IMHE OpenIR
DERIVATION OF HIGH SPATIO-TEMPORAL RESOLUTION LEAF AREA INDEX AND UNCERTAINTY MAPS BY COMBINING LAINET, CACAO AND GPR
语种英语
Gaofei Yin; Ainong Li
2018
会议录名IEEE International Symposium on Geoscience and Remote Sensing IGARSS
会议录编者/会议主办者IEEE
页码5960-5963
会议名称38th IEEE International Geoscience and Remote Sensing Symposium (IGARSS)
会议日期2018
会议地点Valencia, SPAIN
会议举办国SPAIN
产权排序1
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.
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)
ISBN号978-1-5386-7150-4
ISSN号2153-6996
收录类别CPCI
WOS记录号WOS:000451039805196
引用统计
文献类型会议论文
条目标识符http://ir.imde.ac.cn/handle/131551/24500
专题中国科学院水利部成都山地灾害与环境研究所
通讯作者Ainong Li
作者单位Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610041, China
第一作者单位中国科学院水利部成都山地灾害与环境研究所
通讯作者单位中国科学院水利部成都山地灾害与环境研究所
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]//IEEE,2018:5960-5963.
条目包含的文件
文件名称/大小 文献类型 版本类型 开放类型 使用许可
DERIVATION OF HIGH S(346KB)会议论文 开放获取CC BY-NC-SA浏览 请求全文
个性服务
推荐该条目
保存到收藏夹
查看访问统计
导出为Endnote文件
谷歌学术
谷歌学术中相似的文章
[Gaofei Yin]的文章
[Ainong Li]的文章
百度学术
百度学术中相似的文章
[Gaofei Yin]的文章
[Ainong Li]的文章
必应学术
必应学术中相似的文章
[Gaofei Yin]的文章
[Ainong Li]的文章
相关权益政策
暂无数据
收藏/分享
文件名: DERIVATION OF HIGH SPATIO-TEMPORAL RESOLUTION LEAF AREA INDEX AND UNCERTAINTY MAPS BY COMBINING LAINET, CACAO AND GPR.pdf
格式: Adobe PDF
所有评论 (0)
暂无评论
 

除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。