KMS Institute of Mountain Hazards and Environment,CAS
| 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. |
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