| Summary: | Based on the BP neural network, one kind of method of machine learning, we developed a novel method to retrieve sea significant wave height (SWH) and sea surface wind field (SSW) information from spaceborne SAR data in high spatial resolution. Then we produced the relevant data products based on the ESA Sentinel-1 (S1) SAR data in horizontal-horizontal (HH) polarization acquired in the Arctic using the proposed method.The extra-wide (EW) swath mode data have been extensively acquired in the Arctic since the Sentinel-1A and 1B satellites launched in 2014 and 2016, which have a wide coverage of approximately 400 by 400 km and a pixel size of 40 by 40 m. The twins can acquired approximately 2,500 scenes SAR images each month in the Arctic. Based on the above-mentioned method, we processed each S1 SAR image in the Arctic and generated the SWH data and SSW data, which are in the standard CF-1.7 convention of NetCDF-4 format. Each S1 image corresponds to a NC file. The spatial resolution of S1-retrieved SWH and SSW are 2.56 km and 2 km, respectively.We have finished processing the S1 data acquired between 2017 and 2021 in the pan Arctic ocean (above 60 degrees North) and uploaded the corresponding SWH and SSW data. We are continuing to process the ongoing acquired data. Therefore, the published data will be updated often.
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