indicate that shelterbelt planting leads to sequestration of SOC, resulting in the decrease of atmospheric CO₂ concentration. Additionally, shelterbelts also influence organo-mineral association and molecular composition of SOC, which may affect stabilization and dynamics of sequestered SOC

Remote Sensing of Plant Biodiversity

At last, here it is. For some time now, the world has needed a text providing both a new theoretical foundation and practical guidance on how to approach the challenge of biodiversity decline in the Anthropocene. This is a global challenge demanding global approaches to understand its scope and implications. Until recently, we have simply lacked the tools to do so. We are now entering an era in which we can realistically begin to understand and monitor the multidimensional phenomenon of biodiversity at a planetary scale. This era builds upon three centuries of scientific research on biodiversity at site to landscape levels, augmented over the past two decades by airborne research platforms carrying spectrometers, lidars, and radars for larger-scale observations. Emerging international networks of fine-grain in-situ biodiversity observations complemented by space-based sensors offering coarser-grain imagery—but global coverage—of ecosystem composition, function, and structure together provide the information necessary to monitor and track change in biodiversity globally. This book is a road map on how to observe and interpret terrestrial biodiversity across scales through plants—primary producers and the foundation of the trophic pyramid. It honors the fact that biodiversity exists across different dimensions, including both phylogenetic and functional. Then, it relates these aspects of biodiversity to another dimension, the spectral diversity captured by remote sensing instruments operating at scales from leaf to canopy to biome. The biodiversity community has needed a Rosetta Stone to translate between the language of satellite remote sensing and its resulting spectral diversity and the languages of those exploring the phylogenetic diversity and functional trait diversity of life on Earth. By assembling the vital translation, this volume has globalized our ability to track biodiversity state and change. Thus, a global problem meets a key component of the global solution. The editors have cleverly built the book in three parts. Part 1 addresses the theory behind the remote sensing of terrestrial plant biodiversity: why spectral diversity relates to plant functional traits and phylogenetic diversity. Starting with first principles, it connects plant biochemistry, physiology, and macroecology to remotely sensed spectra and explores the processes behind the patterns we observe. Examples from the field demonstrate the rising synthesis of multiple disciplines to create a new cross-spatial and spectral science of biodiversity. Part 2 discusses how to implement this evolving science. It focuses on the plethora of novel in-situ, airborne, and spaceborne Earth observation tools currently and soon to be available while also incorporating the ways of actually making biodiversity measurements with these tools. It includes instructions for organizing and conducting a field campaign. Throughout, there is a focus on the burgeoning field of imaging spectroscopy, which is revolutionizing our ability to characterize life remotely. Part 3 takes on an overarching issue for any effort to globalize biodiversity observations, the issue of scale. It addresses scale from two perspectives. The first is that of combining observations across varying spatial, temporal, and spectral resolutions for better understanding—that is, what scales and how. This is an area of ongoing research driven by a confluence of innovations in observation systems and rising computational capacity. The second is the organizational side of the scaling challenge. It explores existing frameworks for integrating multi-scale observations within global networks. The focus here is on what practical steps can be taken to organize multi-scale data and what is already happening in this regard. These frameworks include essential biodiversity variables and the Group on Earth Observations Biodiversity Observation Network (GEO BON). This book constitutes an end-to-end guide uniting the latest in research and techniques to cover the theory and practice of the remote sensing of plant biodiversity. In putting it together, the editors and their coauthors, all preeminent in their fields, have done a great service for those seeking to understand and conserve life on Earth—just when we need it most. For if the world is ever to construct a coordinated response to the planetwide crisis of biodiversity loss, it must first assemble adequate—and global—measures of what we are losing

Proximal hyperspectral imaging detects diurnal and drought-induced changes in maize physiology

Hyperspectral imaging is a promising tool for non-destructive phenotyping of plant physiological traits, which has been transferred from remote to proximal sensing applications, and from manual laboratory setups to automated plant phenotyping platforms. Due to the higher resolution in proximal sensing, illumination variation and plant geometry result in increased non-biological variation in plant spectra that may mask subtle biological differences. Here, a better understanding of spectral measurements for proximal sensing and their application to study drought, developmental and diurnal responses was acquired in a drought case study of maize grown in a greenhouse phenotyping platform with a hyperspectral imaging setup. The use of brightness classification to reduce the illumination-induced non-biological variation is demonstrated, and allowed the detection of diurnal, developmental and early drought-induced changes in maize reflectance and physiology. Diurnal changes in transpiration rate and vapor pressure deficit were significantly correlated with red and red-edge reflectance. Drought-induced changes in effective quantum yield and water potential were accurately predicted using partial least squares regression and the newly developed Water Potential Index 2, respectively. The prediction accuracy of hyperspectral indices and partial least squares regression were similar, as long as a strong relationship between the physiological trait and reflectance was present. This demonstrates that current hyperspectral processing approaches can be used in automated plant phenotyping platforms to monitor physiological traits with a high temporal resolution