Evaluation of Different Radiative Transfer Models for Microwave Backscatter Estimation of Wheat Fields

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.

Sentinel-1 Backscatter Analysis and Radiative Transfer Modeling of Dense Winter Wheat Time Series

“…The SSRT was chosen because it calculates the direct vegetation volume backscatter and backscatter contributions due to the surface-canopy interactions. The calibration approach of Weiß et al [26] with a non-static empirical parameter (coe f ) influencing the one-way transmissivity of the canopy (T) was used. Therefore, only one empirical parameter was calibrated for the different model combinations.…”

“…The study area (48 • 13 N-48 • 20 N, 11 • 39 E-11 • 45 E, Figure 1) is located to the north of Munich, Bavaria, southern Germany. The test site was established in 2014, and since then, almost every year during the vegetation period, in situ campaigns are carried out to survey the agriculturally relevant key variables [26,[46][47][48][49]. The main crop types within the MNI test site were wheat, maize, and grassland.…”

“…A comparison of the temporal filtered data-using images from different orbits versus using images from the same orbit-shows almost no difference between the filtered output data. After all of the preprocessing steps, the data were resampled to a spatial resolution of 10 × 10 m [26]. In 2017, the MNI test site area was covered by four different Sentinel-1 tracks with individual acquisition geometries regarding incidence angle, azimuth angle, and orbit direction.…”

“…Often, only a single satellite orbit constellation and, therefore, data from one satellite with the same acquisition geometry are used [19,[22][23][24]. Occasionally, the time series used consists of data from the same satellite but related to different orbits and, thus, various azimuth or/and incidence angles [20,23,[25][26][27]. Only a few of these studies also use data from different sensors to retrieve soil moisture values [23,27,28].…”

“…A problem with using these complex models is the provision of additional input data for characterization of the canopy. Therefore, canopy models with fewer input parameters such as the empirical WCM [35], in which only the direct volume backscatter part is considered; a modified version of WCM (MWCM) [36]; or a Single Scattering Radiative Transfer (SSRT) model described by Ulaby [30] or De Roo [37], in which various scattering mechanisms are modeled in a simpler way, are regularly used [26,30,[37][38][39][40]. Additionally, information about the surface scattering contribution under winter wheat fields for the estimation of soil moisture is crucial.…”