CCI土壤湿度产品在中国区的降尺度与融合研究

以欧洲航天局主/被动微波遥感融合的土壤湿度产品数据为主要数据源,结合地表参数、亮度温度、土壤质地、地形、降雨等辅助数据,提出基于移动窗口的线性、非线性联合的土壤湿度降尺度模型,对中国区域2003年1—12月的CCI土壤湿度产品数据进行降尺度,提高CCI土壤湿度数据的空间分辨率,使其更适用于农业、水文模拟等精细尺度的应用要求。为了克服CCI数据冬季缺失较多的情况,利用第3代再分析资料ERA-Interim表层土壤湿度的降尺度和重采样结果与之进行融合,生成空间覆盖完整的2003—2013年中国区月1 km土壤湿度数据集,并利用站点实测数据集对其进行了验证。结果表明,依据主/被动微波融合CCI、ERA-Interim再分析土壤湿度数据以及辅助数据的基于移动窗口和线性非线性集成的中国区地表土壤湿度降尺度和融合方法,提高了CCI土壤湿度数据集的空间分辨率和空间覆盖,同时还一定程度上提高了土壤湿度的估算精度。

Downscaling and merging of the CCI soil moisture product over China

This study used the soil moisture product of ESA CCI(the Climate Change Initiative)and combined land surface parameters, brightness temperature, soil texture, terrain, rainfall and other auxiliary data, to establish a downscaling model for the soil moisture. Based on the moving window method, this model combined linear and nonlinear techniques. It was applied to downscaling the CCI data of soil moisture over China from 2003 Jan. to 2013 Dec. , in order to improve its spatial resolution. The downscaled data will be more suitable for the agriculture, hydrological simulation and other disciplines. Because ESA CCI product is not available over the whole China in winter, we further conducted a merge of ESA CCI product and the ERA-Interim product of soil surface moisture. Finally, we obtained the monthly soil moisture product over China with 1 km spatial resolution from 2003 to 2013. We further used in-situ observations to validate the product and found that this method improved not only the spatial resolution and the coverage, but also the precision.