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

Plot-level rapid screening for photosynthetic parameters using proximal hyperspectral imaging

Photosynthesis is currently measured using time-laborious and/or destructive methods which slows research and breeding efforts to identify crop germplasm with higher photosynthetic capacities. We present a plot-level screening tool for quantification of photosynthetic parameters and pigment contents that utilizes hyperspectral reflectance from sunlit leaf pixels collected from a plot (∼2 m×2 m) in c,max, R2=0.79) maximum electron transport rate in given conditions (J1800, R2=0.59), maximal light-saturated photosynthesis (Pmax, R2=0.54), chlorophyll content (R2=0.87), the Chl a/b ratio (R2=0.63), carbon content (R2=0.47), and nitrogen content (R2=0.49). Model predictions did not improve when using two cameras spanning 400-1800 nm, suggesting a robust, widely applicable and more 'cost-effective' pipeline requiring only a single VNIR camera. The analysis pipeline and methods can be used in any cropping system with modified species-specific PLSR analysis to offer a high-throughput field phenotyping screening for germplasm with improved photosynthetic performance in field trials.</p

Quantitative estimation of vegetation traits and temporal dynamics using 3-D radiative transfer models, high-resolution hyperspectral images and satellite imagery

Large-scale monitoring of vegetation dynamics by remote sensing is key to detecting early signs of vegetation decline. Spectral-based indicators of phys-iological plant traits (PTs) have the potential to quantify variations in pho-tosynthetic pigments, chlorophyll fluorescence emission, and structural changes of vegetation as a function of stress. However, the specific response of PTs to disease-induced decline in heterogeneous canopies remains largely unknown, which is critical for the early detection of irreversible damage at different scales. Four specific objectives were defined in this research: i) to assess the feasibility of modelling the incidence and severity of Phytophthora cinnamomi and Xylella fastidiosa based on PTs and biophysical properties of vegetation; ii) to assess non-visual early indicators, iii) to retrieve PT using radiative transfer models (RTM), high-resolution imagery and satellite observations; and iv) to establish the basis for scaling up PTs at different spatial resolutions using RTM for their retrieval in different vegetation co-vers. This thesis integrates different approaches combining field data, air- and space-borne imagery, and physical and empirical models that allow the retrieval of indicators and the evaluation of each component’s contribution to understanding temporal variations of disease-induced symptoms in heter-ogeneous canopies. Furthermore, the effects associated with the understory are introduced, showing not only their impact but also providing a compre-hensive model to account for it. Consequently, a new methodology has been established to detect vegetation health processes and the influence of biotic and abiotic factors, considering different components of the canopy and their impact on the aggregated signal. It is expected that, using the presented methods, existing remote sensors and future developments, the ability to detect and assess vegetation health globally will have a substantial impact not only on socio-economic factors, but also on the preservation of our eco-system as a whole

Evaluating and Developing Methods for Non-Destructive Monitoring of Biomass and Nitrogen in Wheat and Rice Using Hyperspectral Remote Sensing

Aboveground plant biomass and plant nitrogen are two important parameters for plant growth monitoring, which have a decisive influence on the final yield. Mismanagement of fertilizer or pesticide inputs leads to poor plant growth, environmental pollution, and accordingly, yield loss. Biomass development is driven by nutrient supply, temperature, and phenology. Crop biomass reaches its highest weight at the harvest time. In contrast, plant nitrogen is dependent from fertilizer inputs to the soil and from biomass. Destructive measurement of both parameters is time-consuming and labor-intensive. Remote sensing offers remotely non-direct observation methods from outer space, air space, or close-range in the field by sensors. This dissertation focuses on non-destructive monitoring of plant biomass (the primary parameter) and plant nitrogen (the secondary parameter) using hyperspectral data from non-imaging field spectrometers and the imaging EO-1 Hyperion satellite. The study was conducted on two field crops: winter wheat of two growing seasons of the Huimin test site in the North China Plain; and rice of three growing seasons of the Jiansanjiang test site in the Sanjiang Plain of China. Study fields were set up in different spatial scales, from small experimental scale to large farmers' scale. Extensive field measurements were carried out, including both destructive measuring and non-destructive hyperspectral remote sensing of biomass and plant nitrogen. Besides, two years' Hyperion images were acquired at the Huimin test site. Four different approaches were used to develop the estimation models, which include: vegetation indices (VIs), band combinations, Optimum Multiple Narrow Band Reflectance (OMNBR) and stepwise Multiple Linear Regression (MLR), and derivatives of reflectance. Based on these four approaches, models were constructed, compared, and improved step by step. Additionally, a multiscale approach and a new VI, named GnyLi, were developed. Since experimental and farmers' fields were differently managed, several calibration and validation methods were tested and the field datasets were pooled. All tested approaches and band selections were greatly influenced by single growth stages. The broad band VIs saturated for both crops at the booting stage at the latest and were greatly outperformed by the narrow band VIs with optimized band combinations. Model applications from experimental to farmers' scale using the narrow bands measured by field spectrometers mostly failed due to the effects of different management practices and crop cultivars at both spatial scales. In contrast, the multiscale approach was successfully applied in winter wheat monitoring to transfer data and knowledge from field spectrometer measurements from the experimental scale to the farmers' field scale and the scale that is covered by the Hyperion imagery. The GnyLi and the Normalized Ratio Index (NRI) based on the optimized band combinations performed the best in the up-scaling process in the winter wheat study. In the rice study, MLR or OMNBR models based on 4–6 narrow bands better explained biomass variability compared to VIs based on broad bands and optimized band combinations. The models were more robust when data from different scales were pooled and then randomly divided into calibration and validation datasets. Additional model improvements were obtained using derivatives of reflectance. This dissertation evaluates different hyperspectral remote sensing approaches for non-destructive biomass and plant nitrogen monitoring, with the main focus on biomass estimation. The results and comparisons of different approaches revealed their potentials and limits. Development of new VIs, such as GnyLi, is advantageous due to the saturation problem of broad band VIs. However, the developed VIs need to be tested and improved for different crops and sites. Detection of optimized band combinations facilitates the development of new VIs, which are site-specific and crop-specific. MLR-based models may better explain the biomass variability; nevertheless, with more bands, they are prone to the issues of over-fitting and collinearity. Hence, no more than six bands were recommended to select from the hyperspectral data. Derivatives of reflectance were beneficial at the early growing season of rice when the canopy was strongly influenced by background signals from soil and water. However, their benefits were reduced when more bands were used

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

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

Multitemporal assessment of crop parameters using multisensorial flying platforms

UAV sensors suitable for precision farming (Sony NEX-5n RGB camera; Canon Powershot modified to infrared sensitivity; MCA6 Tetracam; UAV spectrometer) were compared over differently treated grassland. The high resolution infrared and RGB camera allows spatial analysis of vegetation cover while the UAV spectrometer enables detailed analysis of spectral reflectance at single points. The high spatial and six-band spectral resolution of the MCA6 combines the opportunities of spatial and spectral analysis, but requires huge calibration efforts to acquire reliable data. All investigated systems were able to provide useful information in different distinct research areas of interest in the spatial or spectral domain. The UAV spectrometer was further used to assess multiangular reflectance patterns of wheat. By flying the UAV in a hemispherical path and directing the spectrometer towards the center of this hemisphere, the system acts like a large goniometer. Other than ground based goniometers, this novel method allows huge diameters without any need for infrastructures on the ground. Our experimental results shows good agreement with models and other goniometers, proving the approach valid. UAVs are capable of providing airborne data with a high spatial and temporal resolution due to their flexible and easy use. This was demonstrated in a two year survey. A high resolution RGB camera was flown every week over experimental plots of barley. From the RGB imagery a time series of the barley development was created using the color values. From this analysis we could track differences in the growth of multiple seeding densities and identify events of plant development such as ear pushing. These results lead towards promising practical applications that could be used in breeding for the phenotyping of crop varieties or in the scope of precision farming. With the advent of high endurance UAVs such as airships and the development of better light weight sensors, an exciting future for remote sensing from UAV in agriculture is expected

Modeling grassland productivity through remote sensing products

Mixed grasslands in south Canada serve a variety of economic, environmental and ecological purposes. Numerical modeling has become a major method used to identify potential grassland ecosystem responses to environment changes and human activities. In recent years, the focus has been on process models because of their high accuracy and ability to describe the interactions among different environmental components and the ecological processes. At present, two commonly-used process models (CENTURY and BIOME-BGC) have significantly improved our understanding of the possible consequences and responses of terrestrial ecosystems under different environmental conditions. However, problems with these models include only using site-based parameters and adopting different assumptions on interactions between plant, environmental conditions and human activities in simulating such complex phenomenon. In light of this shortfall, the overall objective of this research is to integrate remote sensing products into ecosystem process model in order to simulate productivity for the mixed grassland ecosystem in the landscape level. Data used includes 4-years of field measurements and diverse satellite data (System Pour l’Observation de la Terre (SPOT) 4 and 5, Landsat TM and ETM, Advanced Very High Resolution Radiometer (AVHRR) imagery). Using wavelet analyses, the study first detects that the dominant spatial scale is controlled by topography and thus determines that 20-30 m is the optimum resolution to capture the vegetation spatial variation for the study area. Second, the performance of the RDVI (Renormalized Difference Vegetation Index), ATSAVI (Adjusted Transformed Soil-Adjusted Vegetation Index), and MCARI2 (Modified Chlorophyll Absorption Ratio Index 2) are slightly better than the other VIs in the groups of ratio-based, soil-line-related, and chlorophyll-corrected VIs, respectively. By incorporating CAI (Cellulose Absorption Index) as a litter factor in ATSAVI, a new VI is developed (L-ATSAVI) and it improves LAI estimation capability by about 10%. Third, vegetation maps are derived from a SPOT 4 image based on the significant relationship between LAI and ATSAVI to aid spatial modeling. Fourth, object-oriented classifier is determined as the best approach, providing ecosystem models with an accurate land cover map. Fifth, the phenology parameters are identified for the study area using 22-year AVHRR data, providing the input variables for spatial modeling. Finally, the performance of popular ecosystem models in simulating grassland vegetation productivity is evaluated using site-based field data, AVHRR NDVI data, and climate data. A new model frame, which integrates remote sensing data with site-based BIOME-BGC model, is developed for the mixed grassland prairie. The developed remote sensing-based process model is able to simulate ecosystem processes at the landscape level and can simulate productivity distribution with 71% accuracy for 2005