摘要
由一名新闻记者-机器人与机器学习每日新闻的工作人员新闻编辑-关于人工智能的新研究结果已经发表。根据澳大利亚卡拉汉的新闻报道，由新sRx编辑，研究表明：“土壤湿度(SM)是驱动水文、气候和生态过程的关键变量。”这项研究的资金支持者包括高性能土壤合作研究中心。新闻编辑们从纽卡斯尔大学的研究中获得了一句话："虽然它在空间和时间上都有很大的变化，但在大区域的适当空间和时间尺度上提供关于SM条件的数据有限。卫星SM反演，特别是l波段微波遥感，已成为提供空间连续全球尺度SM信息的可行方案。然而，这些L波段微波SM反演的粗空间分辨率给许多需要大量空间细节的区域和局部SC ALE SM应用带来了不确定性。为了提高粗分辨率卫星SM数据集的空间分辨率，人们进行了大量的研究。基于机器学习（ml）的降尺度模型由于能够捕捉SM与其驱动因素如植被、地表温度、地形和气候条件之间的非线性、复杂关系而近年来得到了广泛的关注。"本文综述了基于ml的SM降尺度方法."
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-New study results on artificial intelligence have been published. According to news reporting out of Callaghan, Australia, by New sRx editors, research stated, "Soil moisture (SM) is a key variable driving hydr ologic, climatic, and ecological processes." Financial supporters for this research include Cooperative Research Centre For H igh Performance Soils. The news editors obtained a quote from the research from Newcastle University: " Although it is highly variable, both spatially and temporally, there is limited data availability to inform about SM conditions at adequate spatial and temporal scales over large regions. Satellite SM retrievals, especially L-band microwave remote sensing, has emerged as a feasible solution to offer spatially continuou s global-scale SM information. However, the coarse spatial resolution of these L -band microwave SM retrievals poses uncertainties in many regional- and local-sc ale SM applications which require a high amount of spatial details. Numerous stu dies have been conducted to develop downscaling algorithms to enhance the spatia l resolution of coarse-resolution satellite-derived SM datasets. Machine Learnin g (ML)-based downscaling models have gained prominence recently due to their abi lity to capture non-linear, complex relationships between SM and its driving fac tors, such as vegetation, surface temperature, topography, and climatic conditio ns. This review paper presents a comprehensive review of the ML-based approaches used in SM downscaling."
NETL
NSTL