Web of Science: 5 cites, Scopus: 6 cites, Google Scholar: cites
Retrieval of high spatiotemporal resolution leaf area index with Gaussian processes, wireless sensor network, and satellite data fusion
Yin, Gaofei (Southwest Jiaotong University. Faculty of Geosciences and Environmental Engineering)
Verger, Aleixandre (Centre de Recerca Ecològica i d'Aplicacions Forestals)
Qu, Yonghua (Beijing Normal University. Institute of Remote Sensing Science and Engineering)
Zhao, Wei (Chinese Academy of Sciences. Institute of Mountain Hazards and Environment)
Xu, Baodong (Huazhong Agricultural University. Macro Agriculture Research Institute)
Zeng, Yelu (Carnegie Institution for Science. Department of Global Ecology)
Liu, Ke (Sichuan Academy of Agricultural Science. Institute of Remote Sensing Application)
Li, Jing (Chinese Academy of Sciences. Institute of Remote Sensing and Digital Earth)
Liu, Qinhuo (Chinese Academy of Sciences. Institute of Remote Sensing and Digital Earth)

Data: 2019
Resum: 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 = 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.
Drets: Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original. Creative Commons
Llengua: Anglès
Document: Article ; recerca ; Versió publicada
Matèria: Leaf area index ; Uncertainty ; Gaussian processes ; Wireless sensor network ; Data fusion ; Landsat ; MODIS ; Validation
Publicat a: Remote sensing (Basel), Vol. 11, Issue 3 (February 2019) , art. 244, ISSN 2072-4292

DOI: 10.3390/rs11030244

18 p, 4.5 MB

El registre apareix a les col·leccions:
Documents de recerca > Documents dels grups de recerca de la UAB > Centres i grups de recerca (producció científica) > Ciències > CREAF (Centre de Recerca Ecològica i d'Aplicacions Forestals)
Articles > Articles de recerca
Articles > Articles publicats

 Registre creat el 2020-01-09, darrera modificació el 2021-08-07

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