地表冻融是水循环和碳循环等系统中的重要变量，准确掌握地表冻融状态及其时空变化对水文过程、气候变化、生态学等研究有重要意义。现有冻融产品在地形、气候、土壤条件复杂的大区域表现不稳定，精度尚不能满足实际应用需求。本文利用再定标的风云3B (FY-3B)和风云3D (FY-3D)微波成像仪数据，以利用结合边缘检测算法和冻融判别式算法发展得到的动态冻融判别式算法为主算法，以季节阈值算法为辅助算法制备中国地区2010-2021年的地表冻融状态数据集。数据集精度验证结果显示：本研究生产的地表冻融数据集在不同季节以及不同气候区精度表现稳定，利用分布在青藏高原、东北根河和华北塞罕坝地区的地面观测站点实测5 cm土壤温度数据验证此冻融数据集的整体精度在86%以上。在对此数据集进行充分验证的基础上，基于2011至2020年的10年中国区地表冻融数据进行了地表冻融的时空变化分析，并分析了地表冻融变化与植被净初级生产力（Net Primary Productivity，NPP）和植被总初级生产力（Gross Primary Productivity，GPP）之间的相关性。分析发现植被NPP/GPP与地表年融化首日和年冻结天数呈负相关，决定系数在0.52～0.72之间，地表融化开始日期越早，年冻结天数越少，植被年NPP/GPP越高，表明本数据集在评估植被生态系统碳储量及气候变化方面具有一定的潜力，本数据集还可为大区域土壤侵蚀、气候变化、水文过程等研究提供高精度的地表冻融状态数据。数据集以H5文件格式存储，下载DOI:10.11888/Cryos.tpdc.300445。

[Objective] Near-surface soil freeze/thaw (F/T) state is an important variable in water cycle and carbon cycle system. Accurately obtaining the F/T state of near-surface soil and its spatial and temporal changes is important for hydrological processes, climate change and ecology study. The main existing F/T products based on passive microwave remote sensing data are unstable in large scale with relatively complex topography, climate and soil conditions, and the accuracy is not yet able to meet the requirements of applications. The microwave radiation imager (MWRI) carried on China"s FY-3 satellite can acquire passive microwave remote sensing data, which is currently less used in near-surface soil F/T monitoring. In this paper, facing the problems of the existing near-surface soil F/T products, based on FY-3/MWRI data, the near-surface soil F/T dataset of China from 2010 to 2021 was presented. [Method] The algorithms used to obtain the dataset consists of a primary algorithm and a secondary algorithm, where the dynamic near-surface soil F/T detection algorithm is the main algorithm and the seasonal threshold algorithm is the auxiliary algorithm. Dynamic F/T detection algorithm is developed based on the union of soil F/T discriminant algorithm and edge detection algorithm and performs stably at large scales. To avoid obvious F/T misclassification, corrected ERA5-Land temperature data were first used to identify areas that are not subject to F/T cycles before generating the near-surface soil F/T dataset. In order to reduce the effect of precipitation and water bodies on the accuracy of F/T dataset, precipitation is labeled using GPM precipitation data and water bodies are labeled using land cover data (GlobeLand30-2010). [Result] Finally, the daily near-surface FY-3B (2010-2019) and FY-3D (2017-2021) F/T dataset consisting of daytime (ascending orbit) and nighttime (descending orbit) are presented. The accuracy of the near-surface soil F/T dataset presented in this paper is stable across seasons and climate zones, and performs best when comparing with the other existing passive microwave remote sensing F/T products. The overall accuracy of the presented F/T dataset is more than 86% based on the evaluation results using the in situ 5 cm soil temperature data obtained from the Qinghai-Tibetan Plateau, the Genhe watershed in Northeastern China, and the Saihanba area in Northern China. [Conclusion] By analyzing the spatial and temporal variations of near-surface soil F/T from 2011 to 2020 based on the presented dataset, we found that the annual thaw onset was delaying, the annual frozen onset was advancing, and the annual frozen days was increasing during the 10-year period over the Qinghai-Tibetan Plateau, whereas there were no significant change over other regions. The vegetation Net Primary Productivity (NPP) and Gross Primary Productivity (GPP) were negatively correlated with the land surface annual thaw onset date and annual frozen days, with the coefficient of determination ranging from 0.52 to 0.72. The earlier the date of land surface thawing and the fewer the annual frozen days, the higher the annual NPP/GPP. These analyses demonstrated the potential application of this presented F/T dataset in studies of climate change, vegetation biomass, and vegetation carbon stocks. The dataset is stored in H5 file format and downloaded at DOI:10.11888/Crvos.tpdc.300445.