To understand the impact of N management on harvestable cover crop systems, seven research trials compared: 1) standard intensive management (SIM) (both fall and spring N application), 2) No fall N, a single spring N application, and 3) Cover N (no N application) effects on winter barley (Hordeum vulgare L.) plant biomass (PB), plant N uptake (PNU), grain yield, residual soil nitrate (RSN), and ammonium (RSA). In general, at winter dormancy, SIM resulted in increased PB and PNU but not RSN or RSA. At cover crop termination; SIM and the No fall N practices increased PNU, and at harvesting stage; they produced higher grain yields than the Cover N practice with little significant effect on RSN or RSA values, under normal climatic conditions. While overall yields for the No fall N treatment were lower (8%) than SIM yields, partial net return was similar due to decreased fertilizer input. Nitrogen use efficiency (NUE) of soft red winter wheat (SRWW) can be improved by characterizing genotypes for NUE using canopy spectral reflectance [(CSR), a cheap, rapid and non-destructive remote sensing tool]. The other objectives of this study were to evaluate the predictive potential of vegetative reflection indices for wheat nitrogen use efficiency (NUE) by genotype and the appropriate stages of CSR sensing. An elite panel of 281 regionally developed SRWW genotypes was screened under low and normal N regimes in two crop seasons for grain yield, N uptake, nitrogen use efficiency for yield (NUEY) and nitrogen use efficiency for protein (NUEP). The best models incorporating CSR data at wheat heading explained a significant proportion of total variation in grain yield, N uptake, NUEY and NUEP. Based on the best linear unbiased predictor values, genotypes were ranked and grouped into quartiles and the most efficient and responsive genotypes were identified. A significant proportion of the genotypes with high NUEY under high N conditions also had high NUEY under N stress; however, this was not the case for NUEP. Similarly, a significant proportion of genotypes with high NUEY also had high NUEP under both normal and low N conditions. / Ph. D.

Spectral reflectance-based, remote sensing technology has been used to adjust in-season nitrogen (N) fertilizer rates for wheat to account for spatial variability in grain yield potential at a sub-meter resolution. The objective of this study was to examine the relationships among spectral reflectance indices, corn tissue N content, chlorophyll measurements, plant size and spacing measurements, and grain yield to develop a similar strategy for variable-rate N management in corn. Irrigated and non-irrigated studies were conducted during the 2002 and 2003 growing seasons in eastern Virginia. Plots were treated with various rates of preplant, starter, and sidedress N fertilizer to establish a wide range of grain yield potential. Spectral measurements, tissue N, chlorophyll measurements, and plant physical measurements were collected at growth stages V6, V8, and V10. At maturity, grain yield was determined and correlated with in-season data and optimum N rate to calibrate in-season, variable-rate N fertilization strategies. Results from these studies indicate that spectral reflectance is well correlated with plant N uptake and chlorophyll meter readings and can also be correlated with final grain yield. These relationships may be used to develop a model to predict in-season, variable N application rates for corn production at a sub-meter resolution. / Master of Science

Master of Science / Department of Agronomy / William T. Schapaugh / Remote sensing technologies might serve as indirect selection tools to improve phenotyping to differentiate genotypes for yield in soybean breeding program as well as the assessment of soybean cyst nematode (SCN), Heterodera glycines. The objective of these studies were to: i) investigate potential use of spectral reflectance indices (SRIs) and canopy temperature (CT) as screening tools for soybean grain yield in an elite, segregating population; ii) determine the most appropriate growth stage(s) to measure SRI’s for predicting grain yield; and iii) estimate SCN population density among and within soybean cultivars utilizing canopy spectral reflectance and canopy temperature. Experiment 1 was conducted at four environments (three irrigated and one rain-fed) in Manhattan, KS in 2012 and 2013. Each environment evaluated 48 F4- derived lines. In experiment 2, two SCN resistant cultivars and two susceptible cultivars were grown in three SCN infested field in Northeast KS, in 2012 and 2013. Initial (Pi) and final SCN soil population (Pf) densities were obtained. Analyses of covariance (ANCOVA) revealed that the green normalized vegetation index (GNDVI) was the best predictive index for yield compared to other SRI’s and differentiated genotype performance across a range of reproductive growth stages. CT did not differentiate genotypes across environments. In experiment 2, relationships between GNDVI, reflectance at single wavelengths (675 and 810 nm) and CT with Pf were not consistent across cultivars or environments. Sudden death syndrome (SDS) may have confounded the relationships between remote sensing data and Pf. Therefore, it would be difficult to assess SCN populations using remote sensing based on these results.

An experiment was conducted to determine whether the water depth (above soil) and soil type would have any influence on the multispectral reflectances of paddy rice, and their calculated vegetation index values. The results showed that, when vegetation cover was low (below 600 grams of dry biomass per square meter), the near infrared (NIR) reflectances decreased very little with water depth. The same was true for red reflectances, but to a lesser degree. Overall the changes were not significant at 0.05 level of significance when the water depth was increased from 2.5 centimeters to 10 centimeters. When the vegetation cover became higher most NIR and red reflectances did not show a significant decrease with the increase of the water depth, and sometimes they even increased slightly up to a water depth of 6.4 cm. Nevertheless both rice cover and water depth as well as soils played an important role in the reflectance pattern in red and NIR bands. Some index values increased and some decreased depending on water depth and rice cover. Statistical analysis of the data showed that rice multispectral responses were mainly controlled by vegetation and minimally influenced by soil and water depths.

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The application of high temporal satellite image data for designation of the spectral characteristic of vegetation Abstract The objektive of this paper is to evaluate possibilities of high temporal satellite data assimilation for continuous monitoring of the spectral characteristic of vegetation. There is also given the suggestion of metodology for processing MERIS data and for continuous monitoring of spectral characteristic of landscape objects. Finally, vegetation cover database for the Czech Republic in the year 2009 is created from sectorial analysis. In the paper there is used the LSU classification and thresholding of vegetation indicies histograms. The universal decision algorithm for classification of vegetation landscape component are described and particular thresholding values for the year 2009 given. The finally product of this paper is Czech vegetation cover database for the year 2009 with overall accuracy of 63,35 %. Accuracy for forest is even over 80 %. Keywords: remote sensing, MERIS, vegetation, spectral reflectance, LSU, BEAM

We argue that one of the early goals of color vision is to distinguish one kind of material from another. Accordingly, we show that when a pair of image regions is such that one region has greater intensity at one wavelength than at another wavelength, and the second region has the opposite property, then the two regions are likely to have arisen from distinct materials in the scene. We call this material change circumstance the 'opposite slope sign condition.' With this criterion as a foundation, we construct a representation of spectral information that facilitates the recognition of material changes. Our theory has implications for both psychology and neurophysiology. In particular, Hering's notion of opponent colors and psychologically unique primaries, and Land's results in two-color projection can be interpreted as different aspects of the visual system's goal of categorizing materials. Also, the theory provides two basic interpretations of the function of double-opponent color cells described by neurophysiologists.

Potato ground cover and spectral data were measured in the Columbia Basin during the 1990 growing season. Three spectral were correlated with ground cover; normalized difference, near infrared-red ratio, and the first derivative of the spectral curve at 750 nm. All models were statistically significant at the 99% level. Normalized was most correlated followed by the near infrared-red ratio, and the first derivative of the spectral curve at 750 nm. / Graduation date: 1992

In this study, the performances of the optical sensors and instruments carried on both ground-based and airborne platforms were evaluated for monitoring crop growing status, detecting the vegetation response to aerial applied herbicides, and identifying crop nitrogen status. Geostatistical analysis on remotely sensed data was conducted to investigate spatial structure of crop canopy normalized difference vegetation index and multispectral imagery. A computerized crop monitoring system was developed that combined sensors and instruments that measured crop structure and spectral data with a global positioning system. The integrated crop monitoring system was able to collect real-time, multi-source, multi-form, and crop related data simultaneously as the tractor-mounted system moved through the field. This study firstly used remotely sensed data to evaluate glyphosate efficacy on weeds applied with conventional and emerging aerial spray nozzles. A weedy field was In this study, the performances of the optical sensors and instruments carried on both ground-based and airborne platforms were evaluated for monitoring crop growing status, detecting the vegetation response to aerial applied herbicides, and identifying crop nitrogen status. Geostatistical analysis on remotely sensed data was conducted to investigate spatial structure of crop canopy normalized difference vegetation index and multispectral imagery. A computerized crop monitoring system was developed that combined sensors and instruments that measured crop structure and spectral data with a global positioning system. The integrated crop monitoring system was able to collect real-time, multi-source, multi-form, and crop related data simultaneously as the tractor-mounted system moved through the field. This study firstly used remotely sensed data to evaluate glyphosate efficacy on weeds applied with conventional and emerging aerial spray nozzles. A weedy field was set up in three blocks and four aerial spray technology treatments were tested. Spectral reflectance measurements were taken using ground-based sensors from all the plots at 1, 8, and 17 days after treatment. The results indicated that the differences among the treatments could be detected with spectral data. This study could provide applicators with guidance equipment configurations that can result in herbicide savings and optimized applications in other crops. The main focus of this research was to apply sensor fusion technology to ground-based and airborne imagery data. Experimental plots cropped with cotton and soybean plants were set up with different nitrogen application rates. The multispectral imagery was acquired by an airborne imaging system over crop field; at the same period, leaf chlorophyll content and spectral reflectance measurements were gathered with chlorophyll meter and spectroradiometer at canopy level on the ground, respectively. Statistical analyses were applied on the data from individual sensor for discrimination with respect to the nitrogen treatment levels. Multisensor data fusion was performed at data level. The results showed that the data fusion of airborne imagery with ground-based data were capable of improving the performance of remote sensing data on detection of crop nitrogen status. The method may be extended to other types of data, and data fusion can be performed at feature or decision level.

No description available.