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遥感技术与应用  2020, Vol. 35 Issue (1): 13-22    DOI: 10.11873/j.issn.1004-0323.2020.1.0013
土壤水分专栏     
基于Sentinel-1及Landsat 8数据的黑河中游农田土壤水分估算
王树果1(),马春锋2(),赵泽斌2,魏龙2
1. 江苏师范大学地理测绘与城乡规划学院,江苏 徐州 221116
2. 中国科学院西北生态环境资源研究院,甘肃 兰州 730000
Estimation of Soil Moisture of Agriculture Field in the Middle Reaches of the Heihe River Basin based on Sentinel-1 and Landsat 8 Imagery
Shuguo Wang1(),Chunfeng Ma2(),Zebin Zhao2,Long Wei2
1. School of Geography, Geomatics and Planning, Jiangsu Normal University, Xuzhou 221116, China
2. Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
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摘要:

土壤水分是陆地表层系统中的关键变量。利用主动微波遥感,特别是合成孔径雷达(Synthetic Aperture Radar, SAR)的观测,在监测和估计表层土壤水分时空分布方面已开展了诸多研究。然而,SAR土壤水分反演仍存在诸多挑战,特别是地表粗糙度和植被的影响。因此,本文提出了一种结合主动微波和光学遥感的优化估计方案,旨在同步反演植被含水量、地表粗糙度和土壤水分。反演算法首先在水云模型的框架下对模型中的植被透过率因子(与植被含水量密切相关)采用3种不同的光学遥感指数——修正的土壤调节植被指数(Modified Soil Adjusted Vegetation Index, MSAVI)、归一化植被指数(Normalized Difference Vegetation Index, NDVI)和归一化水体指数(Normalized Difference Water Index, NDWI)进行参数化估计,用于校正植被层的散射贡献。在此基础上,构造基于SAR观测和Oh模型的代价函数,利用复型洗牌全局优化算法进行土壤水分和地表粗糙度的联合反演。采用Sentinel-1 SAR和Landsat 8多光谱数据在黑河中游开展了反演试验,并利用相应的地面观测数据对结果进行了验证。结果表明反演结果与地面观测具有良好的一致性,其中基于NDWI的植被含水量反演效果最佳,与地面观测比较,土壤水分决定系数(R 2)在0.7以上,均方根误差(RMSE)为0.073 m3/m3;植被含水量R 2大于0.9,RMSE为0.885 kg/m2,表明该方法能够较准确地估计土壤水分。同时发现植被含水量的估计结果,以及植被透过率的参数化方案对土壤水分的反演精度有一定的影响,在未来的研究中需要进一步探索。

关键词: 土壤水分SAR地表粗糙度植被含水量Sentinel-1Landsat 8    
Abstract:

Soil moisture is a key variable in land surface system. Using active microwave remote sensing observations, especially Synthetic Aperture Radar (SAR), has been proven a promising way on the estimation of spatial-temporal distribution of surface soil moisture by a lot of studies. However, there is still challenging in this field, because of the impacts caused by surface roughness and vegetation cover. In this context, this paper proposes an optimal estimation approach combined using SAR and optical remote sensing imagery, in order to retrieve vegetation water content, roughness and soil moisture simultaneously. First, water-cloud model is used to correct vegetation effect on microwave scattering process. In this step, vegetation transmittance factor (closed related to vegetation water content) is estimated by using three optical remote sensing indexes, namely, Modified Soil Adjusted Vegetation Index (MSAVI), Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI). Second, a cost function is constructed based on SAR observations and Oh model simulations, then soil moisture and surface roughness can be estimated through global optimization by shuffled complex evolution algorithm. The proposed method is performed by using Sentinel-1and Landsat 8 data in the middle researches of the Heihe River Basin, retrieved results are validated against ground measurements. Results show a good agreement between remote sensing estimates and ground measurements, which indicates the proposed method can retrieve soil moisture accurately. For soil moisture, the determination coefficient (R 2) is higher than 0.7, the root mean square error (RMSE) is 0.073 m3/m3. With respect to vegetation water content,R 2 is higher than 0.9 and RMSE is 0.885 kg/m2. In the meantime, it is found that the result of estimated vegetation water content and the parameterization scheme of vegetation parameters have pronounced influence on the accuracy of soil moisture estimates, which need to be further addressed in future research.

Key words: Soil moisture    SAR    Surface roughness    Vegetation water content    Sentinel-1    Landsat 8
收稿日期: 2019-06-06 出版日期: 2020-04-01
ZTFLH:  TP79  
基金资助: 江苏省自然科学基金项目(BK20171165);国家自然科学基金项目(41971305);中科院“西部之光”青年人才项目B类
通讯作者: 马春锋     E-mail: swang@jsnu.edu.cn;machf@lzb.ac.cn
作者简介: 王树果(1980-),男,甘肃兰州人,博士,讲师,主要从事定量遥感研究。E?mail:swang@jsnu.edu.cn
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引用本文:

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.

Shuguo Wang, Chunfeng Ma, Zebin Zhao, Long Wei. Estimation of Soil Moisture of Agriculture Field in the Middle Reaches of the Heihe River Basin based on Sentinel-1 and Landsat 8 Imagery. Remote Sensing Technology and Application, 2020, 35(1): 13-22.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2020.1.0013        http://www.rsta.ac.cn/CN/Y2020/V35/I1/13

图1  研究区位置及地面土壤水分观测节点

Sentinel-1获取日期

(D1, YYYYMMDD)

Landsat 8日期(D2, DOY) D1-D2

地面土壤水分

(观测样本数)

植被含水量观测日期

(观测样本数)

20141024 2014301 -4 超级站(1)
20141031 2014301 3 超级站(1)
20141112 2014317 -1 超级站(1)
20141117 2014317 4 超级站(1)
20141124 2014333 -5 超级站(1)
20141201 2014333 2 超级站(1)
20141211 2014349 -4 超级站(1)
20141218 2014349 3 超级站(1)
20150111 2015016 -5 超级站(1)
20150118 2015016 2 超级站(1)
20150216 2015048 -1 超级站(1)
20150221 2015048 4 超级站(1)
20150228 2015064 -5 超级站(1)
20150307 2015064 2 超级站(1)
20160505 2016131 -5 超级站(1)
20160512 2016131 2 超级站(1)+WSN(9) 20160515(10)
20160529 2016147 3 超级站(1)+WSN(9) 20160530(10)
20160902 2016243 3 超级站(1)+WSN(9) 20160830(10)
表1  Sentinel-1、Landsat 8 数据获取日期及地面验证数据的观测日期及有效样本数
图2  3种不同方案估算的植被含水量结果
图3  基于3种植被含水量参数化方案的土壤水分反演结果
图4  2016年5月29日反演得到的土壤水分空间分布(单位:m3/m3)
图5  水云模型参数敏感性分析
1 NRC .Earth Science and Applications from Space: National Imperatives for the Next Decade and Beyond[M].Washington:National Academy Press,2007.
2 Seneviratne S I , Corti T , Davin E L ,et al .Investigating Soil Moisture-Climate Interactions in a Changing Climate: A Review[J].Earth-Science Reviews,2010,99(3-4):125-161.
3 Ulaby F T , Moore R K , Fung A K .Microwave Remote Sensing: Active and Passive, Vol. II-Radar Remote Sensing and Surface Scattering and Emission Theory[M].Addison-Wesley,Advanced Book Program,Reading,Massachusetts,1982:609.
4 de Jeu R , Dorigo W .On the Importance of Satellite Observed Soil Moisture[J].International Journal of Applied Earth Observation and Geoinformation,2016,45:107-109.
5 Kornelsen K C , Coulibaly P .Advances in Soil Moisture Retrieval from Synthetic Aperture Radar and Hydrological Applications[J].Journal of Hydrology,2103,476:460-489.
6 Paloscia S , Pettinato S , Santi E ,et al .Soil Moisture Mapping Using Sentinel-1 Images: Algorithm and Preliminary Validation[J].Remote Sensing of Environment,2013,134:234-248.
7 Dobson M C , Ulaby F T , Hallikainen M T ,et al .Microwave Dielectric Behavior of Wet Soil-part II: Dielectric Mixing Models[J].IEEE Transactions on Geoscience and Remote Sensing,1985,GE- 23(1):35-46.
8 Kim Y , van Zyl J J .A Time-series Approach to Estimate Soil Moisture Using Polarimetric Radar Data[J].IEEE Transactions on Geoscience and Remote Sensing,2009,47(8):2519-2527.
9 Moran M S , Hymer D C , Qi J G ,et al .Soil Moisture Evaluation Using Multi-temporal Synthetic Aperture Radar (SAR) in Semiarid Rangeland[J].Agricultural and Forest Meteorology,2000,105(1-3):69-80.
10 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.
11 Quesney A , Le Hegarat-Mascle S , Taconet O ,et al .Estimation of Watershed Soil Moisture Index from ERS/SAR Data[J].Remote Sensing of Environment,2000,72(3):290-303.
12 Paloscia S .A Summary of Experimental Results to Assess the Contribution of SAR for Mapping Vegetation Biomass and Soil Moisture[J].Canadian Journal of Remote Sensing,2002,28(2):246-261.
13 Joseph A T , Van der Velde R , O'Neill P E ,et al .Effects of corn on C- and L-band Radar Backscatter: A Correction Method for Soil Moisture Retrieval[J].Remote Sensing of Environment,2010,114(11):2417-2430.
14 Zribi M , Le Hetarat-Mascle S , Ottle C ,et al .Surface Soil Moisture Estimation from the Synergistic Use of the (Multi-incidence and Multi-resolution) Active Microwave ERS Wind Scatterometer and SAR Data[J].Remote Sensing of Environment,2003,86(1):30-41.
15 Wang S G , Li X , Han X J ,et al .Estimation of Surface Soil Moisture and Roughness from Multi-angular ASAR Imagery in the Watershed Allied Telemetry Experimental Research (WATER)[J].Hydrology and Earth System Sciences,2011,15(5):1415-1426.
16 Pierdicca N , Castracane P , Pulvirenti L .Inversion of Electromagnetic Models for Bare Soil Parameter Estimation from Multifrequency Polarimetric SAR Data[J].Sensors,2008,8(12):8181-8200.
17 Pierdicca N , Pulvirenti L , Bignami C .Soil Moisture Estimation over Vegetated Terrains Using Multitemporal Remote Sensing Data[J].Remote Sensing of Environment,2010,114(2):440-448.
18 Zhan X W , House P R , Walker J P ,et al .A Method for Retrieving High-resolution Surface Soil Moisture from Hydros L-Band Radiometer and Radar Observations[J].IEEE Transactions on Geoscience and Remote Sensing,2006,44(6):1534-1544.
19 Notarnicola C , Angiulli M , Posa F .Use of Radar and Optical Remotely Sensed Data for Soil Moisture Retrieval over Vegetated Areas[J].IEEE Transactions on Geoscience and Remote Sensing,2006,44(4):925-935.
20 Notarnicola C , Angiulli M , Posa F .Soil Moisture Retrieval from Remotely Sensed Data: Neural Network Approach Versus Bayesian Method[J].IEEE Transactions on Geoscience and Remote Sensing,2008,46(2):547-557.
21 Notarnicola C , Posa F .Bayesian Algorithm for the Estimation of the Dielectric Constant from Active and Passive Remotely Sensed Data[J].IEEE Geoscience and Remote Sensing Letters,2004,1(3):179-183.
22 Paloscia S , Pettinato S , Santi E .Combining L and X Band SAR Data for Estimating Biomass and Soil Moisture of Agricultural Fields[J].European Journal of Remote Sensing,2012,45(1):99-109.
23 Said S , Kothyari U C , Arora M K .ANN-based Soil Moisture Retrieval over Bare and Vegetated Areas Using ERS-2 SAR Data[J].Journal of Hydrologic Engineering,2008,13(6):461-475.
24 Ahmad S , Kalra A , Stephen H .Estimating Soil Moisture Using Remote Sensing Data: A Machine Learning Approach[J].Advances in Water Resources,2010,33(1):69-80.
25 Li X , Cheng G , Liu S ,et al .Heihe Watershed Allied Telemetry Experimental Fesearch(HiWATER): Scientific Objectives and Experimental Design[J].Bulletin of the American Meteorological Society,2013,9(8):1145-1160.
26 Zhang Miao , Jiang Zhirong , Ma Mingguo ,et al .Fine Classification of Planting Structure in the Middle Reaches of Heihe River Basin based on Hyperspectral Compact Airborne Spectrographic Imager(CASI) Data[J].Remote Sensing Technology and Application,2013,28(2):283-289.张苗,蒋志荣,马明国, 等 .基于CASI影像的黑河中游种植结构精细分类研究[J].遥感技术与应用,2013,28(2):283-289.
27 Attema E P , Ulaby F T .Vegetation Modeled as a Water Cloud[J].Radio Science,1978,13(2):357-364.
28 Bindlish R , Barros A P .Parameterization of Vegetation Backscatter in Radar-based, Soil Moisture Estimation[J].Remote Sensing of Environment,2001,76(1):130-137.
29 Wen Yi , Huang Chunlin , Lu Ling ,et al .The Retrieval of Vegetation Water Content based on ASTER Images in Middle of Heihe River Basin[J].Remote Sensing Technology and Application,2015,30(5):876-883.闻熠,黄春林,卢玲, 等 .基于ASTER数据黑河中游植被含水量反演研究[J].遥感技术与应用,2015,30(5):876-883.
30 Gao Y , Walker J P , Allahmoradi M ,et al .Optical Sensing of Vegetation Water Content: A Synthesis Study[J].IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2015,8(4):1456-1464.
31 Jackson T J , Chen D Y , Cosh M ,et al .Vegetation Water Content Mapping Using Landsat Data Derived Normalized Difference Water Index for Corn and Soybeans[J].Remote Sensing of Environment,2004,92(4):475-482.
32 Chen D Y , Huang J F , Jackson T J .Vegetation Water Content Estimation for Corn and Soybeans Using Spectral Indices Derived from MODIS Near- and Short-wave Infrared Bands[J].Remote Sensing of Environment,2005,98(2-3):225-236.
33 Ma C F , Li X , Wang S G .A Global Sensitivity Analysis of Soil Parameters Associated with Backscattering Using the Advanced Integral Equation Model[J].IEEE Transactions on Geoscience and Remote Sensing,2015,53(10):5613-5623.
34 Duan Q Y , Sorooshian S , Gupta V .Effective and Efficient Global Optimization for Conceptual Rainfall-runoff Models[J].Water Resources Research,1992,28(4):1015-1031.
35 Oh Y .Quantitative Retrieval of Soil Moisture Content and Surface Foughness from Multipolarized Radar Observations of Bare Soil Surfaces[J].IEEE Transactions on Geoscience and Remote Sensing,2004,42(3):596-601.
36 Fung A K , Li Z Q , Chen K S .Backscattering from a Randomly Rough Dielectric Surface[J].IEEE Transaction on Geoscience and Remote Sensing,1992,30(2):356-369.
37 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.
38 Ma C F , Li X , Notarnicola C ,et al .Uncertainty Quantification of Soil Moisture Estimations based on a Bayesian Probabilistic Inversion[J].IEEE Transactions on Geoscience and Remote Sensing,2017,55(6):3194-3207.
39 Ma C F , Li X , Chen K S .The Discrepancy Between Backscattering Model Simulations and Radar Observations Caused by Scaling Issues: An Uncertainty Analysis[J].IEEE Transactions on Geoscience and Remote Sensing,2019.doi:10.1109/TGRS. 2019.2899120 .
doi: 10.1109/TGRS. 2019.2899120
40 Das N N , Entekhabi D , Dunbar R S ,et al .The SMAP and Copernicus Sentinel 1A/B Microwave Active-passive High Resolution Surface Soil Moisture Product[J].Remote Sensing of Environment,2019,233:111380.doi:10.1016/j.rse. 2019. 111380 .
doi: 10.1016/j.rse. 2019. 111380
41 Zhu L J , Walker J P , Tsang L ,et al .Soil Moisture Retrieval from Time Series Multi-angular Radar Data Using A Dry down Constrant [J].Remote Sensing of Environment,2019,231:111237.doi:10.1016/j.rse.2019.111237 .
doi: 10.1016/j.rse.2019.111237
42 Han D , Liu S B , Du Y ,et al .Crop Water Content of Winter Wheat Revealed with Sentinel-1 and Sentinel-2 Imagery[J].Sensors,2019,19(18):4013.doi:10.3390/s19184013 .
doi: 10.3390/s19184013
43 Xing M F , He B B , Ni X L ,et al .Retrieving Surface Soil Moisture over Wheat and Soybean Fields during Growing Season Using Modified Water Cloud Model from Radarsat-2 SAR Data[J].Remote Sensing,2019,11(16):1956.doi:10.3390/rs11161956 .
doi: 10.3390/rs11161956
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