Background Soil carbon sequestration is of great significance to achieve carbon neutralization at an early date. Aims The present study was conducted in order to explore the interactive effects of conservation tillage on soil organic carbon (SOC) and aggregate stability, and to understand the response of soil organic carbon to aggregate structure and distribution characteristics. Methods Undisturbed soil from varying depths was collected from the conservation tillage demonstration base under differential tillage practices (NT, no‐tillage mulch; DT, deep‐tillage mulch; CT, conventional‐tillage mulch). SOC and stable‐aggregates of different particle sizes, their number, and structures were analyzed and the relationship between the stability of soil aggregates and SOC was presented. Results The SOC content was the highest under CT treatment (1.67 g kg–1) and the lowest under NT treatment (1.31 g kg–1). In surface soil, the number of macroaggregates (>0.25 mm, M) under NT treatment was the largest (89.1%), the mean weight diameter (MWD) and geometric mean diameter (GMD) of water stability and mechanical aggregates were the highest, and the fractal dimension (D) was the lowest. This indicated that the NT treatment could maintain the aggregate structure of surface soil well. In the short term, DT and CT could increase SOC content, while NT could decrease SOC content. There was a significant correlation between SOC content and MWD (R2 = 0.40), GMD (R2 = 0.38), and D (R2 = 0.44). The more stable the aggregate structure, the higher the soil fixation and maintenance of SOC. Conclusions SOC content and aggregate stability have a synergistic effect, and NT can promote the formation of soil aggregates, improve their stability, and balance the distribution of carbon in soil aggregates with different particle sizes.

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Soil moisture controls the exchange of energy between the land surface and the atmosphere and significantly affects plant growth and productivity. Hyperspectral (350–2500 nm) monitoring of soil moisture content (SMC) could provide a theoretical basis for the real‐time estimation of spatial and temporal variations in soil moisture. During this study, we have developed SMC prediction models in different soil moisture ranges by using the combination of spectral preprocessing methods and optimized spectral indices. Results indicated that first‐order derivative (FD) preprocessing method can highlight the effective information of the spectra and improve the correlation between spectral reflectance and SMC. The spectral reflectance exhibited a strong correlation with SMC near 1400, 1700, 1900, and 2200 nm. The FD‐NDSI‐W0‐model had the best SMC prediction and applicability (R2 = 0.977, root mean square error = 3.413%, relative percent deviation = 6.171) to predict SMCs. Overall, pretreatment methods, combined with the two‐band random combination to optimize the spectral index to build a prediction model of SMC, could be applied for accurate monitoring of SMC.

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