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.

The accurate prediction of soil organic matter (SOM) content is important for sustainable agriculture and effective soil management. This task is particularly challenging due to the variability in factors influencing SOM distribution across different cultivated land types, as well as the site-specific responses of SOM to remote sensing data and environmental covariates, especially in the black soil region of northeastern China, where SOM exhibits significant spatial variability. This study evaluated the variations on the importance of different remote sensing imagery and environmental covariates in different cultivated land zones. A total of 180 soil samples (0–20 cm) were collected from Youyi County, Heilongjiang Province, China, and multi-year synthetic bare soil images from 2014 to 2022 (focusing on April and May) were acquired using Google Earth Engine. Combining three types of environmental covariates such as drainage, climate and topography, the study area was categorized into dry field and paddy field. Then, the SOM prediction model was constructed using random forest regression method and the accuracy of different strategies was evaluated by 10-fold cross-validation. The findings indicated that, (1) in the overall regression analysis, combining drainage and climate variables and multi-year synthetic remote sensing images of May could attain the highest prediction accuracy, and the importance of environmental covariates was ranked as follows: remote sensing (RS) > climate (CLI) > drainage (DN) > Topography (TP). (2) Zonal regression analysis was conducted with a high degree of precision, as evidenced by an R² of 0.72 and an impressively low RMSE of 0.73%. The time window for remote monitoring of SOM was different for dry field and paddy field. More specifically, the optimal time frames for SOM prediction in dryland were identified as April and May, while those for paddy fields were concentrated in May. (3) In addition, the importance of diverse environmental covariates was observed to vary with the cultivated land types. In regions characterized by intricate topography, such as dry fields, the contributions of remote sensing images and climate variables assumed a heightened importance. Conversely, in paddy fields featuring flat terrain, the roles of climate and drainage variables played a more substantial role in influencing the outcomes. These findings underscore the importance of selecting appropriate environmental inputs for improving SOM prediction accuracy.

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.

Environmental variables have a substantial effect on the reliability of soil organic carbon (SOC) mapping. However, it is still challenging to identify which environmental variables are effective in cropland SOC prediction in sandy, saline, and black soil regions. To address this issue, we used the principal component analysis (PCA) method for the feature selection of bands, spectral indexes, and terrain factors for each region. Based on the selection feature, we used global RF and local RF for SOC prediction for these three regions. Our results indicated that (1) climate factors, particularly mean annual precipitation and mean annual temperature, were the most effective predictors in SOC mapping across sandy, saline, and black soil regions, as indicated by their significant contribution to RF model performance (R² > 0.63); (2) followed by climate factors, the Transformed Vegetation Index (TVI) was consistently identified as the most influential variable for SOC prediction among spectral indexes in all three regions; (3) a local regression method based on RF models showed good performance compared to a global model; (4) desertification and salinization were the main reasons for the spatial differences in AH and DM&LD, respectively. The SOC of HL in black soil regions was consistent with the climate change trend because of the latitude difference. This study provides valuable information for constructing a more precise soil prediction strategy for cultivated land in sandy, saline, and black soil regions.

Soil texture is an important physical property of soil. Understanding the spatial distribution of cultivated soil texture in black soil areas is crucial for precise agricultural management and cultivated land protection in these zones. This study utilizes the random forest algorithm, Landsat-8 satellite remote sensing data, and climate- and terrain-related environmental covariates to map the spatial distribution of soil texture and analyze its impact on crop growth. The results show that (1) the order of prediction accuracy differs for different soil texture types; April is determined to have the highest prediction accuracy for silt and sand, while May exhibits the greatest accuracy for clay. (2) Adding environmental variables can significantly improve the accuracy of soil texture predictions; the root mean square error (RMSE) has decreased to varying degrees (silt: 0.84; clay: 0.04; sand: 0.85). (3) Soybean growth has the strongest response to soil texture; clay grain is the key factor affecting crop growth in drought scenarios, and sand grain is the dominant factor influencing flooding. This study improves the accuracy of the remote sensing mapping of soil texture through the combination of remote sensing images and environmental variables and quantitatively evaluates the impact of soil texture on crop growth.