Paul Siqueira

A physical scattering model of repeat-pass InSAR correlation over forested areas is derived by accounting for the changes in the dielectric properties and positions of the scatterers in the scene between overpasses. This derivation is based on the discrete representation of a sparse random medium (such as forest canopy) along with the solution to the Foldy–Lax multiple scattering equations. In addition to taking into account the random motion of scatterers, which has been investigated in previous work, the derived repeat-pass InSAR correlation model in this paper includes the effects of moisture-induced dielectric fluctuations. This is accomplished by incorporating a separate correlation profile that takes into account these fluctuations into the framework. Once constructed, the mathematical formulation of this scattering model is cast into a modified version of the Random Volume over Ground model such that it can separately take into account dielectric fluctuations in the ground and volume components. This model is then validated using a modified version of ESA’s PolSARproSim simulation to show that similar results can be obtained from both the simulation and the theoretical model.

Snow characteristics, such as snow water equivalent (SWE) and snow grain size, are important characteristics for the monitoring of the global hydrological cycle and as indicators of climate change. This paper derives an interferometric synthetic aperture radar (InSAR) scattering model for dense media, such as snow, which takes into account multiple scattering effects through the Quasi‐Crystalline Approximation. The result of this derivation is a simplified version of the InSAR correlation model derived for relating the InSAR correlation measurements to the snowpack characteristics of grain size, volume fraction, and layer depth as well as those aspects of the volume‐ground interaction that affects the interferometric observation (i.e., the surface topography and the ratio of ground‐to‐volume scattering). Based on the model, the sensitivity of the InSAR correlation measurements to the snow characteristics is explored by simulation. Through this process, it is shown that Ka‐band InSAR phase has a good sensitivity to snow grain size and volume fraction, while for lower frequency signals (Ku‐band to L‐band), the InSAR correlation magnitude and phase have a sensitivity to snow depth. Since the formulation depends, in part, on the pair distribution function, three functional forms of the pair distribution function are implemented and their effects on InSAR phase measurements compared. The InSAR scattering model described in this paper is intended to be an observational prototype for future Ka‐band and L‐band InSAR missions, such as NASA's Surface Water and Ocean Topography and NASA‐ISRO Synthetic Aperture Radar missions, planned for launch in the 2020–2021 time frame. This formulation also enables further investigation of the InSAR‐based snow retrieval approaches.

This paper describes an automatic mosaicking algorithm for creating large-scale mosaic maps of forest height. In contrast to existing mosaicking approaches through using SAR backscatter power and/or InSAR phase, this paper utilizes the forest height estimates that are inverted from spaceborne repeat-pass cross-pol InSAR correlation magnitude. By using repeat-pass InSAR correlation measurements that are dominated by temporal decorrelation, it has been shown that a simplified inversion approach can be utilized to create a height-sensitive measure over the whole interferometric scene, where two scene-wide fitting parameters are able to characterize the mean behavior of the random motion and dielectric changes of the volume scatterers within the scene. In order to combine these single-scene results into a mosaic, a matrix formulation is used with nonlinear least squares and observations in adjacent-scene overlap areas to create a self-consistent estimate of forest height over the larger region. This automated mosaicking method has the benefit of suppressing the global fitting error and, thus, mitigating the “wallpapering” problem in the manual mosaicking process. The algorithm is validated over the U.S. state of Maine by using InSAR correlation magnitude data from ALOS/PALSAR and comparing the inverted forest height with Laser Vegetation Imaging Sensor (LVIS) height and National Biomass and Carbon Dataset (NBCD) basal area weighted (BAW) height. This paper serves as a companion work to previously demonstrated results, the combination of which is meant to be an observational prototype for NASA’s DESDynI-R (now called NISAR) and JAXA’s ALOS-2 satellite missions.

This paper describes a novel, simple and efficient approach to estimate forest height over a wide region utilizing spaceborne repeat-pass InSAR correlation magnitude data at L-band. We start from a semi-empirical modification of the RVoG model that characterizes repeat-pass InSAR correlation with large temporal baselines (e.g., 46 days for ALOS) by taking account of the temporal change effect of dielectric fluctuation and random motion of scatterers. By assuming (1) the temporal change parameters and forest backscatter profile/extinction coefficient follow some mean behavior across each inteferogram; (2) there is minimal ground scattering contribution for HV-polarization; and (3) the vertical wavenumber is small, a simplified inversion approach is developed to link the observed HV-polarized InSAR correlation magnitude to forest height and validated using ALOS/PALSAR repeat-pass observations against LVIS lidar heights over the Howland Research Forest in central Maine, US (with RMSE < 4 m at a resolution of 32 hectares). The model parameters derived from this supervised regression are used as the basis for propagating the estimates of forest height to available interferometric pairs for the entire state of Maine, thus creating a state-mosaic map of forest height. The present approach described here serves as an alternative and complementary tool for other PolInSAR inversion techniques when full-polarization data may not be available. This work is also meant to be an observational prototype for NASA’s DESDynI-R (now called NISAR) and JAXA’s ALOS-2 satellite missions.

Estimates of above ground biomass density in forests are crucial for refining global climate models and understanding climate change. Although data from field studies can be aggregated to estimate carbon stocks on global scales, the sparsity of such field data, temporal heterogeneity and methodological variations introduce large errors. Remote sensing measurements from spaceborne sensors are a realistic alternative for global carbon accounting; however, the uncertainty of such measurements is not well known and remains an active area of research. This article describes an effort to collect field data at the Harvard and Howland Forest sites, set in the temperate forests of the Northeastern United States in an attempt to establish ground truth forest biomass for calibration of remote sensing measurements. We present an assessment of the quality of ground truth biomass estimates derived from three different sets of diameter-based allometric equations over the Harvard and Howland Forests to establish the contribution of errors in ground truth data to the error in biomass estimates from remote sensing measurements.

To help optimize the real time scanning of an adaptive radar network, it is necessary to define a performance metric for weighing the competing scanning tasks. A point volume vector velocity estimation methodology can be used in this context to derive the confidence bounds of the vector velocity estimator that can, in turn, be utilized as a quality indicator in devising scanning strategies. This document presents the basic derivation of this estimation process and provides examples of processed weather moment data.

This paper presents an algorithm that derives the vector velocity estimate in a radar network environment considering the velocities that exceed the maximum unambiguous velocity of its observing sensor nodes. At first, an analytical derivation with underlying assumptions is presented and then simulation results are shown and discussed. This unfolding vector velocity algorithm is based on maximum likelihood theory operating on the first three weather radar spectral moments and can potentially provide a real time velocity detection of broader range of velocities than given by the nodal maximum unambiguous velocity.

This paper presents initial results from the lab testing of a Ku-band cross-track interferometric downconverter designed for performing topographic and volumetric depth measurements. This interferometer will be used shortly in the field for measuring topography and to determine its capacity for estimating the volumetric depth of snow.