Spatially seamless, daily FY-3B (SSD_FY3B) surface soil moisture dataset from 2012 to 2018

As an important passive microwave remote sensing dataset, the Fengyun-3B (FY-3B) surface soil moisture (SM) has been applied in a variety of scientific studies. However, due to various effects such as the discontinuous coverage of satellite revisit orbits, the FY-3B SM data have a large range of data missing, which greatly limits the applicability. To solve this problem, we proposed a deep learning network framework called TTP and generated a global spatially seamless, daily FY-3B (SSD_FY3B) SM dataset from 2012-2018. This study can provide methodological support for large-scale FY-3B SM data reconstruction, and the generated dataset can provide data support for research in related fields such as soil science and hydrology.