In this article, a retrieval algorithm based on the use of an artificial neural network (ANN) is proposed for wind speed estimations from cyclone global navigation satellite system (CYGNSS). The delay/Doppler map average and the leading edge slope observables, derived from CYGNSS delay/Doppler maps, are used as inputs to the network, along with geographical, geometry, and hardware antenna information. The derivation of the optimal number of hidden layers and neurons is obtained using statistical metrics of agreement between the CYGNSS data and the wind matchups obtained from modelled winds output by the wavewatch 3 (WW3) model. A cumulative distribution function (CDF) matching step is applied to the network outputs, to impose that the CDF of the retrievals matches that of the matchups. The resulting wind speeds are unbiased with respect to WW3 modeled winds, and deliver a global root mean square (RMS) difference (RMSD) of 1.51 m/s, over a dynamic range of wind speeds up to 32 m/s. The obtained RMSD is the lowest among those seen in literature for wind speed retrievals from CYGNSS. A comparison is carried out between the winds retrieved from the ANN approach and those derived using the fully developed sea approach, which represent the CYGNSS baseline wind product. The comparison highlights that the ANN approach outperforms the baseline approach for both low and high wind speeds and removes most of the geographical biases between baseline winds and WW3 winds seen in monthly maps of wind speeds. The ANN approach could well be applied to the entire CYGNSS dataset to generate an enhanced wind speed product.

The Institute of Electrical and Electronics Engineers (IEEE) Geoscience and Remote Sensing Society (GRSS) created the GRSS "Standards for Earth Observation Technical Committee" to advance the usability of remote sensing products by experts from academia, industry, and government through the creation and promotion of standards and best practices.

<p>The ALtimetry for BIOMass project (ALBIOM) is ESA-funded Permanent Open Call Project that proposes to derive forest biomass using Copernicus Sentinel-3 (S3) SAR altimeter data. The project targets the need to improve our current global observations of biomass as an Essential Climate Variable (ECV) and crucial for bioenergy, risk mitigation activities, and sustainable management of forests. The overall goal is to estimate biomass with sufficient accuracy to be able to increase the existing satellite data for biomass retrieval and to improve the global mapping and monitoring of this fundamental variable.</p><p>The project originates from evidence that radar altimetry backscatter over land responds to a variety of land parameters, including vegetation-related parameters, at the different bands used by past and existing altimeters.</p><p>To achieve the scientific objectives, the project is structured into six conceptual tasks. After a review of the literature and of the existing user needs, a sensitivity analysis is performed to understand the relationship between SAR altimetry backscatter data and land parameters themselves. This is followed by the development of a Sentinel-3 SAR altimeter backscatter simulator over vegetated areas, and then by the development of a biomass inversion algorithm, testing different retrieval methodologies, both theoretical and empirical. A validation task for both the model and the algorithm is carried out over specific test sites of boreal and tropical forests, to finally generate a prototype of biomass product to be reviewed by potential users.</p><p>The sensitivity analysis allows to understand how the S3 Level 1 backscatter power waveforms change with respect to varying biomass, but also how they are affected by other land parameters such as soil moisture, land cover, topography and roughness. This analysis is carried out considering both the single-looked and the multi-looked waveforms, and considering primarily the high-resolution SAR mode, but also the Pseudo Low Resolution Mode (PLRM). The outcome of the sensitivity analysis provides indication of what waveforms, acquisition mode, observables derived from the waveforms and characteristics of the waveforms themselves respond more strongly to biomass variations, and on the degree of influence of the other auxiliary parameters, informing on the best strategies and approaches to adopt for the development of the retrieval algorithm.</p><p>Subsequent to the sensitivity analysis, the S3 altimetry backscatter simulator is developed over vegetated areas, reproducing both the coherent scattering component, which represents the echo from the ground, and incoherent scattering component arising from the forest layers between the ground and the top of canopy. The approach followed is similar to that of the SAVERS simulator, developed for GNSS-Reflectometry, with the signal backscatter attenuation introduced by branches, leaves and trunks modelled through the discrete approach of the Tor Vergata Scattering Model.</p><p>Results from the sensitivity analysis and the initial stages of the simulation development will be presented and discussed at the conference, together with the foreseen approaches for the development of the biomass retrieval algorithm.</p><p> </p>

In February 2019 a Project Authorization Request was approved by the Institute of Electrical and Electronics Engineers (IEEE) Standards Association with the title "Standard for GlobalNavigation Satellite System Reflectometry (GNSS-R) Data and Metadata Content". A Working Group has been assembled to draft this standard with the purpose of unifying and documenting GNSS-R measurements, calibration procedures, and product level definitions. The Working Group (http://www.grssieee.org/community/technical-committees/standards-for-earthobservations/) includes members, collaborators, and contributors from academia, international space agencies, and private industry. In a recent face-to-face meeting held during the ARSI+KEO 2019 Conference, the need was recognized to develop a standard with a wide range of operations, providing procedure guidelines independently of constraints imposed by current limitations on geophysical parameters retrieval algorithms. As such, this effort aims to establish the fundamentals of a potential virtual network of satellites providing inter-comparable data to the scientific community.