Adaptive smoothing to identify spatial structure in global lake ecological processes using satellite remote sensing data

Gong, Mengyi; O’Donnell, Ruth; Miller, Claire; Scott, Marian; Simis, Stefan; Groom, Steve; Tyler, Andrew; Hunter, Peter; Spyrakos, Evangelos; Merchant, Christopher; Maberly, Stephen; Carvalho, Laurence. 2022 Adaptive smoothing to identify spatial structure in global lake ecological processes using satellite remote sensing data. Spatial Statistics, 50, 100615. 19, pp. https://doi.org/10.1016/j.spasta.2022.100615

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Official URL: http://dx.doi.org/10.1016/j.spasta.2022.100615

Abstract/Summary

Satellite remote sensing data are important to the study of environment problems at a global scale. The GloboLakes project aimed to use satellite remote sensing data to investigate the response of the major lakes on Earth to environmental conditions and change. The main challenge to statistical modelling is the identification of the spatial structure in global lake ecological processes from a large number of time series subject to incomplete data and varying uncertainty. This paper introduces a comprehensive modelling procedure, combining adaptive smoothing and functional data analysis, to estimate the smooth curves representing the trend and seasonal patterns in the time series and to cluster the curves over space. Two approaches, based on an irregular basis and an adaptive penalty matrix, are developed to account for the varying uncertainty induced by missing observations and specific constraints (e.g. substantive periods of measurement values of zero in winter). In particular, the adaptive penalty matrix applies a heavier penalty to smooth curve estimates where there is higher uncertainty to prevent over-fitting the noisy/biased data. The modelling procedure was applied to the lake surface water temperature (LSWT) time series from 732 largest lakes globally and the lake chlorophyll-a time series from 535 largest lakes globally. The procedure enabled the identification of nine global lake thermal regions based on the temporal dynamics of LSWT, and the extraction of eight global lake clusters based on the interannual variation in chlorophyll-a and ten clusters to differentiate the seasonal signals.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	https://doi.org/10.1016/j.spasta.2022.100615
UKCEH and CEH Sections/Science Areas:	Water Resources (Science Area 2017-) UKCEH Fellows
ISSN:	2211-6753
Additional Keywords:	satellite remote sensing data, adaptive smoothing, functional data analysis, spatial structure
NORA Subject Terms:	Ecology and Environment
Related URLs:	Dataset Dataset
Date made live:	17 Jun 2022 16:12 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/532771

Item Type:

Publication - Article

Digital Object Identifier (DOI):

https://doi.org/10.1016/j.spasta.2022.100615

UKCEH and CEH Sections/Science Areas:

Water Resources (Science Area 2017-)
UKCEH Fellows

ISSN:

2211-6753

Additional Keywords:

satellite remote sensing data, adaptive smoothing, functional data analysis, spatial structure

NORA Subject Terms:

Ecology and Environment