GSSM: A global seamless soil moisture dataset from 1981 to 2022 matching CCI to SMAP with a novel bias correction method

Abstract. Surface soil moisture is vital for Earth's environmental and energy cycles. However, it is still rare to have remote sensing soil moisture data with a long-term temporal extent, a global seamless spatial coverage, and a near-real-time update frequency. Here, we provided a global seamless soil moisture dataset from July 1981 to December 2022, matching CCI with SMAP through a novel soil moisture data bias correction method (fitting beta CDF matching, BCDF), and filling the gaps of corrected soil moisture through XGBoost Algorithms along with various soil moisture covariates. The new soil moisture dataset was abbreviated as GSSM and it has been validated with in situ observations, original CCI and SMAP data, and simulated gap areas. Results demonstrated that 1) the GSSM has similar accuracy with the SMAP and they are both more accurate than the original CCI data as compared with in situ observations at 399 global sites (averaged R=0.72, averaged ubRMSE<0.05); 2) the GSSM has the global spatial coverage, while filling the gaps of original CCI data through various soil moisture covariates (in artificial gaps verification, averaged R>0.86, averaged ubRMSE<0.04); 3) the GSSM has the same temporal variation characteristics with the original CCI dataset, while it can be combined with SMAP to obtain a long-term and near-real-time soil moisture dataset. Thus, GSSM provides long-term and seamless soil moisture data, paving the way for environmental disaster and water cycle process research.