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

Remote Sensing

Aleixandre Verger¹  Yelu Zeng²  Gaofei Yin³  Ke Liu⁴  Baodong Xu⁵  Wei Zhao⁶  Yonghua Qu⁷  Jing Li⁸  Qinhuo Liu⁸ 

[1] CREAF, 08193 Cerdanyola del Vallès, Catalonia, Spain;Department of Global Ecology, Carnegie Institution for Science, Stanford, CA 94305, USA;Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 610031, China;Institute of Remote Sensing Application, Sichuan Academy of Agricultural Science, Chengdu 610066, China;Macro Agriculture Research Institute, College of Resource and Environment, Huazhong Agricultural University, Wuhan 430070, China;Research Center for Digital Mountain and Remote Sensing Application, Institute of Mountain Hazards and Environment, Chinese Academy Sciences, Chengdu 610010, China;State Key Laboratory of Remote Sensing Science, Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities, Institute of Remote Sensing Science and Engineering, Faculty of Geography Science, Beijing Normal University, Beijing 100875, China;State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China;

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

DOI  :  10.3390/rs11030244

来源: DOAJ

【 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 R² = 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.

【 授权许可】

Unknown