Remote sensing of earth terrain

Abstracts from 46 refereed journal and conference papers are presented for research on remote sensing of earth terrain. The topics covered related to remote sensing include the following: mathematical models, vegetation cover, sea ice, finite difference theory, electromagnetic waves, polarimetry, neural networks, random media, synthetic aperture radar, electromagnetic bias, and others

Geolocating Low-Earth-Orbit satellite data from next-generation millimeter-wave radiometers using natural targets

The main goal of this work is to perform the geolocation error assessment of the channel imagery at 183.31 GHz of the Special Sensor Microwave Imager/Sounder (SSMIS). The frequency around 183.31 GHz still represents the highest channel frequency of current spaceborne microwave and millimeter-wave radiometers. The latter will be extended to frequencies up to 664 GHz, as in the case of EUMETSAT Ice Cloud Imager (ICI). This use of submillimeter observations unfortunately prevents a straightforward geolocation error assessment using landmark-based techniques. This work uses SSMIS data at 183.31 GHz as a submillimeter proxy to identify the most suitable targets for geolocation error validation in very dry atmospheric conditions, as suggested by radiative transfer modeling. Using a yearly SSMIS dataset, 3 candidates landmark targets are selected: i) high-altitude lakes and high-latitude bays using a coastline reference database; ii) Antarctic ice shelves and Arctic shorelines using coastlines derived from Sentinel-1 Synthetic Aperture Radar (SAR) imagery; iii) high altitude mountains using digital elevation model as reference. Data processing is carried out by using spatial cross-correlation methods in the spatial frequency domain and performing a numerical sensitivity analysis to contour displacement. Cloud masking, based on a fuzzy-logic approach, is applied to automatically selected clear-air days. Results show that the average geolocation error is about 6.2 km for mountainous lakes and sea bays and 5.4 km for ice shelves, respectively, with a standard deviation of about 2.7 and 2.0 km. Results are in line with SSMIS previous estimates, whereas annual clear-air days are about 10% for mountainous lakes and sea bays and 18% for ice shelves. The second goal of this work is to investigate ICI channels, focusing on 243 GHz at horizontal polarization (ICI-4). The results of the simulations using radiative transfer model and artificial neural network (ANN) confirm that ICI-4 will be the best candidate to validate the geolocation of the future ICI radiometer. At 243 GHz the atmosphere is less opaque and the surface could be more visible with respect to other frequencies. This work proposes an artificial neural network to reconstruct the 243 GHz starting from real data at 150 GHz and 183 GHz. ANN provides an average value of about 5.8 km with a standard deviation of about 2.7 km. These numbers are in line with those obtained for 183 GHz, but at 243 GHz the number of images that contains visible surface targets are much more with respect to 183 GHz

Radiometer Calibration Using Colocated GPS Radio Occultation Measurements

We present a new high-fidelity method of calibrating a cross-track scanning microwave radiometer using Global Positioning System (GPS) radio occultation (GPSRO) measurements. The radiometer and GPSRO receiver periodically observe the same volume of atmosphere near the Earth's limb, and these overlapping measurements are used to calibrate the radiometer. Performance analyses show that absolute calibration accuracy better than 0.25 K is achievable for temperature sounding channels in the 50-60-GHz band for a total-power radiometer using a weakly coupled noise diode for frequent calibration and proximal GPSRO measurements for infrequent (approximately daily) calibration. The method requires GPSRO penetration depth only down to the stratosphere, thus permitting the use of a relatively small GPS antenna. Furthermore, only coarse spacecraft angular knowledge (approximately one degree rms) is required for the technique, as more precise angular knowledge can be retrieved directly from the combined radiometer and GPSRO data, assuming that the radiometer angular sampling is uniform. These features make the technique particularly well suited for implementation on a low-cost CubeSat hosting both radiometer and GPSRO receiver systems on the same spacecraft. We describe a validation platform for this calibration method, the Microwave Radiometer Technology Acceleration (MiRaTA) CubeSat, currently in development for the National Aeronautics and Space Administration (NASA) Earth Science Technology Office. MiRaTA will fly a multiband radiometer and the Compact TEC/Atmosphere GPS Sensor in 2015.United States. Dept. of Defense. Assistant Secretary of Defense for Research & Engineering (United States. Air Force Contract FA8721-05-C-0002

Innovative Techniques for the Retrieval of Earth’s Surface and Atmosphere Geophysical Parameters: Spaceborne Infrared/Microwave Combined Analyses

With the advent of the first satellites for Earth Observation: Landsat-1 in July 1972 and ERS-1 in May 1991, the discipline of environmental remote sensing has become, over time, increasingly fundamental for the study of phenomena characterizing the planet Earth. The goal of environmental remote sensing is to perform detailed analyses and to monitor the temporal evolution of different physical phenomena, exploiting the mechanisms of interaction between the objects that are present in an observed scene and the electromagnetic radiation detected by sensors, placed at a distance from the scene, operating at different frequencies. The analyzed physical phenomena are those related to climate change, weather forecasts, global ocean circulation, greenhouse gas profiling, earthquakes, volcanic eruptions, soil subsidence, and the effects of rapid urbanization processes. Generally, remote sensing sensors are of two primary types: active and passive. Active sensors use their own source of electromagnetic radiation to illuminate and analyze an area of interest. An active sensor emits radiation in the direction of the area to be investigated and then detects and measures the radiation that is backscattered from the objects contained in that area. Passive sensors, on the other hand, detect natural electromagnetic radiation (e.g., from the Sun in the visible band and the Earth in the infrared and microwave bands) emitted or reflected by the object contained in the observed scene. The scientific community has dedicated many resources to developing techniques to estimate, study and analyze Earth’s geophysical parameters. These techniques differ for active and passive sensors because they depend strictly on the type of the measured physical quantity. In my P.h.D. work, inversion techniques for estimating Earth’s surface and atmosphere geophysical parameters will be addressed, emphasizing methods based on machine learning (ML). In particular, the study of cloud microphysics and the characterization of Earth’s surface changes phenomenon are the critical points of this work

An artificial neural network approach for soil moisture retrieval using passive microwave data

Soil moisture is a key variable that defines land surface-atmosphere (boundary layer) interactions, by contributing directly to the surface energy and water balance. Soil moisture values derived from remote sensing platforms only accounts for the near surface soil layers, generally the top 5cm. Passive microwave data at L-band (1.4 GHz, 21cm wavelength) measurements are shown to be a very effective observation for surface soil moisture retrieval. The first space-borne L-band mission dedicated to observing soil moisture, the European Space Agency's (ESA) Soil Moisture and Ocean Salinity (SMOS) mission, was launched on 2nd November 2009.Artificial Neural Network (ANN) methods have been used to empirically ascertain the complex statistical relationship between soil moisture and brightness temperature in the presence of vegetation cover. The current problem faced by this method is its inability to predict soil moisture values that are 'out-of-range' of the training data.In this research, an optimization model is developed for the Backpropagation Neural Network model. This optimization model utilizes the combination of the mean and standard deviation of the soil moisture values, together with the prediction process at different pre-determined, equal size regions to cope with the spatial and temporal variation of soil moisture values. This optimized model coupled with an ANN of optimum architecture, in terms of inputs and the number of neurons in the hidden layers, is developed to predict scale-to-scale and downscaling of soil moisture values. The dependency on the accuracy of the mean and standard deviation values of soil moisture data is also studied in this research by simulating the soil moisture values using a multiple regression model. This model obtains very encouraging results for these research problems.The data used to develop and evaluate the model in this research has been obtained from the National Airborne Field Experiments in 2005

THz spectroscopy of the atmosphere for climatology and meteorology applications

We present a new satellite-based instrument concept that will enable global measurements of atmospheric temperature and humidity profiles with unprecedented resolution and accuracy, compared to currently planned missions. It will also provide global measurements of essential climate variables related to ice clouds that will better constrain global climate models. The instrument is enabled by the use of superconducting detectors coupled to superconducting filterbank spectrometers, operating between 50GHz and 850 GHz. We present the science drivers, the current instrument concept and status, and predicted performance

Foreground challenge to CMB polarization: present methodologies and new concepts

In this thesis, I focus on the issue of contamination to the polarization of the Cosmic Microwave Background (CMB) anisotropies from diffuse Galactic foregrounds, which is known to be one of the greatest challenges to the detection of the curl (B) modes of the signal, which might be sourced by cosmological gravitational waves. I take parallel approaches along these lines. I apply the most recent techniques capable of parametrizing, fitting, and removing the main known Galactic foregrounds in a multi-frequency CMB dataset to one of the forthcoming powerful CMB polarization experiments, the Large Scale Polarization Explorer (LSPE). I presented the result of the complete simulation done for the parametric component separation pipeline of this experiment. On the other hand, I explored the latest Machine Learning and Artificial Intelligence algorithms and their application in CMB data analysis, specifically component separation and foreground cleaning. I start the investigation of the relevance of Neural Networks (NNs) in the understanding of the physical properties of foregrounds, as it is necessary before the foreground removal layer, by implementing a novel algorithm, which I test on simulated data from future B-mode probes. The results of the implemented NN\u2019s prediction in discerning the correct foreground model address the high accuracy and suitability of this model as a preceding stage for the component separation procedure. Finally, I also investigate how different NNs, as a generative model, could be used for reconstructing CMB anisotropies where the removal is impossible, and data have to be abandoned in the analysis. Lots remain to be done along each of these three investigations, which have been published in scientific journals, and constitute the basis of my future research

Millimeter and sub-millimeter wave radiometers for atmospheric remote sensing from CubeSat platforms

2018 Fall.Includes bibliographical references.To view the abstract, please see the full text of the document

Electromagnetic Wave Theory and Applications

Contains table of contents for Section 3, reports on nine research projects and a list of publications.National Aeronautics and Space Administration Contract 958461U.S. Navy - Office of Naval Research Grant N00014-92-J-1616University of California/Jet Propulsion Laboratory Contract 960408U.S. Army - Corps of Engineers/Cold Regions Research and Engineering Laboratory Contract DACA89-95-K-0014Mitsubishi CorporationU.S. Navy - Office of Naval Research Agreement N00014-92-J-4098Federal Aviation AdministrationDEMACOJoint Services Electronics Program Grant DAAHO4-95-1-003

Atmospheric remote sensing and radiopropagation: from numerical modeling to spaceborne and terrestrial applications

The remote sensing of electromagnetic wave properties is probably the most viable and fascinating way to observe and study physical media, comprising our planet and its atmosphere, at the same time ensuring a proper continuity in the observations. Applications are manifold and the scientific community has been importantly studying and investing on new technologies, which would let us widen our knowledge of what surrounds us. This thesis aims at showing some novel techniques and corresponding applications in the field of the atmospheric remote sensing and radio-propagation, at both microwave and optical wavelengths. The novel Sun-tracking microwave radiometry technique is shown. The antenna noise temperature of a ground-based microwave radiometer is measured by alternately pointing toward-the-Sun and off-the-Sun while tracking it along its diurnal ecliptic. During clear sky the brightness temperature of the Sun disk emission at K and Ka frequency bands and in the under-explored millimeter-wave V and W bands can be estimated by adopting different techniques. Parametric prediction models for retrieving all-weather atmospheric extinction from ground-based microwave radiometers are tested and their accuracy evaluated. Moreover, a characterization of suspended clouds in terms of atmospheric path attenuation is presented, by exploiting a stochastic approach used to model the time evolution of the cloud contribution. A model chain for the prediction of the tropospheric channel for the downlink of interplanetary missions operating above Ku band is proposed. On top of a detailed description of the approach, the chapter presents the validation results and examples of the model-chain online operation. Online operation has already been tested within a feasibility study applied to the BepiColombo mission to Mercury operated by the European Space Agency (ESA) and by exploiting the Hayabusa-2 mission Ka-band data by the Japan Aerospace Exploration Agency (JAXA), thanks to the ESA cross-support service. A preliminary (and successful) validation of the model-chain has been carried out by comparing the simulated signal-to-noise ratio with the one received from Hayabusa-2. At the next ITU World Radiocommunication Conference 2019, Agenda Item 1.13 will address the identification and the possible additional allocation of radio-frequency spectrum to serve the future development of systems supporting the fifth generation of cellular mobile communications (5G). The potential impact of International Mobile Telecommunications (IMT) deployments is shown in terms of received radio frequency interference by ESA’s telecommunication links. Received interference can derive from several radio-propagation mechanisms, which strongly depend on atmospheric conditions, radio frequency, link availability, distance and path topography; at any time a single mechanism, or more than one may be present. Results are shown in terms of required separation distances, i.e. the minimum distance between the earth station and the IMT station ensuring that the protection criteria for the earth station are met