基于FY-4A/AGRI时空特征融合的新疆地区积雪判识

高时间分辨率的积雪判识对于新疆牧区农牧业发展和雪灾预警具有重要作用，针对已有积雪产品易受复杂地形地貌，下垫面类型以及云遮蔽的影响，导致积雪判识精度降低的问题，提出一种利用深度学习方法对风云4号A星多通道辐射扫描计（AGRI）数据与地理信息数据进行多特征时序融合的积雪判识方法：以多时相FY-4A/AGRI多光谱遥感数据，以及高程、坡向、坡度和地表覆盖类型等地形地貌信息作为模型输入，以Landsat 8 OLI提取的高空间分辨率积雪覆盖图作为“真值”标签，构建并训练基于卷积神经网络的积雪判识模型，从而有效区分新疆复杂地形与下垫面地区的云、雪以及无雪地表，最终得到逐小时积雪覆盖范围产品。经数据集和2019年地面气象站实测雪盖验证，该方法精度高于国际主流MODIS逐日积雪产品MOD10A1和MYD10A1，显著降低云雪误判率。

Snow Cover Recognition for Xinjiang based on Fusion of FY-4A/AGRI Spatial and Temporal Characteristics

Snow cover recognition with high temporal resolution plays an important role in the development of agriculture and animal husbandry and snow disaster warning in Xinjiang pastoral areas. To solve the problem that existing snow cover products are susceptible to complex topography， landform， underlying surface type and cloud cover， which leads to the reduced accuracy of snow cover recognition， a deep learning method is proposed to use the data of Fengyun-4A Star Multichannel Radiation Scanner （AGRI） and the number of geographic information.Based on the method of multi-feature time series fusion， a new snow cover recognition model based on convolution neural network is constructed and trained， which takes the multitemporal FY-4A/AGRI multispectral remote sensing data， terrain topographic information such as elevation， aspect， slope， and surface cover type as the input of the model， and the high-resolution snow cover map extracted by Landsat 8-OLI as the "true value" label.Clouds， snow and snow-free surfaces in Xinjiang's complex terrain and underlying areas ultimately lead to hourly snow cover products. It is verified by the data set and the snow coverof meteorological station in 2019 the accuracy of this method is higher than that of MOD10A1 and MYD10A1， the main international MODIS snow products， which significantly reduces the misclassification rate of cloud and snow.