# A decade-long seamless-continuity daily L-band soil moisture product derived from SMOS observations since 2010

## TL;DR

This paper presents a new method to create a continuous, 10-year global soil moisture dataset from satellite data, improving long-term climate and hydrology studies.

## Contribution

A novel automated gap-filling method (DCT-PLS) that generates a seamless, decade-long soil moisture product without external data.

## Key findings

- The method achieved high fidelity with R > 0.9 and RMSE < 0.04 m³/m³ in synthetic gap reconstruction.
- The product matched in-situ measurements with R > 0.7 and unbiased RMSE = 0.057 m³/m³.
- The dataset preserved spatial patterns and seasonal amplitudes globally without boundary artifacts.

## Abstract

Soil moisture (SM) is a fundamental state variable in the Earth system that governs water, energy, and carbon exchanges between land and atmosphere. Current satellite-derived SM products, such as SMOS (Soil Moisture and Ocean Salinity) SM product, exhibit pronounced spatial and temporal discontinuities caused by orbital coverage, radio frequency interference, and retrieval failures, which severely limit their utility to long-term eco-hydrological studies. This study introduces a fully automated gap-filling method that combines Discrete Cosine Transformation with Partial Least Squares (DCT-PLS). The method exploits the intrinsic spatiotemporal coherence of the original SMOS Multi-Temporal and Multi-Angular (MTMA) product without relying on any external ancillary data. It was applied to the entire SMOS MTMA archive to generate a seamless-continuity product (MTMA-SC_SM) at 25 km and daily resolution. Reconstruction of the synthetic gaps confirmed its fidelity (correlation coefficient, R > 0.9; root mean squared error, RMSE, and mean absolute error, MAE < 0.04 m³/m³). When evaluated against the 22 in-situ SM networks, the MTMA-SC_SM product achieved an overall R > 0.7 and unbiased RMSE = 0.057 m³/m³, performing comparably to the original SMOS MTMA retrievals. Spatially, the seamless product preserved mesoscale patterns and seasonal amplitudes across all climate zones, with no discernible boundary artifacts around reconstructed regions. The MTMA-SC_SM dataset constitutes the first decade-long, gap-free, global daily L-band SM record, providing a robust foundation for climate trend assessment and land surface modelling at the global scale.