| Abstract: |
During the last decade, significant progress has been made in the development of satellite-based instruments for earth observation and in particular for monitoring hydrological states of the land surface. L-band passive microwave remote sensing is a well-established technique for surface soil moisture monitoring, especially because of the lower attenuation and scattering of L-band emissions in soils and vegetation compared to microwave emissions at higher frequencies. Currently, two satellites with L-band microwave radiometer instruments are collecting data from space, the ESA Soil Moisture and Ocean Salinity (SMOS) satellite and the NASA Soil Moisture Active Passive (SMAP) satellite [1]. In addition to the estimation of soil moisture at global scale, these two missions provide opportunities for improved understanding and monitoring of plant water status and plant growth [2]. However, it is still a major challenge to disentangle between the L-band emissions originating from the soil and from the vegetation [3]. In that context, the aim of this research study is to improve the accuracy of the soil moisture retrieval by better accounting for the vegetation in the soil moisture retrieval algorithms. A further motivation for this study is to advance the range of vegetation parameters that can be retrieved from L-band brightness temperature signatures. For that, a tower-based experiment was conducted during the 2017 growing season at the TERENO test site in Selhausen (Germany) over a vegetated area covered with typical winter wheat. Time-lapse measurements of passive microwave radiations at different incident angles were performed over two footprint types (natural and manipulated) to disentangle radiative contributions originating from the vegetation from contributions originating from the soil [4]. The manipulated footprints consisted of a reflector covering the soil with plants growing through. The reflector was a wire grid with a mesh-size much smaller than the radiometer wavelength to achieve a highly reflecting surface, while not disturbing hydrology, nor plant growth. The temporal evolution of the vegetation optical depth throughout the season was obtained from the brightness temperature observations over the manipulated footprint by inverting the tau-omega model, which is the zero-order noncoherent solutions of the radiative transfer equations [5]. This information was then used to improve the accuracy of the soil moisture retrieval by constraining the inversion of the microwave data. Results showed that the estimated values of the optical depth throughout the growing season were strongly correlated to the in situ measurements of vegetation water content, vegetation height, vegetation biomass, and leaf area index. Microwave soil moisture retrievals were also in good agreement with the in situ soil moisture measurements. Moreover, the soil moisture retrieval was significantly improved by the use of time-dependent vegetation optical depth information compared to the use of a constant value throughout the year. These first results and further analyses of the experimental data will be presented and discussed at the conference.
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[2] Konings A. G. et al. 2017. L-band vegetation optical depth and effective scattering albedo estimation from SMAP. RSE, 198: 460-470.
[3] Vereecken H. et al. 2012. Characterization of crop canopies and water stress related phenomena using microwave remote sensing methods: A review. VZJ, 11(2).
[4] Jonard F. et al. 2015. Estimation of hydraulic properties of a sandy soil using ground-based active and passive microwave remote sensing. IEEE TGRS, 53(6): 3095-3109.
[5] Wigneron J. P. et al. Modelling the passive microwave signature from land surfaces: A review of recent results and application to the L-band SMOS & SMAP soil moisture retrieval algorithms. RSE, 192: 238-262 |
