Please wait a minute...
img

官方微信

遥感技术与应用  2020, Vol. 35 Issue (1): 23-32    DOI: 10.11873/j.issn.1004-0323.2020.1.0023
土壤水分专栏     
基于Sentinel-1数据的黑河中游土壤水分反演
罗家顺1,2,3,邱建秀1,2,3(),赵天杰4,王大刚1,2,3
1. 中山大学地理科学与规划学院,广东省城市化与地理环境空间模拟重点实验室,广东 广州 510275
2. 广东省地质过程与矿产资源探查重点实验室,广东 广州 510275
3. 南方海洋科学与工程广东省实验室(珠海),广东 珠海 519000
4. 中国科学院遥感与数字地球研究所,遥感科学国家重点实验室,北京 100101
Sentinel-1 based Soil Moisture Estimation in Middle Reaches of Heihe River Basin
Jiashun Luo1,2,3,Jianxiu Qiu1,2,3(),Tianjie Zhao4,Dagang Wang1,2,3
1. Guangdong Provincial Key Laboratory of Urbanization and Geo-simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China
2. Key Laboratory of Mineral Resource & Geological Processes of Guangdong Province, Guangzhou 510275, China
3. Southern Laboratory of Ocean Science and Engineering (Guangdong, Zhuhai), Zhuhai 519000, China
4. State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China
 全文: PDF(2961 KB)   HTML
摘要:

基于Sentinel-1合成孔径雷达 (SAR) 数据及相同时段的中分辨率成像光谱仪(MODIS)和Landsat 8两种归一化植被指数(NDVI),构建变化检测模型以估算黑河中游的高分辨率土壤水分,并探讨模型中具体参数设置对估算精度的影响。结果表明:①在对后向散射系数时间序列的差值 ( Δ σ ) 和植被指数 ( V I ) 进行线性建模过程中,MODIS NDVI和Landsat 8 NDVI这两种植被产品所构建的模型在 Δ σ - V I 空间中所选取的采样点比例分别为2%和4%时,各自取得最优精度; ②以土壤水分反演为目标,使用Landsat 8 NDVI构建的变化检测模型略优于使用MODIS NDVI构建的变化检测模型,两种模型的均方根误差RMSE分别为0.040 m3/m3和0.044 m3/m3,相关系数R分别为0.86和0.83; ③对于变化检测方法的关键参数,若使用低分辨率的SMAP/Sentinel-1 L2_SM_SP土壤水分数据分别代替站点观测的土壤水分初始值和缩放因子 (即两个连续时相土壤水分变化的最大值 Δ M s m a x ) 这两个参数,则土壤水分RMSE将分别增加0.01 m3/m3和0.04 m3/m3。即土壤水分缩放因子这一参数的误差对反演结果的影响大于土壤水分初始值误差对反演结果的影响,故采用高精度的缩放因子进行变化检测估算。研究结论对于利用新兴的Sentinel-1 SAR数据,通过变化检测算法准确获取高分辨率土壤水分信息具有实际参考价值。

关键词: 黑河流域土壤水分变化检测Sentinel-1    
Abstract:

In this study, a change detection model, constructed using the Sentinel-1 Synthetic Aperture Radar (SAR) data and the simultaneous Normalized Difference Vegetation Index (NDVI) products from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat 8 sensors, is applied to estimate soil moisture in middle reaches of the Heihe River Basin, and the effects of two key parameters on retrieval accuracy are comprehensively investigated. The results show that: (1) when constructing the empirical relationship between backscattering coefficient difference ( Δ σ ) and Vegetation Index (VI) required by change detection model, the optimal sampling ratios in the ( Δ σ - V I ) space are approximately 2% and 4% for MODIS NDVI and Landsat 8 NDVI, respectively; (2) the Landsat 8 NDVI-based change detection model slightly outperforms the MODIS NDVI-based model in soil moisture retrieval accuracy, with Root Mean Square Error(RMSE) of 0.040 m3/m3 and 0.044 m3/m3respectively; (3) for the key parameters of the change detection method, replacing the ground-based initial soil moisture and scaling factor (maximum soil moisture difference between two adjacent dates Δ M s m a x ) by the low-resolution SMAP/Sentinel-1 L2_SM_SP data will increase the RMSE by 0.01 m3/m3 and 0.04 m3/m3 respectively. Comparing to the parameter of initial soil moisture, the error in soil moisture scaling factor will lead to more significant degradation in the performance of the change detection method, thus it is recommended to use the high precision scaling factor for soil moisture estimation. This study confirms the promising potential of Sentinel-1 data for retrieving high-resolution soil moisture via change detection method and provides practical insight into its application.

Key words: Heihe River Basin    Soil moisture    Change detection method    Sentinel-1
收稿日期: 2019-06-30 出版日期: 2020-04-01
ZTFLH:  TP79  
基金资助: 国家自然科学基金面上项目(41971031);广东省自然科学基金项目(2016A030310154)
通讯作者: 邱建秀     E-mail: qiujianxiu@mail.sysu.edu.cn
作者简介: 罗家顺(1994-),女,广东增城人,硕士研究生,主要从事微波土壤水分反演研究。E?mail:luojsh3 @mail2.sysu.edu.cn
服务  
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章  
罗家顺
邱建秀
赵天杰
王大刚

引用本文:

罗家顺,邱建秀,赵天杰,王大刚. 基于Sentinel-1数据的黑河中游土壤水分反演[J]. 遥感技术与应用, 2020, 35(1): 23-32.

Jiashun Luo,Jianxiu Qiu,Tianjie Zhao,Dagang Wang. Sentinel-1 based Soil Moisture Estimation in Middle Reaches of Heihe River Basin. Remote Sensing Technology and Application, 2020, 35(1): 23-32.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2020.1.0023        http://www.rsta.ac.cn/CN/Y2020/V35/I1/23

图1  研究区域和验证站点分布图
站名 纬度/°N 经度/°E 海拔/m 植被类型 土壤水分范围/(m3/m3)
大满站 100.372 2 38.855 5 155 6 玉米地 0.06~0.51
黑河遥感站 100.475 6 38.827 0 156 0 人工草地 0.02~0.35
表1  研究区气象站基本情况
图2  在 Δ σ - V I 空间选取不同采样点比例所对应的土壤水分均方根误差
图3  由两种植被指数构建的变化检测经验模型对比
图4  基于两种不同植被指数构建的变化检测模型反演得到的土壤水分对比
图5  3种土壤水分反演方案估算的土壤水分对比
图6  3种土壤水分反演方案在两个站点估算的土壤水分时间序列
图7  黑河中游土壤水分空间分布图
1 Cui Y K , Xiong W T , Hu L ,et al .Applying a Machine Learning Method to Obtain Long Time and Spatio-temporal Continuous Soil Moisture over the Tibetan Plateau[C]∥IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium,2019:6986-6989.
2 Qiu Jianxiu .Research on Root-zone Soil Moisture Estimation Methods and Its Application[D].Beijing:The University of Chinese Academy of Sciences,2014.邱建秀.根层土壤水分估算方法研究及其应用[D].北京:中国科学院研究生院,2014.
3 Wu H , Wilhite D A .An Operational Agricultural Drought Risk Assessment Model for Nebraska, USA[J].Natural Hazards,2004,33(1):1-21.
4 Modanesi S , Massari C , Camici S ,et al .Performance of a Drought Standardized Soil Moisture Index based on ESA CCI Soil Moisture Product: Validation in India Using Crop Data[J].Geophysical Research Abstracts,2019,21:1-1.
5 Gherboudj I , Magagi R , Berg A ,et al .Soil Moisture Retrieval over Agricultural Fields from Multi-polarized and Multi-angular RADARSAT-2 SAR Data[J].Remote Sensing of Environment,2011,115(1):33-43.
6 Scipal K , Drusch M , Wagner W .Assimilation of an ERS Scatterometer Derived Soil Moisture Index in the ECMWF Numerical Weather Prediction System[J].Advances in Water Resources,2008,31(8):1101-1112.
7 Pangaluru K , Velicogna I , Mohajerani Y ,et al .Soil Moisture Variability in India: Relationship of Land Surface--Atmosphere Fields Using Maximum Covariance Analysis[J].Remote Sensing,2019,11(3):335.doi:10.3390/rs11030335 .
doi: 10.3390/rs11030335
8 Jin R , Li X , Liu S M ,et al .Understanding the Heterogeneity of Soil Moisture and Evapotranspiration Using Multiscale Observations from Satellites, Airborne Sensors, and a Ground-based Observation Matrix[J].IEEE Geoscience & Remote Sensing Letters,2017,14(11):2132-2136.
9 Li, Characterization X. ,Controlling, andReduction of Uncertainties in the Modeling and Observation of Land-Surface Systems [J].Science China Earth Sciences,2014,57(1):80-87.
10 Tomer S K , Al B A , Sekhar M ,et al .MAPSM: A Spatio-Temporal Algorithm for Merging Soil Moisture from Active and Passive Microwave Remote Sensing[J].Remote Sensing,2016,8(12):990.doi:10.3390/rs8120990 .
doi: 10.3390/rs8120990
11 Zhuo L , Han D W .The Relevance of Soil Moisture by Remote Sensing and Hydrological Modelling[J].Procedia Engineering,2016,154:1368-1375.
12 Oh Y , Sarabandi K , Ulaby F T .An Empirical Model and An Inversion Technique for Radar Scattering from Bare Soil Surfaces[J].IEEE Transactions on Geoscience and Remote Sensing,1992,30(2):370-381.
13 Dubois P C , Van Zyl J , Engman T .Measuring Soil Moisture with Imaging Radars[J].IEEE Transactions on Geoscience and Remote Sensing,1995,33(4):915-926.
14 Fung A K , Li Z Q , Chen K S .Backscattering from a Randomly Rough Dielectric Surface[J].IEEE Transactions on Geoscience and Remote Sensing,1992,30(2):356-369.
15 Fung A K .Microwave Scattering and Emission Models and Their Applications[M].Artech House,Norwood,MA,1994.
16 Fung A K , Chen K S .Microwave Scattering and Emission Models for Users[M].Artech House,2010:299-330.
17 Chen K S , Wu T D , Tsay M K ,et al .A Note on the Multiple Scattering in An IEM Model[J].IEEE Transactions on Geoscience and Remote Sensing,2000,38 (1):249-256.
18 Chen K S , Wu T D , Tsang L ,et al .Emission of Rough Surfaces Calculated by the Integral Equation Method with Comparison to Three-Dimensional Moment Method Simulations[J].IEEE Transactions on Geoscience and Remote Sensing,2003,41 (1):90-101.
19 Attema E P W , Ulaby F T .Vegetation Modeled as a Water Cloud[J].Radio Science,1978,13(2):357-364.
20 Ulaby F T , Sarabandi K , McDonald K ,et al .Michigan Microwave Canopy Scattering Model[C]∥ Geoscience and Remote Sensing Symposium,1990,11 (7):1223-1253.
21 Wagner W , Lemoine G , Rott H .A Method for Estimating Soil Moisture from ERS Scatterometer and Soil Data[J].Remote Sensing of Environment,1999,70(2):191-207.
22 Wan Youchuan , Chen Jing , Yu Fan ,et al .Retrieving Soil Moisture by Using Spaceborne Advanced Scattorometer[J].Transactions of the Chinese Society of Agricultural Engineering,2014,30(3):70-77.万幼川,陈晶,余凡, 等 .利用星载散射计反演地表土壤水分[J].农业工程学报,2014,30(3):70-77.
23 Zribi M , Kotti F , Amri R ,et al .Soil Moisture Mapping in a Semiarid Region, based on ASAR/Wide Swath Satellite Sata[J].Water Resources Research,2014,50(2):823-835.
24 Pathe C , Wagner W , Sabel D ,et al .Using ENVISAT ASAR Global Mode Data for Surface Soil Moisture Retrieval over Oklahoma, USA[J].IEEE Transactions on Geoscience and Remote Sensing,2009,47(2):468-480.
25 Gao Q , Zribi M , Escorihuela M J ,et al .Synergetic Use of Sentinel-1 and Sentinel-2 Data for Soil Moisture Mapping at 100 m Resolution[J].Sensors,2017,17(9):1966.doi:10.3390/s17091966 .
doi: 10.3390/s17091966
26 Li Xin , Liu Shaomin , Ma Mingguo ,et al .HiWATER: An Integrated Remote Sensing Experiment on Hydrological and Ecological Processes in the Heihe River Basin[J].Advances in Earth Science,2012,27(5):481-498.李新,刘绍民,马明国, 等 .黑河流域生态—水文过程综合遥感观测联合试验总体设计[J].地球科学进展,2012,27(5):481-498.
27 Li X , Cheng G , Liu S M ,et al .Heihe Watershed Allied Telemetry Experimental Research (HiWATER): Scientific Objectives and Experimental Design[J].Bulletin of the American Meteorological Society,2013,94(8):1145-1160.
28 Li Dazhi , Jin Rui , Che Tao ,et al .Soil Moisture Retrieval from Airborne PLMR and MODIS Products in the Zhangye Oasis of Middle Stream of Heihe River Basin,China[J].Advances in Earth Science,2014,29(2):295-305.李大治,晋锐,车涛, 等 .联合机载PLMR微波辐射计和MODIS产品反演黑河中游张掖绿洲土壤水分研究 [J].地球科学进展,2014(2):295-305.
29 Bousbih S , Zribi M , Lili-Chabaane Z ,et al .Potential of Sentinel-1 Radar Data for the Assessment of Soil and Cereal Cover Parameters[J].Sensors,2017,17(11):2617.doi:10.3390/s17112617 .
doi: 10.3390/s17112617
30 Amazirh A , Merlin O , Er-Raki S ,et al .Retrieving Surface Soil Moisture at High Spatio-temporal Resolution from a Synergy between Sentinel-1 Radar and Landsat Thermal Data: A Study Case over Bare Soil[J].Remote Sensing of Environment,2018,211:321-337.
31 Qiu J X , Crow W T , Wagner W ,et al .Effect of Vegetation Index Choice on Soil Moisture Retrievals Via the Synergistic Use of Synthetic Aperture Radar and Optical Remote Aensing[J].International Journal of Applied Earth Observation and Geoinformation,2019,80:47-57.doi:10.1016/j.jag.2019. 03.015
doi: 10.1016/j.jag.2019. 03.015
32 Savitzky A , Golay M J .Smoothing and Differentiation of Data by Simplified Least Squares Procedures[J].Analytical Chemistry,1964,36(8):1627-1639.
33 Chen J , Jönsson P , Tamura M ,et al .A Aimple Method for Reconstructing a High-quality NDVI Time-series Data Set based on the Savitzky-golay Filter[J].Remote Sensing of Environment,2004,91(3-4):332-344.
34 Li X , Cheng G D , Liu S M ,et al .Heihe Watershed Allied Telemetry Experimental Research (HiWATER): Scientific Objectives and Experimental Design[J].Bulletin of the American Meteorological Society,2013,94(8):1145-1160.
35 Liu S M , Li X , Xu Z W ,et al .The Heihe Integrated Observatory Network: A Basin-Scale Land Surface Processes Observatory in China[J].Vadose Zone Journal,2018,17:180072.doi:10.2136/vzj2018.04.0072 .
doi: 10.2136/vzj2018.04.0072
36 Zhu Z L , Tan L , Gao S G ,et al .Observation on Soil Moisture of Irrigated Cropland by Cosmic-ray Probe[J].IEEE Geoscience and Remote Sensing Letters,2015,12(3):472-476.
37 Liu S M , Xu Z W , Wang W Z ,et al .A Comparison of Eddy-Covariance and Large Aperture Scintillometer Measurements with Respect to the Energy Balance Closure Problem[J].Hydrology and Earth System Sciences,2011,15(4):1291-1306.
38 Du Jiaqiang , Wang Yuehui , Shi Huading ,et al .Performance Evaluation of GIMMS NDVI3g and GIMMS NDVIg based on MODIS and Landsat in Tibetan Plateau [J].Transactions of the Chinese Society of Agricultural Engineering,2016,32(22):192-199.杜加强,王跃辉,师华定, 等 .基于MODIS 和Landsat 的青藏高原两代GIMMS NDVI 性能评价[J].农业工程学报,2016,32(22):192-199.
39 Chen Q , Zeng J Y , Cui C Y ,et al .Soil Moisture Retrieval from SMAP: A Validation and Error Analysis Study Using Ground-based Observations over the Little Washita Watershed[J].IEEE Transactions on Geoscience and Remote Sensing,2018,56(3):1394-1408.
[1] 王树果, 马春锋, 赵泽斌, 魏龙. 基于Sentinel-1及Landsat 8数据的黑河中游农田土壤水分估算[J]. 遥感技术与应用, 2020, 35(1): 13-22.
[2] 李雷,郑兴明,赵凯,李晓峰,王广蕊. 基于CCI土壤水分产品的干旱指数精度评价及其对东北地区粮食产量的影响[J]. 遥感技术与应用, 2020, 35(1): 111-119.
[3] 陆峥,韩孟磊,卢麾,彭雪婷,蒙莎莎,刘进,杨晓帆. 基于AMSR2多频亮温的黑河流域中上游土壤水分估算研究[J]. 遥感技术与应用, 2020, 35(1): 33-47.
[4] 胡路,赵天杰,施建成,李尚楠,樊东,王平凯,耿德源,肖青,崔倩,陈德清. 基于地基微波辐射观测的土壤水分反演算法评估[J]. 遥感技术与应用, 2020, 35(1): 74-84.
[5] 陈勇强,杨娜,胡新,佟明远. SMOS与SMAP过境时段表层土壤水分的稳定性研究[J]. 遥感技术与应用, 2020, 35(1): 58-64.
[6] 劳从坤,杨娜,徐少博,汤燕杰,张恒杰. 反演策略对SMOS土壤水分反演算法的影响研究[J]. 遥感技术与应用, 2020, 35(1): 65-73.
[7] 范悦,邱建秀,董建志,张小虎,王大刚. 基于Triple Collocation方法的微波土壤水分产品不确定性分析与时空变化规律研究[J]. 遥感技术与应用, 2020, 35(1): 85-96.
[8] 王树果,刘伟,梁亮. 基于Triple-Collocation方法的微波遥感土壤水分产品不确定性分析及数据融合[J]. 遥感技术与应用, 2019, 34(6): 1227-1234.
[9] 何浩,刘修国,沈永林. 基于视差的高分辨率遥感影像建筑物变化检测[J]. 遥感技术与应用, 2019, 34(6): 1315-1323.
[10] 麻源源,左小清,麻卫峰. 基于PS-InSAR的天津地区沉降监测及分析[J]. 遥感技术与应用, 2019, 34(6): 1324-1331.
[11] 王源,陈富龙,胡祺,唐攀攀. COSMO-SkyMed时序影像南京城市变化检测研究[J]. 遥感技术与应用, 2019, 34(5): 1054-1063.
[12] 张因果,陈芸芝. 一种基于改进土地覆盖更新方法的新增建设用地自动提取[J]. 遥感技术与应用, 2019, 34(5): 1073-1081.
[13] 刘克俭,闫敏,冯琦. 多层土壤观测数据同化的森林碳、水通量模拟[J]. 遥感技术与应用, 2019, 34(5): 950-958.
[14] 申祎,王超,胡佳乐. 一种结合空间与光谱信息的改进CVA变化检测方法[J]. 遥感技术与应用, 2019, 34(4): 799-806.
[15] 王磊, 蒋宗立, 刘时银, 上官冬辉, 张勇. 中巴公路沿线冰川运动特征[J]. 遥感技术与应用, 2019, 34(2): 412-423.