“AI+遥感”精准量化北方干旱流域饲草种植潜力

Core Insights - The research team at the Chinese Academy of Sciences' Aerospace Information Innovation Research Institute has developed a new method that integrates artificial intelligence and remote sensing technology to identify optimal grass planting zones in northern China's arid and semi-arid regions, providing scientific support for ecological protection and high-quality agricultural development in the Yellow River basin [1][3] Group 1: Research and Methodology - The new method addresses the dual pressures of water resource scarcity and food security in northern China's arid regions, which have long struggled with ecological protection and land productivity enhancement [1] - Traditional assessment methods relied heavily on extensive ground sampling and focused on single indicators, making precise and efficient regional planning challenging [1] - The research team constructed a cross-data source integration framework that effectively combines satellite remote sensing, ecological hydrological models, and ground measurement data [1] - Advanced artificial intelligence techniques, such as ensemble learning, were employed to accurately reverse key indicators like irrigation water usage, vegetation productivity, and soil organic carbon, achieving a reversal accuracy exceeding 90% and eliminating over 40% of regional bias [1] Group 2: Practical Applications and Future Prospects - The system can automatically generate a visual representation indicating which plots are most suitable for grass planting and yield the highest input-output benefits, facilitating precise decision-making and resource allocation for management departments [3] - The technology is cost-effective and highly adaptable, not only applicable to the Yellow River middle reaches but also expected to be implemented in other typical arid areas such as the Inner Mongolia-Ningxia ecological transition zone and the Hexi Corridor [3] - The method holds significant reference value for other regions globally facing similar ecological and agricultural challenges [3]