收稿：2016-11-15，
录用：2017-5-21，
纸质出版：2017-09-15
移动端阅览
山区土壤含水量对山区植被生长监测、滑坡预测等工作具有重要意义，因此针对山地低矮植被区域，提出了全极化SAR图像的土壤含水量估计方法。为解决山地区域SAR图像几何形变和极化旋转问题，根据入射角、坡度、坡向信息定义了可测区域与不可测区域，并对可测区域后向散射系数进行校正。其次以密西根模型为基础，发展了低矮植被的散射模型。在假定植被和土壤特征不变的情况下，基于此散射模型并结合校正数据建立了山区土壤含水量反演方法。结果表明，模型反演的土壤含水量和实验点实测值基本一致，两个实验点反演值分别为14%和15%，实测值为11.45%和15.80%，能够满足一般应用的需求。
Soil moisture estimation from mountainous areas is important in many applications
such as vegetation growth monitoring
wildfire warning
and landslide prediction. Synthetic Aperture Radar (SAR) has been widely used to measure soil moisture in recent years due to its capacity to operate under different weather conditions
relatively strong penetrability
and sensitivity to soil moisture. However
the application of SAR to mountainous areas introduces two problems. On the one hand
geometric distortion and polarimetric rotation occur in SAR images. One the other hand
vegetation reduces the sensitivity of SAR images to soil moisture. Therefore
the retrieval of soil moisture from mountainous areas covered with low vegetation is still intractable. To this end
we present a soil moisture estimation method for mountainous areas covered with low vegetation. To solve the problem of geometric distortion and polarimetric rotation caused by undulate terrain in SAR images
the images are classified as measured and unmeasured areas in terms of incident
slope
and aspect angles. Second
backscattering coefficients are corrected in terms of the irradiated area and local coordinates with respect to horizontal and vertical polarizations. Furthermore
a scattering model for low vegetation is developed based on the Michigan microwave canopy scattering model. Under the assumption that the characteristics of vegetation and soil are fixed
the change in backscattering coefficients is only related to the change in soil moisture. Consequently
a method of soil moisture estimation is established by combining the proposed scattering model with the previously corrected data. Two spots with different levels of soil moisture are selected as the experimental sites from Maoxian
Sichuan
China. A field investigation is then carried out to collect the characteristics of vegetation and soil over the study areas. The soil moisture levels measured by two methods from the two experimental sites are 11.45% and 15.80% respectinely. The estimated results from the corresponding Radarsat-2 SAR image are 14% and 15%
with absolute errors of 2.55% and 0.80% and relative errors of 22% and 5%. The experimental results demonstrate that the proposed method is suited for general applications. Moreover
a soil moisture distribution map over the study area is generated
assuming that a similar vegetation observed from the two experimental sites covers the whole study area. Thus
the variation range of soil moisture is acceptable. Different from the current studies focusing on the soil moisture estimation from plain areas covered with monotype vegetation such as crops and grassland
the proposed method can estimate soil moisture from mountainous areas covered with low vegetation. The proposed method monitors the soil moisture in mountains. Our next work will focus on the classification of mountainous areas. The integration of the classification result of the proposed method and the employment of multi-temporal SAR images can provide accurate and continuous monitoring over the observed areas.
Alvarez-Mozos J, Casali J, Gonzalez-Audicana M and Verhoest N E C. 2006. Assessment of the operational applicability of RADARSAT-1 data for surface soil moisture estimation. IEEE Transactions on Geoscience and Remote Sensing, 44(4): 913–924
Attema E P W and Ulaby F T. 1978. Vegetation modeled as a water cloud. Radio Science, 13(2): 357–364
鲍艳松, 刘良云, 王纪华. 2007. 综合利用光学、微波遥感数据反演土壤湿度研究. 北京师范大学学报(自然科学版), 43(3): 228–233
Bao Y S, Liu L Y and Wang J H. 2007. Soil moisture estimation based on optical and microwave remote sensing data. Journal of Beijing Normal University (Natural Science), 43(3): 228–233 (
Chen Q, Li Z, Cai A M and Tian B S. 2010. Surface parameters estimation using Radarsat-2 polarimetric data over wheat covered area//Proceedings of 2010 IEEE International Geoscience and Remote Sensing Symposium. Honolulu, HI, USA: IEEE: 4843–4846 [DOI: 10.1109/IGARSS.2010.5650824]
陈彦, 童玲, 贾明权, 庞少峰. 2012. 一种土壤表面粗糙度测量板及测量方法. 中国, CN102788566A
Chen Y, Tong L, Jia M Q and Pang S F. 2012. A surface roughness measuring board and its measuring method. China, CN102788566A
丁建丽, 姚远. 2013. 干旱区稀疏植被覆盖条件下地表土壤水分微波遥感估算. 地理科学, 33(7): 837–842
Ding J L and Yao Y. 2013. Evaluation of soil moisture contents under sparse vegetation coverage conditions using microwave remote sensing technology in arid region. Scientia Geographica Sinica, 33(7): 837–842 (
Gherboudj I, Magagi R, Berg A A and Toth B. 2011. Soil moisture retrieval over agricultural fields from multi-polarized and multi-angular RADARSAT-2 SAR data. Remote Sensing of Environment, 115(1): 33–43
Jackson T J and Schmugge T J. 1991. Vegetation effects on the microwave emission of soils. Remote Sensing of Environment, 36(3): 203–212
Jagdhuber T, Hajnsek I, Bronstert A and Papathanassiou K P. 2013. Soil moisture estimation under low vegetation cover using a multi-angular polarimetric decomposition. IEEE Transactions on Geoscience and Remote Sensing, 51(4): 2201–2215
Karam M A and Fung A K. 1988. Electromagnetic scattering from a layer of finite length, randomly oriented, dielectric, circular cylinders over a rough interface with application to vegetation. International Journal of Remote Sensing, 9(6): 1109–1134
Lee J S, Schuler D L, Ainsworth T L, Krogager E, Kasilingam D and Boerner W M. 2002. On the estimation of radar polarization orientation shifts induced by terrain slopes. IEEE Transactions on Geoscience and Remote Sensing, 40(1): 30–41
Lin Y C and Sarabandi K. 1999. A Monte Carlo coherent scattering model for forest canopies using fractal-generated trees. IEEE Transactions on Geoscience and Remote Sensing, 37(1): 440–451
刘伟, 施建成, 余琴. 2005. 基于机载极化雷达技术的农作物覆盖区土壤水分估算. 干旱区地理, 28(6): 856–861
Liu W, Shi J C and Yu Q. 2005. Estimation of soil moisture content in vegetated areas using multi-polarization airborne sar. Arid Land Geography, 28(6): 856–861 (
Moran M S, Alonso L, Moreno J F, Mateo M P C, de la Cruz D F and Montoro A. 2012. A RADARSAT-2 quad-polarized time series for monitoring crop and soil conditions in Barrax, Spain. IEEE Transactions on Geoscience and Remote Sensing, 50(4): 1057–1070
Rahman M M, Moran M S, Thoma D P, Bryant R, Collins C D H, Jackson T, Orr B J and Tischler M. 2008. Mapping surface roughness and soil moisture using multi-angle radar imagery without ancillary data. Remote Sensing of Environment, 112(2): 391–402
Said S, Kothyari U C and Arora M K. 2012. Vegetation effects on soil moisture estimation from ERS-2 SAR images. Hydrological Sciences Journal, 57(3): 517–534
Sarabandi K, Senior T B A and Ulaby F T. 1988. Effect of curvature on the backscattering from a leaf. Journal of Electromagnetic Waves and Applications, 2(7): 653–670
Senior T B A and Sarabandi K. 1990. Scattering models for point targets. Radar Polarimetry for Geoscience Applications: 53–110
Senior T B A, Sarabandi K and Ulaby F T. 1987. Measuring and modeling the backscattering cross section of a leaf. Radio Science, 22(6): 1109–1116
Shi J C, Wang J, Hsu A Y, O'Neill P E and Engman E T. 1997. Estimation of bare surface soil moisture and surface roughness parameter using L-band SAR image data. IEEE Transactions on Geoscience and Remote Sensing, 35(5): 1254–1266
Ulaby F T, Dubois P C and van Zyl J. 1996. Radar mapping of surface soil moisture. Journal of Hydrology, 184(1-2): 57–84
Ulaby F T, Long D G, Blackwell W J, Elachi C, Fung A K, Ruf C, Sarabandi K, Zebker H A and Van Zyl J. 2014. Microwave radar and radiometric remote sensing. Ann Arbor, Michigan: University of Michigan Press
Ulaby F T, McDonald K, Sarabandi K and Dobson M C. 1988. Michigan microwave canopy scattering models (MIMICS)//Proceedings of Remote Sensing: Moving Toward the 21st Century Geoscience and Remote Sensing Symposium. Edinburgh, UK: IEEE
Ulaby F T, Moore R K and Fung A K. 1981. Microwave Remote Sensing: Active and Passive. Vol. 1: Microwave Remote Sensing Fundamentals and Radiometry. Dedham, MA: Artech House
Ulaby F T, Moore R K and Fung A K. 1982. Microwave Remote Sensing, Active and Passive: Volume II: Radar Remote Sensing and Surface Scattering and Emission Theory. Dedham, MA: Artech House
谢凯鑫, 张婷婷, 邵芸, 柴勋. 2016. 基于Radarsat-2全极化数据的高原牧草覆盖地表土壤水分反演. 遥感技术与应用, 31(1): 134–142
Xie K X, Zhang T T, Shao Y and Chai X. 2016. Study on soil moisture inversion of plateau pasture using radarsat-2 imagery. Remote Sensing Technology and Application, 31(1): 134–142 (
杨立娟, 武胜利, 张钟军. 2011. 利用主被动微波遥感结合反演土壤水分的理论模型分析. 国土资源遥感(2): 53–58
Yang L J, Wu S L and Zhang Z J. 2011. A model analysis using a combined active/passive microwave remote sensing approach for soil moisture retrieval. Remote Sensing for Land & Resources(2): 53–58 (
相关作者
相关机构
京公网安备11010802024621
