遥感定量反演华北平原地区冬小麦含水量

Remote sensing quantitative retrieval of winter wheat water content in North China plain

针对现有植被水分反演算法在华北平原地区适用性差、反演精度低、不能实施有效监测的问题,该文基于地面实测冬小麦植被含水量(VWC)数据,基于归一化型和比值型植被水分指数这两种常见的指数类型,提出调节植被水分指数以削弱土壤背景的影响,使用多个波段反射率数据反演VWC,提高拟合精度80%以上,发展适用于华北平原的农作物水分含量反演模型。拟合冬小麦植被含水量的决定系数为0.51,均方根误差为0.95(kg·m^-2)。结果表明:调节植被水分指数能够削弱土壤背景影响,大幅度提高植被水分反演精度;同一种指数计算形式中,在水汽吸收谷内,基于更长波段反射率的植被水分指数反演精度更高;归一化型和比值型植被水分指数在反演精度方面无明显优劣,归一化型植被水分指数反演精度。

Aiming at the problem that the vegetation moisture retrieval algorithm in the North China plain has the problem of poor applicability,low precision and cannot be effectively monitored,the adjust vegetation water index that weakening soil background was proposed based on wheat moisture in-situ measurements collected in the North China plain in 2015 in this paper,and the precision of inversion was improved up to 80%.The coefficient of determination is 0.51,and the root mean square error is 0.95(kg·m^-2).The results showed that:1)adjusted vegetation water index could weaken the influence of soil background and improve the precision of vegetation water inversion;2)for a fixed spatial resolution and indices with same form,the longer wavelength remote sensing data were used,the better reversion performance would be achieved;3)normalized difference type water indexes showed a slight superiority to ratio type water indexes.