Digital mapping of soil organic matter under conservation tillage in Northeast China based on multi-source remote sensing

ABSTRACT

The Northeast China black soil region is one of the country’s most important grain-producing areas. However, long-term conventional tillage characterized by deep plowing and intensive soil disturbance has caused severe soil erosion and continuous declines in surface soil organic matter (SOM). In response, conservation tillage has been widely promoted, with pre-sowing crop residue cover (CRC) regarded as a key indicator of its effectiveness. Nevertheless, residue cover substantially weakens soil signals in optical remote sensing, reducing sensitivity to SOM and making accurate SOM mapping under high-coverage conditions particularly challenging. Consequently, achieving robust SOM inversion across CRC conditions has become a critical bottleneck for long-term soil quality monitoring.

To address this challenge, this study identifies the pre-sowing period as the optimal temporal window for SOM remote sensing inversion and develops a SOM estimation framework integrating Sentinel-2 optical imagery, Sentinel-1 SAR data, and multi-source environmental covariates. A total of 585 surface soil samples (0–10 cm) and 117 UAV observations were collected from representative black soil areas in Northeast China. Continuous CRC maps were first generated and used as prior information to partition the study area into bare-soil and residue-covered zones, for which independent random forest regression models were constructed. In bare-soil areas, surface SOM was directly estimated using spectral indices and environmental variables. In residue-covered areas, spectral unmixing was applied to separate soil and residue components, which were combined with SAR penetration features to supplement surface soil dielectric information. In addition, long-term CRC indicators represented by multi-year cumulative values were incorporated to characterize cumulative residue return effects on surface SOM accumulation.

The results demonstrate that the proposed framework significantly improves SOM estimation accuracy in residue-covered areas. Compared with a CRC-agnostic baseline model, R² increased from 0.72 to 0.86 and RMSE decreased from 0.58 to 0.42, corresponding to an approximate 27.6 % reduction in estimation error. High-resolution SOM maps for 2016—2025 reveal a stable northeast-high to southwest-low spatial gradient across the black soil region. High SOM contents (>25 g·kg⁻¹) occur in the Sanjiang Plain and in regions extending from the Lesser Khingan Mountains to the Changbai Mountains, where humus-rich dark brown forest soils predominate. Moderate SOM levels (12–25 g·kg⁻¹) dominate the Songnen and Liaohe Plains, while low SOM contents (0–12 g·kg⁻¹) persist in the southern and western Songnen Plain and aeolian sandy regions of eastern Inner Mongolia. Spatial statistical analysis further indicates that SOM accumulation rates in high-CRC areas are approximately 20 % higher than those in low-coverage regions.

Overall, the proposed multi-source remote sensing framework integrates spectral unmixing, SAR penetration information, and conservation tillage-related features to achieve accurate SOM estimation under CRC conditions. This framework provides a transferable and operational approach for soil quality monitoring under conservation tillage, supporting soil improvement assessment, policy evaluation, and sustainable agricultural management in the Northeast China black soil region.

Keywords: Soil Organic Matter; Conservation Tillage; Multi-source Remote Sensing; Machine Learning; Black Soil Region