融合微波遥感与模型模拟的时空无缝土壤湿度数据生成方法

Spatiotemporal Seamless Soil Moisture Data Generation by Integrating Microwave Remote Sensing and Model Simulation

遥感反演与模型模拟是获取全球土壤湿度数据的两种基本手段,遥感反演可以获得相对较高的空间分辨率,但往往存在时空缝隙;陆面模型能够模拟土壤湿度的时空连续演进,但空间分辨率往往较粗。为此,集成二者的互补优势,提出一种融合微波遥感与模型模拟的全球无缝土壤湿度数据生成方法。具体地,针对SMAP(soil moisture active passive)卫星9 km土壤湿度数据的空缺区域,引入GLDAS Noah 0.25°的模型同化数据,建立二者之间的时空融合模型,通过对模型数据的降尺度实现对遥感数据的填补,进一步基于泊松方程方法进行残差校正,进而生成高精度的9 km日尺度土壤湿度无缝数据。实验结果证明,该方法可以有效结合遥感观测的空间分辨率优势与模型模拟的时空连续优势,提供时空无缝全球土壤湿度数据,更好地满足全球尺度水循环监测与水资源管理的需求。

Objectives:Soil moisture is an important parameter to measure the material and energy exchange between soil and atmosphere.It is a key environmental factor in the fields of hydrology,meteorology,agriculture and so on.Remote sensing inversion and model simulation are two basic means to obtain global soil moisture data.Remote sensing inversion can obtain soil moisture data with relatively high resolution,but there are often time-space gaps while land surface model can simulate spatiotemporal continuous data,but the spatial resolution is often coarse.Both two data cannot meet the fine observation of soil moisture.Therefore,it is very important to develop an effective method to obtain high-resolution,spatiotemporal seamless soil moisture data.Methods:Focusing on their complementary advantages,we propose a global seamless soil moisture data generation method integrating microwave remote sensing and model simulation.Specifically,for the vacant area of 9 km soil moisture data of soil moisture active passive satellite,GLDAS Noah 0.25°model assimilation data is introduced to establish the spatiotemporal fusion model between them.Through this model,Noah data is downscaled to 9 km to fill the vacant area.As for some areas that have not been filled,Noah data is used for fitting,interpolation and filling,and the residual correction is further carried out based on Poisson equation method,and then the spatiotemporal seamless 9 km daily soil moisture data is generated.Results:We evaluated the experimental results by simulation experiment evaluation,site data evaluation and triple collocation evaluation.Experiments show that the global seamless soil moisture data produced in this paper performs well in all evaluation methods.Conclusions:This method can effectively combine the spatial resolution advantage of remote sensing observation with the spatiotemporal continuity advantage of model simulation,provide spatiotemporal seamless global soil moisture data,and better meet the needs of global scale water cycle monitoring and water resources management.