Phased Array Radiometer Calibration Using a Radiated Noise Source

Electronic beam steering capability of phased array antenna systems offer significant advantages when used in real aperture imaging radiometers. The sensitivity of such systems is limited by the ability to accurately calibrate variations in the antenna circuit characteristics. Passive antenna systems, which require mechanical rotation to scan the beam, have stable characteristics and the noise figure of the antenna can be characterized with knowledge of its physical temperature [1],[2]. Phased array antenna systems provide the ability to electronically steer the beam in any desired direction. Such antennas make use of active components (amplifiers, phase shifters) to provide electronic scanning capability while maintaining a low antenna noise figure. The gain fluctuations in the active components can be significant, resulting in substantial calibration difficulties [3]. In this paper, we introduce two novel calibration techniques that provide an end-to-end calibration of a real-aperture, phased array radiometer system. Empirical data will be shown to illustrate the performance of both methods

Evaluation of a surface energy balance method based on optical and thermal satellite imagery to estimate root-zone soil moisture

2014 Fall.Includes bibliographical references.Various remote-sensing methods are available to estimate soil moisture, but few address the fine spatial resolutions (e.g., 30 m grid cells) and root-zone depth requirements of agricultural and other similar applications. One approach that has been previously proposed to estimate fine-resolution soil moisture is to first estimate the evaporative fraction from an energy balance that is inferred from optical and thermal remote-sensing images (e.g., using the ReSET algorithm) and then estimate soil moisture through an empirical relationship to evaporative fraction. A similar approach has also been proposed to estimate the degree of saturation. The primary objective of this study is to evaluate these methods for estimating soil moisture and degree of saturation, particularly for a semiarid grassland with relatively dry conditions. Soil moisture was monitored at twenty-eight field locations in southeastern Colorado with herbaceous vegetation during the summer months of three years. In-situ soil moisture and degree of saturation observations are compared with estimates calculated from Landsat imagery using the ReSET algorithm. The in-situ observations suggest that the empirical relationships with evaporative fraction that have been proposed in previous studies typically provide overestimates of soil moisture and degree of saturation in this region. However, calibrated functions produce estimates with an accuracy that may be adequate for various applications. The estimates produced by this approach are more reliable for degree of saturation than for soil moisture, and the method is more successful at identifying temporal variability than spatial variability in degree of saturation for this region

Sobre el estado del arte en el sensoramiento satelital utilizado para el monitoreo de variables meteorológicas sobre la superficie oceánica

Fil: García Skabar, Yanina. Servicio Meteorológico Nacional. Dirección Nacional de Ciencia e Innovación en Productos y Servicios. Dirección de Productos de Modelación Ambiental y de Sensores Remotos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto Franco Argentino sobre Estudios del Clima y sus Impactos; Argentina.Fil: Vidal, Luciano. Servicio Meteorológico Nacional. Dirección Nacional de Ciencia e Innovación en Productos y Servicios. Dirección de Productos de Modelación Ambiental y de Sensores Remotos; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Ciencias de la Atmósfera y Los Océanos; Argentina.Fil: De Oto, Matías. Servicio Meteorológico Nacional. Dirección Nacional de Ciencia e Innovación en Productos y Servicios. Dirección de Productos de Modelación Ambiental y de Sensores Remotos; Argentina.En esta Nota Técnica se recopila toda aquella información inherente al viento cerca de la superficie oceánica, olas y altura del nivel del mar que se obtienen únicamente a través del sensoramiento satelital. Se realiza una descripción de cuál es el instrumental diseñado para la adquisición de dichas variables y se explica brevemente cómo es su funcionamiento. Se brindan algunas de las plataformas web de fácil acceso en donde estas variables pueden visualizarse de modo interactivo y en algunos casos descargarse. Además, se recopilan todas aquellas misiones satelitales activas y con proyección futura involucradas en la recuperación de las variables mencionadas. Finalmente, se informa de aquellos sitios activos en donde es posible descargar los archivos de forma operativa, se estudia su contenido y cómo es posible su tratamiento, particularmente sobre el Mar Argentino y el océano adyacente.This Technical Note compiles meteorological data information related to surface ocean wind, waves and sea surface height obtained through satellite sensing only. A description of the instrumentation designed for the acquisition of these variables and a brief explanation of how it works is given. We provide some of the easily accessible web platforms where these variables can be viewed interactively and in some cases downloaded. In addition, a survey of active and future satellite missions involved in the collection of the above-mentioned variables is included. Finally, we inform about those active sites where it is possible to download the files in an operative way, discuss their content file information and how it is possible to process them, particularly on the Argentine Continental Shelf and adjacent seas

Science and Technology Directorate Publications and Presentations, January 1-December 31, 2005

This Technical Memorandum (TM) lists the significant publications and presentations of the Science and Technology Directorate during the period January 1-December 31, 2005. Entries in the main part of the document are categorized according to NASA Reports (arranged by report number), Open Literature, and Presentations (arranged alphabetically by title). Most of the articles listed under Open Literature have appeared in refereed professional journals, books, monographs, or conference proceedings. Although many published abstracts are eventually expanded into full papers for publication in scientific and technical journals, they are often sufficiently comprehensive to include the significant results of the research reported. Therefore, published abstracts are listed separately in a subsection under Open Literature. Questions or requests for additional information about the entries in this report should be directed to Dr. J.F. Spann, Jr. (VP60; 961-7512) or to one of the authors

Soil moisture modeling and scaling using passive microwave remote sensing

Soil moisture in the shallow subsurface is a primary hydrologic state governing land-atmosphere interaction at various scales. The primary objectives of this study are to model soil moisture in the root zone in a distributed manner and determine scaling properties of surface soil moisture using passive microwave remote sensing. The study was divided into two parts. For the first study, a root zone soil moisture assessment tool (SMAT) was developed in the ArcGIS platform by fully integrating a one-dimensional vadose zone hydrology model (HYDRUS-ET) with an ensemble Kalman filter (EnKF) data assimilation capability. The tool was tested with dataset from the Southern Great Plain 1997 (SGP97) hydrology remote sensing experiment. Results demonstrated that SMAT displayed a reasonable capability to generate soil moisture distribution at the desired resolution at various depths of the root zone in Little Washita watershed during the SGP97 hydrology remote sensing experiment. To improve the model performance, several outstanding issues need to be addressed in the future by: including "effective" hydraulic parameters across spatial scales; implementing subsurface soil properties data bases using direct and indirect methods; incorporating appropriate hydrologic processes across spatial scales; accounting uncertainties in forcing data; and preserving interactions for spatially correlated pixels. The second study focused on spatial scaling properties of the Polarimetric Scanning Radiometer (PSR)-based remotely sensed surface soil moisture fields in a region with high row crop agriculture. A wavelet based multi-resolution technique was used to decompose the soil moisture fields into larger-scale average soil moisture fields and fluctuations in horizontal, diagonal and vertical directions at various resolutions. The specific objective was to relate soil moisture variability at the scale of the PSR footprint (800 m X 800 m) to larger scale average soil moisture field variability. We also investigated the scaling characteristics of fluctuation fields among various resolutions. The spatial structure of soil moisture exhibited linearity in the log-log dependency of the variance versus scale-factor, up to a scale factor of -2.6 (6100 m X 6100 m) irrespective of wet and dry conditions, whereas dry fields reflect nonlinear (multi-scaling) behavior at larger scale-factors

Modeling and application of soil moisture at varying spatial scales with parameter scaling

The dissertation focuses on characterization of subpixel variability within a satellite-based remotely sensed coarse-scale soil moisture footprint. The underlying heterogeneity of coarse-scale soil moisture footprint is masked by the area-integrated properties within the sensor footprint. Therefore, the soil moisture values derived from these measurements are an area average. The variability in soil moisture within the footprint is introduced by inherent spatial variability present in rainfall, and geophysical parameters (vegetation, topography, and soil). The geophysical parameters/variables typically interact in a complex fashion to make soil moisture evolution and dependent processes highly variable, and also, introduce nonlinearity across spatio-temporal scales. To study the variability and scaling characteristics of soil moisture, a quasi-distributed Soil-Vegetation-Atmosphere-Transfer (SVAT) modeling framework is developed to simulate the hydrological dynamics, i.e., the fluxes and the state variables within the satellite-based soil moisture footprint. The modeling framework is successfully tested and implemented in different hydroclimatic regions during the research. New multiscale data assimilation and Markov Chain Monte Carlo (MCMC) techniques in conjunction with the SVAT modeling framework are developed to quantify subpixel variability and assess multiscale soil moisture fields within the coarse-scale satellite footprint. Reasonable results demonstrate the potential to use these techniques to validate multiscale soil moisture data from future satellite mission e.g., Soil Moisture Active Passive (SMAP) mission of NASA. The results also highlight the physical controls of geophysical parameters on the soil moisture fields for various hydroclimatic regions. New algorithm that uses SVAT modeling framework is also proposed and its application demonstrated, to derive the stochastic soil hydraulic properties (i.e., saturated hydraulic conductivity) and surface features (i.e., surface roughness and volume scattering) related to radar remote sensing of soil moisture

Soil moisture and water stage estimation using precipitation radar

In south-western United States, soil moisture data is important for drought studies in the region which is experiencing a drought for many years, whereas in South Florida, water stage data is required by hydrologists to monitor the hydrological flow in wetlands. Soil moisture data and water stage data are not sufficiently available due to sparse monitoring stations. Installation of dense measuring stations over an extended area is costly and labor intensive. Therefore, there is a need to develop an alternative method of measuring soil moisture and water stage. Microwave remote sensing has proven to be a useful tool in the measurement of various surface variables from space. This research explores the capability of microwave remote sensing to measure soil moisture and water stage on the earth from space. Tropical Rainfall Measuring Mission Precipitation Radar (TRMMPR) provides the Ku -band backscatter measurements that are used to measure soil moisture and water stage. Models that relate soil moisture and water stage to TRMMPR backscatter (σ°) are developed. The dependence of σ° on the dielectrical and physical characteristics of the land surface is used as the basis of this research. The soil moisture content affects σ° by changing the dielectric constant of the surface whereas the vegetation density affects σ° by changing the physical characteristics of the surface. Vegetation density in the model is represented by Normalized Difference Vegetation Index (NDVI). Dependence of σ° on partial submergence of vegetation in inundated areas is used to measure water stage in wetlands of South Florida. The effects of the exposed vegetation above the water surface on the model are assessed by comparing two cases of model run3 (a) that includes NDVI in the model, and (b) that does not include NDVI in the model. Eleven years of data is used in this research where 75% of the data is used for calibration of the model and 25% of the data is used for validation. The estimated values of soil moisture and water stage are compared to the observed values and the performance of the models is assessed by calculating correlation coefficients, calculating root mean square errors, and plotting non-exceedance probability plots for the absolute error between observed and modeled values. The soil moisture and water stage models work reasonably well and are able to estimate soil moisture and water stage with low errors. The soil moisture model works better in low vegetated areas because low vegetation allows the incident radiation to penetrate through the canopy cover and provide measurements from underlying surfaces. The water stage model works better in shrublands where there are no tree trunks and the model has an immediate impact from the vegetation canopy. This research provides an alternate way of measurement of soil moisture and water stage using remote sensing