雪深是描述积雪的重要参数，高精度的雪深产品对天气预报、水文、地表过程等研究有重要作用。国家卫星气象中心自2019年4月起发布了风云三号D星（FY-3D）微波成像仪（MWRI）的被动微波全球雪深/雪水当量产品。相比于FY-3B算法，FY-3D业务化雪深算法在东北林区引入森林覆盖度对森林影响进行了经验校正。为了检验其算法改进效果及业务化产品在东北林区的精度，本文利用黑龙江省伊春市林区的雪线实测数据和气象站点雪深观测数据对FY-3D业务化雪深和雪水当量产品进行了验证，并对验证结果进行了分析。验证表明，FY-3D雪深产品与雪线实测数据、气象站点观测数据的RMSE分别为5cm和13.2cm，FY-3D雪水当量产品与雪线实测数据的RMSE为2.1mm。分析表明FY-3D雪深产品在林区的不确定性主要来源于半经验算法难以消除森林对微波辐射亮温的影响。尽管经过森林辐射校正可以增强亮温梯度与雪深的相关性，但是森林辐射校正的经验性又增加雪深反演结果的不确定性。该工作可为后续基于国产FY-3D亮温数据改进林区雪深反演算法提供参考.

Objective: Snow depth (SD) and SWE (snow water equivalent) are crucial parameter to describe snow cover information. High precision SD and SWE data plays an important role in the study of weather forecast, hydrology, surface processes and other applications. Passive microwave remote sensing is an effective means of observing SD and SWE. Since April 2019, the National Satellite Meteorological Center has released passive microwave global SD and SWE products of Microwave Radiation Imager (MWRI) aboard the Fengyun-3 D Satellite (FY-3D). Compared with FY-3B SD retrieval algorithm, the operational algorithm of FY-3D introduces fractional forest cover for performing empirical correction on forest impact in Northeast China. This study investigates the performance of the improved FY-3D SD and SWE operational algorithm and verify the accuracy of the corresponding products in the forest area in Northeast China. Method: This article obtained the situation of SD in the study area over the years through observation data from meteorological stations in Yichun, Heilongjiang Province. The FY-3D SD and SWE operational products are validated through measured snow course data and SD data observed by meteorological stations in the forest areas. Moreover, the uncertainty of FY-3D SD products and the representativeness of meteorological stations are analyzed. Result: The results indicate that there is strong temporal heterogeneity in SD distribution in the Yichun region. The verification results indicate that the FY-3D SD product exhibits an overall underestimation, and the RMSE is 5cm and 13.2cm, respectively, when compared with the measurements of snow course and the observations of meteorological station. While the RMSE between the FY-3D SWE product and the snow course data is 2.1mm. FY-3D SD operational algorithm, as a semi-empirical algorithm, cannot eliminate the influence of forests on microwave radiation brightness temperature. Although forest radiometric correction can enhance the correlation between brightness temperature gradient and SD, the empirical nature of forest radiometric correction also increases the uncertainty of snow depth inversion results. Conclusion: Analysis shows that the FY-3D algorithm has a lag in response to sudden snow drops due to its lack of response to new snow with an exponential correlation length of 0.11mm. At the beginning of the snow season, when the snow depth remains below 5cm, the change in brightness temperature gradient caused by soil freezing can be misjudged by the inversion algorithm, leading to overestimation of snow depth during this period. In the preliminary exploration of site representativeness, the analysis of the differences between point and surface combined with field observations show that snow in forest areas is deeply influenced by various factors, leading to strong local spatial heterogeneity. This work can provide reference for improving the SD inversion algorithm in forest regions based by domestic FY-3D brightness temperature data in the future.