Land consolidation zoning is the basis of land reclamation planning and provides a precondition for land management work. Zoning is beneficial to scientific decision-making regarding the use of cultivated land resources, and helps to ensure the quality and production level of cultivated land. Hence, land consolidation zoning is of national significance in terms of safeguarding food security. In this paper, an indicator system for land evaluation and zoning is described based on indicators of natural ecological conditions, socioeconomic conditions, land-use conditions, and the land consolidation potential of Hubei province. The application of self-organizing maps and Hierarchical clustering results in 11 and 12 categories, respectively. After comparing and adjusting these results according to the real situation and key points in the land consolidation of each zone, seven integrated categories are determined. Finally, the land consolidation zoning scheme for these seven integrated partitions is shown to be suitable for Hubei province and in line with the real situation. The proposed scheme offers support for land remediation in a coordinated and orderly way, and provides a scientific basis for improving land utilization efficiency and grain production capacity in Hubei province.

The material foundation of sustainable agricultural development is cultivated land resources, and their sustainable use is critical to fostering agricultural sustainability and guaranteeing national food security. In this paper, the multifunctional evaluation framework of the cultivated land system based on the “GESEL” model at the grid scale (5 km × 5 km) is constructed to explore the spatiotemporal evolution characteristics of a multifunctional cultivated land system in two lake plains and the trade-off and synergy between the functions. The five functions are all unstable in time scales, and their spatial distribution characteristics are also different. The trade-off and synergy between the multiple functions of the cultivated land system in the two lake plains from 2000 to 2019 showed significant spatial heterogeneity. Most of the functions were mainly collaborative, and a few were trade-offs. The two lake plains can be divided into four multi-functional cultivated land zones: a grain production leading zone, a distinctive agricultural planting zone, a high-efficiency agricultural development zone, and an ecological agricultural construction zone. This research puts forward some countermeasures and suggestions to promote the sustainable utilization of cultivated land resources.

Cultivated land resources are the material basis of sustainable agricultural development. Climate change, food security, land pollution, and other issues highlight the value of sustainable agricultural development, and the health of the cultivated land system has attracted much attention. By constructing “PSR-VOR” cultivated land system health evaluation framework under the 5 km grid scale and using GIS spatial analysis and mathematical statistics to comprehensively evaluate the health status of the cultivated land system in the two lake plains from 2000 to 2019. The major results have shown that: (1) Over the past 20 years, both the highest and average values of the health index of the cultivated land system have gone down, and the health status of the cultivated land system has changed and gotten worse over time. (2) The health status in the two lake plains has been generally good, mainly in Class I and Class II areas. However, the area of cultivated land with general and poor health status has increased rapidly. On the whole, the health level presents the characteristic of gradually decreasing from the northeast to the southwest and southeast. (3) During the study period, the global Moran’s I value of the cultivated land system health index in the two lake plains increased from 0.686 to 0.729, with significant spatial positive autocorrelation, and the spatial heterogeneity of the cultivated land system health index gradually increased. As shown by the spatial distribution characteristics of high in the north, low in the south, and decreasing from the middle to the outside, the distribution of the high-value cluster area and the low-value cluster area of the cultivated land system health index in the two lake plains has not changed significantly over the past 20 years. (4) The two lake plains are divided into five areas: a moderate optimization area, a collaborative optimization area, a potential improvement area, a key improvement area, and a priority improvement area. The urgency of regulating the health status from the moderate optimization area to the priority improvement area has gradually increased, and the differentiated utilization and management of cultivated land resources need to be carried out according to local conditions.

The accurate mapping of crop planting patterns is vital for sustainable agriculture and food security, particularly in regions with complex cropping systems and limited cloud-free observations. This research focuses on the Jianghan Plain in southern China, where diverse planting structures and persistent cloud cover make consistent monitoring challenging. We integrated multi-temporal Sentinel-2 and Landsat-8 imagery from 2017 to 2021 on the Google Earth Engine platform and applied a sample migration strategy to construct multi-year training data. A random forest classifier was used to identify nine major planting patterns at a 10 m resolution. The classification achieved an average overall accuracy of 88.3%, with annual Kappa coefficients ranging from 0.81 to 0.88. A spatial analysis revealed that single rice was the dominant pattern, covering more than 60% of the area. Temporal variations in cropping patterns were categorized into four frequency levels (0, 1, 2, and 3 changes), with more dynamic transitions concentrated in the central-western and northern subregions. A multiscale geographically weighted regression (MGWR) model revealed that economic and production-related factors had strong positive associations with crop planting patterns, while natural factors showed relatively weaker explanatory power. This research presents a scalable method for mapping fine-resolution crop patterns in complex agroecosystems, providing quantitative support for regional land-use optimization and the development of agricultural policies.

Maize, one of the world’s three major food crops, plays a vital role in global food security. Analyzing the spatiotemporal patterns of maize cultivation in Hunan Province and their influencing factors contributes to enhancing planting quality and efficiency, optimizing production patterns, and supporting provincial food security initiatives. Utilizing maize cultivation data from Hunan Province (2001–2023), this study employed the standard deviation ellipse, center of gravity shift model, and principal component analysis to examine production patterns and their drivers. Key findings include the following: (1) The maize planting area exhibited an overall increasing trend from 2001 to 2023, with a spatial convergence from the northwest towards the east. Cultivation hot spots were identified in Shaoyang, Loudi, and Changde. Maize cultivation was predominantly concentrated in areas with gentle slopes (0–3°) and gradually shifted eastward towards similar terrain. (2) The provincial maize production center of gravity followed a “Z”-shaped trajectory, moving eastward and southward with Loudi City as its core. While the spatial distribution pattern shifted from “northwest–southeast” to “west–east”, the core concentration area maintained its “northwest–southeast” orientation. Concurrently, the fragmentation of cultivated land within the maize planting landscape increased. (3) Maize planting hot spots expanded from the northwest towards the central and eastern regions, extending southward. Cold spot areas shifted from the central region towards the northeast. By the study’s end, the central region had emerged as the core maize planting area. (4) Agricultural production conditions and policy factors were identified as the main drivers of spatiotemporal changes in maize acreage within Hunan Province.