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• This paper proposes a multi-dimensional assessment framework for cropland use stability. • Multi-dimensional cropland use characteristics present significant spatial differentiation. • Cropland stability in Northeast China Plain and Loess Plateau is the highest and lowest. • Urbanization will lead to about 9.31% of high-stability cropland losses in 2030. • Integrating landscape stability and national planning can draft a sustainable-oriented strategy. Shifting to more sustainable use can curb cropland resource degradation and improve production resilience. However, most cropland use assessments are unilaterally focused on biophysical levels and productivity, ignoring the multi-dimensional aspects of degraded cropland. This study attempts to describe cropland use stability in China by proposing a multi-dimensional assessment framework (including quantity, quality, management, service, and risk) to provide a reference for designing land degradation-neutral strategies. Results show that multi-dimensional cropland use characteristics present significant spatial heterogeneity. Western regions are reserve resource agglomerations that can replenish cropland quantity. In contrast, main grain-producing areas cover most current resources with preferably cultivation quality, management efficiency, and ecosystem services, while risk resistance is weak (especially in Middle-lower Yangtze Plain). Generally, the stability of cropland use in Northeast Plain is the highest and in Loess Plateau the lowest. Besides, urbanization will occupy about 9.31% of the high-stability cropland in 2030, of which 70.29% will occur in Northeast Plain and Huang-Huai-Hai Plain. The total potential productivity loss in Huang-Huai-Hai Plain alone will reach 100.1 kt, equivalent to supplying the food demand of 199,200 people annually. Therefore, a sustainable-oriented refined cropland management strategy should be designed according to the hierarchical nested spatial layout of comprehensive planning-stability map-multi-dimensional utilization characteristics to ensure the loss-replenishment balance and stable production. [ABSTRACT FROM AUTHOR]

• Detection of cropland abandonment based on phenological information. • Summarized the inter-annual phenological changes of abandoned cropland. • Detecting cropland abandonment information at the sub-pixel scale. • Spatiotemporal patterns of abandoned cropland in this paper were revealed. Cropland abandonment is a common land-use change with mixed impacts on the environment and rural economic development. Prevalent small family farms and excessive land fragmentation result in cropland abandonment processes that are often gradual and spatially dispersed. These difficulties limit the ability to apply previous remote sensing mapping methods of cropland abandonment to areas with complex underlying surfaces, where mixed pixels and spectral aliasing problems arise. To meet the demands of broad-scale and high-accuracy abandoned cropland mapping, we developed the P C R R S B S - C V model by combining the phenology-based cropland retirement remote sensing (PCRRS) model with the coefficient of variation (CV) and tilled soil fraction (BS). This method combines information regarding crop phenology with Moderate Resolution Imaging Spectroradiometer (MODIS) time-series images. The PCRRS model was used to estimate changes in spectral metrics parameters during the process of cropland abandonment. Additionally, the dead fuel index and normalized difference vegetation index (NDVI) were employed to estimate the BS in mixed pixels (according to crop type). We predicted that use of the BS as the weighting coefficient for the PCRRS model would reduce interference from the mixed pixel problem, while spatial heterogeneity could be reduced by dividing the research area into regional units. Use of the CV of the NDVI time series with phenological information highlights the volatility of crop growth periods and helps to eliminate disturbances associated with woodlands and grasslands. Finally, we demonstrated this method in the Loess Plateau of China and a portion of central Europe; we verified its accuracy using high-resolution images from Google Earth. Our algorithm demonstrated overall accuracy of 82.2 % and can extract cropland abandonment information as little as 20 % of a pixel. The overall root mean square error (RMSE) was controlled below 15 %. In summary, the high accuracy achieved by this method enables the monitoring of cropland abandonment dynamics on a large scale. [ABSTRACT FROM AUTHOR]