基于高分三号卫星合成孔径雷达数据的农田土壤水分反演

doi:10.3785/j.issn.1008-9209.2023.12.183

浙江大学学报（农业与生命科学版）

2024, Vol. 50

Issue (2): 209-220 DOI: 10.3785/j.issn.1008-9209.2023.12.183

研究论文

基于高分三号卫星合成孔径雷达数据的农田土壤水分反演

张琳琳^1,^2,³(

),雷志斌⁴(

),王莉萍⁵,孟庆岩^1,^2,³(

),曾江源¹

^1.中国科学院空天信息创新研究院，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.海南空天信息研究院，海南省地球观测重点实验室，海南三亚 572029
^4.中国地质大学（北京），地球科学与资源学院，北京 100083
^5.杭州国际城市学研究中心浙江省城市治理研究中心，浙江杭州 310000

Retrieval of soil moisture based on Gaofen-3 (GF-3) satellite synthetic aperture radar data over agricultural fields

Linlin ZHANG^1,^2,³(

),Zhibin LEI⁴(

),Liping WANG⁵,Qingyan MENG^1,^2,³(

),Jiangyuan ZENG¹

^1.State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
^2.University of Chinese Academy of Sciences, Beijing 100049, China
^3.Key Laboratory of Earth Observation of Hainan Province, Hainan Aerospace Information Research Institute, Sanya 572029, Hainan, China
^4.School of Earth Sciences and Resources, China University of Geosciences (Beijing), Beijing 100083, China
^5.Center for Urban Governance Studies of Zhejiang Province, Hangzhou International Urbanology Research Center, Hangzhou 310000, Zhejiang, China

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摘要：

土壤水分是农作物生长的基本条件，本研究基于高分三号卫星C波段合成孔径雷达数据，提出新的土壤水分反演算法，并获取区域尺度8 m空间分辨率的农田区土壤水分。首先，通过PROSAIL模型、实测植被冠层含水量、Landsat-8光学数据优选光学植被水分指数，计算水云模型参数并获得土壤直接后向散射系数；其次，利用高级积分方程模型模拟雷达后向散射影响机制，采用雷达影像高低入射角特性计算地表组合粗糙度；最后，利用高分三号卫星同极化雷达数据反演农田区土壤水分，并基于实测数据开展精度验证。结果表明：土壤水分反演值与野外实测值具有良好一致性，垂直极化下反演精度更高，其决定系数为0.595 6，均方根误差为0.041 5 m³/m³。本研究成果可为我国自主研发的高分三号卫星获取高分辨率土壤水分信息提供算法参考。

关键词： 土壤水分; 高分三号卫星; 雷达遥感; 地表粗糙度

Abstract:

Soil moisture is the basic condition for crop growth. A new retrieval algorithm for soil moisture was proposed based on C-band synthetic aperture radar (SAR) data from Gaofen-3 (GF-3) satellite, and soil moisture of agricultural fields with a regional scale spatial resolution of 8 m was obtained. First, the algorithm selected the optical vegetation water index based on PROSAIL model, measured vegetation canopy water content and Landsat-8 optical data. The parameters of water cloud model were calculated， and soil direct backscattering coefficients were obtained. Second, the radar backscattering influence mechanism was simulated using an advanced integral equation model (AIEM), and the combined roughness of soil surface was calculated based on the characteristics of radar data at high and low incidence angles. Finally, soil moisture was retrieved using co-polarization radar data from GF-3 satellite over agricultural fields, and this was verified with measured data. The results showed that there was a high consistency between the measured soil moisture and estimated soil moisture, and vertical-vertical (VV) polarization exhibited higher retrieval accuracy, with a determination coefficient of 0.595 6 and a root mean square error of 0.041 5 m³/m³. The results can provide algorithmic references for the GF-3 satellite to obtain high-resolution soil moisture information.

Key words: soil moisture Gaofen-3 (GF-3) satellite radar remote sensing soil surface roughness

收稿日期: 2023-12-18 出版日期: 2024-04-25

CLC:

P237

基金资助: 遥感科学国家重点实验室开放基金项目(OFSLRSS202208);国家自然科学基金项目(42201384);中国科学院青年创新促进会项目(2023139)

通讯作者: 孟庆岩 E-mail: zhangll@aircas.ac.cn;1529418402@qq.com;mengqy@radi.ac.cn

作者简介: 张琳琳（https://orcid.org/0000-0001-5073-1694），E-mail：zhangll@aircas.ac.cn|雷志斌（https://orcid.org/0009- 0004-5023-4013），E-mail：1529418402@qq.com。

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引用本文:

张琳琳, 雷志斌, 王莉萍, 孟庆岩, 曾江源. 基于高分三号卫星合成孔径雷达数据的农田土壤水分反演[J]. 浙江大学学报（农业与生命科学版）, 2024, 50(2): 209-220.

Linlin ZHANG, Zhibin LEI, Liping WANG, Qingyan MENG, Jiangyuan ZENG. Retrieval of soil moisture based on Gaofen-3 (GF-3) satellite synthetic aperture radar data over agricultural fields. Journal of Zhejiang University (Agriculture and Life Sciences), 2024, 50(2): 209-220.

链接本文:

https://www.zjujournals.com/agr/CN/10.3785/j.issn.1008-9209.2023.12.183 或 https://www.zjujournals.com/agr/CN/Y2024/V50/I2/209

图1 研究区GF-3卫星雷达后向散射系数黑色圆点代表野外试验采样区域，图例代表后向散射系数，dB。

植被水分指数 Vegetation water index	公式 Formula	文献 Reference
简单比值指数 Simple ratio index (SR)	$S R = R N i r R R e d$	[22]
水胁迫指数 Moisture stress index (MSI)	$M S I = R S w i r 1 R N i r$	[23]
归一化差异水指数1640 Normalized difference water index 1640 (NDWI₁₆₄₀)	$N D W I 1640 = R N i r - R S w i r 1 R N i r + R S w i r 1$	[24]
归一化差异水指数2201 NDWI₂₂₀₁	$N D W I 2201 = R N i r - R S w i r 2 R N i r + R S w i r 2$	[24]
归一化植被指数 Normalized difference vegetation index (NDVI)	$N D V I = R N i r - R R e d R N i r + R R e d$	[25]
归一化多波段干旱指数 Normalized multi-band drought index (NMDI)	$N M D I = R N i r - (R S w i r 1 - R S w i r 2) R N i r + (R S w i r 1 + R S w i r 2)$	[26]
四波段干旱指数 Four band combined drought index (FCDI)	$F C D I = R S w i r 1 / R S w i r 2 (R N i r - R G r e e n) / (R N i r + R G r e e n)$	[27]
增强型植被指数 Enhanced vegetation index (EVI)	$E V I = 2.5 R N i r - R R e d R N i r + 6 R S w i r 1 - 7.5 R B l u e + 1$	[28]

表1 8种植被水分指数计算公式

表2 PROSAIL模型的不同参数

图2 植被水分指数与模拟植被冠层含水量的相关性

表3 植被水分指数与野外实测植被冠层含水量的相关性

图3 土壤直接后向散射系数图A. GF-3卫星雷达反演值；B. GF-3卫星雷达反演值与AIEM模型模拟值的散点图。

图4 不同土壤水分条件下入射角与土壤直接后向散射系数的响应关系s=1.0 cm，l=11 cm。

图6 组合粗糙度与土壤直接后向散射系数的响应关系

图7 土壤直接后向散射系数、土壤水分和组合粗糙度间的三维关系

图8 组合粗糙度与高低入射角下土壤直接后向散射系数差值的响应关系

图9 基于GF-3卫星雷达反演的土壤水分图粉色区域为村庄。

图10 土壤水分反演值与实测值的散点图

1	BOJINSKI S, VERSTRAETE M, PETERSON T C, et al. The concept of essential climate variables in support of climate research, applications, and policy[J]. Bulletin of the American Meteorological Society, 2014, 95(9): 1431-1443. DOI: 10.1175/bams-d-13-00047.1 doi: 10.1175/bams-d-13-00047.1
2	CARLSON T. An overview of the “triangle method” for estimating surface evapotranspiration and soil moisture from satellite imagery[J]. Sensors, 2007, 7(8): 1612-1629. DOI: 10.3390/s7081612 doi: 10.3390/s7081612
3	DIRMEYER P A. Using a global soil wetness dataset to improve seasonal climate simulation[J]. Journal of Climate, 2000, 13(16): 2900-2922.
4	JACKSON T J, BINDLISH R, COSH M H, et al. Validation of soil moisture and ocean salinity (SMOS) soil moisture over watershed networks in the U.S.[J]. IEEE Transactions on Geoscience and Remote Sensing, 2012, 50(5): 1530-1543. DOI: 10.1109/TGRS.2011.2168533 doi: 10.1109/TGRS.2011.2168533
5	ENGMAN E T. Progress in microwave remote sensing of soil moisture[J]. Canadian Journal of Remote Sensing, 1990, 16(3): 6-14.
6	ZHANG L L, MENG Q Y, HU D, et al. Comparison of different soil dielectric models for microwave soil moisture retrievals[J]. International Journal of Remote Sensing, 2020, 41(8): 3054-3069. DOI: 10.1080/01431161.2019.1698077 doi: 10.1080/01431161.2019.1698077
7	ZHANG L L, MENG Q Y, ZENG J Y, et al. Evaluation of Gaofen-3 C-band SAR for soil moisture retrieval using different polarimetric decomposition models[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021, 14: 5707-5719. DOI: 10.1109/JSTARS.2021.3083287 doi: 10.1109/JSTARS.2021.3083287
8	HUANG X D, WANG J F, SHANG J L. An adaptive two-component model-based decomposition on soil moisture estimation for C-band RADARSAT-2 imagery over wheat fields at early growing stages[J]. IEEE Geoscience and Remote Sensing Letters, 2016, 13(3): 414-418. DOI: 10.1109/LGRS.2016.2517082 doi: 10.1109/LGRS.2016.2517082
9	蒋金豹,胡丹娟,刘益青,等.基于MIMICS模型的麦田地表土壤含水量反演研究[J].麦类作物学报,2015,35(5):707-713. DOI:10.7606/j.issn.1009-1041.2015.05.20 JIANG J B, HU D J, LIU Y Q, et al. Research of soil moisture retrieval model of wheat covered surface based on MIMICS model[J]. Journal of Triticeae Crops, 2015, 35(5): 707-713. (in Chinese with English abstract) doi: 10.7606/j.issn.1009-1041.2015.05.20
10	雷志斌,孟庆岩,田淑芳,等.基于GF-3和Landsat8遥感数据的土壤水分反演研究[J].地球信息科学学报,2019,21(12):1965-1976. DOI:10.12082/dqxxkx.2019.190115 LEI Z B, MENG Q Y, TIAN S F, et al. Soil moisture retrieval study based on GF-3 and Landsat8 remote sensing data[J]. Journal of Geo-information Science, 2019, 21(12): 1965-1976. (in Chinese with English abstract) doi: 10.12082/dqxxkx.2019.190115
11	BAI X J, HE B B, LI X, et al. First assessment of Sentinel-1A data for surface soil moisture estimations using a coupled water cloud model and advanced integral equation model over the Tibetan Plateau[J]. Remote Sensing, 2017, 9(7): 714. DOI: 10.3390/rs9070714 doi: 10.3390/rs9070714
12	ATTEMA E P W, ULABY F T. Vegetation modeled as a water cloud[J]. Radio Science, 1978, 13(2): 357-364.
13	ULABY F T, SARABANDI K, MCDONALD K, et al. Michigan microwave canopy scattering model[J]. International Journal of Remote Sensing, 1990, 11(7): 1223-1253.
14	BRACAGLIA M, FERRAZZOLI P, GUERRIERO L. A fully polarimetric multiple scattering model for crops[J]. Remote Sensing of Environment, 1995, 54(3): 170-179.
15	方西瑶,蒋玲梅,崔慧珍.基于Sentinel-1雷达数据的青藏高原地区土壤水分反演研究[J].遥感技术与应用,2022,37(6):1447-1459. DOI:10.11873/j.issn.1004-0323.2022.6.1447 FANG X Y, JIANG L M, CUI H Z. Soil moisture retrieval in the Tibetan Plateau based on Sentinel-1 radar data[J]. Remote Sensing Technology and Application, 2022, 37(6): 1447-1459. (in Chinese with English abstract) doi: 10.11873/j.issn.1004-0323.2022.6.1447
16	MENG Q Y, ZHANG L L, XIE Q X, et al. Combined use of GF-3 and Landsat-8 satellite data for soil moisture retrieval over agricultural areas using artificial neural network[J]. Advances in Meteorology, 2018, 2018: 9315132. DOI: 10.1155/2018/9315132 doi: 10.1155/2018/9315132
17	ZHANG L L, MENG Q Y, YAO S, et al. Soil moisture retrieval from the Chinese GF-3 satellite and optical data over agricultural fields[J]. Sensors, 2018, 18(8): 2675. DOI: 10.3390/s18082675 doi: 10.3390/s18082675
18	李震,陈权,任鑫.Envisat-1双极化雷达数据建模及应用[J].遥感学报,2006,10(5):777-782. DOI:10.11834/jrs.200605115 LI Z, CHEN Q, REN X. Modeling Envisat-1 dual-polarized data and its applications[J]. Journal of Remote Sensing, 2006, 10(5): 777-782. (in Chinese with English abstract) doi: 10.11834/jrs.200605115
19	郑磊.基于微波遥感的裸露地表土壤水分反演研究[D].呼和浩特:内蒙古农业大学,2014. ZHENG L. Research on bare surface soil moisture inversion based on the microwave remote sensing[D]. Hohhot: Inner Mongolia Agricultural University, 2014. (in Chinese with English abstract)
20	BOISVERT J B, GWYN Q H J, CHANZY A, et al. Effect of surface soil moisture gradients on modelling radar backscattering from bare fields[J]. International Journal of Remote Sensing, 1997, 18(1): 153-170.
21	张庆君.高分三号卫星总体设计与关键技术[J].测绘学报,2017,46(3):269-277. DO1: 10.11947/j.AGCS.2017.20170049 ZHANG Q J. System design and key technologies of the GF-3 satellite[J]. Acta Geodaetica et Cartographica Sinica, 2017, 46(3): 269-277. (in Chinese with English abstract) doi: 10.11947/j.AGCS.2017.20170049
22	JORDAN C F. Derivation of leaf-area index from quality of light on the forest floor[J]. Ecology, 1969, 50(4): 663-666.
23	HUNT E R, Jr, ROCK B N. Detection of changes in leaf water content using near- and middle-infrared reflectances[J]. Remote Sensing of Environment, 1989, 30(1): 43-54.
24	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. DOI: 10.1016/j.rse.2005.07.008 doi: 10.1016/j.rse.2005.07.008
25	SERRANO L, USTIN S L, ROBERTS D A, et al. Deriving water content of chaparral vegetation from AVIRIS data[J]. Remote Sensing of Environment, 2000, 74(3): 570-581.
26	WANG L L, QU J J. NMDI: a normalized multi-band drought index for monitoring soil and vegetation moisture with satellite remote sensing[J]. Geophysical Research Letters, 2007, 34(20): L20405. DOI: 10.1029/2007GL031021 doi: 10.1029/2007GL031021
27	ZHANG J H, GUO W J. Quantitative retrieval of crop water content under different soil moistures levels[C]//SPIE Pro-ceedings, Volume 6411, Agriculture and Hydrology Applica-tions of Remote Sensing. Goa: SPIE, 2006: 85-93. DOI: 10.1117/12.697957 doi: 10.1117/12.697957
28	HUETE A, DIDAN K, MIURA T, et al. Overview of the radiometric and biophysical performance of the MODIS vegetation indices[J]. Remote Sensing of Environment, 2002, 83(1/2): 195-213. DOI: 10.1016/s0034-4257(02)00096-2 doi: 10.1016/s0034-4257(02)00096-2
29	CHENG Y B, ZARCO-TEJADA P J, RIAÑO D, et al. Estimating vegetation water content with hyperspectral data for different canopy scenarios: relationships between AVIRIS and MODIS indexes[J]. Remote Sensing of Environment, 2006, 105(4): 354-366. DOI: 10.1016/j.rse.2006.07.005 doi: 10.1016/j.rse.2006.07.005
30	马建新.农作物覆盖区土壤含水量多源遥感反演方法[D].焦作:河南理工大学,2016. MA J X. Multi-source remote sensing inversion method of soil water content over agriculture fields[D]. Jiaozuo: Henan Polytechnic University, 2016. (in Chinese with English abstract)
31	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: 90-101. DOI: 10.1109/TGRS.2002.807587 doi: 10.1109/TGRS.2002.807587
32	ZENG J Y, CHEN K S, BI H Y, et al. A comprehensive analysis of rough soil surface scattering and emission predicted by AIEM with comparison to numerical simulations and experi-mental measurements[J]. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(3): 1696-1708. DOI: 10.1109/TGRS.2016.2629759 doi: 10.1109/TGRS.2016.2629759
33	ZRIBI M, DECHAMBRE M. A new empirical model to retrieve soil moisture and roughness from C-band radar data[J]. Remote Sensing of Environment, 2003, 84(1): 42-52. DOI: 10.1016/S0034-4257(02)00069-X doi: 10.1016/S0034-4257(02)00069-X
34	KIM S B, TSANG L, JOHNSON J T, et al. Soil moisture retrieval using time-series radar observations over bare surfaces[J]. IEEE Transactions on Geoscience and Remote Sensing, 2012, 50(5): 1853-1863. DOI: 10.1109/TGRS.2011.2169454 doi: 10.1109/TGRS.2011.2169454
35	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. DOI: 10.1016/j.rse.2003.10.021 doi: 10.1016/j.rse.2003.10.021

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