摘要
微波传感器获得的土壤水分产品空间分辨率一般都很粗,而流域尺度上的研究需要中高分辨率的土壤水分数据。用MODIS逐日地表温度产品MOD11A1和逐日地表反射率产品MOD09GA构建温度—植被指数特征空间,并计算得到TVDI(Temperature Vegetation Dryness Index)指数,它与土壤水分呈负相关关系,能够反映土壤水分的空间分布模式,但并不是真实的土壤水分值。在AMSR-E像元尺度上求得TVDI与土壤水分的负相关系数,进而对VUA AMSR-E土壤水分产品进行降尺度计算得到0.01°分辨率的真实土壤水分值。经NAFE06(The National Airborne Field Experiment 2006)试验地面采样数据验证,降尺度后的土壤水分均方根误差平均值为6.1%。
The near-surface soil moisture data retrieved from passive microwave sensors has the coarse resolution,while the study in a river basin need soil moisture data in high spatial resolution.LST-NDVI space is generated by MODIS Land Surface Temperature Products（MOD11A1） and MODIS Surface Reflectance Products（MOD09GA） and Temperature Vegetation Dryness Index is calculated based on that.The index indicate spatial pattern of soil moisture and which is linear regressions with soil moisture.We calculate the linear regression coefficients between TVDI and near-surface soil moisture at the passive microwave scale,and then calculate soil moisture data at MODIS scale.The root mean square error between 0.01 resolution and ground-measured soil moisture collected during the National Airborne Field Experiment 2006 is 6.1%.
出处
《遥感技术与应用》
CSCD
北大核心
2011年第5期590-597,共8页
Remote Sensing Technology and Application
基金
国家973计划项目"北半球冰冻圈变化及其对气候环境的影响与适应对策"(2010CB951403)
国家自然科学基金项目(41071226)
国家自然科学基金项目(40901160)资助
