Preprints
https://doi.org/10.5194/hess-2022-121
https://doi.org/10.5194/hess-2022-121
 
05 Apr 2022
05 Apr 2022
Status: a revised version of this preprint was accepted for the journal HESS.

Estimating leaf moisture content at global scale from passive microwave satellite observations of vegetation optical depth

Matthias Forkel1, Luisa Schmidt1, Ruxandra-Maria Zotta2, Wouter Dorigo2, and Marta Yebra3,4 Matthias Forkel et al.
  • 1Technische Universität Dresden, Institute of Photogrammetry and Remote Sensing, 01062 Dresden, Germany
  • 2Technische Universität Wien, Department of Geodesy and Geoinformation, 1040 Vienna, Austria
  • 3Fenner School of Environment and Society, Australian National University, ACT 2601, Australia
  • 4School of Engineering, Australian National University, ACT 2601, Australia

Abstract. The moisture content of vegetation canopies controls various ecosystem processes such as plant productivity, transpiration, mortality and flammability. Leaf moisture content (here defined as the ratio of leaf water mass to leaf dry biomass, or live-fuel moisture content, LFMC) is a vegetation property that is frequently used to estimate flammability and the danger of fire occurrence and spread and is widely measured at field sites around the globe. LFMC can be retrieved from satellite observations in the visible and infrared domain of the electromagnetic spectrum, which is however hampered by frequent cloud cover or low sun elevation angles. As an alternative, vegetation water content can be estimated from satellite observations in the microwave domain. For example, studies at local and regional scales have demonstrated the link between LFMC and vegetation optical depth (VOD) from passive microwave satellite observations. VOD describes the attenuation of microwaves in the vegetation layer. However, neither were the relations between VOD and LFMC investigated at large or global scales nor has VOD been used to estimate LFMC. Here we aim to estimate LFMC from VOD at large scales, i.e. at coarse spatial resolution, globally, and at daily time steps over decadal time series. Therefore, our objectives are 1) to investigate the relation between VOD from different frequencies and LFMC derived from optical sensors and a global database of LFMC site measurements; 2) to test different model structures to estimate LFMC from VOD; and 3) to apply the best-performing model to estimate LFMC at global scales. Our results show that VOD is medium to highly correlated with LFMC in areas with medium to high coverage of short vegetation (grasslands, croplands, shrublands). Forested areas show on average weak correlations but the variability in correlations is high. A logistic regression model that uses VOD and additionally leaf area index as predictor to account for canopy biomass reaches the highest performance. Applying this model to global VOD and LAI observations allows estimating LFMC globally over decadal time series at daily temporal sampling. The derived estimates of LFMC can be used to assess large-scale patterns and temporal changes in vegetation water status and fire dynamics.

Matthias Forkel et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • AC1: 'Comment on hess-2022-121', Matthias Forkel, 19 May 2022
  • RC1: 'Comment on hess-2022-121', Anonymous Referee #1, 24 Jun 2022
    • AC2: 'Reply on RC1', Matthias Forkel, 27 Jul 2022
  • RC2: 'Comment on hess-2022-121', Anonymous Referee #2, 03 Jul 2022
    • AC3: 'Reply on RC2', Matthias Forkel, 27 Jul 2022

Matthias Forkel et al.

Matthias Forkel et al.

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Short summary
The live fuel moisture content (LFMC) of vegetation canopies is a driver of wildfires. We investigate the relation between LFMC and passive microwave satellite observations of vegetation optical depth (VOD) and develop a method to estimate LFMC from VOD globally. Our global VOD-based estimates of LFMC can be used to investigate drought effects on vegetation and fire risks.