Soil Moisture Active Passive (SMAP) Project Algorithm Theoretical Basis Document SMAP L1B Radiometer Data Product: L1B_TB

The purpose of the Soil Moisture Active Passive (SMAP) radiometer calibration algorithm is to convert Level 0 (L0) radiometer digital counts data into calibrated estimates of brightness temperatures referenced to the Earth's surface within the main beam. The algorithm theory in most respects is similar to what has been developed and implemented for decades for other satellite radiometers; however, SMAP includes two key features heretofore absent from most satellite borne radiometers: radio frequency interference (RFI) detection and mitigation, and measurement of the third and fourth Stokes parameters using digital correlation. The purpose of this document is to describe the SMAP radiometer and forward model, explain the SMAP calibration algorithm, including approximations, errors, and biases, provide all necessary equations for implementing the calibration algorithm and detail the RFI detection and mitigation process. Section 2 provides a summary of algorithm objectives and driving requirements. Section 3 is a description of the instrument and Section 4 covers the forward models, upon which the algorithm is based. Section 5 gives the retrieval algorithm and theory. Section 6 describes the orbit simulator, which implements the forward model and is the key for deriving antenna pattern correction coefficients and testing the overall algorithm

Contributions to radio frequency interference detection and mitigation in Earth observation

Radio Frequency Interference (RFI) is the most common problem for electronic measuring systems. The presence of those electromagnetic waves can harm the measurements taken from very sensitive instruments, like microwave radiometry or navigation systems. The accuracy and precision are compromised. A first step to mitigate those unwanted effects is to study the RFI properties. Different algorithms have been proposed to detect the interferences, but there is no method that works in all cases. The scope of this dissertation is the design, implementation and testing of different detection and mitigation methods in real-time. Performed surveys and characterization of RFI sources provide a great contribution to optimize the current mitigation techniques. In the mitigation area, two real-time hardware systems have been implemented: a wavelet denoise system to model the RFI and mitigate it, and a circuit to allow a navigation system to continue operational under the effects of a jammer.El problema més comú en els sistemes electrònics de mesura són les interferències electromagnètiques. La presència d'aquests senyals pot danyar les mesures preses per instruments molt sensibles, com radiòmetres de microones o sistemes de navegació. L'exactitud i precisió es veuen compromeses. El primer pas per mitigar aquests efectes no desitjats és estudiar les propietats de les interferències electromagnètiques. Diversos algoritmes han estat proposats per detectar interferències, però no hi ha mètode que funcioni bé en tots els casos . Aquest treball comprèn el disseny, implementació i comprovació de diferents mètodes de detecció i mitigació en temps real. Els estudis i caracterització de les fonts d'interferències són una gran contribució per a optimitzar les tècniques de mitigació actuals. En el tema de mitigació, dos sistemes en temps real han estat implementats en hardware: un sistema que utilitza wavelets per modelar la interferència i mitigar-la, i un circuit que permet a un sistema de navegació continuar funcionant sota els efectes d'un interferidor comercial ( jammer )

Development of Radio Frequency Interference Detection Algorithm for Passive Microwave Remote Sensing

Radio Frequency Interference (RFI) signals are man-made sources that are increasingly plaguing passive microwave remote sensing measurements. RFI is of insidious nature, with some signals low power enough to go undetected but large enough to impact science measurements and their results. With the launch of the European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) satellite in November 2009 and the upcoming launches of the new NASA sea-surface salinity measuring Aquarius mission in June 2011 and soil-moisture measuring Soil Moisture Active Passive (SMAP) mission around 2015, active steps are being taken to detect and mitigate RFI at L-band. An RFI detection algorithm was designed for the Aquarius mission. The algorithm performance was analyzed using kurtosis based RFI ground-truth. The algorithm has been developed with several adjustable location dependant parameters to control the detection statistics (false-alarm rate and probability of detection). The kurtosis statistical detection algorithm has been compared with the Aquarius pulse detection method. The comparative study determines the feasibility of the kurtosis detector for the SMAP radiometer, as a primary RFI detection algorithm in terms of detectability and data bandwidth. The kurtosis algorithm has superior detection capabilities for low duty-cycle radar like pulses, which are more prevalent according to analysis of field campaign data. Most RFI algorithms developed have generally been optimized for performance with individual pulsed-sinusoidal RFI sources. A new RFI detection model is developed that takes into account multiple RFI sources within an antenna footprint. The performance of the kurtosis detection algorithm under such central-limit conditions is evaluated. The SMOS mission has a unique hardware system, and conventional RFI detection techniques cannot be applied. Instead, an RFI detection algorithm for SMOS is developed and applied in the angular domain. This algorithm compares brightness temperature values at various incidence angles for a particular grid location. This algorithm is compared and contrasted with other algorithms present in the visibility domain of SMOS, as well as the spatial domain. Initial results indicate that the SMOS RFI detection algorithm in the angular domain has a higher sensitivity and lower false-alarm rate than algorithms developed in the other two domains.Ph.D.Atmospheric and Space SciencesUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/86308/1/samisra_1.pd

Radio frequency interference in microwave radiometry: statistical analysis and study of techniques for detection and mitigation

Microwave radiometry field has been increasing its performance with higher accuracy measurements, leading to a more presence in the remote sensing field. Several space-borne, air-borne and ground-based radiometers have been developed to perform measurement campaigns; however, the actual sensitivity of a radiometer is often limited by man-made radio emissions such as radars, broadcasting emissions, wireless communications and many other communication systems based on electromagnetic waves, limiting the improvement in the radiometers¿ performance. Consequently, in order to maintain the accuracy in the radiometric measurements, it has been researched in the Radio Frequency Interference (RFI) detection and mitigation systems and algorithms for the microwave radiometry field. The scope of this doctoral thesis is the development and testing of RFI detection and mitigation algorithms in order to enhance radiometric measurements performed by the Multifequency Experimental Radiometer with Interference Tracking for Experiments over Land and Littoral (MERITXELL). The MERITXELL has been developed during this thesis with the idea studying the RFI present in several radiometric bands and the way to mitigate it, as well as to obtain data from diverse frequency bands and devices in only one measurement campaign