被动微波土壤水分产品真实性检验研究进展
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摘要
随着全球气候变化和陆面数据同化研究对土壤水分反演精度要求的不断提高,大尺度被动微波土壤水分产品的真实性检验变得极为重要。如何获取可以代表卫星观测尺度"真值"、并能表征空间异质性的土壤水分观测场,成为被动微波土壤水分产品真实性检验的关键问题。土壤水分观测场的构建涉及地面观测、同步观测、尺度转换等关键环节,可通过"点代面"和"多源信息融合"这2个技术途径进行构建。简要总结了应用于大尺度土壤水分的5种典型的真实性检验方法,包括实测样本数据检验、影像数据交叉检验、模型模拟检验、影响因素检验和传统地统计检验。这5种方法或缺乏对先验知识的有效利用,或忽略地面实测的重要性,或在综合利用多源数据类型的先验知识信息方面不足。随着贝叶斯最大熵理论的发展,基于贝叶斯最大熵理论和先验知识的大尺度土壤水分产品真实性检验有望发展成为一种可靠的方法。贝叶斯最大熵理论的优势在于,能够提供灵活的数据利用方式,使多种来源、多种类型的数据集有机会同时被用于卫星观测尺度的时空分析,生成高分辨率土壤水分数字地图,从而为大尺度土壤水分产品的真实性检验研究提供一个新的途径。
The validation of large-scale soil moisture products in passive microwave remote sensing takes a key role for global climate change and data assimilation studies. However, it is difficult to obtain the real soil moisture values that can represent the soil moisture values and reflect spatial heterogeneity of large-scale passive microwave remote sensing products. It has become a key issue for the validation of passive microwave soil moisture products. At present, two technical approaches were used to build soil moisture observation network: the scale-up methods and the multi-source information fusion methods. Both two approaches involved the ground observation, simultaneous observation, and scale conversion, etc. The progress made in validation methods of large-scale soil moisture products was reviewed in this paper, and five typical methods were summarized, including the measured sample test, image data cross-test, model simulation test, influencing factor test and traditional geostatistical test. The first two methods belongs to the "spot generation" scale conversion test, and they attach importance to the spatial representation of the ground sample. Theoretical basis of the model simulation test and influencing factor test is the relationship between soil moisture and prior knowledge. However, these two methods neglect the importance of sample data. Traditional geostatistical test takes into account both sample points and prior knowledge, but it is insufficient in the comprehensive utilization of prior knowledge information of multi-source data types. Currently, the Bayesian maximum entropy method is widely used in spatial prediction. In some studies, this method has been applied to multi-source data fusion. The advantage of Bayesian maximum entropy test provides a flexible way of data utilization. This method allows multiple sources and types of data sets to be used for spatial analysis of satellite observation scale at the same time, generates high resolution reference map of soil moisture observation field, completes the validation of soil moisture products, and provides a basis for the authenticity test of large-scale soil moisture products. Thus, it would provide a new way for the validation of large-scale soil moisture products.
The validation of large-scale soil moisture products in passive microwave remote sensing takes a key role for global climate change and data assimilation studies. However, it is difficult to obtain the real soil moisture values that can represent the soil moisture values and reflect spatial heterogeneity of large-scale passive microwave remote sensing products. It has become a key issue for the validation of passive microwave soil moisture products. At present, two technical approaches were used to build soil moisture observation network: the scale-up methods and the multi-source information fusion methods. Both two approaches involved the ground observation, simultaneous observation, and scale conversion, etc. The progress made in validation methods of large-scale soil moisture products was reviewed in this paper, and five typical methods were summarized, including the measured sample test, image data cross-test, model simulation test, influencing factor test and traditional geostatistical test. The first two methods belongs to the "spot generation" scale conversion test, and they attach importance to the spatial representation of the ground sample. Theoretical basis of the model simulation test and influencing factor test is the relationship between soil moisture and prior knowledge. However, these two methods neglect the importance of sample data. Traditional geostatistical test takes into account both sample points and prior knowledge, but it is insufficient in the comprehensive utilization of prior knowledge information of multi-source data types. Currently, the Bayesian maximum entropy method is widely used in spatial prediction. In some studies, this method has been applied to multi-source data fusion. The advantage of Bayesian maximum entropy test provides a flexible way of data utilization. This method allows multiple sources and types of data sets to be used for spatial analysis of satellite observation scale at the same time, generates high resolution reference map of soil moisture observation field, completes the validation of soil moisture products, and provides a basis for the authenticity test of large-scale soil moisture products. Thus, it would provide a new way for the validation of large-scale soil moisture products.
引文
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[9] GRUHIER C,DE ROSNAY P,HASENAUER S,et al.Soil moisture active and passive microwave products:intercomparison and evaluation over a Sahelian site[J].Hydrology and Earth System Sciences,2010,14(1):141-156.
[10] JACKSON T J,BINDLISH R,COSH M.Validation of AMSR-E soil moisture products using in situ observations[J].Journal of the Remote Sensing Society of Japan,2009,29(1):263-270.
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[12] JACKSON T J,COSH M H,BINDLISH R,et al.Validation of advanced microwave scanning radiometer soil moisture products[J].IEEE Transactions on Geoscience and Remote Sensing,2010,48(12):4256-4272.
[13] HU W,SHAO M G,REICHARDT K.Using a new criterion to identify sites for mean soil water storage evaluation[J].Soil Science Society of America Journal,2010,74(3):762.
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[15] HU W,SHAO M G,HAN F P,et al.Watershed scale temporal stability of soil water content[J].Geoderma,2010,158(3/4):181-198.
[16] WANG J H,GE Y,HEUVELINK G,et al.Upscaling in situ soil moisture observations to pixel averages with spatio-temporal geostatistics[J].Remote Sensing,2015,7(9):11372-11388.
[17] 晋锐,李新,阎保平,等.黑河流域生态水文传感器网络设计[J].地球科学进展,2012,27(9):993-1005.JIN R,LI X,YAN B P,et al.Introduction of Eco-hydrological Wireless Sensor Network in the Heihe River Basin[J].Advances in Earth Sciences,2012,27(9):993-1005.(in Chinese with English abstract)
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[19] CROW W T,BERG A A,COSH M H,et al.Upscaling sparse ground-based soil moisture observations for the validation of coarse-resolution satellite soil moisture products[J].Reviews of Geophysics,2012,50(2):2011RG000372.
[20] LOEW A,SCHLENZ F.A dynamic approach for evaluating coarse scale satellite soil moisture products[J].Hydrology and Earth System Sciences,2011,15(1):75-90.
[21] GAO S G,ZHU Z L,LIU S M,et al.Estimating the spatial distribution of soil moisture based on Bayesian maximum entropy method with auxiliary data from remote sensing[J].International Journal of Applied Earth Observation and Geoinformation,2014,32:54-66.
[22] VACHAUD G,PASSERAT DE SILANS A,BALABANIS P,et al.Temporal stability of spatially measured soil water probability density function[J].Soil Science Society of America Journal,1985,49(4):822.
[23] COSH M H,JACKSON T J,STARKS P,et al.Temporal stability of surface soil moisture in the Little Washita River watershed and its applications in satellite soil moisture product validation[J].Journal of Hydrology,2006,323(1/2/3/4):168-177.
[24] LIN H.Temporal stability of soil moisture spatial pattern and subsurface preferential flow pathways in the shale hills catchment[J].Vadose Zone Journal,2006,5(1):317-340.
[25] GRAYSON R B,WESTERN A W,CHIEW F H S,et al.Preferred states in spatial soil moisture patterns:local and nonlocal controls[J].Water Resources Research,1997,33(12):2897-2908.
[26] MOHANTY B P,SKAGGS T H.Spatio-temporal evolution and time-stable characteristics of soil moisture within remote sensing footprints with varying soil,slope,and vegetation[J].Advances in Water Resources,2001,24(9/10):1051-1067.
[27] COSH M.Watershed scale temporal and spatial stability of soil moisture and its role in validating satellite estimates[J].Remote Sensing of Environment,2004,92(4):427-435.
[28] MARTíNEZ-FERNáNDEZ J,CEBALLOS A.Mean soil moisture estimation using temporal stability analysis[J].Journal of Hydrology,2005,312(1/2/3/4):28-38.
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