The CYGNSS constellation of eight satellites was launched in December 2016 into a low inclination Earth orbit. Each satellite carries a four-channel bi-static radar receiver which measures signals transmitted by GPS satellites and scattered back into space by the Earth surface. Over the ocean, surface roughness, near-surface wind speed and air-sea latent heat flux are estimated from the direct measurements of surface scattering cross section. Over the land, estimates of soil moisture and flood inundation are also possible. An overview and the current status of the mission will be presented, together with highlights of recent scientific results.
An inversion algorithm based on a nonlinear optimization technique is used to retrieve a subset of forest canopy parameters that can be used in ecosystem modeling. The inversion algorithm uses a parametric model derived from a discrete component forest scattering model which includes all major scattering contributions to radar backscatter. The parametric model, however, is derived for cases where only one mechanism dominates the scattering process, in this case, branch layer volume scattering. The free parameters are taken to be the real and imaginary parts of the dielectric constant of branch layer constituents. The parametric model and the inversion algorithm are validated with synthetic data and applied to two data sets taken at different dates over one of the OTTER sites (Santiam Pass). The inversion algorithm is shown to be a useful tool in the quantitative monitoring of canopy moisture status, as well as a building block of its future versions which will include a more comprehensive set of forest scattering parameters as unknowns.
In this paper a hybrid technique of image classification, modeling, and inversion algorithm is introduced in order to extract vegetation biomass from polarimetric SAR data. The development of the hybrid technique has evolved from SAR data analysis and the mere fact that the SAR signal, being correlated with vegetation type and moisture content, saturates as the biomass increases. The technique consists of the following steps: (1) classification of the image to land cover map, (2) classification of the SAR image into scattering mechanisms, (3) formulation of a multilayer forest backscatter model, (4) piecewise inversion of the model to estimate the vegetation water content for various components of the forest canopy, and (5) estimation of the forest biomass by combining the inversion and classification results. This technique has been tested over the boreal forest in Canada by using the multipolarization, multifrequency AIRSAR data. The results indicate that the use of the hybrid technique enhances the estimation of the forest biomass. The general form of the technique and its various components can be regarded as a basic approach to resolve the problem of biomass estimation with SAR data. The modification of this technique into more sophisticated forms can improve the biomass estimation in particular over dense tropical forests.
Conference Committee Involvement (2)
Microwave Remote Sensing of the Atmosphere and Environment VI
19 November 2008 | Noumea, New Caledonia
Microwave Remote Sensing of the Atmosphere and Environment V
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