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

作　　者：张新平[1,2] 乔治 李皓[2] 闫杰[2] 张芳芳[3] 赵栋锋 王得祥 康海斌[1] 杨航[1] 冯扬 Zhang Xinping;Qiao Zhi;Li Hao;Yan Jie;Zhang Fangfang;Zhao Dongfeng;Wang Dexiang;Kang Haibin;Yang Hang;Feng Yang(College of Forestry,Northwest A&F University,Yangling 712100,China;College of Art and Design,Xi’an University of Technology,Xi’an 710054,China;Branch School of Gaoling District Xi’an City,Shaanxi Agricultural Broadcasting and Television School,Xi’an 710200,China;College of Landscape Architecture and Arts,Northwest A&F University,Yangling 712100,China;Yan'an Forestry Survey Planning and Design Institute,Yan'an 716000,China)

机构地区：[1]西北农林科技大学林学院,陕西杨凌712100 [2]西安理工大学艺术与设计学院,陕西西安710054 [3]陕西省农业广播电视学校西安市高陵分校,陕西西安710200 [4]西北农林科技大学风景园林与艺术学院,陕西杨凌712100 [5]延安市林业勘察规划设计院,陕西西安716000

出　　处：《遥感技术与应用》2020年第1期120-131,共12页Remote Sensing Technology and Application

基　　金：国家“十二五”科技支撑计划课题“环境友好型城镇景观林构建技术研究与示范”(2015BAD07B06);国家自然科学基金项目“秦岭松栎林建群种更新格局对种子扩散过程及影响因素的响应”(31470644);文化部文化艺术研究项目“西北地区工业遗产型产业园地域文化创意因子植入及景观活化研究”(17DH17);教育部人文社科青年基金项目“工业遗产型创意产业园文化传承及地域认同研究:内涵重塑、业态培育、主题营造”(18YJC760063);陕西省社科界重大理论与现实问题研究项目“基于生态视角的陕西关中地区农村人居环境建设模式研究”(20192097)。

摘　　要：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 e

关 键 词：归一化植被指数 土壤湿度 卫星遥感 地表温度 地表反射率 

分 类 号：TP79[自动化与计算机技术—检测技术与自动化装置]