Quantitative remote sensing of vegetation properties and functioning under normal and dry conditions

  • Bagher Bayat U.S. Geological Survey (Creator)
  • D. Baldocchi (Data Collector)
  • S. Ma University of California at Berkeley (Data Collector)

Dataset

Description

The main idea of this research is to exploit multiple observations including time-series of optical, thermal (TIR) and soil moisture data for remote sensing of vegetation properties and functioning under normal and dry conditions. It is significant to investigate the information content of such observations and quantify the impact of their synergistic use to explain drought effects on vegetation functioning. Therefore, understanding how much information one can get from different sensors (e.g., optical, TIR and soil moisture) to see vegetation (here for annual C3 grasses) properties and functioning (notably canopy photosynthesis [gross primary production (GPP)] and evapotranspiration (ET)) variations during a drought episode and whether combined use of this information can enhance vegetation functioning estimations is of great interest. This study describes the importance of plant functioning, drought effects, application of remote sensing and in-situ observations, methods for plant functioning assessment, the proposed coupled modeling approach. For more information, the reader is refereed to the digital version of the thesis here: https://library.itc.utwente.nl/papers_2018/phd/bayat.pdf
Date made available10 Mar 2019
PublisherDANS easy
Date of data production10 Nov 2018

Cite this

Bayat, B. (Creator), Baldocchi, D. (Data Collector), Ma, S. (Data Collector) (10 Mar 2019). Quantitative remote sensing of vegetation properties and functioning under normal and dry conditions. DANS easy. 10.17026/dans-z5e-nhxv