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
  <sup>*</sup>

doi:1001-8166(2014)02-0295-11

地球科学进展 ›› 2014, Vol. 29 ›› Issue (2): 295 -305. doi: 1001-8166(2014)02-0295-11

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 ^*

李大治 ^1, ^2, ³(

), 晋锐 ^1, ^3, ^*(

), 车涛 ^1, ³, 高莹 ⁴, 耶楠 ⁵, 王树果 ^1, ³

1.中国科学院寒区旱区环境与工程研究所,甘肃兰州 730000
2. 中国科学院大学,北京 100049
3.中国科学院寒旱所黑河遥感试验研究站,甘肃兰州 730000
4.Monash University, Department of Civil Engineering, Australia, Melbourne 3800
5. 南京大学地理信息科学系,南京 210093

收稿日期:2013-12-18 修回日期:2014-01-23 出版日期:2014-03-10
通讯作者: 晋锐 E-mail:lidazhi@lzb.ac.cn;jinrui@lzb.ac.cn
基金资助:
[HT6SS][ZK(]国家自然科学基金重大研究计划项目#cod#x0201c;黑河流域生态#cod#x02014;水文过程综合遥感观测试验：综合集成与航空微波遥感#cod#x0201d;（编号：91125001）;中国科学院西部行动计划（三期）项目#cod#x0201c;黑河流域生态#cod#x02014;水文遥感产品生产算法研究与应用试验#cod#x0201d;（编号：KZCX2-XB3-15）资助.

Soil Moisture Retrieval from Airborne PLMR and MODIS Productsinthe ZhangyeOasisof MiddleStream ofHeihe River Basin, China

Li Dazhi ^1, ^2, ³, Jin Rui ^1, ^3, ^*, Che Tao ^1, ³, Walker Jeffrey ⁴, Gao Ying ⁴, Ye Nan ⁵, Wang Shuguo ^1, ³

1.Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou 730000, China
2.University of Chinese Academy of Sciences, Beijing 100049, China
3.Heihe Remote Sensing Experimental Research Station, CAREERI, CAS,Lanzhou 730000, China
4.Monash University, Department of Civil Engineering, Melbourne 3800, Australia
5.Nanjing University, Department ofGeographical Information Science, Nanjing 210093,China

Received:2013-12-18 Revised:2014-01-23 Online:2014-03-10 Published:2014-02-10
Contact: Jin Rui E-mail:lidazhi@lzb.ac.cn;jinrui@lzb.ac.cn

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土壤水分是气候、水文学研究中的重要变量,微波遥感是获取区域地表土壤水分的重要手段,而L波段更是微波土壤水分反演的最优波段。依托HiWATER黑河中游绿洲试验区的地面观测及机载PLMR微波辐射计亮温数据,利用微波辐射传输模型L-MEB,并将MODIS地表温度产品（MOD11A1）和叶面积指数产品（MYD15A2）作为模型及反演中的先验辅助信息,借助LM优化算法,通过PLMR双极化多角度的亮温观测,针对土壤水分、植被含水量（VWC）和地表粗糙度这3个主要参数,分别进行土壤水分单参数反演、土壤水分与VWC或粗糙度的双参数反演以及这3个参数的同时反演。通过对不同反演方法的比较可以得出结论,多源辅助数据及PLMR双极化、多角度信息的应用可以显著降低反演的不确定性,提高土壤水分反演精度。证明在合理的模型参数和反演策略下,SMOS的L-MEB模型和产品算法可以达到0.04 cm³/cm³的反演精度,另外无线传感器网络可以在遥感产品真实性检验中起到重要作用。

关键词: PLMR微波辐射计, 土壤水分反演, MODIS, 绿洲农田, HiWATER

Soil moisture is one of important variables in the climate and hydrology research. Remote sensing can map the soil moisture distribution at regional or global scale. Microwave remote sensing now has been the main method to retrieve soil moisture information, especially using satellite-based passive microwave radiometer. L-band is most suitable for the microwave remote sensing of soil moisture due to its longer wavelength. ESA#cod#x02019;s Soil Moisture and Ocean Salinity (SMOS) satellite uses MIRAS (Microwave Imaging Radiometer by Aperture Synthesis) to get multi-angle and dualpolarized brightness temperatures of land surface at L-band. SMOS aims to get global surface soil moisture through radiative transfer model (L-MEB) and multiparameter retrieval method. SMOS Level 2 surface soil moisture algorithm uses an iterative method to minimize a cost function formulated by difference between modeled and measured brightness temperature.PLMR (Polarimetric L-band Multibeam Radiometer) is an airborne simulator of SMOS MIRAS that can measure passive microwave radiation of land surface at L-band (1.4 GHz) dual polarization and in three different angles(7#cod#x000b0;,21.5#cod#x000b0; and 38.5#cod#x000b0;).This paper uses airborne PLMR radiometer data combined with MODIS LST (MOD11A1) and LAI (MOD15A2) products to retrieve surface soil moisture in the artificial oasis experimental area of HiWATER by L-MEB radiative transfer model and LM (Levenberg-Marquardt) optimization algorithm. The three retrieving strategies are tested, including single, two and three parameters selected from soil moisture, vegetation water content and surface roughness. The comparison analysis shows the multi-angle and dual-polarized PLMR brightness temperatures combined with prior information from operational remote sensing products can obviously reduce the uncertainty of retrieval process and improve the retrieval accuracy. This paper proves that with reasonable model parameters and retrieval method, the L-MEB model can achieve 0.04 cm³/cm³ accuracy requirement for soil moisture retrieval. This paper also reveals the importance of using wireless sensor network in the verification of remote sensing products.

Key words: PLMR radiometer, Soil moisture retrieval, MODIS, Oasis farmland, HiWATER.

中图分类号:

P237

图1 HiWATER黑河中游人工绿洲试验区土地利用图 ^{[

13
]}

Fig.1 Land use map of artificial oasis experimental area in the middle stream of Heihe River Basin ^{[

13
]}

图2 PLMR多角度双极化微波亮温（2012年7月10日）

Fig.2 PLMR multi-angle and dual-polarized brightness temperatures

图3 MODIS LST与LAI产品验证

Fig.3 Verification of MODIS LST and LAI

图4 使用标定参数的L-MEB模拟与实测对比

Fig.4 Compare L-MEB modeling results with PLMR observation

表1 L-MEB模型参数的标定值

Table 1 Calibrated prior parameters of L-MEB model

地表覆盖类型	N_H	N_V	tt_H	tt_V	#cod#x003c9;_H	#cod#x003c9;_V	H_r	#cod#x003c4;_NAD
玉米	0	-2	2	4	0.05	0	0.55	(0.6#cod#x000d7;LAI+1.4)#cod#x000d7;0.12
未分类作物	0	-1	3	4	0.05	0.05	0.4	(0.7#cod#x000d7;LAI)#cod#x000d7;0.12
荒漠与裸地	0.5	-4	-	-	-	-	0.95	-

表1 L-MEB模型参数的标定值

Table 1 Calibrated prior parameters of L-MEB model

地表覆盖类型	N_H	N_V	tt_H	tt_V	#cod#x003c9;_H	#cod#x003c9;_V	H_r	#cod#x003c4;_NAD
玉米	0	-2	2	4	0.05	0	0.55	(0.6#cod#x000d7;LAI+1.4)#cod#x000d7;0.12
未分类作物	0	-1	3	4	0.05	0.05	0.4	(0.7#cod#x000d7;LAI)#cod#x000d7;0.12
荒漠与裸地	0.5	-4	-	-	-	-	0.95	-

表2 不同地类的反演参数取值

Table 2 Retrieving Parameters of different land use types

地表覆盖类型	面积比例	H_r反演初值	H_r标准差	H_r值域	#cod#x003c4;_NAD反演初值	#cod#x003c4;_NAD标准差	#cod#x003c4;_NAD值域	SM反演初值(cm³/cm³)	SM标准差(cm³/cm³)	SM值域(cm³/cm³)
玉米	40.28%	0.55	0.2	0~2	(0.6#cod#x000d7;LAI+1.4)#cod#x000d7;0.12	0.1	0~0.6	0.2	0.3	0~0.5
未分类作物	18%	0.4	0.2	0~2	(0.7#cod#x000d7;LAI)#cod#x000d7;0.12	0.1	0~0.6	0.2	0.3	0~0.5
荒漠与裸地	31.36%	0.95	0.2	0~2	-	-	-	0.05	0.3	0~0.5

表2 不同地类的反演参数取值

Table 2 Retrieving Parameters of different land use types

地表覆盖类型	面积比例	H_r反演初值	H_r标准差	H_r值域	#cod#x003c4;_NAD反演初值	#cod#x003c4;_NAD标准差	#cod#x003c4;_NAD值域	SM反演初值(cm³/cm³)	SM标准差(cm³/cm³)	SM值域(cm³/cm³)
玉米	40.28%	0.55	0.2	0~2	(0.6#cod#x000d7;LAI+1.4)#cod#x000d7;0.12	0.1	0~0.6	0.2	0.3	0~0.5
未分类作物	18%	0.4	0.2	0~2	(0.7#cod#x000d7;LAI)#cod#x000d7;0.12	0.1	0~0.6	0.2	0.3	0~0.5
荒漠与裸地	31.36%	0.95	0.2	0~2	-	-	-	0.05	0.3	0~0.5

图5 单参数（SM）反演结果

Fig.5 Single parameter (SM) retrieval results

图6 双参数反演结果

Fig.6 Two parameters retrieval results

图7 三角度双极化的三参数反演结果

Fig.7 Three parameters retrieval results using dual polarization measurements ofthree angles

图8 三参数反演的对比试验

Fig.8 Three parameters retrieval results comparing

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