| 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 Publication | IEEE International Symposium on Geoscience and Remote Sensing IGARSS |
| Author of Source | IEEE |
| Pages | 5960-5963 |
| meeting | 38th IEEE International Geoscience and Remote Sensing Symposium (IGARSS) |
| Conference Date | 2018 |
| Conference Place | Valencia, SPAIN |
| Country | SPAIN |
| Contribution Rank | 1 |
| Abstract | 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. |
| Keyword | 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 | 978-1-5386-7150-4 |
| ISSN | 2153-6996 |
| Indexed By | CPCI |
| WOS ID | WOS:000451039805196 |
| Citation statistics | |
| Document Type | 会议论文 |
| Identifier | http://ir.imde.ac.cn/handle/131551/24500 |
| Collection | 数字山地与遥感应用中心 |
| Corresponding Author | Ainong Li |
| Affiliation | Institute 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 |
| Recommended Citation 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. |
| Files in This Item: | ||||||
| File Name/Size | DocType | Version | Access | License | ||
| DERIVATION OF HIGH S（346KB） | 会议论文 | 开放获取 | CC BY-NC-SA | View Application Full Text | ||
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