基于可见光红外与被动微波遥感的土壤水分协同反演

Monitoring land surface soil moisture: co-inversion of visible, infrared and passive microwave sensing data

利用MODIS传感器的可见光、红外波段数据反演土壤水分在一定时段内的基准值,用被动微波传感器AMSR-E数据反演其变化量,提出将被动微波遥感数据与热红外遥感数据在模型级别协同反演大范围地表土壤水分的方法,这样每天可输出1 km×1 km的升、降轨土壤水分反演结果.以新疆为研究区,对上述方法进行了土壤水分协同反演实验,以地面实测数据为参考的验证结果表明,所提模型得到的土壤水分值与地面实测值之间相关性较高,均方根误差较小,优于单一传感器数据的反演结果,可更好地满足新疆土壤水分监测的需求.

To effectively retrieve large-scale daily soil moisture,this study proposed a model-level integrated approach termed co-inversion of visible,infrared and passive microwave remote sensing data.Specifically,the MODIS data are used to derive soil moisture base,and the AMSR-E data are employed to estimate daily variation of land surface soil moisture over a large area.The soil moisture information over the large area is then estimated by integrating these two parts： base and variation.Improvements inherent in the proposed approach enable daily 1 km×1 km soil moisture estimation of the entire study area,even when some areas were covered with clouds.Verification with ground truthing data in Xinjiang,China shows that the co-inversion of thermal and passive microwave remotely sensed data can achieve better estimation of soil moisture than each single data source or model.The square correlation coefficient is 0.86 and RSME is 3.99 when the estimated soil moisture is compared with the ground truthings.The results proved that the co-inversion model outperformed either the MODIS or AMSR-E inversion of soil moisture over large areas,and can meet the needs of Xinjiang＇s soil moisture monitoring.