A Comparison of X-ray and Radio Emission from the Supernova Remnant Cassiopeia A

We compare the radio and soft X-ray brightness as a function of position within the young supernova remnant Cassiopeia A. A moderately strong correlation (r = 0.7) was found between the X-ray emission (corrected for interstellar absorption) and radio emission, showing that the thermal and relativistic plasmas occupy the same volumes and are regulated by common underlying parameters. The logarithmic slope of the relationship, ln(Sx-ray) = 1.2 x Sradio + ln(k) implies that the variations in brightness are primarily due to path length differences. The X-ray and radio emissivities are both high in the same general locations, but their more detailed relationship is poorly constrained and probably shows significant scatter. The strongest radio and X-ray absorption is found at the western boundary of Cas A. Based on the properties of Cas A and the absorbing molecular cloud, we argue that they are physically interacting. We also compare ASCA derived column densities with 21 cm H I and 18 cm OH optical depths in the direction of Cas A, in order to provide an independent estimate of ISM properties. We derive an average value for the H I spin temperature of about 40 K and measure the ratio of OH to molecular hydrogen to be nominally larger than previous estimates. Keywords: Cas A, Cassiopeia A, interstellar medium, molecular clouds, radio astronomy, supernova remnants, X-ray astronomyComment: To appear in Vol. 446 of The Astrophysical Journal on Aug. 1, 1996; 10 pages with 5 embedded figures; replaced because of updated reference

Using Temporal Changes in Drought Indices to Generate Probabilistic Drought Intensification Forecasts

In this study, the potential utility of using rapid temporal changes in drought indices to provide early warning of an elevated risk for drought development over subseasonal time scales is assessed. Standardized change anomalies were computed each week during the 2000–13 growing seasons for drought indices depicting anomalies in evapotranspiration, precipitation, and soil moisture. A rapid change index (RCI) that encapsulates the accumulated magnitude of rapid changes in the weekly anomalies was computed each week for each drought index, and then a simple statistical method was used to convert the RCI values into drought intensification probabilities depicting the likelihood that drought severity as analyzed by the U.S. Drought Monitor (USDM) would worsen in subsequent weeks. Local and regional case study analyses revealed that elevated drought intensification probabilities often occur several weeks prior to changes in the USDM and in topsoil moisture and crop condition datasets compiled by the National Agricultural Statistics Service. Statistical analyses showed that the RCI-derived probabilities are most reliable and skillful over the central and eastern United States in regions most susceptible to rapid drought development. Taken together, these results suggest that tools used to identify areas experiencing rapid changes in drought indices may be useful components of future drought early warning systems

Demonstration of a Daily High-Resolution (375-m) ALEXI Evapotranspiration Product for the NENA Region

While the current constellation of geostationary sensors provides near-global coverage (60N to 60S) – it requires merging data from 7 satellites [resolving time differences; view angles; atmospheric correction]. Polar orbiting sensors such as MODIS and VIIRS provide daily global coverage of LST at higher resolutions than GEO sensors but at only two times per day

Demonstration of a Daily High-Resolution (375-m) ALEXI Evapotranspiration Product for the NENA Region

No abstract availabl

Keys to successful balanced scorecard implementation and use based on published implementation attempts

In recent years many companies have evolved from being centrally located and managed to decentralized, multi-national companies consisting of many separate entities to be strategically managed. In response to this and other changes, such as the need for better measurement of performance, a strategic management tool was developed called the Balanced Scorecard (BSC). This research provides a tool to guide and evaluate BSC implementation. A meta-synthesis approach was used to examine qualitative BSC data available in the literature that suggested eleven keys to successful BSC implementation and use. These keys are then used to benchmark an implementation in a government logistics organization

Utility of thermal sharpening over Texas high plains irrigated agricultural fields

Irrigated crop production in the Texas high plains (THP) is dependent on water extracted from the Ogallala Aquifer, an area suffering from sever water shortage. Water management in this area is therefore highly important. Thermal satellite imagery at high temporal (~daily) and high spatial (~100 m) resolutions could provide important surface boundary conditions for vegetation stress and water use monitoring, mainly through energy balance models such as DisALEXI. At present, however, no satellite platform collects such high spatiotemporal resolution data. The objective of this study is to examine the utility of an image sharpening technique (TsHARP) for retrieving land surface temperature at high spatial resolution (down to 60 m) from moderate spatial resolution (1 km) imagery, which is typically available at higher (~daily) temporal frequency. A simulated sharpening experiment was applied to Landsat 7 imagery collected over the THP in September 2002 to examine its utility over both agricultural and natural vegetation cover. The Landsat thermal image was aggregated to 960 m resolution and then sharpened to its native resolution of 60 m and to various intermediate resolutions. The algorithm did not provide any measurable improvement in estimating high-resolution temperature distributions over natural land cover. In contrast, TsHARP was shown to retrieve high-resolution temperature information with good accuracy over much of the agricultural area within the scene. However, in recently irrigated fields, TsHARP could not reproduce the temperature patterns. Therefore we conclude that TsHARP is not an adequate substitute for 100-m-scale observations afforded by the current Landsat platforms. Should the thermal imager be removed from follow-on Landsat platforms, we will lose valuable capacity to monitor water use at the field scale, particularly in many agricultural regions where the typical field size is ~100 X 100 m. In this scenario, sharpened thermal imagery from instruments like MODIS or VIIRS would be the suboptimal alternative

Utility of thermal sharpening over Texas high plains irrigated agricultural fields

Irrigated crop production in the Texas high plains (THP) is dependent on water extracted from the Ogallala Aquifer, an area suffering from sever water shortage. Water management in this area is therefore highly important. Thermal satellite imagery at high temporal (~daily) and high spatial (~100 m) resolutions could provide important surface boundary conditions for vegetation stress and water use monitoring, mainly through energy balance models such as DisALEXI. At present, however, no satellite platform collects such high spatiotemporal resolution data. The objective of this study is to examine the utility of an image sharpening technique (TsHARP) for retrieving land surface temperature at high spatial resolution (down to 60 m) from moderate spatial resolution (1 km) imagery, which is typically available at higher (~daily) temporal frequency. A simulated sharpening experiment was applied to Landsat 7 imagery collected over the THP in September 2002 to examine its utility over both agricultural and natural vegetation cover. The Landsat thermal image was aggregated to 960 m resolution and then sharpened to its native resolution of 60 m and to various intermediate resolutions. The algorithm did not provide any measurable improvement in estimating high-resolution temperature distributions over natural land cover. In contrast, TsHARP was shown to retrieve high-resolution temperature information with good accuracy over much of the agricultural area within the scene. However, in recently irrigated fields, TsHARP could not reproduce the temperature patterns. Therefore we conclude that TsHARP is not an adequate substitute for 100-m-scale observations afforded by the current Landsat platforms. Should the thermal imager be removed from follow-on Landsat platforms, we will lose valuable capacity to monitor water use at the field scale, particularly in many agricultural regions where the typical field size is ~100 X 100 m. In this scenario, sharpened thermal imagery from instruments like MODIS or VIIRS would be the suboptimal alternative

Utility of thermal image sharpening for monitoring field-scale evapotranspiration over rainfed and irrigated agricultural regions

The utility of a thermal image sharpening algorithm (TsHARP) in providing fine resolution land surface temperature data to a Two-Source-Model for mapping evapotranspiration (ET) was examined over two agricultural regions in the U.S. One site is in a rainfed corn and soybean production region in central Iowa. The other lies within the Texas High Plains, an irrigated agricultural area. It is concluded that in the absence of fine (sub-field scale) resolution thermal data, TsHARP provides an important tool for monitoring ET over rainfed agricultural areas. In contrast, over irrigated regions, TsHARP applied to kilometer-resolution thermal imagery is unable to provide accurate fine resolution land surface temperature due to significant sub-pixel moisture variations that are not captured in the sharpening procedure. Consequently, reliable estimation of ET and crop stress requires thermal imagery acquired at high spatial resolution, resolving the dominant length-scales of moisture variability present within the landscape

Development OF A Multi-Scale Framework for Mapping Global Evapotranspiration

As the worlds water resources come under increasing tension due to dual stressors of climate change and population growth, accurate knowledge of water consumption through evapotranspiration (ET) over a range in spatial scales will be critical in developing adaptation strategies. Remote sensing methods for monitoring consumptive water use (e.g, ET) are becoming increasingly important, especially in areas of significant water and food insecurity. One method to estimate ET from satellite-based methods, the Atmosphere Land Exchange Inverse (ALEXI) model uses the change in mid-morning land surface temperature to estimate the partitioning of sensible and latent heat fluxes which are then used to estimate daily ET. This presentation will outline several recent enhancements to the ALEXI modeling system, with a focus on global ET and drought monitoring

A Method for Objectively Integrating Soil Moisture Satellite Observations and Model Simulations Toward a Blended Drought Index

With satellite soil moisture (SM) retrievals becoming widely and continuously available, we aim to develop a method to objectively integrate the drought indices into one that is more accurate and consistently reliable. The datasets used in this paper include the Noah land surface modelbased SM estimations, AtmosphereLandExchangeInverse modelbased Evaporative Stress Index, and the satellite SM products from the Advanced Scatterometer, WindSat, Soil Moisture and Ocean Salinity, and Soil Moisture Operational Product System. Using the Triple Collocation Error Model (TCEM) to quantify the uncertainties of these data, we developed an optically blended drought index (BDI_b) that objectively integrates drought estimations with the lowest TCEMderived rootmeansquareerrors in this paper. With respect to the reported drought records and the drought monitoring benchmarks including the U.S. Drought Monitor, the Palmer Drought Severity Index and the standardized precipitation evapotranspiration index products, the BDI_b was compared with the sample average blending drought index (BDI_s) and the RMSEweighted average blending drought indices (BDI_w). Relative to the BDI_s and the BDI_w, the BDI_b performs more consistently with the drought monitoring benchmarks. With respect to the official drought records, the developed BDI_b shows the best performance on tracking drought development in terms of time evolution and spatial patterns of 2010Russia, 2011USA, 2013New Zealand droughts and other reported agricultural drought occurrences over the 20092014 period. These results suggest that model simulations and remotely sensed observations of SM can be objectively translated into useful information for drought monitoring and early warning, in turn can reduce drought risk and impacts