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遥感技术与应用  2020, Vol. 35 Issue (1): 74-84    DOI: 10.11873/j.issn.1004-0323.2020.1.0074
土壤水分专栏     
基于地基微波辐射观测的土壤水分反演算法评估
胡路1,2(),赵天杰1(),施建成1,李尚楠3,樊东2,王平凯4,耿德源1,2,肖青1,崔倩5,陈德清5
1.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
2.中国科学院大学,北京 100049
3.93920部队,陕西 西安 710061
4.上海航天电子通讯设备研究所,上海 201109
5.水利部信息中心,北京 100053
Evaluation of Soil Moisture Retrieval Algorithms based on Ground-based Microwave Radiation Observation
Lu Hu1,2(),Tianjie Zhao1(),Jiancheng Shi1,Shannan Li3,Dong Fang2,Pingkai Wang4,Deyuan Geng1,2,Qing Xiao1,Qing Cui5,Deqing Chen5
1.State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China
2.University of Chinese Academy of Sciences, Beijing 100049, China
3.Unit 93920, Xi’an 710061, China
4.Institute of Aerospace Electronic Communication Equipment, Shanghai 201109, China
5.Ministry of Water Resources Information Center, Beijing 100053, China
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摘要:

当前常用的被动微波土壤水分反演算法有水平极化单通道算法、垂直极化单通道算法、双通道算法、微波极化差比值算法和扩展双通道算法,5种反演算法具有不同的差异,对这些反演算法进行系统的评估和分析将有助于反演算法的改进和星载高精度土壤水分产品的发布。为了避免直接采用卫星产品验证时的尺度匹配、空间异质性等问题,基于地基L波段微波辐射观测以及配套的土壤和植被参数测量数据,对这5种反演算法进行了实现、对比和分析,得出以下结论:①单通道算法具有最佳的反演性能,水平极化单通道算法反演结果具有最高的相关性(相关性系数R=0.83),垂直极化单通道算法反演结果具有最小的反演误差(均方根误差RMSE=0.028 m3/m3,偏差BIAS= -0.011 m3/m3),但单通道算法需要精确的植被含水量输入;②其余3种算法能脱离植被辅助数据的使用,性能略差但也能满足星载微波传感器的探测指标要求(小于等于0.04 m3/m3);其中,扩展双通道算法和微波极化差比值算法的土壤水分反演结果比双通道算法略差,但本例中扩展双通道算法在植被含水量反演方面更具优势。

关键词: 土壤水分微波辐射计L波段反演算法    
Abstract:

The commonly used passive microwave soil moisture inversion algorithms include Single Channel Algorithm at H polarization (SCA-H), Single Channel Algorithm at V polarization (SCA-V), Dual-Channel Algorithm (DCA), Microwave Polarization Ratio Algorithm (MPRA) and Extended Dual Channel Algorithm (E-DCA). The five retrieval algorithms have different performance, systematic evaluation and analysis of these inversion algorithms will contribute to the improvement of the retrieval algorithm and the release of satellite soil moisture products. Verification of satellite product could bring some problems, such as scale matching and spatial heterogeneity. In order to avoid these issues, the above five soil moisture inversion algorithms are implemented, compared and analyzed based on ground-based microwave radiometer observation and supporting soil and vegetation parameter measurement data. The results show: (1) SCA has the best inversion performance. SCA-H has the highest correlation (R=0.83), and SCA-V has the smallest inversion error (RMSE=0.028 m3/m3, BIAS=-0.011 m3/m3), but SCA needs the accurate vegetation water content as an input. (2) The other three algorithms can get rid of the use of vegetation-aided data, with slightly poor performance but also meet the satellite detection requirements (less than or equal to 0.04 m3/m3). Among them, E-DCA and MPRA are slightly worse than the DCA. However, E-DCA is more advantageous in the vegetation water content inversion in our study.

Key words: Soil moisture    Microwave radiometer    L band    Retrieval algorithm
收稿日期: 2019-02-16 出版日期: 2020-04-01
ZTFLH:  TP79  
基金资助: 国家重点研发计划(2016YFE0117300);国家重大科学研究计划(2015CB953701);“十三五”民用航天预先研究项目“陆地水资源卫星系统技术”(Y7D0070038);中国科学院青年创新促进会项目(2016061)
通讯作者: 赵天杰     E-mail: hulu@radi.ac.cn;zhaotj@radi.ac.cn
作者简介: 胡 路(1994-),男,湖北潜江人,硕士研究生,主要从事微波遥感反演土壤水分研究。E?mail:hulu@radi.ac.cn
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引用本文:

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.

Lu Hu,Tianjie Zhao,Jiancheng Shi,Shannan Li,Dong Fang,Pingkai Wang,Deyuan Geng,Qing Xiao,Qing Cui,Deqing Chen. Evaluation of Soil Moisture Retrieval Algorithms based on Ground-based Microwave Radiation Observation. Remote Sensing Technology and Application, 2020, 35(1): 74-84.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2020.1.0074        http://www.rsta.ac.cn/CN/Y2020/V35/I1/74

图1  L波段观测亮温组成部分(忽略了大气影响)
图2  单通道算法流程图
图3  车载辐射计观测视场图
图4  玉米地观测亮温、土壤温湿度和降雨量
图5  不同ω和b参数下的单通道算法模拟结果
观测模式RMSE最小R最大
Dayscan

ω=0.02

b=0.06

ω=0.02

b=0.05

Nightscan

ω=0.01

b=0.05

ω=0.08

b=0.07

表1  不同观测模式下的最佳ω和b参数值
方法时间RRMSE(m3/m3BIAS(m3/m3)
SCA-H2:00 AM0.850.028-0.021
6:00 AM0.730.030-0.018
所有数据0.830.029-0.018
SCA-V2:00 AM0.810.025-0.013
6:00 AM0.660.029-0.011
所有数据0.780.028-0.011
DCA2:00 AM0.620.030-0.010
6:00 AM0.590.027-0.013
所有数据0.580.031-0.012
MPRA2:00 AM0.630.038-0.014
6:00 AM0.430.042-0.012
所有数据0.580.040-0.014
E-DCA2:00 AM0.600.038-0.032
6:00 AM0.580.037-0.030
所有数据0.580.038-0.030
表2  不同反演算法精度验证
图6  5种方法反演结果对比
图7  植被含水量反演结果与实测对比
方法RRMSE(kg/m2)BIAS(kg/m2)
DCA0.591.030.37
MPRA0.501.901.00
E-DCA0.690.89-0.38
表3  植被含水量反演方法精度验证
图8  不同深度的土壤湿度与观测亮温散点图
图9  L波段理论穿透深度变化
图10  40°入射角MPDI变化图
图11  DCA和MPRA反演精度随初值的变化
1 Purdy A J,Fisher J B,Goulden M L,et al.SMAP Soil Moisture Improves Global Evapotranspiration[J].Remote Sensing of Environment,2018,219:1-14.
2 Kim S,Paik K,Johnson F M,et al.Building a Flood-warning Framework for Ungauged Locations Using Low Resolution, Open-access Remotely Sensed Surface Soil Moisture, Precipitation, Soil, and Topographic Information [J].IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2018,11:375-387.
3 Mladenova I E,Bolten J D,Crow W,et al.Enhancing the USDA Fas Crop Forecasting System Using SMAP L3 Soil Moisture Observations [J].IEEE International Geoscience and Remote Sensing Symposium,2018:5375-5377.
4 O'Neill P,Chan S,Njoku E G,et al.Algorithm Theoretical Basis Document Level 2 & 3 Soil Moisture (Passive) Data Products [M].Pasadena,CA: Jet Propulsion Laboratory,2018.
5 Chan S,Bindlish R,O’Neill P,et al.Assessment of the SMAP Passive Soil Moisture Product [J].IEEE Transactions on Geoscience and Remote Sensing,2016,54(8):4994-5007.
6 Zeng J Y,Chen K S,Bi H Y,et al.A Preliminary Evaluation of the SMAP Radiometer Soil Moisture Product over United States and Europe Using Ground-based Measurements [J].IEEE Transactions on Geoscience and Remote Sensing,2016,54(8):4929-4940.
7 Ma C F,Li X,Wei L,et al.Multi-scale Validation of SMAP Soil Moisture Products over Cold and Arid Regions in Northwestern China Using Distributed Ground Observation Data[J].Remote Sensing,2017,9(4):327.doi:10.3390/rs9040327.
doi: 10.3390/rs9040327
8 Miernecki M,Wigneron J P,Baeza E L,et al.Comparison of SMOS and SMAP Soil Moisture Retrieval Approaches Using Tower-based Radiometer Data over A Vineyard Field [J].Remote sensing of Environment,2014,154:89-101.
9 J-P Wigneron,Kerr Y,Waldteufel P,et al.L-band Microwave Emission of the Biosphere (L-MEB) Model: Description and Calibration Against Experimental Data Sets over Crop Fields [J].Remote Sensing of Environment,2007,107(4):639–655.
10 Jackson T J.Measuring Surface Soil Moisture Using Microwave Remote Sensing[J].Hydrological Processes,1993,7:139-152.
11 Jackson,and Schmugge T J T J.Vegetation Effects on the Microwave Emission from Soils[J].Remote Sensing of Environment,1991,36:203-212.
12 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.
13 Schmugge T J,Chang A,Newton R W.Effect of Surface Roughness on the Microwave Emission from Soils[J].Journal of Geophysical Research,1979,84(C9):5699-5706.
14 Wang J R,Choudhury B J.Renote Sensing of Soil Moisture Content over Bare Field at 1.4 GHz Frequency[J].Journal of Geophysical Research,1981,86(6):5277-5282.
15 Wang J R,Schmugge T J.An Empirical Model for the Complex Dielectric Permittivity of Soils as A Function of Water Content [J].IEEE Transactions on Geoscience and Remote Sensing,1980,18(4):288-295.
16 Dobson M C,Ulaby F T,Hallikainen M,et al.Microwave Dielectric Behavior of Wet Soil-Part II: Dielectric Mixing Models [J].IEEE Transactions on Geoscience and Remote Sensing,1985,23(1):35-46.
17 Mironov V L,Kosolapova L G,Fomin S V.Physically and Mineralogically based Spectroscopic Dielectric Model for Moist Soils [J].IEEE Transactions on Geoscience and Remote Sensing,2009,47(7):2059-2070.
18 Njoku E G,Li L.Retrieval of Land Surface Parameters Using Passive Microwave Measurement at 6~18 GHz[J].IEEE Transaction on Geoscience and Remote Sensing,1999,37(1):79-93.
19 Press W H,Flannery B P,Teukolsky S A,et al.Numerical Recipes[M].New York:Cambridge University,1989.
20 Owe M,De Jeu R,Walker J.A Methodology for Surface Soil Moisture and Vegetation Optical Depth Retrieval Using the Microwave Polarization Difference Index[J].IEEE Transactions on Geoscience and Remote Sensing,2001,39(8):1643-1654.
21 Meesters A G C A,De Jeu R,Manfred Owe.Analytical Derivation of the Vegetation Optical Depth from the Microwave Polarization Difference Index [J].IEEE Transactions on Geoscience and Remote Sensing Letters,2005,2(2):121-123.
22 Montpetit B,Royer A,Wigneron J P,et al.Evaluation of Multi-frequency Bare Soil Microwave Reflectivity Models[J].Remote Sensing of Environment,2015,162:186-195.
23 Guo Peng.Passive Microwave Soil Moisture Retrieval based on SMAP Configurations[D].Beijing:University of Chinese Academy of Sciences,2013.
23 郭鹏.基于SMAP的被动微波土壤水分反演[D].北京:中国科学院大学,2013.
24 Ulaby F T,Elachi C.Radar Polarimetry for Geoscience Applications [M].Norwood,MA: Artech House,1990.
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