The ground waters of Runnels County, Texas, are highly contaminated with nitrate. The average nitrate concentration of 230 water samples was 250 mg/I NO3. The natural variations of the stable nitrogen isotopes N14 and N15 identified natural soil nitrate as the predominant source. Nitrate from animal wastes was of minor importance. The δN15 range of natural soil nitrate was +2 to +8% whereas the δN15 range of animal waste nitrate was +10 to +20%‐ (Atmospheric nitrogen was used as a standard for mass spectrometric analysis. Experimental error for sample preparation and isotopic analysis was ±1 %.) More than 66 percent of the ground‐water nitrates analyzed were in the δN15 range of natural soil nitrates. Dryland farming since 1900 has caused the oxidation of the organic nitrogen in the soil to nitrate. Minimal fertilizer has been used because of the lack of suitable water for irrigation. During the period 1900‐1950, nitrate was leached below the root zone but not to the water table. Extensive terracing after the drought in the early 1950's has raised the water table approximately 6 meters and has leached the nitrate into the ground water. Tritium dates indicate that the ground water is less than 20 years old.

-2 -Extremely dry conditions characterized by amounts of precipitable water vapor (PWV) as low as 1-2 mm commonly occur in high-latitude regions during the winter months. While such dry atmospheres carry only a few percent of the latent heat energy compared to tropical atmospheres, the effects of low vapor amounts on the polar radiation budget -both directly through modulation of longwave radiation and indirectly through the formation of clouds -are considerable. Accurate measurements of precipitable water vapor (PWV) during such dry conditions are needed to improve polar radiation models for use in understanding and predicting change in the climatically sensitive polar regions. To this end, the strong water vapor absorption line at 183.3 10 GHz provides a unique means of measuring low amounts of PWV. Weighting h c t i o n analysis, forward model calculations based upon a 7-year radiosonde dataset, and retrieval simulations consistently predict that radiometric measurements made using several millimeter-wavelength (MMW) channels near the 183 GHz line, together with established microwave (MW) measurements at the 22.235 GHz water vapor line and -3 1 GHz atmospheric absorption window can be used to determine within 5% uncertainty the full range of PWV expected in the Arctic. This unique collective capability stands in spite of accuracy limitations stemming from uncertainties due to the sensitivity of the vertical distribution of temperature and water vapor at MMW channels.In this study the potential of MMW radiometry using the 183 GHz line for measuring low amounts of PWV is demonstrated both theoretically and experimentally. The study uses data obtained during March 1999 as part of an experiment conducted at the Department of Energy's Cloud and Radiation Testbed (CART) near Barrow, Alaska. Several radiometers from both NOAA and NASA were deployed during the experiment to provide the first combined MMW and MW ground-based data set during dry arctic conditions. Single-channel retrievals of PWV were performed using the MW and MMW data. Discrepancies in the retrieved values were found to be consistent with differences observed between measured brightness temperatures (TBs) and forward-modeled TBs based on concurrent radiosonde profiles. These discrepancies are greater than can be explained by measurement error alone and are attributed to absorption model uncertainty. We discuss here the measurements, retrieval technique, and line model discrepancies along with difficulties and potential of MMWMW PWV measurement.

Remote detection of non-native invasive plant species using geospatial imagery may significantly improve monitoring, planning and management practices by eliminating shortfalls, such as observer bias and accessibility involved in ground-based surveys. The use of remote sensing for accurate mapping invasion extent and pattern offers several advantages, including repeatability, large area coverage, complete instead of sub-sampled assessments and greater cost-effectiveness over ground-based methods. It is critical for locating, early mapping and controlling small infestations before they reach economically prohibitive or ecologically significant levels over larger land areas. This study was designed to explore the ability of hyperspectral imagery for mapping infestation of musk thistle (Carduus nutans) on a native grassland during the preflowering stage in mid-April and during the peak flowering stage in mid-June using the support vector machine classifier and to assess and compare the resulting mapping accuracy for these two OPEN ACCESSRemote Sens. 2013, 5 613 distinctive phenological stages. Accuracy assessment revealed that the overall accuracies were 79% and 91% for the classified images at preflowering and peak flowering stages, respectively. These results indicate that repeated detection of the infestation extent, as well as infestation severity or intensity, of this noxious weed in a spatial and temporal context is possible using hyperspectral remote sensing imagery.

The prevalence of wheat streak mosaic, caused by Wheat streak mosaic virus, was assessed using Landsat 5 Thematic Mapper (TM) images in two counties of the Texas Panhandle during the 2005–2006 and 2007–2008 crop years. In both crop years, wheat streak mosaic was widely distributed in the counties studied. Healthy and diseased wheat were separated on the images using the maximum likelihood classifier. The overall classification accuracies were between 89.47 and 99.07% for disease detection when compared to “ground truth” field observations. Omission errors (i.e., pixels incorrectly excluded from a particular class and assigned to other classes) varied between 0 and 12.50%. Commission errors (i.e., pixels incorrectly assigned to a particular class that actually belong to other classes) ranged from 0 to 23.81%. There were substantial differences between planted wheat acreage reported by the United States Department of Agriculture-National Agricultural Statistics Service (USDA-NASS) and that detected by image analyses. However, harvested wheat acreage reported by USDA-NASS and that detected by image classifications were closely matched. These results indicate that the TM image can be used to accurately detect and quantify incidence of wheat streak mosaic over large areas. This method appears to be one of the best currently available for identification and mapping disease incidence over large and remote areas by offering a repeatable, inexpensive, and synoptic strategy during the course of a growing season.

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