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]