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.

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.

The η ring is one of the narrow rings of Uranus, consisting of a dense core that is 1–2 km wide and a diffuse outer sheet spanning about 40 km. Its dense core lies just exterior to the 3:2 Inner Lindblad Resonance of the small moon Cressida. We fit the η ring radius residuals and longitudes from a complete set of both ground-based and Voyager stellar and radio occultations of the Uranian rings spanning 1977–2002. We find variations in the radial position of the η ring that are likely generated by this resonance, and take the form of a 3-lobed structure rotating at an angular rate equal to the mean motion of the moon Cressida. The amplitude of these radial oscillations is 0.667 ± 0.113 km, which is consistent with the expected shape due to the perturbations from Cressida. The magnitude of these variations provides the first measurement of the mass and density of the moon Cressida ( kg and g cm⁻³) or, indeed, any of Uranus’ small inner moons. A better grasp of inner Uranian satellite masses will provide another clue to the composition, dynamical stability, and history of Uranus’ tightly packed system of small moons.

The Voyager 2 Radio Science Subsystem occultations of the Uranian α and β rings exhibit quasi-periodic optical depth variations with radial wavelengths that vary with longitude. These patterns may be wakes from small moonlets orbiting exterior to these rings. Based on the observed structures in the rings, we estimate that the moonlets would need to be located ∼100 km exterior to the rings’ semimajor axes ( km for α and km for β) and be 2–7 km in radius. Such moonlets could help keep the rings confined. Due to their small radii and presumed low albedo, the expected brightness of these moonlets is on the order of the noise in Voyager 2 images.

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.