Opportunities and limitations of crop phenotyping in southern european countries

ReviewThe Mediterranean climate is characterized by hot dry summers and frequent droughts. Mediterranean crops are frequently subjected to high evapotranspiration demands, soil water deficits, high temperatures, and photo-oxidative stress. These conditions will become more severe due to global warming which poses major challenges to the sustainability of the agricultural sector in Mediterranean countries. Selection of crop varieties adapted to future climatic conditions and more tolerant to extreme climatic events is urgently required. Plant phenotyping is a crucial approach to address these challenges. High-throughput plant phenotyping (HTPP) helps to monitor the performance of improved genotypes and is one of the most effective strategies to improve the sustainability of agricultural production. In spite of the remarkable progress in basic knowledge and technology of plant phenotyping, there are still several practical, financial, and political constraints to implement HTPP approaches in field and controlled conditions across the Mediterranean. The European panorama of phenotyping is heterogeneous and integration of phenotyping data across different scales and translation of “phytotron research” to the field, and from model species to crops, remain major challenges. Moreover, solutions specifically tailored to Mediterranean agriculture (e.g., crops and environmental stresses) are in high demand, as the region is vulnerable to climate change and to desertification processes. The specific phenotyping requirements of Mediterranean crops have not yet been fully identified. The high cost of HTPP infrastructures is a major limiting factor, though the limited availability of skilled personnel may also impair its implementation in Mediterranean countries. We propose that the lack of suitable phenotyping infrastructures is hindering the development of new Mediterranean agricultural varieties and will negatively affect future competitiveness of the agricultural sector. We provide an overview of the heterogeneous panorama of phenotyping within Mediterranean countries, describing the state of the art of agricultural production, breeding initiatives, and phenotyping capabilities in five countries: Italy, Greece, Portugal, Spain, and Turkey. We characterize some of the main impediments for development of plant phenotyping in those countries and identify strategies to overcome barriers and maximize the benefits of phenotyping and modeling approaches to Mediterranean agriculture and related sustainabilityinfo:eu-repo/semantics/publishedVersio

Heavy metal soil contamination detection using combined geochemistry and field spectroradiometry in the United Kingdom

Technological advances in hyperspectral remote sensing have been widely applied in heavy metal soil contamination studies, as they are able to provide assessments in a rapid and cost-effective way. The present work investigates the potential role of combining field and laboratory spectroradiometry with geochemical data of lead (Pb), zinc (Zn), copper (Cu) and cadmium (Cd) in quantifying and modelling heavy metal soil contamination (HMSC) for a floodplain site located in Wales, United Kingdom. The study objectives were to: (i) collect field- and lab-based spectra from contaminated soils by using ASD FieldSpec® 3, where the spectrum varies between 350 and 2500 nm; (ii) build field- and lab-based spectral libraries; (iii) conduct geochemical analyses of Pb, Zn, Cu and Cd using atomic absorption spectrometer; (iv) identify the specific spectral regions associated to the modelling of HMSC; and (v) develop and validate heavy metal prediction models (HMPM) for the aforementioned contaminants, by considering their spectral features and concentrations in the soil. Herein, the field- and lab-based spectral features derived from 85 soil samples were used successfully to develop two spectral libraries, which along with the concentrations of Pb, Zn, Cu and Cd were combined to build eight HMPMs using stepwise multiple linear regression. The results showed, for the first time, the feasibility to predict HMSC in a highly contaminated floodplain site by combining soil geochemistry analyses and field spectroradiometry. The generated models help for mapping heavy metal concentrations over a huge area by using space-borne hyperspectral sensors. The results further demonstrated the feasibility of combining geochemistry analyses with filed spectroradiometric data to generate models that can predict heavy metal concentrations

Assessing the potential of remote sensing to discriminate invasive Seriphium plumosum from grass

The usefulness of remote sensing to discriminate Seriphium plumosum from grass using a field spectrometer data was investigated in this study. Analysis focused on wavelength regions that showed potential of discriminating S. plumosum from grass which were determined from global pair spectral comparison between S. plumosum and grass. Assessment of reflectance differences done at individual and plot levels using original spectra and spectra simulated based on bands of Landsat and SPOT 5 images. The simulations were done to investigate the possibility of extending field based information into airborne and spaceborne remote sensing techniques. Results showed reflectance spectra of S. plumosum and grass to be relatively comparable. Comparisons at all levels of analysis using original spectra did not show noteworthy reflectance difference in all regions used in the analysis. Similarly, simulated spectra did not show significant differences. The results therefore did not appear to encourage the potential of upscaling the application to airborne and spaceborne remote sensing techniques. There were, however, some shortcomings that made it difficult to draw conclusive remarks on whether the plant can be differentiated from grass. These included, firstly, not all species were in the same phenology. Secondly, spectral measurements were not necessarily taken in an ideal scenario of optimal sunny conditions. It is therefore advised that a similar study be carried out that will address the shortcomings of this study. Furthermore, studies on the biochemical composition of both S. plumosum and grass species are needed, since they explain spectral properties of plants

Enviromentální aplikace obrazové spektroskopie

The main purpose of this thesis is to use Image Spectroscopy as a tool to monitor the environmental conditions in a region affected by anthropogenic activities via estimating both geochemical and biochemical parameters on a regional scale. The research has been carried on the Sokolov lignite mine, NW Bohemia, a region affected by long-term extensive mining. The thesis is divided into two thematic parts. First part is devoted to applications of Image Spectroscopy into Acid Mine Drainage mapping and its related issues (chapters 2 and 3). In chapter 2 the equivalent mineral end-members were successfully derived from the ASTER image data (Advanced Space-borne Thermal Emission and Reflection Radiometer satellite data). In the chapter 3 the pH was estimated on the basis of mineral and image spectroscopy. The Multi Range Spectral Feature Fitting (MRSFF) technique was utilized for mineral mapping and the multiple regression model using the fit images, the results of MRSFF, as inputs was constructed to estimate the surface pH and statistical significant accuracy was attained. In the second thematic part (chapters 4-6) Image Spectroscopy is applied into monitoring of vegetation stress. A new statistical method was developed to assess the physiological status of macroscopically undamaged foliage of Norway...Předložená disertační práce se věnuje aplikaci metod obrazové spektroskopie jako moderního nástroje pro environmentální monitoring, přičemž se zaměřuje na modelování vybraných geochemických a biochemických parametrů Disertační práce je členěna do dvou tematických celků. První z nich (kapitoly 2 a 3) je věnován aplikaci minerální a obrazové spektroskopie pro vymezení plošného výskytu povrchové acidifikace (anglický termín: AMD - Acid Mine Drainage) a modelování povrchového pH. Druhá tematická část (kapitoly 4, 5 a 6) se věnuje zhodnocení fyziologického stavu smrkových porostů. V kapitole 2 jsou s využitím satelitních dat ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer satellite data) plošně vymezeny kyselé zvětralinové povrchy (pH<4), jež charakterizuje výskyt jarositu a lignitu (hnědé uhlí). Kapitola 3 se věnuje vytvoření modelu pro odhad povrchového pH odkrytých substrátů s využitím leteckých hyperspektrálních dat HyMap (07/2009). Tato studie je jednou z prvních, jež aplikuje metody obrazové spektroskopie pro kvantitativní modelování pH v prostředí povrchových dolů vyznačující se vysokou heterogenitou. V druhé tematické části je obrazová spektroskopie aplikována do oblasti monitoringu zdravotního stavu lesních smrkových porostů, které se vyskytují v bezprostředním okolí...Department of Applied Geoinformatics and CartographyKatedra aplikované geoinformatiky a kartografieFaculty of SciencePřírodovědecká fakult

Applying Neural Networks to Hyperspectral and Multispectral Field Data for Discrimination of Cruciferous Weeds in Winter Crops

In the context of detection of weeds in crops for site-specific weed control, on-ground spectral reflectance measurements are the first step to determine the potential of remote spectral data to classify weeds and crops. Field studies were conducted for four years at different locations in Spain. We aimed to distinguish cruciferous weeds in wheat and broad bean crops, using hyperspectral and multispectral readings in the visible and near-infrared spectrum. To identify differences in reflectance between cruciferous weeds, we applied three classification methods: stepwise discriminant (STEPDISC) analysis and two neural networks, specifically, multilayer perceptron (MLP) and radial basis function (RBF). Hyperspectral and multispectral signatures of cruciferous weeds, and wheat and broad bean crops can be classified using STEPDISC analysis, and MLP and RBF neural networks with different success, being the MLP model the most accurate with 100%, or higher than 98.1%, of classification performance for all the years. Classification accuracy from hyperspectral signatures was similar to that from multispectral and spectral indices, suggesting that little advantage would be obtained by using more expensive airborne hyperspectral imagery. Therefore, for next investigations, we recommend using multispectral remote imagery to explore whether they can potentially discriminate these weeds and crops

Remote Sensing as a Precision Farming Tool in the Nile Valley, Egypt

Detecting stress in plants resulting from different stressors including nitrogen deficiency, salinity, moisture, contamination and diseases, is crucial in crop production. In the Nile Valley, crop production is hindered perhaps more fundamentally by issues of water supply and salinity. Predicting stress in crops by conventional methods is tedious, laborious and costly and is perhaps unreliable in providing a spatial context of stress patterns. Accurate and quick monitoring techniques for crop status to detect stress in crops at early growth stages are needed to maximize crop productivity. In this context, remotely sensed data may provide a useful tool in precision farming. This research aims to evaluate the role of in situ hyperspectral and high spatial resolution satellite remote sensing data to detect stress in wheat and maize crops and assess whether moisture induced stress can be distinguished from salinity induced stress spectrally. A series of five greenhouse based experiments on wheat and maize were undertaken subjecting both crops to a range of salinity and moisture stress levels. Spectroradiometry measurements were collected at different growth stages of each crop to assess the relationship between crop biophysical and biochemical properties and reflectance measurements from plant canopies. Additionally, high spatial resolution satellite images including two QuickBird, one ASTER and two SPOT HRV were acquired in south-west Alexandria, Egypt to assess the potential of high spectral and spatial resolution satellite imagery to detect stress in wheat and maize at local and regional scales. Two field work visits were conducted in Egypt to collect ground reference data and coupled with Hyperion imagery acquisition, during winter and summer seasons of 2007 in March (8-30: wheat) and July (12-17: maize). Despite efforts, Hyperion imagery was not acquired due to factors out with the control of this research. Strong significant correlations between crop properties and different vegetation indices derived from both ground based and satellite platforms were observed. RDVI showed a sensitive index to different wheat properties (r > 0.90 with different biophysical properties). In maize, GNDVIbr and Cgreen had strong significant correlations with maize biophysical properties (r > 0.80). PCA showed the possibility to distinguish between moisture and salinity induced stress at the grain filling stages. The results further showed that a combined approach of high (2-5 m) and moderate (15-20) spatial resolution satellite imagery can provide a better mechanistic interpretation of the distribution and sources of stress, despite the typical small size of fields (20-50 m scale). QuickBird imagery successfully detects stress within field and local scales, whereas SPOT HRV imagery is useful in detecting stress at a regional scale, and therefore, can be a robust tool in identifying issues of crop management at a regional scale. Due to the limited spectral capabilities of high spatial resolution images, distinguishing different sources of stress is not directly possible, and therefore, hyperspectral satellite imagery (e.g. Hyperion or HyspIRI) is required to distinguish between moisture and salinity induced stress. It is evident from the results that remotely sensed data acquired by both in situ hyperspectral and high spatial resolution satellite remote sensing can be used as a useful tool in precision farming in the Nile Valley, Egypt. A combined approach of using reliable high spatial and spectral satellite remote sensing data could provide better insight about stress at local and regional scales. Using this technique as a precision farming and management tool will lead to improved crop productivity by limiting stress and consequently provide a valuable tool in combating issues of food supply at a time of rapid population growth

Latin America:A development pole for phenomics

Latin America and the Caribbean (LAC) has long been associated with the production and export of a diverse range of agricultural commodities. Due to its strategic geographic location, which encompasses a wide range of climates, it is possible to produce almost any crop. The climate diversity in LAC is a major factor in its agricultural potential but this also means climate change represents a real threat to the region. Therefore, LAC farming must prepare and quickly adapt to a climate that is likely to feature long periods of drought, excessive rainfall and extreme temperatures. With the aim of moving towards a more resilient agriculture, LAC scientists have created the Latin American Plant Phenomics Network (LatPPN) which focuses on LAC’s economically important crops. LatPPN’s key strategies to achieve its main goal are: 1) training of LAC members on plant phenomics and phenotyping, 2) establish international and multidisciplinary collaborations, 3) develop standards for data exchange and research protocols, 4) share equipment and infrastructure, 5) disseminate data and research results, 6) identify funding opportunities and 7) develop strategies to guarantee LatPPN’s relevance and sustainability across time. Despite the challenges ahead, LatPPN represents a big step forward towards the consolidation of a common mind-set in the field of plant phenotyping and phenomics in LAC

Avaliação da composição e estrutura ripária Mediterrânica baseada em SIG e detecção remota

Doutoramento em Engenharia Florestal e dos Recursos Naturais - Instituto Superior de AgronomiaRiparian forests are responsible for many functions considered essential to the preservation of the ecological condition of fluvial corridors. The aim of this thesis is to characterize the structural and compositional patterns of the riparian vegetation in relation to its ecological quality using remote detection and geographic information systems. Separability analyses allowed to characterize and distinguish the spectral patterns and divergent optical behavior between the main riparian forests of Portugal. Spectroradiometry analyses enable the identification of the optimal bands for the remote detection of the alien invasive species Arundo donax, giant reed, from the surrounding vegetation, taking into account its seasonal spectral variability. The Geostatistical techniques combined with the application of landscape metrics, in high spatial resolution images, allowed the remote identification of the structural patterns for riparian forests and for the riparian areas invaded by the giant reed. It was obtained a relation between the observed degradation patterns and a gradient of human disturbance in the surrounding areas of fluvial corridors. The combination of the spectral and geometric attributes allowed to increasing giant reed mapping accuracy in riparian habitats, using a semi-automatic technique

Summaries of the Third Annual JPL Airborne Geoscience Workshop. Volume 1: AVIRIS Workshop

This publication contains the preliminary agenda and summaries for the Third Annual JPL Airborne Geoscience Workshop, held at the Jet Propulsion Laboratory, Pasadena, California, on 1-5 June 1992. This main workshop is divided into three smaller workshops as follows: (1) the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) workshop, on June 1 and 2; (2) the Thermal Infrared Multispectral Scanner (TIMS) workshop, on June 3; and (3) the Airborne Synthetic Aperture Radar (AIRSAR) workshop, on June 4 and 5. The summaries are contained in Volumes 1, 2, and 3, respectively

Characterization of a Highly Biodiverse Floodplain Meadow Using Hyperspectral Remote Sensing within a Plant Functional Trait Framework

We assessed the potential for using optical functional types as effective markers to monitor changes in vegetation in floodplain meadows associated with changes in their local environment. Floodplain meadows are challenging ecosystems for monitoring and conservation because of their highly biodiverse nature. Our aim was to understand and explain spectral differences among key members of floodplain meadows and also characterize differences with respect to functional traits. The study was conducted on a typical floodplain meadow in UK (MG4-type, mesotrophic grassland type 4, according to British National Vegetation Classification). We compared two approaches to characterize floodplain communities using field spectroscopy. The first approach was sub-community based, in which we collected spectral signatures for species groupings indicating two distinct eco-hydrological conditions (dry and wet soil indicator species). The other approach was “species-specific”, in which we focused on the spectral reflectance of three key species found on the meadow. One herb species is a typical member of the MG4 floodplain meadow community, while the other two species, sedge and rush, represent wetland vegetation. We also monitored vegetation biophysical and functional properties as well as soil nutrients and ground water levels. We found that the vegetation classes representing meadow sub-communities could not be spectrally distinguished from each other, whereas the individual herb species was found to have a distinctly different spectral signature from the sedge and rush species. The spectral differences between these three species could be explained by their observed differences in plant biophysical parameters, as corroborated through radiative transfer model simulations. These parameters, such as leaf area index, leaf dry matter content, leaf water content, and specific leaf area, along with other functional parameters, such as maximum carboxylation capacity and leaf nitrogen content, also helped explain the species’ differences in functional dynamics. Groundwater level and soil nitrogen availability, which are important factors governing plant nutrient status, were also found to be significantly different for the herb/wetland species’ locations. The study concludes that spectrally distinguishable species, typical for a highly biodiverse site such as a floodplain meadow, could potentially be used as target species to monitor vegetation dynamics under changing environmental conditions