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Pasetto, Damiano; Arenas‐Castro, Salvador; Bustamante, Javier; Casagrandi, Renato; Chrysoulakis, Nektarios; Cord, Anna F.; Dittrich, Andreas; Domingo‐Marimon, Cristina; El Serafy, Ghada; Karnieli, Arnon; Kordelas, Georgios A.; Manakos, Ioannis; Mari, Lorenzo; Monteiro, Antonio; Palazzi, Elisa; Poursanidis, Dimitris; Rinaldo, Andrea; Terzago, Silvia; Ziemba, Alex; Ziv, Guy

Integration of satellite remote sensing data in ecosystem modelling at local scales: Practices and trends

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  • Media type: E-Article
  • Title: Integration of satellite remote sensing data in ecosystem modelling at local scales: Practices and trends
  • Contributor: Pasetto, Damiano; Arenas‐Castro, Salvador; Bustamante, Javier; Casagrandi, Renato; Chrysoulakis, Nektarios; Cord, Anna F.; Dittrich, Andreas; Domingo‐Marimon, Cristina; El Serafy, Ghada; Karnieli, Arnon; Kordelas, Georgios A.; Manakos, Ioannis; Mari, Lorenzo; Monteiro, Antonio; Palazzi, Elisa; Poursanidis, Dimitris; Rinaldo, Andrea; Terzago, Silvia; Ziemba, Alex; Ziv, Guy
  • imprint: Wiley, 2018
  • Published in: Methods in Ecology and Evolution
  • Language: English
  • DOI: 10.1111/2041-210x.13018
  • ISSN: 2041-210X
  • Keywords: Ecological Modeling ; Ecology, Evolution, Behavior and Systematics
  • Origination:
  • Footnote:
  • Description: <jats:title>Abstract</jats:title><jats:p> <jats:list> <jats:list-item><jats:p>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 (<jats:styled-content style="fixed-case">SRS</jats:styled-content>) products are frequently integrated to in situ data in the development of ecosystem models (<jats:styled-content style="fixed-case">EM</jats:styled-content>s) 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 <jats:styled-content style="fixed-case">SRS</jats:styled-content> data with <jats:styled-content style="fixed-case">EM</jats:styled-content>s, with particular attention to the challenges modellers face for applications at local scales (e.g. small watersheds).</jats:p></jats:list-item> <jats:list-item><jats:p>We critically review the literature on progress made towards integration of <jats:styled-content style="fixed-case">SRS</jats:styled-content> data into terrestrial <jats:styled-content style="fixed-case">EM</jats:styled-content>s: (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.</jats:p></jats:list-item> <jats:list-item><jats:p>The number of applications provided in the literature shows that <jats:styled-content style="fixed-case">EM</jats:styled-content>s may profit greatly from the inclusion of spatial parameters and forcings provided by vegetation and climatic‐related <jats:styled-content style="fixed-case">SRS</jats:styled-content> products. Limiting factors for the application of such models to local scales are: (1) mismatch between the resolution of <jats:styled-content style="fixed-case">SRS</jats:styled-content> products and model grid; (2) unavailability of specific products in free and public online repositories; (3) temporal gaps in <jats:styled-content style="fixed-case">SRS</jats:styled-content> 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 <jats:styled-content style="fixed-case">SRS</jats:styled-content> data for local applications. Moreover, we suggest coupling models with data assimilation techniques to improve their forecast abilities.</jats:p></jats:list-item> <jats:list-item><jats:p>This review encourages the use of <jats:styled-content style="fixed-case">SRS</jats:styled-content> data in <jats:styled-content style="fixed-case">EM</jats:styled-content>s for local applications, and underlines the necessity for a closer collaboration among <jats:styled-content style="fixed-case">EM</jats:styled-content> developers and remote sensing scientists. With more upcoming satellite missions, especially the Sentinel platforms, concerted efforts to further integrate <jats:styled-content style="fixed-case">SRS</jats:styled-content> into modelling are in great demand and these types of applications will certainly proliferate.</jats:p></jats:list-item> </jats:list> </jats:p>
  • Access State: Open Access