基于Landsat影像和不规则梯形方法遥感反演延安城市森林表层土壤水分

Remotely Sensed Retrieving the Surface Soil Moisture of Yan’an Urban Forest based on Landsat Image and Trapezoid Method

反演模型对土壤水分评估结果有重要影响,基于此,以黄土沟壑区城市森林表层土壤为研究对象,以3期Landsat影像和实地土壤水分传感器测定数据为数据源,分别通过像元在二维空间(LST-NDVI与STR-NDVI,LST为地表温度,NDVI为归一化植被指数,STR为短波红外转换反射系数)中的散点图及其拟合的干燥边界与湿润边界,获取TOTRAM(热学—光学不规则梯形模型)和OPTRAM(光学不规则梯形模型)的参数,然后在像素水平上(30 m×30 m)反演出延安城市森林表层土壤水分(W),验证两模型的精度,并比较两模型估算结果的差异及线性边界与非线性边界对反演结果的影响。结果发现:①除OPTRAM模型在Landsat 7和Landsat 8上干湿边界呈现非线性外,像素在LST-NDVI空间和STR—NDVI空间中的干湿边界均呈线性,且包络成不规则梯形形状;②与实地测定数据相比,TOTRAM与OPTRAM两模型的平均误差(ME)分别为0.009和0.0455,表明两模型估算结果均偏高,但OPTRAM模型的均方根误差(RMSE)较TOTRAM模型更接近0。OPTRAM模型估算的W值均匀地分布在1∶1参考线两侧,且位于参考线上的点数多于TOTRAM模型,表明OPTRAM准确度高于TOTRAM模型,且非线性边界的反演精度高于线性边界;③与TOTRAM模型相比,OPTRAM模型估算出的W空间分异规律与土地利用/覆被类型具有较高的相关性,且OPTRAM模型对植被覆盖度极低的区域敏感。因此,在后续研究中,应在OPTRAM模型中探讨干湿边界复杂性与模型准确性改善之间的关系,同时考虑周围环境、降雨量、森林干扰和NDVI饱和等因素对两模型估算准确性的影响。

It is crucial for soil moisture assessment to know the prediction accuracy of inversion model.Urban forest surface soil in a gully-loess region(Yan’an),was taken as the research object,and the three scenes of Landsat satellite remotely sensed imagery in different periods and soil moisture sensor in situ measurement data were used as the data source.The parameters of TOTRAM(Thermal-Optical TRApezoid Model)and OPTRAM(OPtical TRApezoid Model)were obtained through the scatter diagram of pixels in two-dimensional spaces(LST-NDVI and STR-NDVI,LST is land surface temperature,NDVI is normalized vegetation index,and STR is shortwave infrared conversion reflection coefficient)and their fitting dry edge and wet edge,respectively.Then,the w values(soil moisture in percentage)of Yan’an urban forest were retrieved at the pixel level(30 m by 30 m),the accuracy of the two models was verified,the differences between the estimated results of the two models,and the influence of linear and nonlinear edge on the inversion results were compared.The results indicate that:(1)Except that the dry edge and wet edge of OPTRAM models on Landsat 7 and Landsat 8 were non-linear,the other dry and wet edges of pixels in LST-NDVI space and STR-NDVI space are almost linear and enveloped into a trapezoid shape.(2)Compared with the field measurement data,the mean error(ME)of TOTRAM and OPTRAM were 0.009 and 0.0455,respectively,which indicating that the estimation results of both models were relatively high,but the root mean square error(RMSE)of the OPTRAM model was closer to zero than the TOTRAM model.The value of w estimated by the OPTRAM model is evenly distributed on both sides of the 1∶1 reference line,and the number of points on the reference line is more than that of the TOTRAM model in scatterplots,indicating that the accuracy of OPTRAM is higher than that of the TOTRAM model,moreover,the inversion precision of nonlinear edge is higher than that of linear edge.Thus,in further research,the relationship between the complexity of the dry edge and wet edge and the model’s accuracy improvement should be discussed in the OPTRAM model,and the influences of surrounding environment,rainfall,forest disturbance and NDVI saturation on the estimation accuracy of the two models need to be considered.