The Case for a New Frontiers–Class Uranus Orbiter: System Science at an Underexplored and Unique World with a Mid-scale Mission

Current knowledge of the Uranian system is limited to observations from the flyby of Voyager 2 and limited remote observations. However, Uranus remains a highly compelling scientific target due to the unique properties of many aspects of the planet itself and its system. Future exploration of Uranus must focus on cross-disciplinary science that spans the range of research areas from the planet’s interior, atmosphere, and magnetosphere to the its rings and satellites, as well as the interactions between them. Detailed study of Uranus by an orbiter is crucial not only for valuable insights into the formation and evolution of our solar system but also for providing ground truths for the understanding of exoplanets. As such, exploration of Uranus will not only enhance our understanding of the ice giant planets themselves but also extend to planetary dynamics throughout our solar system and beyond. The timeliness of exploring Uranus is great, as the community hopes to return in time to image unseen portions of the satellites and magnetospheric configurations. This urgency motivates evaluation of what science can be achieved with a lower-cost, potentially faster-turnaround mission, such as a New Frontiers–class orbiter mission. This paper outlines the scientific case for and the technological and design considerations that must be addressed by future studies to enable a New Frontiers–class Uranus orbiter with balanced cross-disciplinary science objectives. In particular, studies that trade scientific scope and instrumentation and operational capabilities against simpler and cheaper options must be fundamental to the mission formulation

New Frontiers-class Uranus Orbiter: Exploring the feasibility of achieving multidisciplinary science with a mid-scale mission

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The case for a New Frontiers-class Uranus Orbiter:System science at an underexplored and unique world with a mid-scale mission

Current knowledge of the Uranian system is limited to observations from the flyby of Voyager 2 and limited remote observations. However, Uranus remains a highly compelling scientific target due to the unique properties of many aspects of the planet itself and its system. Future exploration of Uranus must focus on cross-disciplinary science that spans the range of research areas from the planet's interior, atmosphere, and magnetosphere to the its rings and satellites, as well as the interactions between them. Detailed study of Uranus by an orbiter is crucial not only for valuable insights into the formation and evolution of our solar system but also for providing ground truths for the understanding of exoplanets. As such, exploration of Uranus will not only enhance our understanding of the ice giant planets themselves but also extend to planetary dynamics throughout our solar system and beyond. The timeliness of exploring Uranus is great, as the community hopes to return in time to image unseen portions of the satellites and magnetospheric configurations. This urgency motivates evaluation of what science can be achieved with a lower-cost, potentially faster-turnaround mission, such as a New Frontiers–class orbiter mission. This paper outlines the scientific case for and the technological and design considerations that must be addressed by future studies to enable a New Frontiers–class Uranus orbiter with balanced cross-disciplinary science objectives. In particular, studies that trade scientific scope and instrumentation and operational capabilities against simpler and cheaper options must be fundamental to the mission formulation

Assessing Grapevine Nutrient Status from Unmanned Aerial System (UAS) Hyperspectral Imagery

This study aimed to identify the optimal sets of spectral bands for monitoring multiple grapevine nutrients in vineyards. We used spectral data spanning 400–2500 nm and leaf samples from 100 Concord grapevine canopies, lab-analyzed for six key nutrient values, to select the optimal bands for the nutrient regression models. The canopy spectral data were obtained with unmanned aerial systems (UAS), using push-broom imaging spectrometers (hyperspectral sensors). The novel use of UAS-based hyperspectral imagery to assess the grapevine nutrient status fills the gap between in situ spectral sampling and UAS-based multispectral imaging, avoiding their inherent trade-offs between spatial and spectral resolution. We found that an ensemble feature ranking method, utilizing six different machine learning feature selection methods, produced similar regression results as the standard PLSR feature selection and regression while generally selecting fewer wavelengths. We identified a set of biochemically consistent bands (606, 641, and 1494 nm) to predict the nitrogen content with an RMSE of 0.17% (using leave-one-out cross-validation) in samples with nitrogen contents ranging between 2.4 and 3.6%. Further studying is needed to confirm the relevance and consistency of the wavelengths selected for each nutrient model, but ensemble feature selection showed promise in identifying stable sets of wavelengths for assessing grapevine nutrient contents from canopy spectra

Toward improved crop management using spectral sensing with unmanned aerial systems

Remote sensing applications in agriculture are established for large-scale monitoring with satellite and airborne imagery, but unmanned aerial systems (UAS) are poised to bring in-field mapping capabilities to the hands of individual farmers. UAS imaging holds several advantages over traditional methods, including centimeter-scale resolution, reduced atmospheric absorption, flexible timing of data acquisitions, and ease of use. In this work, we present two studies using UAS imaging of specialty crops in upstate New York to work towards improved crop management applications. The first study is an investigation of multispectral imagery obtained over table beet fields in Batavia, NY during the 2018 and 2019 seasons to be used in root yield modeling. We determined optimal growth stages for future observations and establish the importance of quantifying early growth via determination of canopy area, a feature unattainable with lower resolution imaging. We developed models for root mass and count based on area-augmented imagery of our raw study plots and their corresponding ground truth data for practical testing with independent data sets. The second study was designed to determine an optimal subset of wavelengths derived from hyperspectral imagery that are related to grapevine nutrients for improved vineyard nutrient monitoring. Our ensemble wavelength selection and regression algorithm chose wavelengths consistent with known absorption features related to nitrogen content in vegetation. Our model achieved a leave-one-out cross-validation root-mean-squared error of 0.17% nitrogen in our dried vine-leaf samples with 2.4-3.6% nitrogen. This is an improvement upon published studies of typical UAS multispectral sensors used to assess grapevine nitrogen status. With further testing on new data, we can determine consistently selected wavelengths and guide the design of specialty multispectral sensors for improved grapevine nutrient management

Dusty, Dense, Bright, Dark, Narrow, and Broad: the Rings in our Solar System and a Few of the Things They Can Tell Us

The observations of planetary rings acquired by the Cassini and Voyager missions and from the Earth have launched large advancements in our knowledge of planetary ring formation, evolution, and dynamics. Today, the study of the ring systems around our giant planets has evolved further into using the rings themselves as a tool to learn about their home planetary systems. In Chapter moonlets we use two of the narrow rings of Uranus to probe for unseen nearby small moons who gravitationally interact with the rings and produce wake-like structures. These small moons both provide a possible solution for the confinement of their neighboring narrow rings and may represent a subset of the upper end of the ring particle size distribution. In Chapter eta we detect the effects of a resonant induced radial mode in the Uranian eta ring. The amplitude of the radial oscillations observed in the eta ring allow us to estimate the mass of the perturbing moon Cressida. This is the first such measurement of small inner Uranian moon's mass and density. In Chapter roche we track the presence and strength of periodic brightness variations in Saturn's dusty Roche Division and find that they are most likely caused by the seasonally varying planetary period oscillations of Saturn's magnetic field. This is just one of many ways in which the rings of Saturn have been found to be interacting with their magnetospheric environment.doctoral, Ph.D., Physics -- University of Idaho - College of Graduate Studies, 2019-1

Predicting Table Beet Root Yield with Multispectral UAS Imagery

Timely and accurate monitoring has the potential to streamline crop management, harvest planning, and processing in the growing table beet industry of New York state. We used unmanned aerial system (UAS) combined with a multispectral imager to monitor table beet (Beta vulgaris ssp. vulgaris) canopies in New York during the 2018 and 2019 growing seasons. We assessed the optimal pairing of a reflectance band or vegetation index with canopy area to predict table beet yield components of small sample plots using leave-one-out cross-validation. The most promising models were for table beet root count and mass using imagery taken during emergence and canopy closure, respectively. We created augmented plots, composed of random combinations of the study plots, to further exploit the importance of early canopy growth area. We achieved a R2 = 0.70 and root mean squared error (RMSE) of 84 roots (~24%) for root count, using 2018 emergence imagery. The same model resulted in a RMSE of 127 roots (~35%) when tested on the unseen 2019 data. Harvested root mass was best modeled with canopy closing imagery, with a R2 = 0.89 and RMSE = 6700 kg/ha using 2018 data. We applied the model to the 2019 full-field imagery and found an average yield of 41,000 kg/ha (~40,000 kg/ha average for upstate New York). This study demonstrates the potential for table beet yield models using a combination of radiometric and canopy structure data obtained at early growth stages. Additional imagery of these early growth stages is vital to develop a robust and generalized model of table beet root yield that can handle imagery captured at slightly different growth stages between seasons

Forecasting Table Beet Root Yield Using Spectral and Textural Features from Hyperspectral UAS Imagery

New York state is among the largest producers of table beets in the United States, which, by extension, has placed a new focus on precision crop management. For example, an operational unmanned aerial system (UAS)-based yield forecasting tool could prove helpful for the efficient management and harvest scheduling of crops for factory feedstock. The objective of this study was to evaluate the feasibility of predicting the weight of table beet roots from spectral and textural features, obtained from hyperspectral images collected via UAS. We identified specific wavelengths with significant predictive ability, e.g., we down-select >200 wavelengths to those spectral indices sensitive to root yield (weight per unit length). Multivariate linear regression was used, and the accuracy and precision were evaluated at different growth stages throughout the season to evaluate temporal plasticity. Models at each growth stage exhibited similar results (albeit with different wavelength indices), with the LOOCV (leave-one-out cross-validation) R2 ranging from 0.85 to 0.90 and RMSE of 10.81–12.93% for the best-performing models in each growth stage. Among visible and NIR spectral regions, the 760–920 nm-wavelength region contained the most wavelength indices highly correlated with table beet root yield. We recommend future studies to further test our proposed wavelength indices on data collected from different geographic locations and seasons to validate our results