DERIVATION OF HIGH SPATIO-TEMPORAL RESOLUTION LEAF AREA INDEX AND UNCERTAINTY MAPS BY COMBINING LAINET, CACAO AND GPR

CORC > 成都山地灾害与环境研究所 > 中国科学院水利部成都山地灾害与环境研究所 > 数字山地与遥感应用中心

	DERIVATION OF HIGH SPATIO-TEMPORAL RESOLUTION LEAF AREA INDEX AND UNCERTAINTY MAPS BY COMBINING LAINET, CACAO AND GPR
	Gaofei Yin; Ainong Li
	2018
会议日期	2018
会议地点	Valencia, SPAIN
关键词	Leaf area index (LAI) uncertainty high spatio-temporal resolution Gaussian Process Regression (GPR) LAINet observation system Consistent Adjustment of the Climatology to Actual Observations (CACAO)
页码	5960-5963
国家	SPAIN
英文摘要	We 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.
源文献作者	IEEE
产权排序	1
会议录	IEEE International Symposium on Geoscience and Remote Sensing IGARSS
语种	英语
ISSN号	2153-6996
ISBN号	978-1-5386-7150-4
WOS记录号	WOS:000451039805196
内容类型	会议论文
源URL	[http://ir.imde.ac.cn/handle/131551/24500]
专题	成都山地灾害与环境研究所_数字山地与遥感应用中心
通讯作者	Ainong Li
作者单位	Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610041, China
推荐引用方式 GB/T 7714	Gaofei Yin,Ainong Li. DERIVATION OF HIGH SPATIO-TEMPORAL RESOLUTION LEAF AREA INDEX AND UNCERTAINTY MAPS BY COMBINING LAINET, CACAO AND GPR[C]. 见:. Valencia, SPAIN. 2018.