Soil moisture, as a crucial factor in the eco-hydrological process, is of great importance for food production, land management in response to water and soil loss, geomorphic processes, and environmental protection. Understanding the spatial variability of soil moisture induced by different land use types and topographic features is conducive to advancing the adjustment of the land use structure and preventing soil erosion on the hillslopes in the black soil (Mollisols) area of Northeast China. Classical statistical methods and Canonical Correspondence Analysis were used to analyze the spatial heterogeneity of soil moisture at 0–20, 20–40, and 40–60 cm on slopes, to identify the main controlling factors and their relative contributions. The results suggested that: the average soil moisture content followed a decreasing order of grassland > shrubland > soybean land > maize land > adzuki bean (Vigna angularis) land > forestland; the profile soil moisture content (SMC) patterns could be divided into four types, related to the comprehensive influence of vegetation types, root system characteristics, and topographic attributes; the spatial variability of soil moisture was strongly influenced by slope gradient, followed by land use types and elevation and slope position, while slope aspect had the least impact; and finally, land use type had a greater impact on the deep layer than the surface layer, while on the contrary, the influence of the topographic attributes on the deep layer was smaller than on the surface layer. Land use types and topographical elements work together on the soil moisture variability and vertical patterns at differing depths. This study provides an insight into policy making of land resource management and can be used in the modeling of hydrological processes.

Land use change accounts for a large proportion of the carbon emissions produced each year, especially in highly developed traditional heavy industry and agriculture areas. In this study, we estimated the carbon emissions from land use in the Black Soil Region of Northeast China (BSRNC) from 1990 to 2020. We utilized seven periods of land use remote sensing image data spanning the years 1990, 1995, 2000, 2005, 2010, 2015, and 2020, with a 30-m grid resolution. Additionally, socio-economic data was incorporated into the analysis. The preprocessing of the remote sensing images involved several steps using ENVI 5.5, including radiometric correction, fusion, mosaic, and cropping. The land types were classified into six major categories: cropland, forest land, grassland, water area, construction land, and unused land, using the LUCC classification system. The IPCC coefficient method was used to calculate the trends in carbon emissions from land use, and the logarithmic mean Divisia index (LMDI) method was applied to analyze the influencing factors. The main conclusions are as follows: (1) From 1990 to 2020, the net carbon emissions from land use in the BSRNC increased from 11.91 × 10⁴ t to 253.29 × 10⁴ t, with an annual growth rate of 8.04%. (2) Spatially, land use carbon emissions exhibited an agglomeration pattern that gradually weakened and the regional emission differences gradually narrowed. (3) Income level was identified as the most important factor influencing land use carbon emissions in the BSRNC from 1990 to 2020. Land use efficiency had a inhibitory effect on net carbon emissions, reducing land use carbon emissions by 1730.63 × 10⁴ t.

Enhancing cultivated land system resilience is a fundamental prerequisite for improving land use efficiency and thus addressing climate change. Taking the Songnen Plain—a major grain production area in China—as the study region, this study constructs a definition of cultivated land system resilience from three dimensions (“resistance–adaptability–reconstruction”). An index system for resilience evaluation is established, and methods such as three-dimensional Euclidean distance and K-means clustering are employed to investigate the spatiotemporal differentiation characteristics of cultivated land resilience in the Songnen Plain from 2001 to 2021. Based on the findings, zoning is performed and corresponding management strategies are proposed: (1) Overall resilience in the Songnen Plain increased from 0.4450 in 2001 to 0.7469 in 2021; enhanced resistance played the most significant role in promoting this increase. (2) The Songnen Plain exhibited pronounced spatial differentiation in cultivated land resilience, characterized by higher resilience in the eastern region and weaker resilience in the central and western regions. (3) The zoning results reveal significant disparities in resilience levels within the study area; targeted measures are thus required to address key problems in each zone. This study provides theoretical insights and empirical conclusions for formulating differentiated protection policies for cultivated land systems, thereby ensuring the sustainable development of the Songnen Plain’s cultivated land system.

Rapid and efficient evaluation of cultivated land quality in black soil regions at the farm scale using remote sensing techniques is crucial for resource protection. However, current studies face challenges in developing convenient and reliable models that directly leverage raw spectral reflectance. Therefore, this study develops and validates a deep learning framework specifically for this task. The framework first selects remote sensing images from typical periods using a Random Forest model in Google Earth Engine (GEE). Subsequently, the raw spectral reflectance data from these images, without any transformation into vegetation indices, are directly input into an optimized BO-Stacking-TabNet model. This model is enhanced through a two-step Stacking ensemble process and a Bayesian optimization algorithm. A case study at Shuanghe Farm in Northeast China shows that (1) compared to the BO-Stacking-TabNet model using vegetation indices as input, the BO-Stacking-TabNet model based on spectral reflectance as the input indicator achieved an improvement of 10.62% in Accuracy, 1.55% in Precision, 11.05% in Recall, and 10.18% in F1-score. (2) Compared to the original TabNet model, the BO-Stacking-TabNet model optimized by the two-step Stacking process and Bayesian optimization algorithm improved Accuracy by 2.13%, Precision by 12.59%, Recall by 1.83%, and F1-score by 2.19%. These results demonstrate the reliability of the new farm-scale black soil region cultivated land evaluation method we proposed. The method provides significant references for future research on cultivated land quality assessment at the farm scale in terms of remote sensing image data processing and model construction.

The spatial configuration of cultivated land is crucial for modern agricultural production; therefore, research on cultivated land aggregation and spatial connectivity holds significant importance for enhancing agricultural production efficiency and ensuring food security. This study selected Daqing City, China, as the research area and constructed a three-level nested framework of “patch–local–regional” scales. The aggregation degree was calculated through landscape pattern indices and the MSPA model, and connectivity was evaluated using the Omniscape algorithm based on circuit theory to explore the spatiotemporal evolution patterns of cultivated land configuration and analyze their spatial correlations, proposing classified optimization strategies. The results indicate the following: (1) the spatiotemporal distribution characteristics of cultivated land aggregation in Daqing City exhibit a spatial pattern of “high in the north and south, low in the middle,” with an overall declining trend from 2000 to 2020; (2) high-connectivity areas are primarily distributed in Lindian County in the north and Zhaozhou and Zhaoyuan Counties in the south, while low-connectivity areas are concentrated in the central urban area and surrounding regions; (3) the aggregation degree and connectivity demonstrate positive spatial correlation, with the Global Moran’s index increasing from 0.358 in 2000 to 0.413 in 2020; and (4) based on the aggregation degree and connectivity characteristics, the study area can be classified into four types: scattered imbalance–isolated dysfunction, regular imbalance–connected dysfunction, scattered improvement–connected optimization, and regular improvement–connected optimization. This study provides new research perspectives for cultivated land protection. The proposed multi-scale aggregation–connectivity research method and classification system offer important reference value for the efficient utilization and management optimization of cultivated land.