| Abstract: |
This research study focuses on the global estimation of gravimetric moisture content of vegetation, a state parameter, important for Earth System monitoring and modelling of its dynamics.
A novel physics-based approach is proposed in the following to globally assess gravimetric moisture of vegetation (m_g) and thus, retrieving information about the amount of water [kg] per amount of wet biomass [kg].
The essential feature of the proposed m_g-retrieval is that it relies on multi-sensor data from radiometer, radar & lidar to solve a physics equation, and to obtain m_g-estimates. More exactly, this multi-sensor retrieval approach utilizes lidar returns, radar scattering, and particularly radiometer-based vegetation optical depth (VOD) values. The attenuation-based VOD provides predominantly information about the intrinsic vegetation moisture. Radar scattering, in contrast, provides largely information about the extrinsic vegetation structure. In addition, lidar measurements combined with a DEM provide the required vegetation height estimates. The multi-sensor retrieval is based on a physical model of VOD [1], modified with a two-phase dielectric mixing model for vegetation canopies from [2] to retrieve m_g. In order to link the vegetation permittivity and m_g, the dielectric mixing model of [3] is incorporated. One advantage of the proposed attenuation-based, multi-sensor approach is its physics-based nature, which requires no calibration, empirical fit, or in situ measurements.
Several data sets were used to realize the above-described retrieval. Firstly, the vegetation volume fraction δ is retrieved from radar data of the SMAP mission for the period it was active (April to July 2015). Secondly, the vegetation optical depth τ is obtained from a multi-temporal dual-channel algorithm using collocated SMAP radiometer data within the same time period [4]. The lidar-based tree heights were provided by the retrieval algorithm of [5] using ICESat GLAS data.
Results show intuitively high m_g-values for regions with distinct precipitation and high vegetation cover and, vice versa, low m_g-values for regions with low precipitation and sparse vegetation cover. However, there are also ‘counter-intuitive’ isolated cases, such as the Sahel zone, with high m_g-values. This could be a result of the high water availability during the rainy season in combination with yet low vegetation cover and will be subject of future research.
When comparing the obtained m_g-estimates with literature values, nearly the same mean m_g values were achieved, especially for the IGBP-classes “mixed forest” and “closed shrublands”. A spatial comparison at the local scale resulted in an even better fit for the “savanna”-classes with discrepancies of only 10% instead of the 20% obtained at the global scale. Global estimates have been compared with the local ones provided by Grant [6]. Results show the two m_g-retrievals compare well.
[1] Schmugge T.J., Jackson T.J. (1992): A Dielectric Model of the Vegetation Effects on the Microwave Emission from Soils. IEEE Transactions on Geoscience and Remote Sensing, Vol. 30, No. 4, 757 – 760.
[2] Ulaby F.T., Moore R.K., Fung A.K. (1986): Microwave Remote Sensing. Active and Passive. Volume III. From Theory to Applications. Book-Mart Press, New Jersey.
[3] Ulaby F.T., El-Rayes M.A. (1987): Microwave Dielectric Spectrum of Vegetation – Part II: Dual Dispersion Model. IEEE Transactions on Geoscience and Remote Sensing, Vol. Ge-25, No. 5, 550 – 557.
[4] Konings A.G., Piles, M., Rötzer K., McColl K.A., Chan K.S., Entekhabi D. (2016): Vegetation optical depth and scattering albedo retrieval using time series of dual-polarized L-band radiometer observations. Remote Sensing of Environment, Vol. 172, Pages 178–189.
[5] Simard M., Pinto N., Fisher J.B., and Baccini A. (2011): Mapping forest canopy height globally with spaceborne lidar, J. Geophys. Res., 116.
[6] Grant J.P. (2016): Global-scale dynamic monitoring of vegetation water status for improving carbon flux estimates (VEGWAC), ESA STSE Final report, Lund University, Sweden.
|
