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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]

Semi-arid regions belong to important ecological transitional zones with a high ecosystem vulnerability and sensitivity. In recent years, due to the influence of climate change and human activities, the land covers of semi-arid regions have experienced dramatic changes. Therefore, timely and accurate land cover mapping of semi-arid regions is of great significance. Deep learning has been a hotspot in land cover mapping; however, it requires a large number of labelled samples. To tackle this data-hunger issue, we resolve to a multi-gate semi-supervised learning (SSL) method that could use a huge number of unlabelled samples to improve the classification performance. Specifically, the proposed method consists of a probability gate, an uncertainty gate and a consistency gate, aiming to generate high-quality pseudo-labels from the unlabelled datasets. Experimental results from Zhangbei, China indicate that the proposed method yields a good performance with an overall accuracy of 90.31%, which is 4.31% higher than that of traditional supervised learning (SL) methods. Ablation studies also verify the effectiveness of the gated mechanism in selecting unlabelled data. [ABSTRACT FROM AUTHOR]

Wheat is a major staple food crop in China. Accurate and cost-effective wheat mapping is exceedingly critical for food production management, food security warnings, and food trade policy-making in China. To reduce confusion between wheat and non-wheat crops for accurate growth stage wheat mapping, we present a novel approach that combines a random forest (RF) classifier with multi-sensor and multi-temporal image data. This study aims to (1) determine whether an RF combined with multi-sensor and multi-temporal imagery can achieve accurate winter wheat mapping, (2) to find out whether the proposed approach can provide improved performance over the traditional classifiers, and (3) examine the feasibility of deriving reliable estimates of winter wheat-growing areas from medium-resolution remotely sensed data. Winter wheat mapping experiments were conducted in Boxing County. The experimental results suggest that the proposed method can achieve good performance, with an overall accuracy of 92.9% and a kappa coefficient (κ) of 0.858. The winter wheat acreage was estimated at 33,895.71 ha with a relative error of only 9.3%. The effectiveness and feasibility of the proposed approach has been evaluated through comparison with other image classification methods. We conclude that the proposed approach can provide accurate delineation of winter wheat areas. [ABSTRACT FROM AUTHOR]