一种被动微波土壤水分降尺度新算法

Extended application of a downscaling algorithm for large-scale soil moisture acquired by passive microwave radiometer

文中利用被动微波辐射计AMSR-E土壤水分数据和MODIS数据,在频谱降尺度算法框架下,针对文中数据特征,修正模型拟合方程,并引入遥感物理蒸散模型,发展了一种新的大尺度土壤水分数据降尺度算法。此后通过反演土壤水分数据与全球能量与水循环协调观测计划的(CEOP)亚澳季风计划蒙古实验区内地表实测数据的比较,证明该方法可获得数值、时间变化趋势均与实测数据吻合较好的高分辨率土壤水分数据,确认了该降尺度方法较高的可信度及准确性。

Soil moisture is an important part of global energy cycle. Obtaining quantitative soil moisture accurate- ly is of great significance for environmental protection, agricultural production monitoring, global change study, etc. There are many remote sensing platforms providing soil moisture data in different temporal and scales, each of which has its own advantages and disadvantages. By using AMSR -E soil moisture data by passive microwave radiometers and MODIS data, taking Mongolia and Asia as the verification zone, we spatial gained devel- oped a new downscaling algorithm for large - scale soil moisture data under the framework of the spectrum down- scaling algorithm by improving the model fitting equation and introducing the evapotranspiration model of remote sensing physics. The comparison with ground measured data from the Asia - Australia Monsoon Project of Coordi- nated Energy and water- cycle Observation Project （CEOP） in Mongolia study area shows that the downscaled soil moisture data and its changing trends are in good agreement with measured data. Hence it is confirmed that the downscaling method is of high accuracy and credibility.