IMHE OpenIR
DERIVATION OF HIGH SPATIO-TEMPORAL RESOLUTION LEAF AREA INDEX AND UNCERTAINTY MAPS BY COMBINING LAINET, CACAO AND GPR
Language英语
Gaofei Yin; Ainong Li
2018
Source PublicationIEEE International Symposium on Geoscience and Remote Sensing IGARSS
Author of SourceIEEE
Pages5960-5963
meeting38th IEEE International Geoscience and Remote Sensing Symposium (IGARSS)
Conference Date2018
Conference PlaceValencia, SPAIN
CountrySPAIN
Contribution Rank1
AbstractWe proposed a framework to generate high spatio-temporal resolution leaf area index (LAI) and uncertainty maps based on the integration of LAINet observation system, Consistent Adjustment of the Climatology to Actual Observations (CACAO) method and Gaussian process regression (GPR). LAINet, which is a wireless sensor network based automatic LAI observation instrument, was used to provide temporally continuous field measurements; CACAO, a data blending method, was used to blend the high and low spatial resolution remote sensing observations to obtain high spatio-temporal resolution remote sensing observations synchronous with the field measurements. GPR, a machine learning regression algorithm, was used to upscale the spatially discrete field measurements to spatially explicit LAI maps, and get the concomitant uncertainty maps. The performance of the proposed method was evaluated over a crop site, where seven LAI maps and their accompanying uncertainty maps all with 30 m and 8 days resolutions were generated. Results show that the framework can provide accurate LAI retrievals. In addition, the concomitant uncertainty maps provide insight into the reliability of the LAI retrievals. This paper contributes to precision agriculture and validation activities for coarse resolution LAI products.
KeywordLeaf area index (LAI) uncertainty high spatio-temporal resolution Gaussian Process Regression (GPR) LAINet observation system Consistent Adjustment of the Climatology to Actual Observations (CACAO)
ISBN978-1-5386-7150-4
ISSN2153-6996
Indexed ByCPCI
WOS IDWOS:000451039805196
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Document Type会议论文
Identifierhttp://ir.imde.ac.cn/handle/131551/24500
Collection中国科学院水利部成都山地灾害与环境研究所
Corresponding AuthorAinong Li
AffiliationInstitute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610041, China
First Author Affilication中国科学院水利部成都山地灾害与环境研究所
Corresponding Author Affilication中国科学院水利部成都山地灾害与环境研究所
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Gaofei Yin,Ainong Li. DERIVATION OF HIGH SPATIO-TEMPORAL RESOLUTION LEAF AREA INDEX AND UNCERTAINTY MAPS BY COMBINING LAINET, CACAO AND GPR[C]//IEEE,2018:5960-5963.
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