Detection of water in interstellar regions by its microwave radiation.
Water in interstellar regions detected by microwave emission from 6 sub 16 to 5 sub 23 rotational transition
Cheung, A. C.↗
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LAVA Subsystem Integration and Testing for the RESOLVE Payload of the Resource Prospector Mission: Mass Spectrometers and Gas Chromatography
The Regolith and Environment Science & Oxygen and Lunar Volatile Extraction (RESOLVE) payload is part of Resource Prospector (RP) along with a rover and a lander that are expected to launch in 2020. RP will identify volatile elements that may be combined and collected to be used for fuel, air, and water in order to enable deeper space exploration. The Resource Prospector mission is a key part of In-Situ Resource Utilization (ISRU). The demand for this method of utilizing resources at the site of exploration is increasing due to the cost of resupply missions and deep space exploration goals. The RESOLVE payload includes the Lunar Advanced Volatile Analysis (LAVA) subsystem. The main instrument used to identify the volatiles evolved from the lunar regolith is the Gas Chromatograph-Mass Spectrometer (GC-MS). LAVA analyzes the volatiles emitted from the Oxygen and Volatile Extraction Node (OVEN) Subsystem. The objective of OVEN is to obtain, weigh, heat and transfer evolved gases to LAVA through the connection between the two subsystems called the LOVEN line. This paper highlights the work completed during a ten week internship that involved the integration, testing, data analysis, and procedure documentation of two candidate mass spectrometers for the LAVA subsystem in order to aid in determining which model to use for flight. Additionally, the examination of data from the integrated Resource Prospector '15 (RP' 15) field test will be presented in order to characterize the amount of water detected from water doped regolith samples.
RESOLVE↗
Lunar Advanced Volatile Analysis Integration and Testing
The Regolith and Environment Science Oxygen and Lunar Volatile Extraction (RESOLVE) payload is part of Resource Prospector (RP) along with a rover and a lander that are expected to launch in 2020. RP will identify volatile elements that may be combined and collected to be used for fuel, air, and water in order to enable deeper space exploration. The Resource Prospector mission is a key part of In-Situ Resource Utilization (ISRU). The demand for this method of utilizing resources at the site of exploration is increasing due to the cost of resupply missions and deep space exploration goals. The RESOLVE payload includes the Lunar Advanced Volatile Analysis (LAVA) subsystem. The main instrument used to identify the volatiles evolved from the lunar regolith is the Gas Chromatograph-Mass Spectrometer (GC-MS). LAVA analyzes the volatiles emitted from the Oxygen and Volatile Extraction Node (OVEN) Subsystem. The objective of OVEN is to obtain, weigh, heat and transfer evolved gases to LAVA through the connection between the two subsystems called the LOVEN line. This paper highlights the work completed during a ten week internship that involved the integration, testing, data analysis, and procedure documentation of two candidate mass spectrometers for the LAVA subsystem in order to aid in determining which model to use for flight. Additionally, the examination of data from the integrated Resource Prospector '15 (RP' 15) field test will be presented in order to characterize the amount of water detected from water doped regolith samples.
In-Situ Resource Utilization↗
Alexandrite lidar for the atmospheric water vapor detection and development of powerful tunable sources in IR
New tunable solid-state lasers, such as alexandrite and Ti-sapphire lasers, provide a powerful technique to detect various molecules in the atmosphere whose absorption bands are in the infrared region. The differential absorption lidar (DIAL) system to measure the tropospheric water vapor has been investigated by many authors, in an early stage, by dye and ruby lasers. Using the alpha band of water vapor, the longest detection range can be obtained with high accuracy, and the alexandrite laser is the most suitable laser for this purpose. In this paper, we describe the detection of water vapor in the atmosphere by an alexandrite lidar, and the development of powerful tunable sources based on Raman lasers in the infrared region.
Uchiumi, M.↗
Rapid toxicity detection in water quality control utilizing automated multispecies biomonitoring for permanent space stations
The objective of this study was to evaluate proposed design characteristics and applications of automated biomonitoring devices for real-time toxicity detection in water quality control on-board permanent space stations. Simulated tests in downlinking transmissions of automated biomonitoring data to Earth-receiving stations were simulated using satellite data transmissions from remote Earth-based stations.
Morgan, E. L.↗
Electronics for a Martian water vapor detection system
Electronic circuitry for aluminum oxide detector of water content in Martian atmosphere and surface
MARS /PLANET/↗
Electromagnetic detection of lunar subsurface water
Radar detection of lunar subsurface water from lunar orbiting satellite
Jiracek, G. R.↗
Supporting Aquaculture in the Chesapeake Bay Using Artificial Intelligence to Detect Poor Water Quality with Remote Sensing
Reliable information on water quality is not currently available at the space and time scales that are required for aquaculture and other resource management needs. For example, shellfish growing areas maybe impacted by harmful algal blooms or runoff from land that increases turbidity, lowers salinity, or introduces contaminants. Shellfish resource managers in the Chesapeake Bay are especially concerned with sources of bacteria from land such as failing onsite waste systems, failing wastewater infrastructure, and concentrated animal feeding operations. There is an urgent need for remote sensing of water quality indicators beyond chlorophyll-a and suspended sediments to augment field sampling programs. Artificial Intelligence trained with simultaneous in situ and satellite observations is explored in preparation for future hyperspectral satellite missions, which offer potential to detect additional water quality indicators not previously possible. This first step identifies and develops a method to harmonize disparate, unlinked aquatic datasets to derive information about where water quality is likely degraded.
Stephanie Schollaert Uz↗
Detection of water vapor in Halley's comet
Gaseous, neutral H2O was detected in the coma of comet Halley on 22.1 and 24.1 December 1985 Universal Time. Nine spectral lines of the nus band (2.65 micrometers) were found by means of a Fourier transform spectrometer on the NASA-Kuiper Airborne Observatory. The water production rate was about 6 x 10 to the 28th molecules per second on 22.1 December and 1.7 x 10 to the 29th molecules per second on 24.1 December UT. The numbers of spectral lines and their intensities are in accord with nonthermal-equilibrium cometary models. Rotational populations are derived from the observed spectral line intensities and excitation conditions are discussed. The ortho-para ratio was found to be 2.66 + or - 0.13, corresponding to a nuclear-spin temperature of 32 K (+5 K, -2 K), possibly indicating that the observed water vapor originated from a low-temperature ice.
Mumma, M. J.↗
Evaluation of computer-aided procedure for detecting surface water
Results from an evaluation of a computer-aided procedure for processing ERTS-1 data to detect and locate surface water are presented. The procedure was evaluated using data from a study area in the vicinity of the Lake Somerville area in Washington County, Texas. The procedure consisted of (1) selecting water training fields, (2) aggregating the training samples together and clustering them into unimodal clusters, (3) computing the mean vector and covariance matrix for each cluster, (4) classifying all of the study area into classes corresponding to the clusters using the maximum likelihood classifier, and (5) thresholding out the nonwater pixels.
Source record↗
On the Detection of Water on the Moon
Permafrost layer existence beneath lunar surface, noting possible detection techniques
MOLECULAR SPECTRUM↗
Cassini/CIRS Observations of Water Vapor in Saturn's Stratosphere
The Composite Infrared Spectrometer (CIRS) on the Cassini spacecraft has obtained numerous spectra of Saturn at varying spectral and spatial resolutions since Saturn Orbit Insertion in 2004. Emission lines due to water vapor in Saturn's stratosphere were first detected using whole-disk observations from the Infrared Space Observatory (Feuchtgruber et al 1997) and subsequently confirmed by the Submillimeter Wave Astronomy Satellite (Rergin et al 2000). CIRS has detected water and the data permit the retrieval of the latitudinal variation of water on Saturn. Emission lines of H2O on Saturn are very weak in the CIRS data. Thus. large spectral averages as well as improvements in calibration are necessary to detect water vapor. Zonally averaged nadir spectra were produced every 10 degrees of latitude. Stratospheric temperatures in the 0.5 - 5.0 mbar range were obtained by inverting spectra of CH4 in the v4 band centered at 1304 cm(exp -1). The origin of water vapor is believed to be from the ablation of micrometeorites containing water ice, followed by photochemistry. This external source of oxygen originates either from the Saturn system (from the rings or perhaps from Enceladus) or from the interplanetary medium. Connerney (1986) proposed a mechanism to transport water from the inner edge of the B-ring along magnetic field lines to specific latitudes (50N and 44S) on Saturn. Prange et al (2006) interpreted a minimum in the abundance of acetylene from ultraviolet spectra near 41S on Saturn as possibly due to an enhanced influx of water. Existing CIRS far-IR spectra are at relatively low spatial resolution, but observations at closer range planned for the extended mission will be able to test the "ring rain" mechanism by searching for localized water vapor enhancement at midlatitudes.
Bjoraker, G. L.↗
Cassini/CIRS Observations of Water Vapor in Saturn's Stratosphere
The Composite Infrared Spectrometer (CIRS) on the Cassini spacecraft has obtained numerous spectra of Saturn at varying spectral and spatial resolutions since Saturn Orbit Insertion in 2001. Emission lines due to water vapor in Saturn's stratosphere were first detected using whole-disk observations from the Infrared Space Observatory [1] and subsequently confirmed by the Submillimeter Wave Astronomy Satellite [2], CIRS has detected water and the data permit the retrieval of the latitudinal variation of water on Saturn. Emission lines of H2O on Saturn are very weak in the CIRS data. Thus, large spectral averages as well as improvements in calibration are necessary to detect water vapor. long integrations at the full 0.5/cm spectral resolution were performed at targeted latitudes on Saturn. High emission angles were chosen to enhance stratospheric emission. Over the course of the prime and extended mission a set of observations has been built up spaced roughly every 10 degrees of latitude. Stratospheric temperatures in the 0.5 - 5.0 mbar range were obtained by inverting spectra of CH4 in the v'4 band centered at 1501/cm. The origin of water vapor is believed to be from the ablation of micrometeorites containing eater ice, followed by photochemistry. This external source of oxygen originates either from the Saturn system (from the rings or perhaps from Enceladus) or from the interplanetary medium. Connerney [3] proposed a mechanism to transport water from the inner edge of the B-ring along magnetic field lines to specific latitudes (50N and 44S) on Saturn. Prange et al [4] interpreted a minimum in the abundance of acetylene from ultraviolet spectra gear 41S on Saturn as possibly due to an enhanced influx of water. We will be able to test the "ring rain" mechanism by searching, for localized water vapor enhancement at mid-latitudes. Our results may be used to constrain photochemical models of Saturn's stratosphere [5].
Bjoraker, Gordon↗
Leaf water stress detection utilizing thematic mapper bands 3, 4 and 5 in soybean plants
The total and diffuse radiance responses of Thematic Mapper bands 3 (0.63-0.69 microns), 4 (0.76-0.90 microns), and 5 (1.55-1.75 microns) to water stress in a soybean canopy are compared. Polarization measurements were used to separate the total from the diffuse reflectance; the reflectances were compared statistically at a variety of look angles at 15 min intervals from about 09.00 until 14.00 hrs EST. The results suggest that remotely sensed data collected in the photographic infrared region (TM4) are sensitive to leaf water stress in a 100 percent canopy cover of soybeans, and that TM3 is less sensitive than TM4 for detection of reversible foliar water stress. The mean values of TM5 reflectance data show similar trends to TM4. The primary implication of this study is that remote sensing of water stress in green plant canopies is possible in TM4 from ground-based observations primarily through the indirect link of leaf geometry.
Holben, B. N.↗
Water Across Synthetic Aperture Radar Data (WASARD): SAR Water Body Classification for the Open Data Cube
The detection of inland water bodies from Synthetic Aperture Radar (SAR) data provides a great advantage over water detection with optical data, since SAR imaging is not impeded by cloud cover. Traditional methods of detecting water from SAR data involves using thresholding methods that can be labor intensive and imprecise. This paper describes Water Across Synthetic Aperture Radar Data (WASARD): a method of water detection from SAR data which automates and simplifies the thresholding process using machine learning on training data created from Geoscience Australia’s WOFS algorithm. Of the machine learning models tested, the Linear Support Vector Machine was determined to be optimal, with the option of training using solely the VH polarization or a combination of the VH and VV polarizations. WASARD was able to identify water in the target area with a correlation of 97% with WOFS. Sentinel-1, Open Data Cube, Earth Observations, Machine Learning, Water Detection 1. INTRODUCTION Water classification is an important function of Earth imaging satellites, as accurate remote classification of land and water can assist in land use analysis, flood prediction, climate change research, as well as a variety of agricultural applications [2]. The ability to identify bodies of water remotely via satellite is immensely cheaper than contracting surveys of the areas in question, meaning that an application that can accurately use satellite data towards this function can make valuable information available to nations which would not be able to afford it otherwise. Highly reliable applications for the remote detection of water currently exist for use with optical satellite data such as that provided by LANDSAT. One such application, Geoscience Australia’s Water Observations from Space (WOFS) has already been ported for use with the Open Data Cube [6]. However, water detection using optical data from Landsat is constrained by its relatively long revisit cycle of 16 days [5], and water detection using any optical data is constrained in that it lacks the ability to make accurate classifications through cloud cover [2]. The alternative solution which solves these problems is water detection using SAR data, which images the Earth using cloud-penetrating microwaves. Because of its advantages over optical data, much research has been done into water detection using SAR data. Traditionally, this has been done using the thresholding method, which involves picking a polarization band and labeling all pixels for which this band’s value is below a certain threshold as containing water. The thresholding method works since water tends to return a much lower backscatter value to the satellite than land [1]. However, this method can be flawed since estimating the proper threshold is often imprecise, complicated, and labor intensive for the end user. Thresholding also tends to use data from only one SAR polarization, when a combination of polarizations can provide insight into whether water is present. [2] In order to alleviate these problems, this paper presents an application for the Open Data Cube to detect water from SAR data using support vector machine (SVM) classification. 2. PLATFORM WASARD is an application for the Open Data Cube, a mechanism which provides a simple yet efficient means of ingesting, storing, and retrieving remote sensing data. Data can be ingested and made analysis ready according to whatever specifications the researcher chooses, and easily resampled to artificially alter a scene’s resolution. Currently WASARD supports water detection on scenes from ESA’s Sentinel-1 and JAXA’s ALOS. When testing WASARD, Sentinel-1 was most commonly used due to its relatively high spatial resolution and its rapid 6 day revisit cycle [5]. With minor alterations to the application's code, however, it could support data from other satellites. 3. METHODOLOGY Using supervised classification, WASARD compares SAR data to a dataset pre-classified by WOFS in order to train an SVM classifier. This classifier is then used to detect water in other SAR scenes outside the training set. Accuracy was measured according to the following metrics: Precision: a measure of what percentage of the points WASARD labels as water are truly water Recall: a measure of what percentage of the total water cover WASARD was able to identify. F1 Score: a harmonic average of the precision and recall scores Both precision and recall are calculated at the end of the training phase, when the trained classifier is compared to a testing dataset. Because the WOFS algorithm’s classifications are used as the truth values when training a WASARD classifier, when precision and recall are mentioned in this paper, they are always with respect to the values produced by WOFS on a similar scene of Landsat data, which themselves have a classification accuracy of 97% [6]. Visual representations of water identified by WASARD in this paper were produced using the function wasard_plot(), which is included in WASARD. 3.1 Algorithm Selection The machine learning model used by WASARD is the Linear Support Vector Machine (SVM). This model uses a supervised learning algorithm to develop a classifier, meaning it creates a vector which can be multiplied by the vector formed by the relevant data bands to determine whether a pixel in a SAR scene contains water. This classifier is trained by comparing data points from selected bands in a SAR scene to their respective labels, which in this case are “water” or “not water” as given by the WOFS algorithm. The SVM was selected over the Random Forest model, which outperformed the SVM in training speed, but had a greater classification time and lower accuracy, and the Multilayer Perceptron Artificial Neural Network, which had a slightly higher average accuracy than the SVM, but much greater training and classification times. Figure 1: Visual representation of the SVM Classifier. Each white point represents a pixel in a SAR scene. In Figure 1, the diagonal line separating pixels determined to be water from those determined not to be water represents the actual classification vector produced by the SVM. It is worth noting that once the model has been trained, classification of pixels is done in a similar manner as in the thresholding method. This is especially true if only one band was used to train the model. 3.1 Feature Selection Sentinel-1 collects data from two bands: the Vertical/Vertical polarization (VV) and the Vertical/Horizontal polarization (VH). When 100 SVM classifiers were created for each polarization individually, and for the combination of the two, the following results were achieved: Figure 2: Accuracy of classifiers trained using different polarization bands. Precision and Recall were measured with respect to the values produced by WOFS. Figure 2 demonstrates that using both the VV and VH bands trades slightly lower recall for significantly greater precision when compared with the VH band alone, and that using the VV band alone is inferior in both metrics. WASARD therefore defaults to using both the VV and VH bands, and includes the option to use solely the VH band. The VV polarization’s lower precision compared to the VH polarization is in contrast to results from previous research and may merit further analysis [4]. 3.2 Training a Classifier The steps in training a classifier with WASARD are 1. Selecting two scenes (one SAR, one optical) with the same spatial extents, and acquired close to each other in time, with a preference that the scenes are taken on the same day. 2. Using the WOFS algorithm to produce an array of the detected water in the scene of optical data, to be used as the labels during supervised learning 3. Data points from the selected bands from the SAR acquisition are bundled together into an array with the corresponding labels gathered from WOFS. A random sample with an equal number of points labeled “Water” and “Not Water” is selected to be partitioned into a training and a testing dataset 4. Using Scikit-Learn’s LinearSVC object, the training dataset is used to produce a classifier, which is then tested against the testing dataset to determine its precision and recall The result is a wasard_classifier object, which has the following attributes: 1. f1, recall, and precision: 3 metrics used to determine the classifier’s accuracy 2. Coefficient: Vector which the SVM uses to make its predictions. The classifier detects water when the dot product of the coefficient and the vector formed by the SAR bands is positive 3. Save(): allows a user to save a classifier to the disk in order to use it without retraining 4. wasard_classify(): Classifies an entire xarray of SAR data using the SVM classifier All of the above steps are performed automatically when the user creates a wasard_classifier object. 3.3 Classifying a Dataset Once the classifier has been created, it can be used to detect water in an xarray of SAR data using wasard_classify(). By taking the dot product of the classifier’s coefficients and the vector formed by the selected bands of SAR data, an array of predictions is constructed. A classifier can effectively be used on the same spatial extents as the ones where it was trained, or on any area with a similar landscape. While
Kreiser, Zachary↗
Mars - Detection of atmospheric water vapor during the southern hemisphere spring and summer season
Mars atmospheric water vapor detection during Southern Hemisphere spring and summer season
Barker, E. S.↗
Ocean Observation Instrument
The Airborne Ocean Color Imager (AOCI) was developed by Daedalus Enterprises, Inc. for Ames Research Center under a Small Business Innovation Research (SBIR) contract as a simulator for an advanced oceanographic satellite instrument. The instrument measures water temperature and detects water color in nine wavelengths. Water color indicates chlorophyll content or phytoplankton. After EOCAP assistance and technical improvements, the AOCI was successfully commercialized by Daedalus Enterprises, Inc. One version provides commercial fishing fleets with information about fish locations, and the other is used for oceanographic research.
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