Retrieval of Leaf Area Index (LAI) and Soil Water Content (WC) Using Hyperspectral Remote Sensing under Controlled Glass House Conditions for Spring Barley and Sugar Beet

Leaf area index (LAI) and water content (WC) in the root zone are two major hydro-meteorological parameters that exhibit a dominant control on water, energy and carbon fluxes, and are therefore important for any regional eco-hydrological or climatological study. To investigate the potential for retrieving these parameter from hyperspectral remote sensing, we have investigated plant spectral reflectance (400-2,500 nm, ASD FieldSpec3) for two major agricultural crops (sugar beet and spring barley) in the mid-latitudes, treated under different water and nitrogen (N) conditions in a greenhouse experiment over the growing period of 2008. Along with the spectral response, we have measured soil water content and LAI for 15 intensive measurement campaigns spread over the growing season and could demonstrate a significant response of plant reflectance characteristics to variations in water content and nutrient conditions. Linear and non-linear dimensionality analysis suggests that the full band reflectance information is well represented by the set of 28 vegetation spectral indices (SI) and most of the variance is explained by three to a maximum of eight variables. Investigation of linear dependencies between LAI and soil WC and pre-selected SI's indicate that: (1) linear regression using single SI is not sufficient to describe plant/soil variables over the range of experimental conditions, however, some improvement can be seen knowing crop species beforehand; (2) the improvement is superior when applying multiple linear regression using three explanatory SI's approach. In addition to linear investigations, we applied the non-linear CART (Classification and Regression Trees) technique, which finally did not show the potential for any improvement in the retrieval process

The Detection of Potato Cyst Nematode (PCN) Infestation Using Remotely Sensed Imagery

Potato cyst nematodes (PCN) Globodera pallida Stone and G. rostochiensis (Wollenweber) are economically-important pests which cause significant losses to potato production world-wide. Population control is a major objective of a sustainable management strategy as nematodes are persistent and reproductive rates can be high. Current methods of determining PCN population densities are both expensive and time consuming: an advance on current sampling methods would facilitate the application of precision farming methods to PCN management. The potential for using light reflected from the potato canopy as an assay to detect PCN infestation is investigated. Spectral reflectance was measured from the canopy of commercial potato crops, individual plants and single leaves with a field spectrometer, airborne remote sensing and from SPOT and Ikonos satellite images. Comparisons between the reflectance from healthy and infected plants revealed a significant reduction in green (c. 550nm) reflectance from infested plants, and this was consistent across seasons, cultivars and imaging methods. The strongest results were seen early in the crop cycle, although full canopy development is desirable in airborne and satellite image analysis. Yield-limiting pathogens are frequently reported as increasing visible reflectance by reducing chlorophyll concentration in the leaves of afflicted plants. The spectral response to PCN infestation is distinct from that from common plant stress factors and a PCN- specifrc detector system is proposed. A viable commercial PCN assay would need to be timely, reliable and cost-effective. Whilst aircraft and satellite platforms offer the rapid acquisition of data from large areas, these imaging methods are heavily dependent on weather conditions. Results from data collected using a hand-held chlorophyll meter in this investigation indicate that a closed, or semi-closed imaging system (enclosing or covering the plant canopy) would be suited to the detection of PCN under field conditions and less susceptible to inclement weather

The Agronomic Use and Application of Canopy Reflectance within the Visible and Near-Infrared Wavebands and its Relation with Nitrogen Fertilization in Energy Cane Production in Louisiana

Spectral vegetation index-based models that are used to estimate yield potential are commonly developed from the relationship between early-season crop canopy reflectance readings and actual yield obtained at harvest. Plant population stand can influence cane yield potential and nutrient requirement. This study was conducted at LSU AgCenter Sugar Research Station in St. Gabriel, LA to evaluate (1) the relation between estimated early season biomass yield and the spectral vegetation indices acquired at the same time, (2) nitrogen (N) response pattern between early-season biomass production and yield at harvest, and (3) the relationship between coefficient of variation (CV) among normalized difference vegetation index (NDVI) readings and stand population of cane planted as whole stalk and billets. Treatments were applied in split plots with a randomized complete block design with four replications. Varieties (Ho 02-113, US 72-114) and N application rates (0, 56, 112, and 224 kg N ha-1) were assigned as main plots and sub-plots, respectively. Another experiment was conducted with planting schemes (whole stalks and billets) and varieties (Ho 02-113, US 72-114, Ho 06-9001, Ho 06-9002, L 01-299, and L 03-371) arranged as main and sub-plots, respectively. Biomass clippings and canopy spectral reflectance readings using Jaz® spectrometer were collected at three, four, and five weeks after N application (WAN). Results showed that early-season biomass yield and its canopy reflectance collected at the same time were correlated. Overall, the relationships between vegetation indices (VIs) and biomass were best described with quadratic model at four WAN. Reflectance from red wavelengths (670 and 690 nm) and VI computed from them consistently performed better than the reflectance from red-edge wavelengths in relating early-season biomass production. Variables collected at four and five WAN showed similar response pattern to variable N rates as with harvest. Under favorable weather, billet-planted cane produced higher initial plant population compared to whole stalk-planted cane in 2013. Negative correlation was found between CV among NDVI and plant population. Coefficient of variation among red-based vegetation indices produced better correlation with plant population than those from different wavelengths. Variety had no effect on canopy spectral reflectance

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 Application in Biomass Crop Production Systems in Oklahoma

This study was conducted to evaluate the combined effects of nitrogen and cropping systems on biomass yield and quality and to describe the spatial variation of biomass yield, soil carbon and nitrogen within a switchgrass field. Field plots at Stillwater and Woodward in Oklahoma consisting of five nitrogen treatments and three cropping systems were used for the nitrogen x cropping system study and an 8 ha switchgrass field at Chickasha, Oklahoma was used to describe the spatial variability at fine (2.5 m sampling distance) and coarse scale (10 m sampling distance). Remote sensing technique was used to monitor biomass yield and quality to better understand N requirement and usage for production. Semivariogram were used to evaluate spatial variability of the soil parameters and biomass yield. The results of this study showed that maximum yield was produced at both locations with less than 84 kg N ha 1 and high biomass sorghum has potential to produce biomass yield > 20 Mg ha 1 under normal conditions in Oklahoma. The study results also showed that perennial grass systems are more reliable sources of biomass yield, especially under adverse climatic conditions of Oklahoma. Final biomass yield of high biomass sorghum could be predicted using both broadband (aerial photograph) and narrowband (GreenSeeker) normalized difference vegetation index (NDVI) from July to close to harvest, while biomass yield in the perennial grass was best predicted during June to July. Comparing simple ratios and best narrowband indices with partial least square regression (PLSR) models suggested that while PLSR calibration models produced significantly lower error and higher r2 for predicting biomass yield and N concentration within a growing season, the simple ratios and best narrowband indices were more stable and reliable when used for prediction across growing seasons. Spatial pattern in switchgrass field was described using both ground and aerial imagery. The NDVI computed from aerial imagery provided good precision at the fine scale in describing the spatial distribution of switchgrass yield. Remote sensing application in biomass production systems can greatly improve prediction models for predicting biomass yield and quality in feedstock materials with use of optimal hyperspectral narrowband.Plant & Soil Scienc

High-throughput field phenotyping in cereals and implications in plant ecophysiology

[eng] Global climate change effects on agroecosystems together with increasing world population is already threatening food security and endangering ecosystem stability. Meet global food demand with crops production under climate change scenario is the core challenge in plant research nowadays. Thus, there is an urgent need to better understand the underpinning mechanisms of plant acclimation to stress conditions contributing to obtain resilient crops. Also, it is essential to develop new methods in plant research that permit to better characterize non-destructively plant traits of interest. In this sense, the advance in plant phenotyping research by high throughput systems is key to overcome these challenges, while its verification in the field may clear doubts on its feasibility. To this aim, this thesis focused on wheat and secondarily on maize as study species as they make up the major staple crops worldwide. A large panoply of phenotyping methods was employed in these works, ranging from RGB and hyperspectral sensing to metabolomic characterization, besides of other more conventional traits. All research was performed with trials grown in the field and diverse stressor conditions representative of major constrains for plant growth and production were studied: water stress, nitrogen deficiency and disease stress. Our results demonstrated the great potential of leave-to-canopy color traits captured by RGB sensors for in-field phenotyping, as they were accurate and robust indicators of grain yield in wheat and maize under disease and nitrogen deficiency conditions and of leaf nitrogen concentration in maize. On the other hand, the characterization of the metabolome of wheat tissues contributed to elucidate the metabolic mechanisms triggered by water stress and their relationship with high yielding performance, providing some potential biomarkers for higher yields and stress adaptation. Spectroscopic studies in wheat highlighted that leaf dorsoventrality may affect more than water stress on the reflected spectrum and consequently the performance of the multispectral/hyperspectral approaches to assess yield or any other relevant phenotypic trait. Anatomy, pigments and water changes were responsible of reflectance differences and the existence of leaf-side-specific responses were discussed. Finally, the use of spectroscopy for the estimation of the metabolite profiles of wheat organs showed promising for many metabolites which could pave the way for a new generation phenotyping. We concluded that future phenotyping may benefit from these findings in both the low-cost and straightforward methods and the more complex and frontier technologies.[cat] Els efectes del canvi climàtic sobre els agro-ecosistemes i l’increment de la població mundial posa en risc la seguretat alimentària i l’estabilitat dels ecosistemes. Actualment, satisfer les demandes de producció d’aliments sota l’escenari del canvi climàtic és el repte central a la Biologia Vegetal. Per això, és indispensable entendre els mecanismes subjacents de l’aclimatació a l’estrès que permeten obtenir cultius resilients. També és precís desenvolupar nou mètodes de recerca que permetin caracteritzar de manera no destructiva els trets d’interès. L’avenç del fenotipat vegetal amb sistemes d’alt rendiment és clau per abordar aquests reptes. La present tesi s’enfoca en el blat i secundàriament en el panís com a espècies d’estudi ja que constitueixen els cultius bàsics arreu del món. Un ampli ventall de mètodes de fenotipat s’han utilitzat, des sensors RGB a híper-espectrals fins a la caracterització metabolòmica. La recerca s’ha dut a terme en assajos de camp i s’han avaluat diversos tipus d’estrès representatius de les majors limitacions pel creixement i producció vegetal: estrès hídric i biòtic i deficiència de nitrogen. Els resultats demostraren el gran potencial dels trets del color RGB (des de la planta a la capçada) pel fenotipat de camp, ja que foren indicadors precisos del rendiment a blat i panís sota condicions de malaltia i deficiència de nitrogen i de la concentració de nitrogen foliar a panís. La caracterització metabolòmica de teixits de blat contribuí a esbrinar els processos metabòlics endegats per l’estrès hídric i la seva relació amb comportament genotípic, proporcionant bio-marcadors potencials per rendiments més alts i l’adaptació a l’estrès. Estudis espectroscòpics en blat van demostrar que la dorsoventralitat pot afectar més que l’estrès hídric sobre l’espectre de reflectància i consegüentment sobre el comportament de les aproximacions multi/híper-espectrals per avaluar el rendiment i d’altres trets fenotípics com anatòmics i contingut de pigments. Finalment, l’ús de l’espectroscòpia per l’estimació del contingut metabòlic als teixits de blat resulta prometedor per molts metabòlits, la qual cosa obre les portes per a un fenotipat de nova generació. El fenotipat pot beneficiar-se d’aquestes troballes, tant en els mètodes de baix cost com de les tecnologies més sofisticades i d’avantguarda

Almost 25 years of chromatographic and spectroscopic analytical method development for petroleum hydrocarbons analysis in soil and sediment: State-of-the-art, progress and trends

This review provides a critical insight into the selection of chromatographic and spectroscopic techniques for semi-quantitative and quantitative detection of petroleum hydrocarbons in soil and sediment matrices. Advantages and limitations of both field screening and laboratory-based techniques are discussed and recent advances in chemometrics to extract maximum information from a sample by using the optimal pre-processing and data mining techniques are presented. An integrated analytical framework based on spectroscopic techniques integration and data fusion for the rapid measurement and detection of on-site petroleum hydrocarbons is proposed. Furthermore, factors influencing petroleum hydrocarbons analysis in contaminated samples are discussed and recommendations on how to reduce their influence provided

Determination of autumn senescence in subtropical sourveld grasslands, KwaZulu-Natal, South Africa, based on remote sensing techniques: an approach towards forage quality and quantity assessment.

Doctoral Degree. University of KwaZulu-Natal, Pietermaritzburg.Abstract available in PDF

Detecting and mapping forest nutrient deficiencies: eucalyptus variety (Eucalyptus grandis x and Eucalyptus urophylla) trees in KwaZulu-Natal, South Africa.

Doctoral Degree. University of KwaZulu-Natal, Pietermaritzburg.Abstract available in PDF