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To ensure national food and ecological security in China, it is essential to develop various reserve resources for cultivated land based on existing farmland advantages. Owing to abundant saline-alkali land reserves, the Yellow River Delta has enormous potentials for agriculture development. However, water scarcity and imbalance between water and land resources allocation have significantly limited the efficient utilization of saline-alkali land in this region. According to the development status regarding salinealkali soil amelioration and crop production, this study analyzes water and land resource conditions, clarifies major challenges for the sustainable development of local agriculture from the perspectives of water resource supply, groundwater table regulation, management and utilization system of saline-alkali land, technological innovation, and follow-up engineering maintenance, and proposes strategies for efficient use and coordinated allocation of water and land resources in the region. The strategies include: (1) increasing water supply from the Yellow River and connecting the coastal hydrographic net to make optimal allocation of regional water resources; (2) improving irrigation and drainage systems and auxiliary projects to enhance the quality of arable lands; and (3) accelerating demonstration and promotion of key technologies for saline-alkali soil amelioration to fully leverage the supporting function of sciences and technologies. The research results can provide a reference for comprehensive governance and utilization of saline-alkali land and agricultural sustainable development.

Due to their important role in maintaining temperature and soil moisture, agricultural plastic covers have been widely utilized around the globe for improving crop-growing conditions, which include both plastic-covered greenhouses (PCGs) and plastic-mulched farmlands (PMFs). However, it is a challenging and long-neglected issue to separate PCGs from PMFs due to their spectral similarity. The objective of this study is to propose a deep semantic segmentation model for accurate PCG and PMF mapping based on very high-resolution satellite images and to improve the model's spatial generalization capability using a transfer learning strategy. Specifically, the proposed semantic segmentation model has an encoder-decoder structure, where the encoder is composed of a new convolutional neural network for discriminative spatial feature learning, while the decoder utilizes a multi-task strategy to improve the predictions on the boundaries. Meanwhile, a transfer learning framework is adopted to increase mapping performance and generalization ability under limited samples. Experimental results in several typical regions across the Eurasian continent show that the proposed model could separate PCGs from PMFs accurately with a mean overall accuracy of 94.49% and an average mIoU of 0.8377. Ablation studies verify the role of encoder-decoder and transfer learning strategy in improving classification performance. [ABSTRACT FROM AUTHOR]

The greenhouse is the fastest growing food production approach and has become the symbol of protected agriculture with the development of agricultural modernization. Previous studies have verified the effectiveness of remote sensing techniques for mono-temporal greenhouse mapping. In practice, long-term monitoring of greenhouse from remote sensing data is vital for the sustainable management of protected agriculture and existing studies have been limited in understanding its spatiotemporal dynamics. This study aimed to generate multi-temporal greenhouse maps in a typical protected agricultural region (Shouguang region, north China) from 1990 to 2018 using Landsat imagery and the Google Earth Engine and quantify its spatiotemporal dynamics that occur as a consequence of the development of protected agriculture in the study area. The multi-temporal greenhouse maps were produced using random forest supervised classification at seven-time intervals, and the overall accuracy of the results greater than 90%. The total area of greenhouses in the study area expanded by 1061.94 km 2 from 1990 to 2018, with the largest growth occurring in 1995–2010. And a large number of increased greenhouses occurred in 10–35 km northwest and 0–5 km primary roads buffer zones. Differential change trajectories between the total area and number of patches of greenhouses were revealed using global change metrics. Results of five landscape metrics showed that various landscape patterns occurred in both spatial and temporal aspects. According to the value of landscape expansion index in each period, the growth mode of greenhouses was from outlying to edge-expansion and then gradually changed to infilling. Spatial heterogeneity, which measured by Shannon's entropy, of the increased greenhouses was different between the global and local levels. These results demonstrated the advantage of utilizing Landsat imagery and Google Earth Engine for monitoring the development of greenhouses in a long-term period and provided a more intuitive perspective to understand the process of this special agricultural production approach than relevant social science studies. [ABSTRACT FROM AUTHOR]