Mesospheric vertical thermal structure and winds on Venus from HHSMT CO spectral-line observations

We report vertical thermal structure and wind velocities in the Venusian mesosphere retrieved from carbon monoxide (12CO J=2-1 and 13CO J=2-1) spectral line observations obtained with the Heinrich Hertz Submillimeter Telescope (HHSMT). We observed the mesosphere of Venus from two days after the second Messenger flyby of Venus (on June 5 2007 at 23:10 UTC) during five days. Day-to-day and day-to-night temperature variations and short-term fluctuations of the mesospheric zonal flow were evident in our data. The extensive layer of warm air detected recently by SPICAV at 90 - to 100 km altitude is also detected in the temperature profiles reported here. These data were part of a coordinated ground-based Venus observational campaign in support of the ESA Venus Express mission. Furthermore, this study attempts to cross-calibrate space- and ground-based observations, to constrain radiative transfer and retrieval algorithms for planetary atmospheres, and to contribute to a more thorough understanding of the global patterns of circulation of the Venusian atmosphere.Comment: 35 pages, 18 figures. Shortcut URL to this page: http://www.sciencedirect.com/science/journal/0032063

Remote sensing vegetation index methods to evaluate changes in greenness and evapotranspiration in riparian vegetation in response to the Minute 319 environmental pulse flow to Mexico

During the spring of 2014, 130 million m(3) of water were released from the United States' Morelos Dam on the lower Colorado River to Mexico, allowing water to reach the Gulf of California for the first time in 13 years. Our study assessed the effects of water transfer or ecological environmental flows from one nation to another, using remote sensing. Spatial applications for water resource evaluation are important for binational, integrated water resources management and planning for the Colorado River, which includes seven basin states in the US plus two states in Mexico. Our study examined the effects of the historic binational experiment (the Minute 319 agreement) on vegetative response along the riparian corridor. We used 250 m Moderate Resolution Imaging Spectroradiometer (MODIS), Enhanced Vegetation Index (EVI) and 30 m Landsat 8 satellite imagery to track evapotranspiration (ET) and the normalized difference vegetation index (NDVI). Our analysis showed an overall increase in NDVI and evapotranspiration (ET) in the year following the 2014 pulse, which reversed a decline in those metrics since the last major flood in 2000. NDVI and ET levels decreased in 2015, but were still significantly higher (P < 0.001) than pre-pulse (2013) levels. Preliminary findings show that the decline in 2015 persisted into 2016 and 2017. We continue to analyse results for 2018 in comparison to short-term (2013-2018) and long-term (2000-2018) trends. Our results support the conclusion that these environmental flows from the US to Mexico via the Minute 319 "pulse" had a positive, but short-lived (1 year), impact on vegetation growth in the delta.Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Vegetation‐groundwater dynamics at a former uranium mill site following invasion of a biocontrol agent: A time series analysis of Landsat normalized difference vegetation index data

Because groundwater recharge in dry regions is generally low, arid and semiarid environments have been considered well-suited for long-term isolation of hazardous materials (e.g., radioactive waste). In these dry regions, water lost (transpired) by plants and evaporated from the soil surface, collectively termed evapotranspiration (ET), is usually the primary discharge component in the water balance. Therefore, vegetation can potentially affect groundwater flow and contaminant transport at waste disposal sites. We studied vegetation health and ET dynamics at a Uranium Mill Tailings Radiation Control Act (UMTRCA) disposal site in Shiprock, New Mexico, where a floodplain alluvial aquifer was contaminated by mill effluent. Vegetation on the floodplain was predominantly deep-rooted, non-native tamarisk shrubs (Tamarix sp.). After the introduction of the tamarisk beetle (Diorhabda sp.) as a biocontrol agent, the health of the invasive tamarisk on the Shiprock floodplain declined. We used Landsat normalized difference vegetation index (NDVI) data to measure greenness and a remote sensing algorithm to estimate landscape-scale ET along the floodplain of the UMTRCA site in Shiprock prior to (2000-2009) and after (2010-2018) beetle establishment. Using groundwater level data collected from 2011 to 2014, we also assessed the role of ET in explaining seasonal variations in depth to water of the floodplain. Growing season scaled NDVI decreased 30% (p <.001), while ET decreased 26% from the pre- to post-beetle period and seasonal ET estimates were significantly correlated with groundwater levels from 2011 to 2014 (r(2) =.71; p =.009). Tamarisk greenness (a proxy for health) was significantly affected by Diorhabda but has partially recovered since 2012. Despite this, increased ET demand in the summer/fall period might reduce contaminant transport to the San Juan River during this period.Public domain articleThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

A chemical survey of exoplanets with ARIEL

Thousands of exoplanets have now been discovered with a huge range of masses, sizes and orbits: from rocky Earth-like planets to large gas giants grazing the surface of their host star. However, the essential nature of these exoplanets remains largely mysterious: there is no known, discernible pattern linking the presence, size, or orbital parameters of a planet to the nature of its parent star. We have little idea whether the chemistry of a planet is linked to its formation environment, or whether the type of host star drives the physics and chemistry of the planet’s birth, and evolution. ARIEL was conceived to observe a large number (~1000) of transiting planets for statistical understanding, including gas giants, Neptunes, super-Earths and Earth-size planets around a range of host star types using transit spectroscopy in the 1.25–7.8 μm spectral range and multiple narrow-band photometry in the optical. ARIEL will focus on warm and hot planets to take advantage of their well-mixed atmospheres which should show minimal condensation and sequestration of high-Z materials compared to their colder Solar System siblings. Said warm and hot atmospheres are expected to be more representative of the planetary bulk composition. Observations of these warm/hot exoplanets, and in particular of their elemental composition (especially C, O, N, S, Si), will allow the understanding of the early stages of planetary and atmospheric formation during the nebular phase and the following few million years. ARIEL will thus provide a representative picture of the chemical nature of the exoplanets and relate this directly to the type and chemical environment of the host star. ARIEL is designed as a dedicated survey mission for combined-light spectroscopy, capable of observing a large and well-defined planet sample within its 4-year mission lifetime. Transit, eclipse and phase-curve spectroscopy methods, whereby the signal from the star and planet are differentiated using knowledge of the planetary ephemerides, allow us to measure atmospheric signals from the planet at levels of 10–100 part per million (ppm) relative to the star and, given the bright nature of targets, also allows more sophisticated techniques, such as eclipse mapping, to give a deeper insight into the nature of the atmosphere. These types of observations require a stable payload and satellite platform with broad, instantaneous wavelength coverage to detect many molecular species, probe the thermal structure, identify clouds and monitor the stellar activity. The wavelength range proposed covers all the expected major atmospheric gases from e.g. H2O, CO2, CH4 NH3, HCN, H2S through to the more exotic metallic compounds, such as TiO, VO, and condensed species. Simulations of ARIEL performance in conducting exoplanet surveys have been performed – using conservative estimates of mission performance and a full model of all significant noise sources in the measurement – using a list of potential ARIEL targets that incorporates the latest available exoplanet statistics. The conclusion at the end of the Phase A study, is that ARIEL – in line with the stated mission objectives – will be able to observe about 1000 exoplanets depending on the details of the adopted survey strategy, thus confirming the feasibility of the main science objectives.Peer reviewedFinal Published versio

Application and Comparison of the MODIS-Derived Enhanced Vegetation Index to VIIRS, Landsat 5 TM and Landsat 8 OLI Platforms: A Case Study in the Arid Colorado River Delta, Mexico

The Enhanced Vegetation Index (EVI) is a key Earth science parameter used to assess vegetation, originally developed and calibrated for the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Terra and Aqua satellites. With the impending decommissioning of the MODIS sensors by the year 2020/2022, alternative platforms will need to be used to estimate EVI. We compared Landsat 5 (2000–2011), 8 (2013–2016) and the Visible Infrared Imaging Radiometer Suite (VIIRS; 2013–2016) to MODIS EVI (2000–2016) over a 420,083-ha area of the arid lower Colorado River Delta in Mexico. Over large areas with mixed land cover or agricultural fields, we found high correspondence between Landsat and MODIS EVI (R2 = 0.93 for the entire area studied and 0.97 for agricultural fields), but the relationship was weak over bare soil (R2 = 0.27) and riparian vegetation (R2 = 0.48). The correlation between MODIS and Landsat EVI was higher over large, homogeneous areas and was generally lower in narrow riparian areas. VIIRS and MODIS EVI were highly similar (R2 = 0.99 for the entire area studied) and did not show the same decrease in performance in smaller, narrower regions as Landsat. Landsat and VIIRS provide EVI estimates of similar quality and characteristics to MODIS, but scale, seasonality and land cover type(s) should be considered before implementing Landsat EVI in a particular area.Open Access Article. UA Open Access Publishing Fund.This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Estimating Productivity Measures in Guayule Using UAS Imagery and Sentinel-2 Satellite Data

Guayule (Parthenium argentatum Gray) is a perennial desert shrub currently under investigation as a viable commercial alternative to the Par&aacute; rubber tree (Hevea brasiliensis), the traditional source of natural rubber. Previous studies on guayule have shown a close association between morphological traits or biomass and rubber content. We collected multispectral and RGB-derived Structure-from-motion (SfM) data using an unmanned aircraft system (UAS; drone) to determine if incorporating both high-resolution normalized difference vegetation index (NDVI; an indicator of plant health) and canopy height (CH) information could support model predictions of crop productivity. Ground-truth resource allocation in guayule was measured at four elevations (i.e., tiers) along the crop&rsquo;s vertical profile using both traditional biomass measurement techniques and a novel volumetric measurement technique. Multiple linear regression models estimating fresh weight (FW), dry weight (DW), fresh volume (FV), fresh-weight-density (FWD), and dry-weight-density (DWD) were developed and their performance compared. Of the crop productivity measures considered, a model predicting FWD (i.e., the fresh weight of plant material adjusted by its freshly harvested volume) and incorporating NDVI, CH, NDVI:CH interaction, and tier parameters reported the lowest mean absolute percentage error (MAPE) between field measurements and predictions, ranging from 9 to 13%. A reduced FWD model incorporating only NDVI and tier parameters was developed to explore the scalability of model predictions to medium spatial resolutions with Sentinel-2 satellite data. Across all UAS surveys and corresponding satellite imagery compared, MAPE between FWD model predictions for UAS and satellite data were below 3% irrespective of soil pixel influence

Estimating Productivity Measures in Guayule Using UAS Imagery and Sentinel-2 Satellite Data

Guayule (Parthenium argentatum Gray) is a perennial desert shrub currently under investigation as a viable commercial alternative to the Pará rubber tree (Hevea brasiliensis), the traditional source of natural rubber. Previous studies on guayule have shown a close association between morphological traits or biomass and rubber content. We collected multispectral and RGB-derived Structure-from-motion (SfM) data using an unmanned aircraft system (UAS; drone) to determine if incorporating both high-resolution normalized difference vegetation index (NDVI; an indicator of plant health) and canopy height (CH) information could support model predictions of crop productivity. Ground-truth resource allocation in guayule was measured at four elevations (i.e., tiers) along the crop’s vertical profile using both traditional biomass measurement techniques and a novel volumetric measurement technique. Multiple linear regression models estimating fresh weight (FW), dry weight (DW), fresh volume (FV), fresh-weight-density (FWD), and dry-weight-density (DWD) were developed and their performance compared. Of the crop productivity measures considered, a model predicting FWD (i.e., the fresh weight of plant material adjusted by its freshly harvested volume) and incorporating NDVI, CH, NDVI:CH interaction, and tier parameters reported the lowest mean absolute percentage error (MAPE) between field measurements and predictions, ranging from 9 to 13%. A reduced FWD model incorporating only NDVI and tier parameters was developed to explore the scalability of model predictions to medium spatial resolutions with Sentinel-2 satellite data. Across all UAS surveys and corresponding satellite imagery compared, MAPE between FWD model predictions for UAS and satellite data were below 3% irrespective of soil pixel influence