globalchange  > 影响、适应和脆弱性
DOI: 10.1029/2012JD018150
论文题名:
Soil moisture retrieval from multi-instrument observations: Information content analysis and retrieval methodology
作者: Kolassa J.; Aires F.; Polcher J.; Prigent C.; Jimenez C.; Pereira J.M.
刊名: Journal of Geophysical Research Atmospheres
ISSN: 21698996
出版年: 2013
卷: 118, 期:10
起始页码: 4847
结束页码: 4859
语种: 英语
英文关键词: neural networks ; retrieval ; satellite observations ; soil moisture ; surface hydrology
Scopus关键词: Atmospheric temperature ; Data fusion ; Neural networks ; Soil moisture ; Soil surveys ; Uncertainty analysis ; Information content analysis ; Innovative approaches ; retrieval ; Retrieval uncertainty ; Satellite observations ; Soil moisture retrievals ; Spatial and temporal scale ; Surface hydrology ; Algorithms ; algorithm ; information system ; methodology ; satellite data ; soil moisture ; surface temperature
英文摘要: An algorithm has been developed that employs neural network technology to retrieve soil moisture from multi-wavelength satellite observations (active/passive microwave, infrared, and visible). This represents the first step in the development of a methodology aiming to combine beneficial aspects of existing retrieval schemes. Several quality metrics have been developed to assess the performance of a retrieval product on different spatial and temporal scales. Additionally, an innovative approach to estimate the retrieval uncertainty has been proposed. An information content analysis of different satellite observations showed that active microwave observations are best suited to capture the soil moisture temporal variability, while the amplitude of the surface temperature diurnal cycle is best suited to capture the spatial variability. In a synergy analysis, it has been found that through the combination of all observations the retrieval uncertainty could be reduced by 13%. Furthermore, it was found that synergy benefits are significantly larger using a data fusion approach compared to an a posteriori combination of retrieval products, supporting the combination of different retrieval methodology aspects in a single algorithm. In a comparison with model data, it was found that the proposed methodology also shows potential to be used for the evaluation of modeled soil moisture. A comparison with in situ observations showed that the algorithm is well able to capture soil moisture spatial variabilities. It was concluded that the temporal performance can be improved through incorporation of other existing retrieval approaches. Key Points Multi-instrument soil moisture retrieval algorithm has been developedData fusion was found to perform better than a posteriori combinationAlgorithm has been evaluated and used to create soil moisture data base ©2012. American Geophysical Union. All Rights Reserved.
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/63725
Appears in Collections:影响、适应和脆弱性
气候减缓与适应

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作者单位: Estellus S.A.S., 93 Boulevard de Sébastopol, F-75014, Paris, France; Laboratoire d'Etude du Rayonnement et de la Matière en Astrophysique, CNRS, Paris, France; Laboratoire de Météorologie Dynamique, CNRS, Paris, France; Instituto Superior de Agronomia, Universidade Tecnica de Lisboa, Lisbon, Portugal

Recommended Citation:
Kolassa J.,Aires F.,Polcher J.,et al. Soil moisture retrieval from multi-instrument observations: Information content analysis and retrieval methodology[J]. Journal of Geophysical Research Atmospheres,2013-01-01,118(10)
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