Title
Integration of satellite remote sensing data in ecosystem modelling at local scales: Practices and trends
Author
Pasetto, Damiano (Swiss Federal Institute of Technology)
Arenas-Castro, Salvador (Universidade do Porto)
Bustamante, Javier (University of the Balearic Islands)
Casagrandi, Renato (Politecnico di Milano)
Chrysoulakis, Nektarios (Foundation for Research and Technology - Hellas (FORTH))
Cord, Anna F. (Helmholtz Centre for Environmental Research - UFZ)
Dittrich, Andreas (Helmholtz Centre for Environmental Research - UFZ)
Domingo‐Marimon, Cristina (Universitat Autònoma de Barcelona)
El Serafy, G.Y.H. (TU Delft Mathematical Physics; Deltares)
Karnieli, Arnon (Ben-Gurion University of the Negev)
Kordelas, Georgios A. (Centre for Research and Technology-Hellas)
Manakos, Ioannis (Centre for Research and Technology-Hellas)
Mari, Lorenzo (Politecnico di Milano)
Monteiro, Antonio (Universidade do Porto)
Palazzi, Elisa (Institute of Atmospheric Sciences and Climate of the National Research Council)
Poursanidis, Dimitris (Foundation for Research and Technology - Hellas (FORTH))
Rinaldo, Andrea (École Polytechnique de Lausanne; Università degli Studi di Padova)
Terzago, Silvia (Institute of Atmospheric Sciences and Climate of the National Research Council)
Ziemba, A.M. (TU Delft Mathematical Physics; Deltares)
Ziv, Guy (University of Leeds)
Date
2018
Abstract
Spatiotemporal ecological modelling of terrestrial ecosystems relies on climatological and biophysical Earth observations. Due to their increasing availability, global coverage, frequent acquisition and high spatial resolution, satellite remote sensing (SRS) products are frequently integrated to in situ data in the development of ecosystem models (EMs) quantifying the interaction among the vegetation component and the hydrological, energy and nutrient cycles. This review highlights the main advances achieved in the last decade in combining SRS data with EMs, with particular attention to the challenges modellers face for applications at local scales (e.g. small watersheds). We critically review the literature on progress made towards integration of SRS data into terrestrial EMs: (1) as input to define model drivers; (2) as reference to validate model results; and (3) as a tool to sequentially update the state variables, and to quantify and reduce model uncertainty. The number of applications provided in the literature shows that EMs may profit greatly from the inclusion of spatial parameters and forcings provided by vegetation and climatic-related SRS products. Limiting factors for the application of such models to local scales are: (1) mismatch between the resolution of SRS products and model grid; (2) unavailability of specific products in free and public online repositories; (3) temporal gaps in SRS data; and (4) quantification of model and measurement uncertainties. This review provides examples of possible solutions adopted in recent literature, with particular reference to the spatiotemporal scales of analysis and data accuracy. We propose that analysis methods such as stochastic downscaling techniques and multi-sensor/multi-platform fusion approaches are necessary to improve the quality of SRS data for local applications. Moreover, we suggest coupling models with data assimilation techniques to improve their forecast abilities. This review encourages the use of SRS data in EMs for local applications, and underlines the necessity for a closer collaboration among EM developers and remote sensing scientists. With more upcoming satellite missions, especially the Sentinel platforms, concerted efforts to further integrate SRS into modelling are in great demand and these types of applications will certainly proliferate.
Subject
data assimilation
ecohydrological models
satellite remote sensing
stochastic downscaling
To reference this document use:
http://resolver.tudelft.nl/uuid:a56ea0b2-2010-4961-906e-0a544a4f77d2
DOI
https://doi.org/10.1111/2041-210X.13018
ISSN
2041-210X
Source
Methods in Ecology and Evolution, 9 (8), 1810-1821
Part of collection
Institutional Repository
Document type
review
Rights
© 2018 Damiano Pasetto, Salvador Arenas-Castro, Javier Bustamante, Renato Casagrandi, Nektarios Chrysoulakis, Anna F. Cord, Andreas Dittrich, Cristina Domingo‐Marimon, G.Y.H. El Serafy, Arnon Karnieli, Georgios A. Kordelas, Ioannis Manakos, Lorenzo Mari, Antonio Monteiro, Elisa Palazzi, Dimitris Poursanidis, Andrea Rinaldo, Silvia Terzago, A.M. Ziemba, Guy Ziv