基于Landsat８OLI数据的黄土高原植被含水量的估算模型研究

植被含水量(VWC)能够指示植被的水分状况,对植被生长、火灾、旱灾以及生态环境安全监测等具有重要意义,也是微波遥感估算土壤水分的重要参数之一.光谱指数法是估算植被含水量最常用的方法之一.结合地面观测及Landsat８OLI传感器遥感影像,对平凉地区的植被含水量进行了遥感估算模型研究,结果表明:①平凉地区叶片含水量(FMC)与植被光谱指数没有相关关系,而等效水深(EWT)则与各植被光谱指数具有显著的相关关系(均超过９５％显著性水平),其中RVI２与EWT的相关关系最显著且最稳定;②利用RVI２对研究区EWT进行遥感估算,其均方根误差(RMSE)为０．１８３,平均相对误差为８．９％,平均相对误差绝对值为２６．４％;③研究区内大部分农田的植被含水量为０．６~０．９kg/m２,少数农田的植被含水量达到１kg/m２ 以上,这与实际考查基本一致,基本能够反映研究区内农田EWT的空间变化特征.

Vegetation Water Content Retrieved Using Landsat 8 OLI Remote Sensing Data on Loess Plateau

Vegetation water content(VWC) can indicate moisture condition of vegetation,so it’s important and meaningful to vegetation growth,fire,drought and ecological safety monitoring,also it is an important parameter for estimating soil moisture by microwave remote sensing.Building a statistical model between VWC and Spectral vegetation index is one of the most common methods to estimate VWC.Fuel Moisture Content (FMC),Relative Water Content(RWT) and Equivalent Water Thickness (EWT) were three expression for VWC.Taking Pingliang as an example,FMC and EWT were calculated basing on the ground observation,and then EWT was estimated by using Landsat8 OLI remote sensing data in this paper.Results shown that:① Fuel Moisture Content (FMC) is not correlated with spectral vegetation indices,while EWT is significant correlated with spectral vegetation indices with more than 95% significant level.Using cross validation concept,an experiment is executed,which shows the relations between RVI2 and EWT is most significant and most stable.② EWT was estimated by RVI2 ,its Root Mean Square Error(RMSE) is 0.183,the average relative error is 8.9%,and the average absolute value of relative error is 26.4%.③ Most of EWT was 0.6~0.9 kg/m2on farmland in study area,and a small of it was reaches 1 kg/m2or more,consistent with the field situation ,and can reflect the spatial distribution of EWT.