Particle heterogeneity is one of the key indicators to estimate the variability in the soil properties and compositions. This study aims to explore the effects of the soil particles settling and bedding on the greenhouse gas emissions from the depositional area. The layer-specific physicochemical properties were also considered during this time. Specifically, one type of Mollisol (local people named as the black soil) was sampled from a typical eroding slope in the northeastern China. Then six classes were fractionated by their respective settling velocities. Six structures were obtained to bed the differently-sized soil particles in sequence: S1 was the thinnest structure with only > 500 μm particles L1, whereas S6 was the thickest structure with the six particle classes being layered in sequence from the coarsest particles L1 at the bottom to the finest particles L6 on top. The emissions of greenhouse gases CO₂, N₂O and CH₄ were finally collected from the incubated structures on a daily basebasis. The results showed that: 1) The coarse particles settled first (L1) on the bottom, whereas, the fine particles settled the last and then covered on the top (L6). The porosity gradually decreased from 23.79% of coarse layer L1 to 1.0% of fine layer L6. By contrast, the soil water content increased generally at the highest moisture content of 68.78%, as the soil particles were settled with the L5. 2) Carbon and nitrogen were rich in the L2-L4 layer, where the soil organic carbon and total nitrogen were peaked in the L2 layer (with 27.34 and 2.35 g/kg, respectively), and the maximum dissolved organic carbon and nitrogen appeared in the L3 and L4 layer (with 519.31, and 19.69 mg/kg, respectively). 3) The average greenhouse gas emission rates decreased significantly during the seven incubation days, as the sedimentary layers were thicker (P<0.05). Correspondingly, there was no increase in the gas flux with the thickening of the sedimentary soil layer. The layer-by-layer deposition process was separated from the coarse and fine particles, where the coarse particles were preferentially deposited as the lower layers, while the fine ones were stacked over forming upper layers. Consequently, there was a gradual decrease trend of the porosity and relative diffusion coefficient of gas from the bottom to the top. As such, it is expected to effectively inhibit the transfer of greenhouse gases via the depositional profile. Overall, the layer-by-layer settling and bedding can be used to reconstruct the deposition process in the field, and then to effectively capture the potential impacts of depositional layer structure on greenhouse gas emissions. The finding can overcome the limitations of traditional sampling, indicating the outstanding variations of small-scale layers and bedding. The settling and bedding patterns can also be characterized to quantify the greenhouse gas emissions from different depositional settings.

Rapid monitoring of dissolved organic carbon (DOC) content is often required in the frozen black soil. It is crucial to assess the soil fertility under different conditions. Nutrient migration and transformation can also be used to elucidate the carbon cycling in the field. Hyperspectral remote sensing has been widely applied to monitor the soil organic matter, due to its rapidity and efficiency. However, the soil freezing can alter the spectral characteristics. It also remains unclear on the accuracy of inversion models under frozen conditions. In this study, a hyperspectral inversion model was established for the DOC content in the frozen black soil. A systematic comparison was made on the accuracy differences of the models over various moisture gradients under frozen versus unfrozen states. Typical seasonally frozen black soil was selected from the Heshan Farm in Heilongjiang Province, China. Random samples were collected with three DOC levels. The DOC content was then measured. Pure water was added at different gradients after measurement. Ultimately, 120 experimental samples were obtained with the moisture contents ranging from 3.57% to 30.14% and DOC concentrations between 171.80 and 322.75 mg/kg. Surface hyperspectral reflectance was collected under unfrozen and frozen states using an AvaField-3 field spectroradiometer. Five spectral preprocessing approaches were applied: raw spectral reflectance (REF), first-order differential reflectance (FDR), second-order differential reflectance (SDR), logarithm of reciprocal (LR), and standard normal variable (SNV). Variable importance in projection (VIP) was used to select the sensitive bands. A one-dimensional convolutional neural network (1D-CNN) was employed for the feature extraction.The dataset was partitioned using the Kennard-Stone (KS) algorithm with a calibration-to-validation ratio of 2:1, resulting in 80 samples for the calibration set and 40 for the validation set. The DOC inversion models were constructed using four machine learning models, including backpropagation neural network (BPNN), random forest (RF), tabular prior-fitted network (TabPFN), and extreme gradient boosting (XGBoost). The optimal model was selected from the low and high moisture gradient categories. The performance of the model was finally evaluated using the coefficient of determination for calibration (R_C²), coefficient of determination for prediction (R_P²), root mean square error (RMSE), and ratio of performance to deviation (RPD). The results indicated that: 1) All four models effectively predicted the soil DOC content. The accuracy was ranked in descending order under both unfrozen and frozen states: RF > XGBoost > TabPFN > BPNN. The RF-FDR model demonstrated the best performance was achieved (unfrozen: R_P²=0.851, RMSE=13.095 mg/kg, RPD=2.410; frozen: R_P²=0.808, RMSE=14.830 mg/kg, RPD=2.123). While the BPNN-LR model demonstrated the worst performance (unfrozen: R_P²=0.742, RMSE=17.200 mg/kg, RPD=1.889; frozen: R_P²=0.687, RMSE=18.945 mg/kg, RPD=1.670). 2) Soil freezing status significantly dominated the accuracy of DOC inversion among models. Comparative analysis revealed that the optimal preprocessing remained consistent before and after freezing. However, the freezing reduced the inversion accuracy of all models, with the most substantial decline in BPNN (R_P² and RPD decreased by 7.36% and 11.62%, respectively), and the least in XGBoost (R_P² and RPD decreased by 4.70% and 7.25%, respectively). 3) Moisture content gradients considerably influenced the accuracy of the model. The high-moisture models outperformed the low-moisture ones under unfrozen conditions, with R_P² and RPD higher by 0.067~0.082 and 0.206~0.392, respectively. There was a decrease in the accuracy of both low-moisture and high-moisture models under frozen conditions. The low-moisture models were reduced significantly (R_P² and RPD decreased by 16.67%-17.71% and 13.76%-18.69%, respectively). The RF-FDR model can also provide the technical support for the hyperspectral monitoring of the DOC content in seasonally frozen black soil.

Snowmelt runoff and soil freezing have been combined to seriously exert on the rill networks and soil erosion intensity in the black soil region of northeastern China. The dynamic development of rill networks can be used to quantitatively extract the parameters of erosion. Previous studies have focused primarily on the runoff erosion caused by rainfall. Only a few studies were to consider the combined effects of snowmelt runoff and freezing. Currently, available methods cannot fully meet the high precision of rill morphology. Therefore, it is very necessary to systematically investigate the rill erosion under combined effects using high-precision extraction. This study aims to simulate the snowmelt runoff scouring on the frozen (-20 °C freezing) and non-frozen slopes (10 °C), refilled with black soil from northeastern China. A 0 °C ice-water mixture was used to simulate the snowmelt runoff scouring. Meanwhile, three-dimensional laser scanning was employed to obtain the point cloud data of the slope surface after different times of scouring, until the eroded surface stabilized. The Digital Surface Model (DSM) of Difference (DoD) with point cloud reverse engineering was used to detect and parameterize the micro-topographic pattern (e.g., development of rill networks and erosion area, erosion volume, rill length, and rill density). The results show that there was a significant impact of freezing-induced temperature on the development of rill networks: (1) The frozen slope was more prone to develop the rill networks. The erosion rate after each scouring was significantly higher than that on the non-frozen slopes. Upon reaching stability, the erosion area, erosion volume, and rill length were 291%, 557%, and 437% of those on the non-frozen slopes, respectively; (2) The cumulative erosion volume of the frozen and non-frozen slopes showed similar patterns during the rill development. But there were noticeable differences along the cross-sectional direction of the rills. On the frozen slopes, the width-depth ratio (R_W/D) of the rill cross-sections decreased rapidly at the rill intersections with accelerated downcutting, whereas, the width and depth of rills increased linearly and stably. On the non-frozen slopes, the R_W/D at the mid-slope cross-sections increased with the number of scouring events, thus accelerating lateral erosion, while the R_W/D at other sections decreased with the number of scouring events with accelerated downcutting; (3) The relative error range was -12.70% to 4.42% for the Differential Digital Surface Model (DoD) with ArcGIS hydrological processing and GeoMagic point cloud reverse engineering models for the frozen slopes, with an absolute mean relative error of 5.48%, and an extraction accuracy of over 95%. The combined extraction with GeoMagic and ArcGIS showed a great contribution to obtain the rill parameters on the freeze-treated slopes. The finding can serve as a promising potential to the rill development under complex eroding settings.