干旱区稀疏植被覆盖条件下地表土壤水分微波遥感估算

doi:10.13249/j.cnki.sgs.2013.07.837

摘要/Abstract

摘要：

当前在干旱区土壤水分研究工作中,开展利用微波遥感技术高精度估算稀疏植被覆盖条件下地表粗糙度参数的研究是一项非常有意义的工作。基于微波遥感数据,结合土壤水分实测资料,以IEM模型为基础,分析雷达后向散射系数与土壤含水量、地表粗糙度参数之间的关系,利用最小二乘法和非线性回归的方法,建立光滑地表条件下的土壤含水量模型。结果表明：模型提取的土壤水分与实测值之间有很好的相关性,决定系数达到0.886,所建立的土壤水分信息提取模型在裸土以及稀疏植被覆盖地区能够得到较好的应用效果。

关键词: 土壤水分, 雷达, IEM模型, 后向散射系数

Abstract:

Soil moisture information can be extracted by microwave remote sensing technology; however, the problem is how to eliminate the influence of ground surface roughness and vegetation coverage on the backscattering data. Ground surface roughness is the key factor determining the data accuracy in northwest China where the vegetation coverage is small. Thus, evaluating and modeling the surface roughness parameters with a high precision is an important task for the arid land soil moisture study. Using IEM model, the response relationships between the different types of backscattering coefficient and the radar systematic coefficient and soil surface parameters are simulated. Results show that the backscattering coefficient is different from these parameters’ change. Based on these achievements, the best radar parameters which can reflect the soil moisture are put forwarded. The corresponding relationships between the radar backscattering parameters and soil moisture contents, ground surface roughness parameters (such as, mean root height method, persistence length method and combined roughness method) are analyzed, and the experimental model which can reflect the soil moisture of relatively smooth ground surface is developed by using least squares method and non-linear regression method. At last, for a ground truth testing analysis, using the obtained RADAR-SAT2 data, the experimental model is applied in Weigan River and Kuch River delta oasis area. Results show that the data obtained from the experimental model has relatively high corresponding relationship with the soil moisture data collected in situ, and the determination coefficient is up to 0.886, implying the experimental model can be used in relatively wide scale for analyzing and modeling the soil moisture contents of some bare soil surface or the area whose vegetation density is low and vegetation coverage is small.

Key words: soil moisture, radar, IEM model, backscattering coefficient

中图分类号:

TP751.1

丁建丽, 姚远. 干旱区稀疏植被覆盖条件下地表土壤水分微波遥感估算[J]. 地理科学, 2013, 33(7): 837-843.

Jian-li DING, Yuan YAO. Evaluation of Soil Moisture Contents Under Sparse Vegetation Coverage Conditions Using Microwave Remote Sensing Technology in Arid Region[J]. SCIENTIA GEOGRAPHICA SINICA, 2013, 33(7): 837-843.

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参考文献 37

[1]	钟若飞,郭华东,王为民.被动微波遥感反演土壤水分进展研究[J].遥感技术与应用,2005,20(1):49-57.
[2]	鲍艳松,刘良云,王纪华.综合利用微波遥感数据反演土壤湿度研究[J].北京师范大学学报(自然科学版),2007,43(3):228-233.
[3]	吐尔逊·艾山,塔西甫拉提·特依拜,买买提·阿扎提,等.渭干河灌区地下水埋深与矿化度时空动态分布[J].地理科学,2011,31(9):1131-1137.
[4]	宋长春,邓伟. 吉林西部地下水特征及其与土壤盐渍化的关系[J].地理科学,2010,20(3):246-250.
[5]	李震,郭华东,施建成,等.综合主动和被动微波数据监测土壤水分变化[J].遥感学报,2002,6(6):481-485.
[6]	詹志明,冯兆东.区域遥感土壤水分模型的方法初探[J].水土保持研究,2002,9(3):227-230.
[7]	韩桂红,塔西甫拉提·特依拜,买买提·沙吾提,等.渭-库绿洲地下水对土壤盐渍化和其逆向演替过程的影响[J].地理科学,2012,32(3):362-367.
[8]	赵全升,冯娟,安乐生.德州市深层地下水水质演化研究[J].地理科学,2009,29(5):767-773.
[9]	邵晓梅,严昌荣,徐振剑.土壤水分监测与模拟研究[J].地理科学进展,2004,23(3):58-66.
[10]	肖飞,杜耘,凌峰,等.长江中游四湖流域湖泊变迁与湖区土壤空间格局的关联分析[J].湿地科学,2012,10(1):8-14.
[11]	GAO Junqin,OUYANG Hua,LEI Guangchun,et al.Effects of Temperature,Soil Moisture, Soil Type and Their Interactions on Soil Carbon Mineralization in Zoigê Alpine Wetland,Qinghai-Tibet Plateau[J].Chinese Geographical Science,2011,21(1):27-35.
[12]	陈怀亮,毛留喜,冯定原.遥感监测土壤水分的理论、方法及研究进展[J].遥感技术与应用,1999,14(2):55-65.
[13]	许小燕,汪志农,张志韬.表层土壤水分反演深层土壤水分的研究进展[J].陕西农业科学,2005, (3):81-84.
[14]	周鹏,丁建丽,王飞,等.植被覆盖地表土壤水分遥感反演[J].遥感学报,2010,14(5):966-973.
[15]	马红章,柳钦火,闻建光,等.裸露地表土壤水分的L波段被动微波最佳角度反演算法[J].农业工程学报,2010, 26(11):24-29.
[16]	刘万侠,刘旭,翁丰惠,等.基于AMSR-E被动微波遥感数据的广东省土壤水分变化监测[J].热带地理,2011,31(3):272-277.
[17]	杨立娟,武胜利,张钟军.利用主被动微波遥感结合反演土壤水分的理论模型分析[J].国土资源遥感,2011,(2):53-58.
[18]	余凡,赵英时. ASAR和TM数据协同反演植被覆盖地表土壤水分的新方法[J].中国科学,2011,41(4):532-540.
[19]	Mo T,Schmugge T J,Wang J R.Calculation of the microwave brightness temperature of rough soil surface: bare field[J].IEEE Transactions on Geoscience and Remote Sensing,1987,25(1):47-54.
[20]	Jackson T J,Le Vine D E.Mapping surface soil moisture using an aircraft-based passive microwave instrument: algorithm and example[J].Journal of Hydrology,1996,184:85-99.
[21]	Shi J,Wang J R,Hsu A Y,et al.Estimation of bare surface soil moisture and surface roughness parameter using L-band SAR inage data[J].IEEE Transactions on Geoscience and Remote Sensing,1997,35(5):1254-1266.
[22]	Topp G C,Davis J L,Anna A P.Electromagnetic daaterminationof soil water content: measurements in claxial transmission lines[J].Water Resources Research,1980,16(3):574-582.
[23]	魏小兰,李震,陈权.S波段雷达数据反演土壤水分的模拟分析和验证[J].地球信息科学,2008,10(1):97-101.
[24]	Hallikainen M T,Ulaby F T,Dobson M C,et al.Microwave dielectric behavior of wet soil-Part I:empirical models and experimental observations[J]. IEEE Transactions on Geoscience and Remote Sensing, 1985, 23(1): 25-34.
[25]	Ledieu J,de Ridder P,de Clerck P, et al. A method of measuring soil moisture by time-domain reflectometry[J]. Journal of Hydrology,1986,88(3-4):319-328.
[26]	Dobson M C,Ulaby F T,Hallikainen M T,et al.Microwave dielectric behavior of wet soil-Part II:dielectric mixing models[J].IEEE Transaction on Geoscience and Remote Sensing,1985,23(1):35-46.
[27]	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.
[28]	Mironov V L,Dobson M C,Kaupp V H.Generalized refractive mixing dielectric model for moist soils[J].IEEE Transactions on Geoscience and Remote Sensing,2004,42(4):773-785.
[29]	Fung A K,Li Z,Chen K S.Backscattering from a randomly rough dielectric surface[J].IEEE Transactions on Geoseience and Remote Sensing,1992,30(2):356-369.
[30]	李森. 基于IEM的多波段、多极化SAR土壤水分反演算法研究[D].北京:中国农业科学院,2007.
[31]	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 Geoseience and Remote Sensing,1992,30(2):370-381.
[32]	邵芸,郭华东,范湘涛,等.水稻时域散射特征分析及其应用研究[J].遥感学报,2001,5(5):340-345.
[33]	张永红,林宗坚,张继贤,等. SAR影像几何校正[J].测绘学报,2002,31(2):134-138.
[34]	杜培军. RADARSAT图像滤波研究[J].中国矿业大学学报,2002,31(2):132-137.
[35]	Zribi M Ciarletti,Dechambre M.A new empirical model to inverse soil moisture and roughness using two radar configurations[C].IEEE:Geoscience and Remote Sensing Symposium,2002:2223-2225.
[36]	戴国俊,王金玉,杨建生,等.应用统计软件SPSS拟合生长曲线方程[J].畜牧与兽医,2006,38(9):28-29.
[37]	郝拉娣,于化东.标准差与标准误差[J].编辑学报,2005,17(2):116-118.

输入参数	入射角	土壤含水量	均方根高度	相关长度
范围	10°~60°	5%~50%	0.3~0.9 cm	5~30 cm
步长	2°	3%	0.1 cm	3 cm

θ	A(θ)	B(θ)	C(θ)	标准偏差	R²	θ	A(θ)	B(θ)	C(θ)	标准偏差	R²
10	2.474	2.405	12.380	0.607	0.831	36	2.954	3.387	1.103	0.526	0.919
12	2.492	2.604	11.565	0.678	0.825	38	2.982	3.416	0.568	0.539	0.912
14	2.512	2.796	10.638	0.659	0.827	40	3.012	3.469	-0.053	0.465	0.926
16	2.537	2.832	9.658	0.600	0.852	42	3.073	3.506	-0.465	0.403	0.932
18	2.578	2.901	8.065	0.687	0.815	44	3.146	3.559	-1.011	0.398	0.936
20	2.602	2.985	7.049	0.596	0.895	46	3.201	3.625	-1.368	0.423	0.930
22	2.691	3.024	6.123	0.632	0.829	48	3.267	3.687	-1.769	0.369	0.942
24	2.725	3.068	5.326	0.645	0.823	50	3.301	3.712	-2.145	0.354	0.951
26	2.767	3.102	4.505	0.651	0.825	52	3.364	3.765	-2.687	0.312	0.962
28	2.802	3.159	3.724	0.598	0.898	54	3.412	3.829	-3.269	0.201	0.989
30	2.842	3.211	3.012	0.603	0.836	56	3.478	3.897	-3.755	0.102	0.995
32	2.897	3.275	2.225	0.643	0.823	58	3.521	3.946	-4.052	0.326	0.960
34	2.921	3.326	1.687	0.586	0.901	60	3.586	4.012	-4.368	0.128	0.992