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遥感技术与应用  2020, Vol. 35 Issue (1): 120-131    DOI: 10.11873/j.issn.1004-0323.2020.1.0120
土壤水分专栏     
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
张新平1,2(),乔治2,李皓2,闫杰2,张芳芳3,赵栋锋1,5,王得祥1(),康海斌1,杨航1,冯扬4
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
Remotely Sensed Retrieving the Surface Soil Moisture of Yan’an Urban Forest based on Landsat Image and Trapezoid Method
Xinping Zhang1,2(),Zhi Qiao2,Hao Li2,Jie Yan2,Fangfang Zhang3,Dongfeng Zhao1,5,Dexiang Wang1(),Haibin Kang1,Hang Yang1,Yang Feng4
1. College of Forestry, Northwest A&F University, Yangling 712100, China
2. College of Art and Design, Xi’an University of Technology, Xi’an 710054, China
3. Branch School of Gaoling District Xi’an City, Shaanxi Agricultural Broadcasting and Television School, Xi’an 710200, China
4. College of Landscape Architecture and Arts, Northwest A&F University, Yangling 712100, China
5. Yan'an Forestry Survey Planning and Design Institute, Yan'an 716000, China
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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。

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    
Abstract:

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, LSTis land surface temperature,NDVIis 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 0.045 5, 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.

Key words: Normalized Difference Vegetation Index (NDVI)    Soil moisture    Satellite remote sensing    Land surface temperature    Surface reflectance
收稿日期: 2018-08-16 出版日期: 2020-04-01
ZTFLH:  TP79  
基金资助: 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)
通讯作者: 王得祥     E-mail: jhonxinping81@nwsuaf.edu.cn;wangdx66@sohu.com
作者简介: 张新平(1981-),男,陕西柞水人,讲师,博士,主要从事景观规划与遥感监测研究。E?mail:jhonxinping81@nwsuaf.edu.cn
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引用本文:

张新平,乔治,李皓,闫杰,张芳芳,赵栋锋,王得祥,康海斌,杨航,冯扬. 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[J]. 遥感技术与应用, 2020, 35(1): 120-131.

Xinping Zhang,Zhi Qiao,Hao Li,Jie Yan,Fangfang Zhang,Dongfeng Zhao,Dexiang Wang,Haibin Kang,Hang Yang,Yang Feng. Remotely Sensed Retrieving the Surface Soil Moisture of Yan’an Urban Forest based on Landsat Image and Trapezoid Method. Remote Sensing Technology and Application, 2020, 35(1): 120-131.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2020.1.0120        http://www.rsta.ac.cn/CN/Y2020/V35/I1/120

图1  延安城市森林现状图及表层土壤水分实测样点
卫星(传感器) 日期 模型 干燥边界 湿润边界
id sd R 2 iw sw R 2
L5(TM) 1995-06-08 TOTRAM 313.90 -6.90 0.812 3 298.20 -3.30 0.907 2
(10:25 a.m.) OPTRAM 0.15 1.15 0.953 8 1.15 2.95 0.935 6
L7(ETM+) 2001-05-31 TOTRAM 327.65 -25.15 0.912 6 307.80 -14.95 0.706 2
(11:09 a.m.) OPTRAM -0.10 2.78 0.793 8 0.70 6.02 0.950 0
L8(OLI&TIRS) 2015-07-01 TOTRAM 314.45 -4.75 0.574 6 309.30 -12.15 0.758 5
(11:18 a.m.) OPTRAM 0.15 2.55 0.791 4 1.25 8.55 0.801 5
表1  基于延安城市森林的Landsat系列卫星数据获取的TOTRAM与OPTRAM模型的参数(线性边界)
图2  反演表层土壤水分的TOTRAM与OPTRAM模型参数概念图
卫星 模型 边界类型 曲线方程 R 2
L7

TOTRAM

(x=NDVI)

干燥边界 y=317.903 1+69.034 2x-317.974 0x 2+476.945 9x 3-268.520 0x 4 0.977 5
湿润边界 y=302.832 9-14.042 2x+106.471 7x 2-244.677 6x 3+149.357 9x 4 0.982 8
L7

OPTRAM

(x=NDVI)

干燥边界 y=0.993 3-12.763 1x+70.953 1x 2-128.957 4x 3+82.146 5x 4 0.972 2
湿润边界 y=0.044 9+21.923 2x-83.412 7x 2+156.310 0x 3-87.360 2x 4 0.996 0
L8

TOTRAM

(x=NDVI)

干燥边界 y=317.182 8-44.295 1x+197.230 6x 2-325.459 7x 3+156.739 9x 4 0.649 7
湿润边界 y=304.781 3+39.125 8x-265.385 1x 2+567.742 2x 3-412.209 7x 4 0.852 4
L8

OPTRAM

(x=NDVI)

干燥边界 y=0.179 2+1.727 8x+7.473 8x 2-29.221 2x 3+31.030 1x 4 0.760 3
湿润边界 y=2.220 9-9.448 2x+76.920 1x 2-180.963 7x 3+151.225 9x 4 0.960 3
表2  基于延安城市森林的Landsat系列卫星数据获取的TOTRAM与OPTRAM模型的参数(非线性边界)
图3  三期影像在STR-NDVI (OPTRAM) 和LST-NDVI (TOTRAM)空间中的像元分布图
图4  95个样点表层土壤水分实测数值与两模型估算值的比较
图5  基于OPTRAM and TOTRAM参数化方法生成的三期土壤湿度二维散点图比较
图6  基于OPTRAM 与 TOTRAM及线性边界生成的3期延安城市森林土壤水分空间分布图
图7  基于OPTRAM 与 TOTRAM及非线性边界生成的2期延安城市森林土壤水分空间分布图
图8  线性边界和非线性边界反演的表层土壤水分比较
图9  1995~2015年年均降雨量、夏季NDVI与森林干扰指数年变化趋势分空间分布图[37]
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