Global efforts to restore the world’s degraded croplands require knowledge on the degree and extent of accelerated soil organic carbon (SOC) loss induced by soil erosion. However, the methods for assessing where and to what extent erosion takes place are still inadequate for precise detection of erosion hotspots at high spatial resolution. Drawing on recent advances in multitemporal Sentinel-2 remote sensing to create a bare soil composite that reflects erosion-induced variations in soil spectral signatures, this study attempted to develop a spectra-based soil erosion mapping approach to pinpoint eroded hotspots in a typical catchment located in the black soil region of northeast China as characterized by undulating landscapes. We built a ground-truth dataset consisting of three classes of soils representing Severe, Moderate and Low erosion intensity because of their inter-class contrasts in estimated erosion rates from ¹³⁷Cs tracing. The spectral separability of different erosion classes was first tested by a combined principal component and linear discriminant analysis (PCA-LDA) against laboratory hyperspectral data and then validated against Sentinel-2-derived broadband spectra. The results show that PCA-LDA produced excellent classification accuracy (Kappa coefficient > 0.9) for both data sources and even more so for Sentinel-2 spectra, highlighting the effectiveness of the multitemporal approach to extract bare soil pixels. Further investigations into the spectral curves enabled identification of distinctive spectral features representative of shifting soil albedo and biochemical composition due to erosion-induced SOC mobilization. A classification scheme comprising the spectral features was applied to the Sentinel-2 bare soil composite for pixel-wise soil erosion mapping, in which 15.9% of the cropland area was detected as erosion hotspots, while the Moderate class occupied 65.4%. Comparing the erosion map to a NDVI map demonstrated the negative impact of soil erosion on crop growth from a spatial perspective, highlighting the potential of the proposed approach to aid targeted cropland management for food security and climate.

The staking (elevating creeping vines above the ground with poles) of yam is practiced to optimize crop yield, but its effect on soil loss due to crop harvesting (SLCH-soil adhering to harvested tubers) and its associated carbon loss has not been investigated globally. A 3-year field experiment was conducted to study the yam (Dioscorea rotundata) staking effect on SLCH and to examine the environmental effect of soil carbon loss. Staking reduced soil loss due to crop harvesting by 55.6% and increased yam yield by 33.3% when compared to un-staking. Soil carbon loss and root hair weight per tuber yield decreased by 47.7 and 58.4%, respectively, under staking compared with un-staking practices. The un-staking with higher moisture (≃42%) exported two times more soil-available nutrients (N, P, K and Ca) than staking. SLCH was also linearly related to root hair weight (R² = 0.88–0.75; p < 0.05) and moisture content (R² = 0.79–0.63; p < 0.05). The lower ratio of root hair weight to tuber yield coupled with moderate mound moisture in staking neutralized its higher tuber yield effect on SLCH by reducing soil loss and its carbon loss. Thus, yam staking mitigates soil loss and its carbon loss which can increase the sequestration potential of soil carbon stock.

Monitoring soil CO₂ is essential for accurately quantifying the sources and sinks of atmospheric greenhouse gases and for providing carbon emission reduction strategies. However, the limited portability and high cost of conventional soil CO₂ monitoring equipment have severely restricted large-scale and long-term field observations. To address these constraints, this study has successfully designed and fabricated a portable and low-cost soil respiration system (SRS) based on non-dispersive infrared (NDIR) sensor technology and Long-range radio (LoRa) wireless communication. The SRS enables multi-point synchronous measurements and remote data transmission. Its reliability was rigorously evaluated through both simulated and field comparative experiments against the LI-8100A. The results demonstrated a high level of agreement between the measurements of the SRS and the LI-8100A, with the coefficients of determination (R²) of 0.996 and 0.997, respectively, for the simulation and field experiments, with the corresponding root mean square error (RMSE) of 0.090 and 0.089 μmol·m⁻²·s⁻¹. The Bland–Altman analysis further confirmed the consistency between the two systems, with over 95% of the data points falling within the acceptable limits of agreement. These findings indicate that the self-developed SRS substantially reduces costs while maintaining reliable measurement accuracy. With its wireless transmission and multi-point deployment capabilities, the SRS offered an efficient and practical solution for addressing the challenges of monitoring spatial heterogeneity of soil respiration, demonstrating considerable potential for broader application in CO₂ flux monitoring research.