Abstract
This study aimed to analyze existing microwave surface (Oh, Dubois, Water Cloud Model “WCM”, Integral Equation Model “IEM”) and canopy (Water Cloud Model “WCM”, Single Scattering Radiative Transfer “SSRT”) Radiative Transfer (RT) models and assess advantages and disadvantages of different model combinations in terms of VV polarized radar backscatter simulation of wheat fields. The models are driven with field measurements acquired in 2017 at a test site near Munich, Germany. As vegetation descriptor for the canopy models Leaf Area Index (LAI) was used. The effect of empirical model parameters is evaluated in two different ways: (a) empirical model parameters are set as static throughout the whole time series of one growing season and (b) empirical model parameters describing the backscatter attenuation by the canopy are treated as non-static in time. The model results are compared to a dense Sentinel-1 C-band time series with observations every 1.5 days. The utilized Sentinel-1 time series comprises images acquired with different satellite acquisition geometries (different incidence and azimuth angles), which allows us to evaluate the model performance for different acquisition geometries. Results show that total LAI as vegetation descriptor in combination with static empirical parameters fit Sentinel-1 radar backscatter of wheat fields only sufficient within the first half of the vegetation period. With the saturation of LAI and/or canopy height of the wheat fields, the observed increase in Sentinel-1 radar backscatter cannot be modeled. Probable cause are effects of changes within the grains (both structure and water content per leaf area) and their influence on the backscatter. However, model results with LAI and non-static empirical parameters fit the Sentinel-1 data well for the entire vegetation period. Limitations regarding different satellite acquisition geometries become apparent for the second half of the vegetation period. The observed overall increase in backscatter can be modeled, but a trend mismatch between modeled and observed backscatter values of adjacent time points with different acquisition geometries is observed.
Highlights
Soil moisture plays an important role in land surface processes, such as water and energy fluxes
Complexities in Radiative Transfer (RT) models for surface backscatter calculations range from simple empirical regression-based models [18,19,20,21] and different empirical models based on the Water Cloud approach (WCM surface part) [22,23,24,25], over semi-empirical models from Oh (Oh92, Oh Model 2004 (Oh04)) [26,27] or Dubois (Dubois95), [28] to physical-based models, like the Integral Equation Model (IEM) in its original form [29] or refined versions [30,31,32]
The incidence angle is implemented within the used RT models, whereas the models do not account for difference azimuth angles
Summary
Soil moisture plays an important role in land surface processes, such as water and energy fluxes. Microwave data has proven to be a suitable tool for long-term soil moisture derivation of large areas and different land cover types [3,4,5,6,7,8,9]. With different available Synthetic Aperture Radar (SAR) data from different sensors and for different usage in terms of absolute accuracy and spatial scale, various soil moisture retrieval approaches, like change detection, microwave data fusion (active and passive), differential Synthetic Aperture Radar (SAR) interferometry, or SAR polarimetry, are available [15]. Land surface parameters, like soil moisture, can be derived by using Radiative Transfer (RT) models. Despite the large numbers of existing models, there is still the need of an algorithm generating soil moisture maps with acceptable accuracy of 3–4% [17]
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