浏览全部资源
扫码关注微信
Published： 07 April 2021 ，
扫 描 看 全 文
郭鹏，赵天杰，施建成，孙彦龙，黄硕，牛升达.2021.变角度下主被动微波遥感联合降尺度方法.遥感学报，25（4）： 952-961
Guo P，Zhao T J，Shi J C，Sun Y L，Huang S and Niu S D. 2021. Downscaling algorithm using active-passive microwave observations with variable incident angle. National Remote Sensing Bulletin， 25（4）：952-961
被动微波遥感土壤水分空间分辨率低，无法满足干旱监测、洪水预测以及灌溉管理等区域水利和农业等行业应用需求。中国在民用空间基础设施中规划论证的“陆地水资源卫星”搭载了雷达和辐射计主被动一体化微波载荷，通过主被动联合降尺度可以获取高分辨率（~5 km）的土壤水分，但其采用了一维合成孔径技术，主被动微波传感器观测的地面入射角是变化的，这给土壤水分反演及降尺度带来诸多挑战。本文从主被动微波遥感的物理机理和谱分析两种角度出发，利用闪电河流域的航空飞行试验数据，分析研究了基于主被动微波观测时间序列回归分析和基于谱分析的降尺度算法在辐射计和雷达入射角不同时的适用性。结果表明，在辐射计入射角度22.5°—27.5°时，基于主被动微波观测时间序列回归分析方法在27.5°时降尺度的结果最好，V极化和H极化的RMSE分别为7.57 K和7.46 K。基于谱分析方法在辐射计入射角度为22.5°和25°时得到的降尺度结果较好，V极化和H极化的最小RMSE分别为7.13 K和6.61 K，比基于主被动微波观测时间序列回归分析方法分别降低了0.44 K和0.85 K。基于主被动时间序列观测回归分析的降尺度方法，依赖于主被动微波观测的时间序列观测，当时序观测较短时，可能会因为回归分析的不稳定对降尺度结果造成较大的影响，而基于谱分析的降尺度方法则不需要依赖于长时间的时序观测。
Soil moisture derived from passive microwave remote sensing has a low spatial resolution
which cannot meet the requirement for the application of regional hydrological and agricultural activities
such as meteorological forecast
flood forecast
and irrigation management. The integrated microwave load of radar and radiometer loaded on “land-water resource satellite
” which is planned and demonstrated by Chinese civil space infrastructure
will obtain high-resolution soil moisture with the combination of active and passive microwave observation by using a one-dimensional synthetic aperture imaging observation mode. However
the incidence angle changes during observation. Present downscaling algorithms with combination of active and passive microwave observations are developed under the condition of fixed incident angles of radar and radiometer. In this study
downscaling algorithms based on time series regression analysis and spectral analysis are tested using the air flight experimental data of a lightning river basin to demonstrate that the feasibility of each for application to the incidence angles of radiometer and radar differs.
The downscaling method based on time series regression analysis is the basic algorithm for Soil Moisture Active and Passive. The principal theoretical basis for this method is the linear relationship between the brightness temperature observation of radiometer and the backscatter coefficient observation of radar
and the linear relationship is related to surface roughness
vegetation
and incident angle. The spectral analysis downscaling method was first proposed and applied to remote sensing water vapor downscaling. Its theoretical basis is to obtain a high-resolution image by correctly simulating the spatial characteristics of the image spectral domain.
The downscaling results based on active and passive observation regression analysis can reproduce spatial details
but the root-mean-square error (RMSE) is large. The minimum RMSE of V polarization is 7.57 K
and the minimum RMSE of H polarization is 7.46 K. The downscaling results based on spectral analysis can basically reflect the spatial distribution of the original observation. Nevertheless
evident plate phenomena occur in some areas
the spatial transition is not smooth
and the traces of downscaling are obvious. The minimum RMSE of H polarization is 7.13 K
and that of V polarization is 6.61 K. In accordance with the RMSE
the overall accuracy of the spectral downscaling method is higher than that of the time series regression analysis.
The downscaling method based on the regression analysis of active and passive observation depends on the time series observation of active and passive microwave observation. The method of spectrum analysis does not need to regress to determine the relationship between active and passive microwave. It directly uses low-resolution passive radiometer observation and high-resolution radar copolarization (
vv
) observation for downscaling and does not need to rely on long-term time observation. The experimental results indicate that the time series regression analysis downscaling method can obtain the best results when the incident angle of radiometer is 27.5° and that of radar is 52.5° or 55°. The minimum RMSEs of V and H polarizations are 7.57 K and 7.46 K
respectively. The minimum RMSEs of V and H polarizations are 7.13 K and 6.61 K
respectively
for the spectral analysis downscaling method
which are 0.44k and 0.85 K
respectively
lower than those of the time series regression analysis downscaling method.
微波遥感辐射计雷达降尺度土壤水
microwave remote sensingradiometerradardownscalingsoil water
Anderson M C, Norman J M, Mecikalski J R, Otkin J A and Kustas W P. 2007. A climatological study of evapotranspiration and moisture stress across the continental United States based on thermal remote sensing: 2. Surface moisture climatology. Journal of Geophysical Research: Atmospheres, 112(D11): D11112 [DOI: 10.1029/2006JD007507http://dx.doi.org/10.1029/2006JD007507]
Das N N, Entekhabi D, Dunbar R S, Colliander A, Chen F, Crow W, Jackson T J, Berg A, Bosch D D, Caldwell T, Cosh M H, Collins C H, Lopez-Baeza E, Moghaddam M, Rowlandson T, Starks P J, Thibeault M, Walker J P, Wu X L, O'Neill P E, Yueh S and Njoku E G. 2018. The SMAP mission combined active-passive soil moisture product at 9 km and 3 km spatial resolutions. Remote Sensing of Environment, 211: 204-217 [DOI: 10.1016/j.rse.2018.04.011http://dx.doi.org/10.1016/j.rse.2018.04.011]
Das N N, Entekhabi D and Njoku E G. 2011. An algorithm for merging SMAP radiometer and radar data for high-resolution soil-moisture retrieval. IEEE Transactions on Geoscience and Remote Sensing, 49(5): 1504-1512 [DOI: 10.1109/TGRS.2010.2089526http://dx.doi.org/10.1109/TGRS.2010.2089526]
Das N N, Entekhabi D, Njoku E G, Shi J J C, Johnson J T and Colliander A. 2014. Tests of the SMAP combined radar and radiometer algorithm using airborne field campaign observations and simulated data. IEEE Transactions on Geoscience and Remote Sensing, 52(4): 2018-2028 [DOI: 10.1109/TGRS.2013.2257605http://dx.doi.org/10.1109/TGRS.2013.2257605]
Dobriyal P, Qureshi A, Badola R and Hussain S A. 2012. A review of the methods available for estimating soil moisture and its implications for water resource management. Journal of Hydrology, 458-459: 110-117 [DOI: 10.1016/j.jhydrol.2012.06.021http://dx.doi.org/10.1016/j.jhydrol.2012.06.021]
Entekhabi D, Njoku E G, O'Neill P E, Kellogg K H, Crow W T, Edelstein W N, Entin J K, Goodman S D, Jackson T J, Johnson J, Kimball J, Piepmeier J R, Koster R D, Martin N, McDonald K C, Moghaddam M, Moran S, Reichle R, Shi J C, Spencer M W, Thurman S W, Tsang L and Van Zyl J. 2010. The Soil Moisture Active Passive (SMAP) mission. Proceedings of the IEEE, 98(5): 704-716 [DOI: 10.1109/JPROC.2010.2043918http://dx.doi.org/10.1109/JPROC.2010.2043918]
Guo P, Shi J C and Zhao T J. 2013. A downscaling algorithm for combining radar and radiometer observations for SMAP soil moisture retrieval//IEEE International Geoscience and Remote Sensing Symposium. Melbourne, VIC, Australia: IEEE: 731-734 [DOI: 10.1109/IGARSS.2013.6721261http://dx.doi.org/10.1109/IGARSS.2013.6721261]
Jackson T J. 1993. III. Measuring surface soil moisture using passive microwave remote sensing. Hydrological Processes, 7(2): 139-152 [DOI: 10.1002/hyp.3360070205http://dx.doi.org/10.1002/hyp.3360070205]
Kerr Y H. 2007. Soil moisture from space: where are we? Hydrogeology Journal, 15(1): 117-120 [DOI: 10.1007/s10040-006-0095-3http://dx.doi.org/10.1007/s10040-006-0095-3]
Kim Y and van Zyl J J. 2009. A time-series approach to estimate soil moisture using polarimetric radar data. IEEE Transactions on Geoscience and Remote Sensing, 47(8): 2519-2527 [DOI: 10.1109/TGRS.2009.2014944http://dx.doi.org/10.1109/TGRS.2009.2014944]
Legates D R, Mahmood R, Levia D F, DeLiberty T L, Quiring S M, Houser C and Nelson F E. 2011. Soil moisture: a central and unifying theme in physical geography. Progress in Physical Geography, 35(1): 65-86 [DOI: 10.1177/0309133310386514http://dx.doi.org/10.1177/0309133310386514]
Liang S, Bai R, Chen X, Cheng J, Fan W, He T, Jia K, Jiang B, Jiang L, Jiao Z, Liu Y, Ni W, Qiu F, Song L, Sun L, Tang B, Wen J, Wu G, Xie D, Yao Y, Yuan W, Zhang Y, Zhang Y, Zhang Y, Zhang X, Zhao T and Zhao X.2020. Review of China’s land surface quantitative remote sensing development in 2019. Journal of Remote Sensing (Chinese), 24(6): 618-671
梁顺林,白瑞,陈晓娜,程洁,范闻捷,何涛,贾坤,江波,蒋玲梅,焦子锑,刘元波,倪文俭,邱凤,宋柳霖,孙林,唐伯惠,闻建光,吴桂平,谢东辉,姚云军,袁文平,张永光,张玉珍,张云腾,张晓通,赵天杰,赵祥. 2020. 2019年中国陆表定量遥感发展综述.遥感学报, 24(6): 618-671 [DOI: 10.11834/jrs.20209476http://dx.doi.org/10.11834/jrs.20209476］
Loew A, Stacke T, Dorigo W, de Jeu R and Hagemann S. 2013. Potential and limitations of multidecadal satellite soil moisture observations for selected climate model evaluation studies. Hydrology and Earth System Sciences, 17(9): 3523-3542 [DOI: 10.5194/hess-17-3523-2013http://dx.doi.org/10.5194/hess-17-3523-2013]
Lu H, Koike T, Fujii H, Ohta T and Tamagawa K. 2009. Development of a physically-based soil moisture retrieval algorithm for spaceborne passive microwave radiometers and its application to AMSR-E. Journal of the Remote Sensing Society of Japan, 29(1): 253-262 [DOI: 10.11440/rssj.29.253http://dx.doi.org/10.11440/rssj.29.253]
Montopoli M, Pierdicca N and Marzano F S. 2012. Spectral downscaling of integrated water vapor fields from satellite infrared observations. IEEE Transactions on Geoscience and Remote Sensing, 50(2): 415-428 [DOI: 10.1109/TGRS.2011.2161996http://dx.doi.org/10.1109/TGRS.2011.2161996]
Montzka C, Jagdhuber T, Horn R, Bogena H R, Hajnsek I, Reigber A and Vereecken H. 2016. Investigation of SMAP fusion algorithms with airborne active and passive L-band microwave remote sensing. IEEE Transactions on Geoscience and Remote Sensing, 54(7): 3878-3889 [DOI: 10.1109/TGRS.2016.2529659http://dx.doi.org/10.1109/TGRS.2016.2529659]
Narayan U, Lakshmi V and Jackson T J. 2006. High-resolution change estimation of soil moisture using L-band radiometer and radar observations made during the SMEX02 experiments. IEEE Transactions on Geoscience and Remote Sensing, 44(6): 1545-1554 [DOI: 10.1109/TGRS.2006.871199http://dx.doi.org/10.1109/TGRS.2006.871199]
Njoku E G and Chan S K. 2006. Vegetation and surface roughness effects on AMSR-E land observations. Remote Sensing of Environment, 100(2): 190-199 [DOI: 10.1016/j.rse.2005.10.017http://dx.doi.org/10.1016/j.rse.2005.10.017]
Njoku E G and Li L. 1999. Retrieval of land surface parameters using passive microwave measurements at 6-18 GHz. IEEE Transactions on Geoscience and Remote Sensing, 37(1): 79-93 [DOI: 10.1109/36.739125http://dx.doi.org/10.1109/36.739125]
Njoku E G, Wilson W J, Yueh S H, Dinardo S J, Li F K, Jackson T J, Lakshmi V and Bolten J. 2002. Observations of soil moisture using a passive and active low-frequency microwave airborne sensor during SGP99. IEEE Transactions on Geoscience and Remote Sensing, 40(12): 2659-2673 [DOI: 10.1109/TGRS.2002.807008http://dx.doi.org/10.1109/TGRS.2002.807008]
Ochsner T E, Cosh M H, Cuenca R H, Dorigo W A, Draper C S, Hagimoto Y, Kerr Y H, Larson K M, Njoku E G, Small E E and Zreda M. 2013. State of the art in large-scale soil moisture monitoring. Soil Science Society of America Journal, 77(6): 1888-1919 [DOI: 10.2136/sssaj2013.03.0093http://dx.doi.org/10.2136/sssaj2013.03.0093]
Owe M, de Jeu R and Holmes T. 2008. Multisensor historical climatology of satellite-derived global land surface moisture. Journal of Geophysical Research: Earth Surface, 113(F1): F01002. [DOI: 10.1029/2007JF000769http://dx.doi.org/10.1029/2007JF000769]
Peng J, Loew A, Merlin O and Verhoest N E C. 2017. A review of spatial downscaling of satellite remotely sensed soil moisture. Reviews of Geophysics, 55(2): 341-366 [DOI: 10.1002/2016RG000543http://dx.doi.org/10.1002/2016RG000543]
Piles M, Entekhabi D and Camps A. 2009. A change detection algorithm for retrieving high-resolution soil moisture from SMAP radar and radiometer observations. IEEE Transactions on Geoscience and Remote Sensing, 47(12): 4125-4131 [DOI: 10.1109/TGRS.2009.2022088http://dx.doi.org/10.1109/TGRS.2009.2022088]
Robinson D A, Campbell C S, Hopmans J W, Hornbuckle B K, Jones S B, Knight R, Ogden F, Selker J and Wendroth O. 2008. Soil moisture measurement for ecological and hydrological watershed-scale observatories: a review. Vadose Zone Journal, 7(1): 358-389 [DOI: 10.2136/vzj2007.0143http://dx.doi.org/10.2136/vzj2007.0143]
Seneviratne S I, Wilhelm M, Stanelle T, Hurk B, Hagemann S, Berg A, Cheruy F, Higgins M E, Meier A, Brovkin V, Claussen M, Ducharne A, Dufresne J, Findell K L, Ghattas J, Lawrence D M, Malyshev S, Rummukainen M and Smith B. 2013. Impact of soil moisture-climate feedbacks on CMIP5 projections: first results from the GLACE-CMIP5 experiment. Geophysical Research Letters, 40(19): 5212-5217 [DOI: 10.1002/grl.50956http://dx.doi.org/10.1002/grl.50956]
Shi J C, Jiang L M, Zhang L X, Chen K S, Wigneron J P, Chanzy A and Jackson T J. 2006. Physically based estimation of bare-surface soil moisture with the passive radiometers. IEEE Transactions on Geoscience and Remote Sensing, 44(11): 3145-3153 [DOI: 10.1109/TGRS.2006.876706http://dx.doi.org/10.1109/TGRS.2006.876706]
Wagner W, Lemoine G and Rott H. 1999. A method for estimating soil moisture from ERS scatterometer and soil data. Remote Sensing of Environment, 70(2): 191-207 [DOI: 10.1016/S0034-4257(99)00036-Xhttp://dx.doi.org/10.1016/S0034-4257(99)00036-X]
Wigneron J P, Calvet J C, Pellarin T, Van de Griend A A, Berger M and Ferrazzoli P. 2003. Retrieving near-surface soil moisture from microwave radiometric observations: current status and future plans. Remote Sensing of Environment, 85(4): 489-506 [DOI: 10.1016/S0034-4257(03)00051-8http://dx.doi.org/10.1016/S0034-4257(03)00051-8]
Wigneron J P, Waldteufel P, Chanzy A, Calvet J C and Kerr Y. 2000. Two-dimensional microwave interferometer retrieval capabilities over land surfaces (SMOS mission). Remote Sensing of Environment, 73(3): 270-282 [DOI: 10.1016/S0034-4257(00)00103-6http://dx.doi.org/10.1016/S0034-4257(00)00103-6]
Wu X L, Walker J P, Das N N, Panciera R and Rüdiger C. 2014. Evaluation of the SMAP brightness temperature downscaling algorithm using active-passive microwave observations. Remote Sensing of Environment, 155: 210-221 [DOI: 10.1016/j.rse.2014.08.021http://dx.doi.org/10.1016/j.rse.2014.08.021]
Zhan X W, Houser P R, Walker J P and Crow W T. 2006. A method for retrieving high-resolution surface soil moisture from hydros L-band radiometer and radar observations. IEEE Transactions on Geoscience and Remote Sensing, 44(6): 1534-1544 [DOI: 10.1109/TGRS.2005.863319http://dx.doi.org/10.1109/TGRS.2005.863319]
Zhao T J, Shi J C, Lv L, Xu H, Sun Y, Chen D, Cui Q, Huang S, Niu S, Li X,Yan G, Jia L, Chen L, Liu Q, Zhao K, Zheng X, Zhao L, Zheng C, Ji D, Xiong C, Wang T, Li R, Pan J, Wen J, Yu C, Zheng Y, Jiang L, Chai L, Lu H, Yao P, Ma J. Lv H, Wu J, Zhao W, Yang N, Guo P, Li Y, Hu L, Geng D, Zhang Z, Hu J and Du A. 2020. Comprehensive remote sensing experiment of water cycle and energy balance in the Shandian River basin (Chinese). Journal of Remote Sensing (Chinese), 25(4):871-887
赵天杰, 施建成, 吕利清, 徐红新, 孙彦龙, 陈德清, 崔倩,黄硕,牛升达,李秀伟, 阎广建, 贾立, 陈良富, 柳钦火，赵凯, 郑兴明, 赵利民, 郑超磊, 姬大彬, 熊川, 王天星, 李睿, 潘金梅, 闻建光, 穆西晗，余超, 郑姚闽, 蒋玲梅, 柴琳娜, 卢麾, 姚盼盼, 马建威, 吕海深, 武建军, 赵伟, 杨娜, 郭鹏, 李玉霞, 胡路, 耿德源, 张子谦, 胡建峰, 杜爱萍. 2021. 闪电河流域水循环和能量平衡遥感综合试验. 遥感学报, 25(4): 871-887
Zhao T J, Shi J C, Lv L, Xu H, Chen D, Cui Q, Jackson T J,Yan G, Jia L, Chen L, Zhao K, Zheng X, Zhao L, Zheng C, Ji D, Xiong C, Wang T, Li R, Pan J, Wen J, Yu C, Zheng Y, Jiang L, Chai L, Lu H, Yao P, Ma J. Lv H, Wu J, Zhao W, Yang N, Guo P, Li Y, Hu L, Geng D and Zhang Z. 2020. Soil moisture experiment in the Luan River supporting new satellite mission opportunities. Remote Sensing of Environment, 240: 111680 [DOI: 10.1016/j.res.2020.111680http://dx.doi.org/10.1016/j.res.2020.111680]
相关文章
相关作者
相关机构
京公网安备11010802024621