全球30 m地表覆盖遥感制图的总体技术

以2019年09号超强台风“利奇马”引发的浙江省台州临海市受淹为例，利用浙江省水文站和气象站降水量观测资料对国家级地面-卫星-雷达三源融合实况格点降水数据（CMPAS-5km，CMA Multisource Precipitation Analysis System）进行检验评估；然后，基于CMPAS-5km计算的面雨量和降水分布权重栅格驱动FloodArea二维水动力淹没模型，在“暴雨情景”下进行洪涝模拟，并借助哨兵1号合成孔径雷达卫星（Sentinel-1 SAR）资料和地面验证点开展模拟受淹验证。结果表明：CMPAS-5km降水与站点降水的时空一致性较好，与水文站、“水文站+气象站”两套资料的过程降水量相关系数均达0.89，与水文站逐小时平均降水的相关系数为0.79、均方根误差为1.7 mm·h^-1；在台州受淹模拟区，CMPAS-5km、水文站及气象站三者的小时面雨量变化趋势较为一致，过程累积面雨量分别为305.5 mm、304.1 mm和283.7 mm；CMPAS-5km在数据稳定性、时效性、精细度及数据接口上较其他两套资料有优势；在SAR影像中新增水体明显区域与FloodArea模拟结果匹配较好，模拟水深与4个验证点的实际水深的误差均在±22%以内。

Google Earth Engine: Planetary-scale geospatial analysis for everyone

Taking the flood inundation event in Linhai, Taizhou, Zhejiang Province caused by the super severe typhoon (1909) Lekima for example, we have verified and evaluated the 5km real-time data (CMPAS-5km) from the CMA Multi-source Precipitation Analysis System by using the precipitation data obtained by hydrological and meteorological stations over Zhejiang Province. In addition, we haveconductedflood simulations under the rainstorm scenarios based on the spatial distribution with weighted grid of area precipitation and accumulated precipitation calculated by CMPAS-5km to drive the FloodArea two-dimensional hydrodynamic inundation model, and performed verification of the simulated flooding by using Sentinel-1 SAR images and representative flooded points. The results show that the precipitation from CMPAS-5km and that from the stations have good temporal and spatial consistency, and the correlation coefficient (R) ofprecipitation from CMPAS-5km with those from hydrological stations and