Snow Cover Reconstruction via Multisource Data Fusion Using Fengyun Satellites in Upper Jinsha River Basin
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摘要: 为了解决云层覆盖导致的积雪覆盖遥感监测产品数据缺失的问题，提出了一种基于风云卫星等多源数据融合的雪盖重建方法，通过7个去云步骤实现了金沙江上游高精度逐日无云雪盖监测.结果显示，融合后的雪盖产品总体精度能达到0.94，Kappa系数为0.80，比广泛使用的美国IMS雪冰产品的积雪监测效果更好.然而，融合雪盖产品的总体精度在海拔4.3 km和5.6 km左右有轻微下降，且产品精度随坡向的变化而出现差异.此外，融合雪盖产品在森林地区的精确率（0.18）远小于其他地区.未来在改进雪盖重建方法时，应考虑不同坡向的积雪分布以及森林地区的积雪特征，以提升多源融合无云雪盖产品的精度.Abstract: To address the issue of missing data in snow cover products caused by cloud contamination in remote sensing imagery, this study proposes a snow cover reconstruction method based on the fusion of multisource data, including Fengyun satellite observations. A seven-step cloud removal process was employed to achieve daily, accurate, cloud-free snow cover monitoring in the upper Jinsha River basin. The results indicate that the reconstructed snow cover product attains an overall accuracy of 0.94 and a Kappa coefficient of 0.80, outperforming the widely used IMS product from the United States. However, the overall accuracy slightly declines within snow transition zones at altitudes of approximately 4.3 km and 5.6 km, and it varies according to slope aspect. Moreover, the precision of the reconstructed product in forested areas is considerably lower (0.18) than in other land cover types. Future enhancements to the reconstruction method should incorporate slope aspect and forest-specific characteristics to improve the accuracy of multisource fused, cloud-free snow cover products.
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图 2 用于精度检验的Landsat-8影像的时空分布
a. Landsat-8 WRS-2分幅条带矢量图；b. 所使用的Landsat-8影像逐月数量分布
Fig. 2. Spatiotemporal distribution of Landsat-8 images used for accuracy assessment
图 5 融合雪盖产品分别在(a)冬季、(b)春季、(c)夏季和(d)秋季的SCF，以及IMS雪冰产品分别在(e)冬季、(f)春季、(g)夏季和(h)秋季的SCF
Fig. 5. Snow cover frequency (SCF) of the reconstructed snow cover products in winter (a), spring (b), summer (c), and autumn (d), and SCF of the IMS products in winter (e), spring (f), summer (g), and autumn (h)
图 6 融合雪盖产品和IMS产品分别在冬季(a)、春季(b)、夏季(c)和秋季(d)的像元SCF出现频率分布
Fig. 6. Frequency distribution of SCF for the reconstructed snow cover products and IMS products in winter (a), spring (b), summer (c), and autumn (d)
图 7 2019—2023年融合雪盖产品和IMS的精确率(a)、召回率(b)、总体精度(c)、Kappa系数(d)和F1分数(e)
Fig. 7. Precision (a), recall (b), overall accuracy (c), Kappa coefficient (d), and F1-score (e) of the reconstructed snow cover products and IMS products from 2019 to 2023
图 8 2019年1月11日金沙江上游局部积雪分布
a. Landsat-8影像；b. IMS产品；c. 融合产品
Fig. 8. Local snow distribution in the upper Jinsha River on January 11, 2019
图 9 2019—2023年融合雪盖和IMS产品精度随海拔高程的变化
a. IMS产品的像元数量随高程的变化；b. 融合雪盖产品的像元数量随高程的变化；c. 产品精确率随高程的变化；d. 召回率随高程的变化；e. 总体精度随高程的变化
Fig. 9. Variation in the accuracy of reconstructed snow cover and IMS products with altitude from 2019 to 2023
图 10 2019—2023年融合雪盖和IMS产品精度随坡向的变化
a. 精确率；b. 召回率；c.总体精度
Fig. 10. Variation in the accuracy of reconstructed snow cover and IMS products with slope aspect from 2019 to 2023
图 11 2019—2023年融合雪盖和IMS产品精度随土地覆盖类型的变化
a. 精确率；b. 召回率；c. 总体精度；d. Kappa系数；e. F1分数
Fig. 11. Variation in the accuracy of reconstructed snow cover and IMS products with land cover types from 2019 to 2023
图 12 2019—2023年融合雪盖和IMS产品在不同森林类型下的精度变化
常绿针叶林：ENF，常绿阔叶林：EBF，落叶针叶林：DNF，落叶阔叶林：DBF，混生林MF，郁闭灌丛CSH，稀疏灌丛OSH；a. 精确率；b. 召回率；c. 总体精度；d. Kappa系数；e. F1分数图
Fig. 12. Variation in the accuracy of reconstructed snow cover and IMS products across different forest types from 2019 to 2023
表 1 主要数据源特性
Table 1. Characteristics of the main data sources
数据源 时间分辨率 空间分辨率 重采样方法 重采样后的分辨率 重分类后的像元 FY-3D 1 d 1 km - 除站点观测外，其他数据的空间分辨率均与FY-3D保持一致，为1 km 无雪地表(25)、湖泊(37)、海洋(39)、云(50)、海/湖冰(100)、积雪(200) Terra/Aqua MODIS 1 d 500 m 众数重采样 IMS 1 d 1 km 最邻近重采样 站点观测 1 d - - 无雪地表(25)、积雪(200) Landsat-8 16 d 30 m 双三次插值 DEM - 30 m 双线性插值 - 
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表 3 多源融合雪盖重建算法每一步骤的云覆盖百分比
Table 3. Cloud coverage percentages at each step of the snow cover reconstruction algorithm
FY3D_SNC Step 1 Step 2 Step 3 Step 4 Step 5 Step 6 Step 7 2019年 53.02% 43.23% 38.25% 21.66% 18.55% 16.12% 5.07% 0 2020年 52.87% 39.65% 32.35% 16.65% 13.96% 12.23% 4.25% 0 2021年 52.72% 36.52% 30.34% 15.75% 13.64% 11.77% 3.37% 0 2022年 55.07% 39.20% 31.77% 16.91% 14.20% 12.41% 3.79% 0 2023年 58.24% 38.92% 32.82% 18.39% 15.95% 13.78% 4.69% 0
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表 4 融合雪盖产品和IMS总体精度表
Table 4. The accuracy of the reconstructed snow cover products and IMS products
Precision Recall Accuracy Kappa F1-score 融合雪盖产品 0.78 0.89 0.94 0.80 0.83 IMS 0.60 0.87 0.88 0.64 0.71
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