NASA NTRS · 20130000006
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vanZyl, Jakob. 2012-04-16. SMAP Radar Soil Moisture Algorithms. https://ntrs.nasa.gov/citations/20130000006
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In remote sensing, radiometric measurements taken in the mid-wave infrared and beyond (λ >3μm) are commonly reported in units of Kelvin by utilizing Planck’s radiation law to relate measured radiance and target brightness temperature (Tb). Thus, it is desirable to match this formalism in thermal polarimetry and report the unnormalized Stokes parameters in units of K instead of radiance (Wm−2sr−1). This approach also al-lows common performance metrics in long-wave infrared (LWIR) imaging such as Noise Equivalent DifferentialTemperature (NEDT) to be modified and extended to metrics of polarimetric accuracy and precision. However, since the relationship between measured radiance and Tb are non-linear, the conversion of I, Q, and U in units of radiance to Tb,Tb,Q, and Tb,U in K is ambiguous. As a solution a metric of performance for thermal linear Stokes polarimetry, the Stokes resolved differential temperature (SRDT), is introduced.
The review of optical instrumentation, forward modeling, and inverse problem solution for the polarimetric aerosol remote sensing from space is presented. The special emphasis is given to the description of current airborne and satellite imaging polarimeters and also to modern satellite aerosol retrieval algorithms based on the measurements of the Stokes vector of reflected solar light as detected on a satellite. Various underlying surface reflectance models are discussed and evaluated.
In this paper we show our first POLINSAR results using the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) developed by the Jet Propulsion Laboratory (JPL). UAVSAR is a L-band repeat-pass polarimetric and interferometric system designed for measuring vegetation structure and monitoring crustal deformations. In order to extract canopy height from POLINSAR data and account for temporal decorrelation, we formulate a physical model of the temporal-volumetric coherence, random motion over ground (RMOG) model. Canopy height extracted from single-baseline UAVSAR data using the RMOG model is shown to be in agreement with canopy height measured by the Land, Vegetation, and Ice Sensor (LVIS) lidar.