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Retrieval of High Spatiotemporal Resolution Leaf Area Index with Gaussian Processes, Wireless Sensor Network, and Satellite Data Fusion

文献类型: 外文期刊

作者: Verger, Aleixandre 1 ; Qu, Yonghua 2 ; Zhao, Wei 3 ; Xu, Baodong 4 ; Zeng, Yelu 5 ; Liu, Ke 6 ; Li, Jing 7 ; Liu, Qinhuo 7 ;

作者机构: 1.CREAF, Cerdanyola Del Valles 08193, Catalonia, Spain

2.Beijing Normal Univ, State Key Lab Remote Sensing Sci, Beijing Key Lab Remote Sensing Environm & Digital, Inst Remote Sensing Sci & Engn,Fac Geog Sci, Beijing 100875, Peoples R China

3.Chinese Acad Sci, Inst Mt Hazards & Environm, Res Ctr Digital Mt & Remote Sensing Applicat, Chengdu 610010, Sichuan, Peoples R China

4.Huazhong Agr Univ, Coll Resource & Environm, Macro Agr Res Inst, Wuhan 430070, Hubei, Peoples R China

5.Carnegie Inst Sci, Dept Global Ecol, Stanford, CA 94305 USA

6.Sichuan Acad Agr Sci, Inst Remote Sensing Applicat, Chengdu 610066, Sichuan, Peoples R China

7.Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China

关键词: leaf area index; uncertainty; Gaussian processes; wireless sensor network; data fusion; Landsat; MODIS; validation

期刊名称: REMOTE SENSING

ISSN: 2072-4292

年卷期: 2019 年 11 卷 3 期

页码:

收录情况: SCI

摘要: Many applications, including crop growth and yield monitoring, require accurate long-term time series of leaf area index (LAI) at high spatiotemporal resolution with a quantification of the associated uncertainties. We propose an LAI retrieval approach based on a combination of the LAINet observation system, the Consistent Adjustment of the Climatology to Actual Observations (CACAO) method, and Gaussian process regression (GPR). First, the LAINet wireless sensor network provides temporally continuous field measurements of LAI. Then, the CACAO approach generates synchronous reflectance data at high spatiotemporal resolution (30-m and 8-day) from the fusion of multitemporal MODIS and high spatial resolution Landsat satellite imagery. Finally, the GPR machine learning regression algorithm retrieves the LAI maps and their associated uncertainties. A case study in a cropland site in China showed that the accuracy of LAI retrievals is 0.36 (12.7%) in terms of root mean square error and R-2 = 0.88 correlation with ground measurements as evaluated over the entire growing season. This paper demonstrates the potential of the joint use of newly developed software and hardware technologies in deriving concomitant LAI and uncertainty maps with high spatiotemporal resolution. It will contribute to precision agriculture, as well as to the retrieval and validation of LAI products.

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