Remote sensing and modeling fusion for investigating the ecosystem water-carbon coupling processes.

Academic Article


  • The water and carbon cycles are tightly linked and play a key role in the material and energy flows between terrestrial ecosystems and the atmosphere, but the interactions of water and carbon cycles are not quite clear. The global climate change and intensive human activities could also complicate the water and carbon coupling processes. Better understanding the coupled water-carbon cycles and their spatiotemporal evolution can inform management and decision-making efforts regarding carbon uptake, food production, water resources, and climate change. The integration of remote sensing and numeric modeling is an attractive approach to address the challenge. Remote sensing can provide extensive data for a number of variables at regional scale and support models, whereas process-based modeling can facilitate investigating the processes that remote sensing cannot well handle (e.g., below-ground and lateral material movement) and backcast/forecast the impacts of environmental change. Over the past twenty years, an increasing number of studies using a variety of remote sensing products together with numeric models have examined the water-carbon interactions. This article reviewed the methodologies for integrating remote sensing data into these models and the modeling of water-carbon coupling processes. We first summarized the major remote sensing datasets and models used for studying the coupled water-carbon cycles. We then provided an overview of the methods for integrating remote sensing data into water-carbon models, and discussed their strengths and challenges. We also prospected the development of potential new remote sensing datasets, modeling methods, and their potential applications in the field of eco-hydrology.
  • Authors

  • Sun, Pengcheng
  • Wu, Yiping
  • Xiao, Jingfeng
  • Hui, Jinyu
  • Hu, Jingyi
  • Zhao, Fubo
  • Qiu, Linjing
  • Liu, Shuguang
  • Status

    Publication Date

  • December 20, 2019
  • Has Subject Area

    Published In


  • Biogeochemistry
  • Ecological processes
  • Hydrology
  • Modeling
  • Regional water-carbon cycles
  • Remote sensing data
  • Digital Object Identifier (doi)

    Pubmed Id

  • 31476506
  • Start Page

  • 134064
  • Volume

  • 697