The black soil region of northeastern China, one of the world’s major black soil belts, is China’s main grain-producing area, producing a quarter of China’s commercial grain. However, over-exploitation and unsustainable management practices have led to a steady decline in the quality of arable land. Scientific and reasonable zoning of arable land is the key to ensuring that black soil arable land achieves sustainable development. In this study, the 317 districts and counties under the jurisdiction of Heilongjiang, Jilin, and Liaoning Provinces in the northeast region and the four eastern leagues of the Inner Mongolia Autonomous Region were taken as the study area, and arable land zoning in the northeast black soil region was explored through group analysis. Ten types of indicators were selected according to the four levels of climate, soil, vegetation, and topography of the northeast black soil region, including average precipitation and average temperature for many years at the climate level, organic matter content and soil texture (including clay, silt, and sand) at the soil level, NDVI and EVI indicators at the vegetation level, and DEM and slope indicators at the topographic level. In accordance with the principle of distinguishing differences and summarizing commonalities, nine scenarios of dividing the northeast black soil zones into 2 regions to 10 regions were explored, and these nine zoning scenarios were evaluated in terms of zoning. The results showed that (1) the spatial variability of cropland zoning in the northeast black soil zone based on four indicators, namely climate, soil, vegetation, and topography, was significant; (2) the results of the nine types of zoning based on cropland in the northeast black soil zone showed that intra-zonal zoning was optimal when zoning the northeast black soil zone into six types of zones, which enhanced the variability between the zones and the consistency within the zones; and (3) the assessment of large-scale cropland zoning using the pseudo F-statistic and area-weighted standard deviation methods revealed similarities in their outcomes. The results provide a scientific basis for the subregional protection of arable land in the black soil zone and help to formulate effective policies for different regions.

Soil organic matter (SOM) is essential for assessing land quality and enhancing soil fertility. Understanding SOM spatial and temporal changes is crucial for sustainable soil management. This study investigates the spatial and temporal variations and influencing factors of SOM content in the Songnen Plain (SNP) and Sanjiang Plain (SJP) of Heilongjiang Province, China, based on high-precision SOC content data (RMSE = 4.84 g/kg⁻¹, R² = 0.75, RPIQ = 2.43) from 1984 to 2021, with geostatistical analyses and geodetector models. This study aims to quantitatively reveal and compare the long-term spatial and temporal characteristics of SOM changes and their influencing factors across these two plains. The results show that SOM content in both plains has decreased over the past 37 years. In the SNP, the average SOM decreased from 48.61 g/kg to 45.6 g/kg, representing a reduction of 3.01 g/kg, or a 6.10% decrease; SOM decreased spatially from northeast to southwest, covering 63.1% of the area. In the SJP, the average SOM declined from 48.41 g/kg to 44.31 g/kg, a decrease of 4.1 g/kg, or an 8.50% decrease; no pronounced spatial pattern was observed, but the declining area comprises 67.49%. Changing SOM hotspots are concentrated in southern SNP and central and northwestern SJP, showing clear heterogeneity across counties. Geodetector model analysis indicates annual mean temperature as the primary driver of SOM variations in SNP; while elevation is the main driver in SJP, the combined explanatory power of multiple factors surpasses individual ones. There is a positive correlation between SOM and temperature in SNP, and policy protection positively influences SOM in both plains. These findings provide insights into the differential protection of SOM in SNP and SJP.

In Northeast China, transplanted rice cultivation has been adopted to extend the rice growing season and boost yields, responding to the limitations of the cumulative temperature zone and high food demand. However, direct-seeded rice offers advantages in water conservation and labour efficiency. The precise and timely monitoring of the distribution of different rice planting types is key to ensuring food security and promoting sustainable regional development. This study explores the feasibility of mapping various rice planting types using only early-stage satellite data from the rice growing season. We focused on Daxing Farm in Fujin City, Jiamusi City, Heilongjiang Province, for cropland plot extraction using Planet satellite imagery. Utilizing Sentinel-2 satellite imagery, we analysed the differences in rice’s modified normalized difference water index (MNDWI) during specific phenological periods. A multitemporal Gaussian mixture model (GMM) was developed, integrated with the maximum expectation algorithm, to produce binarized classification outcomes. These results were employed to detect surface changes and map the corresponding rice cultivation types. The probability of various rice cultivation types within arable plots was quantified, yielding a plot-level rice-cultivation-type mapping product. The mapping achieved an overall accuracy of 91.46% in classifying rice planting types, with a Kappa coefficient of 0.89. The area extraction based on arable land parcels showed a higher R² by 0.1109 compared to pixel-based area extraction and a lower RMSE by 0.468, indicating more accurate results aligned with real statistics and surveys, thus validating our study’s method. This approach, not requiring labelled samples or many predefined parameters, offers a new method for rapid and feasible mapping, especially suitable for direct-seeded rice areas in Northeast China. It fills the gap in mapping rice distribution for different planting types, supporting water management in rice fields and policies for planting-method changes.

In recent decades, the integrity and security of the ecosystem in the Sanjiang Plain have faced severe challenges due to land reclamation. Understanding the impact of paddy field expansion on regional ecosystem services (ESs), as well as revealing the trade-offs and synergies (TOS) between these services to achieve optimal resource allocation, has become an urgent issue to address. This study employs the InVEST model to map the spatial and temporal dynamics of five key ESs, while the Optimal Parameter Geodetector (OPGD) identifies primary drivers of these changes. Correlation analysis and Geographically Weighted Regression (GWR) reveal intricate TOS among ESs at multiple scales. Additionally, the Partial Least Squares-Structural Equation Model (PLS-SEM) elucidates the direct impacts of paddy field expansion on ESs. The main findings include the following: (1) The paddy field area in the Sanjiang Plain increased from 5775 km² to 18,773.41 km² from 1990 to 2020, an increase of 12,998.41 km² in 40 years. And the area of other land use types has generally decreased. (2) Overall, ESs showed a recovery trend, with carbon storage (CS) and habitat quality (HQ) initially decreasing but later improving, and consistent increases were observed in soil conservation, water yield (WY), and food production (FP). Paddy fields, drylands, forests, and wetlands were the main ES providers, with soil type, topography, and NDVI emerging as the main influencing factors. (3) Distinct correlations among ESs, where CS shows synergies with HQ and SC, while trade-offs are noted between CS and both WY and FP. These TOS demonstrate significant spatial heterogeneity and scale effects across subregions. (4) Paddy field expansion enhances regional SC, WY, and FP, but negatively affects CS and HQ. These insights offer a scientific basis for harmonizing agricultural development with ecological conservation, enriching our understanding of ES interrelationships, and guiding sustainable ecosystem management and policymaking.