【文/观察者网 柳白】一边是从0到47%的飞跃，另一边是从88%到9%的暴跌，中美遥感研究论文产出 占比的一升一降，见证了中国遥感技术实力从"起跑"到"领跑"的突破。 香港英文媒体《南华早报》11月18日发文指出，一项最新的研究显示，中国数十年来国家层面持续的大 规模资金投入，已将地球观测科学的主导权从美国夺走。不仅是相关领域的论文研究，中国在专利方面 同样占据全球主导，且在量子计算等相关新技术领域持续投入，实现了纳米级空间测距等技术突破。业 内人士表示，除非美国大幅调整政府资金优先级，否则短期内难以重获该领域的创新领导地位。 2015年，纽约大学教授黛布拉·莱弗坐在布鲁克林的办公桌前，审阅着又一叠遥感领域的研究论文。浏 览到作者所属机构时，她愣住了，那些曾经满是美国大学和美国国家航空航天局（NASA）实验室署名 的期刊，如今已开始刊登来自北京、武汉和上海的研究成果。 在接下来的几年里，最初的涓涓细流逐渐汇成浪潮，而后演变为一场"海啸"。 文章说，回溯20世纪90年代，美国在遥感领域的主导地位，堪比如今硅谷在软件行业的统治力。当时的 美国机构贡献了该领域近90%的研究成果，而中国的相关论文几乎为零。 但到20 ...

记者从中国科学院空天信息创新研究院获悉，该院研究员曾江源团队提出了一种融合机器学习与插 值方法的新型技术框架，有效解决了全球卫星土壤水分遥感数据产品中常见的大范围数据缺失问题，显 著提高了数据的完整性和实用性。相关研究论文日前发表于《环境遥感》。 作为反映地球生态健康状况的核心指标，土壤水分对农业灌溉、干旱预警、气候变化分析等具有重 要价值。目前，全球土壤水分数据主要依赖卫星遥感获取，然而受卫星轨道、地表复杂地形、人为信号 干扰等多种因素影响，原始数据常出现大量缺失，限制了其在实际科研与应用中的使用效果。 "当前填补缺失数据主要有两类方法：一类是传统插值法，依赖已知数据推测缺失区域，适用于小 范围缺失，但在大片空白区容易失效；另一类是基于大数据分析的机器学习方法，能够通过分析全球数 据，寻找土壤水分与降雨、植被等因素间的联系来进行预测，但结果容易趋向'平均'，难以准确反映特 别干旱或湿润地区的真实情况。"曾江源介绍。 针对上述问题，研究团队创新采用"优势互补"思路，将两类方法深度融合。他们运用堆叠异质集成 技术，先分别利用插值和机器学习方法生成初步填补结果，再通过智能算法优化整合，形成同时兼顾整 体准确性和局 ...

Core Insights - The research team from the Chinese Academy of Sciences has developed a new technology framework that integrates machine learning and interpolation methods to address the common issue of large-scale data gaps in global satellite soil moisture products, significantly enhancing data completeness and usability [1][2] Group 1: Technology Development - The new technology combines traditional interpolation methods, which are effective for small data gaps, with machine learning techniques that analyze global data to predict soil moisture based on relationships with rainfall and vegetation [1][2] - The innovative approach utilizes "stacked" heterogeneous ensemble techniques to generate initial fill results from both methods, followed by optimization through intelligent algorithms, ensuring both overall accuracy and local detail in the final data [2] Group 2: Application and Impact - This technology demonstrates strong versatility and can be extended to repair various remote sensing data products, including surface temperature, vegetation parameters, and atmospheric components, providing higher quality data support for agriculture management, ecological protection, disaster monitoring, and climate change research [2]

Core Viewpoint - The article highlights significant advancements in the precise monitoring and accounting of carbon emissions from large coal-fired power plants, which are crucial for achieving global carbon neutrality goals [1][2]. Group 1: Research Breakthroughs - The Chinese Academy of Sciences has achieved a breakthrough in remote sensing and carbon emission estimation, enabling high-precision dynamic quantification and mapping of CO2 emissions from large coal-fired power plants [2]. - The research team developed a new satellite remote sensing approach, marking the first time high-precision dynamic quantification of CO2 emissions from large coal-fired power plants has been realized internationally [2][4]. Group 2: Importance of Carbon Emission Monitoring - Coal-fired power plants account for approximately 50% of global carbon emissions from fossil fuel combustion, making accurate carbon accounting essential for global carbon assessments and the electricity sector [3]. - Traditional methods of calculating emissions rely heavily on self-reported data from power plants, which can lead to discrepancies and lack of comparability due to the absence of unified international accounting standards [3]. Group 3: Methodological Innovations - The research team introduced an innovative optimization algorithm that significantly enhances the efficiency of identifying background carbon emission levels and improves the accuracy of smoke plume trajectory inversion [4]. - The study successfully quantified CO2 emissions from 14 large coal-fired power plants, with emissions ranging from 21.54 thousand tons to 82.3 thousand tons per day, demonstrating a marked improvement in inversion accuracy [4].