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遥感技术与应用  2021, Vol. 36 Issue (3): 564-570    DOI: 10.11873/j.issn.1004-0323.2021.3.0564
森林遥感专栏     
基于多源遥感数据协同反演森林地表土壤水分研究
孙景霞(),张冬有(),侯宇初
哈尔滨师范大学寒区地理环境监测与空间信息服务黑龙江省重点实验室,哈尔滨师范大学地理科学学院,黑龙江 哈尔滨 150025
Multi-source Remote Sensing Data Cooperates to Retrieve Forest Surface Soil Moisture
Jingxia Sun(),Dongyou Zhang(),Yuchu Hou
Heilongjiang Province Key Laboratory of Geographical Environment Monitoring and Spatial Information Service in Cold Regions,Harbin Normal University,School of Geographical Sciences,Harbin Normal University,Harbin 150025,China
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摘要:

土壤水分在土壤监测中是一项重要的指标,对于农业生产、生态环境以及水资源管理有着重要的影响。随着遥感建模与反演理论的不断成熟,其逐渐成为分析土壤指标的重要技术与手段。因此,利用光学影像与雷达影像数据,以大兴安岭地区漠河市为研究区域,分别建立以Landsat 8为数据源的土壤水分反演模型和由Landsat 8影像数据与GF-3卫星数据协同反演的土壤水分反演模型,将反演结果与实际测得数据进行对比验证,并评价所建立的反演模型。结果表明:①对研究区地温进行反演,利用地表温度(Ts)与归一化差异湿度指数NDMI构建Ts-NDMI特征空间,结合实测数据可以发现Ts-NDMI特征空间土壤水分反演模型的反演结果与实测土壤含水量为负相关性;②协同GF-3卫星数据和Landsat 8遥感影像数据所建立的土壤水分反演模型能得到质量较高的反演结果,且在高植被覆盖度地区,利用该协同反演模型得到的反演结果比利用单一光学数据源所建模型得到的反演结果精度高,为今后高植被覆盖度地区土壤湿度的研究提供了新途径。

关键词: Landsat 8GF?3土壤水分协同反演温度植被干旱指数    
Abstract:

Soil moisture is an important index in soil monitoring, which has an important impact on agricultural production, ecological environment and water resources management. With the remote sensing modeling and remote sensing inversion theory have gradually become important techniques and means to estimate soil indicators. Therefore, using the optical image data and radar image data, with Mohe City of Daxing'anling area as research area, to establish model of soil moisture inversion based on Landsat8 data and the model based on Landsat8 image data and high-resolution 3 remote sensing image data, the inversion results compared with the measured data analysis, and make evaluation on the model. The results showed that: (1) The surface temperature in the study area was inverted, and the TS-NDMI feature space was constructed by using surface temperature (Ts) and normalized difference humidity index NDMI. Combined with the measured data, it could be found that the inversion results of ts-NDMI feature space soil water inversion model were negatively correlated with the measured soil water content;(2) The soil moisture retrieval model based on GF-3 satellite data and Landsat 8 remote sensing data can get better retrieval results, and in areas with high vegetation coverage, the results obtained from this model are more accurate than those from a single optical data source, which provides a new way for the study of soil moisture in high vegetation coverage areas.

Key words: Landsat 8    GF-3    Soil moisture    Collaborative inversion    Temperature Vegetation Drought Index
收稿日期: 2020-06-27 出版日期: 2021-07-22
ZTFLH:  S152.7  
基金资助: 国家自然科学基金项目“大兴安岭森林生物量与多年冻土退化响应关系研究”(41671064)
通讯作者: 张冬有     E-mail: sunjx0410@126.com;zhangdy@163.com
作者简介: 孙景霞(1995-),女,黑龙江牡丹江人,硕士研究生,主要从事多年冻土与森林生态研究。E?mail:sunjx0410@126.com
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引用本文:

孙景霞,张冬有,侯宇初. 基于多源遥感数据协同反演森林地表土壤水分研究[J]. 遥感技术与应用, 2021, 36(3): 564-570.

Jingxia Sun,Dongyou Zhang,Yuchu Hou. Multi-source Remote Sensing Data Cooperates to Retrieve Forest Surface Soil Moisture. Remote Sensing Technology and Application, 2021, 36(3): 564-570.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2021.3.0564        http://www.rsta.ac.cn/CN/Y2021/V36/I3/564

图1  干边与湿边拟合方程图
经验参数草地冬小麦放牧地所有植被
A0.001 40.001 80.000 90.001 2
B0.084 00.138 00.032 00.091 0
表1  水云模型经验参数
图2  光学数据反演结果与实测值的比较
图3  雷达数据协同光学数据反演结果与实测值的比较
模型相关系数(R2均方根误差(RMSE)
光学数据反演模型0.3520.0469
雷达数据协同光学数据反演模型0.6680.0250
表2  两种模型反演结果的对比
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