Abstract: Remote sensing inversion products for soil organic matter (SOM) are fundamental to monitor and assess soil quality in the black soil region of Northeast China. However, current research has largely prioritized the vertical optimization of inversion algorithms for single products, while ignoring systematic horizontal comparisons among different products, which limits their practical application. Based on a systematic literature review, we synthesized the spatial distribution characteristics, mainstream methodologies, and current status of data products for remote sensing-based inversion of soil organic matter (SOM) in the region. We found that 1) 77.5% of existing studies are concentrated in the Songnen and Sanjiang Plains, while regions such as eastern Inner Mongolia remain underrepresented; 2) a dominant paradigm has emerged, integrating multispectral data, environmental covariates and machine learning techniques; 3) there are inconsistencies among publicly available SOM products, with estimate discrepancies exceeding 30%. There are three major challenges: limited data sources, multiple interfering factors, and insufficient model interpretability and applicability of models. In the future, low altitude remote sensing data should be actively introduced, a ground aerospace multi-level remote sensing fusion system should be constructed, innovative modeling and promotion methods should be developed, grid-based datasets should be built, and data sharing should be promoted to fully explore the application value of soil data.

Key words: remote sensing inversion, soil organic matter, data product assessment, black soil region of Northeast China