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

Abstract

This study evaluates a temporally dense VV-polarized Sentinel-1 C-band backscatter time series (revisit time of 1.5 days) for wheat fields near Munich (Germany). A dense time series consisting of images from different orbits (varying acquisition) is analyzed, and Radiative Transfer (RT)-based model combinations are adapted and evaluated with the use of radar backscatter. The model shortcomings are related to scattering mechanism changes throughout the growth period with the use of polarimetric decomposition. Furthermore, changes in the RT modeled backscatter results with spatial aggregation from the pixel to field scales are quantified and related to the sensitivity of the RT models, and their soil moisture output are quantified and related to changes in backscatter. Therefore, various (sub)sets of the dense Sentinel-1 time series are analyzed to relate and quantify the impact of the abovementioned points on the modeling results. The results indicate that the incidence angle is the main driver for backscatter differences between consecutive acquisitions with various recording scenarios. The influence of changing azimuth angles was found to be negligible. Further analyses of polarimetric entropy and scattering alpha angle using a dual polarimetric eigen-based decomposition show that scattering mechanisms change over time. The patterns analyzed in the entropy-alpha space indicate that scattering mechanism changes are mainly driven by the incidence angle and not by the azimuth angle. Besides the analysis of differences within the Sentinel-1 data, we analyze the capability of RT model approaches to capture the observed Sentinel-1 backscatter changes due to various acquisition geometries. For this, the surface models “Oh92” or “IEM_B” (Baghdadi’s version of the Integral Equation Method) are coupled with the canopy model “SSRT” (Single Scattering Radiative Transfer). To resolve the shortcomings of the RT model setup in handling varying incidence angles and therefore the backscatter changes observed between consecutive time steps of a dense winter wheat time series, an empirical calibration parameter (coef) influencing the transmissivity (T) is introduced. The results show that shortcomings of simplified RT model architectures caused by handling time series consisting of images with varied incidence angles can be at least partially compensated by including a calibration coefficient to parameterize the modeled transmissivity for the varying incidence angle scenarios individually.

High Spatial and Temporal Soil Moisture Retrieval in Agricultural Areas Using Multi-Orbit and Vegetation Adapted Sentinel-1 SAR Time Series

“…Focusing on agricultural areas, predominantly located on flat surfaces, the influence of the azimuth angle is substantially lower [71]. Via the investigation of the influence of azimuthal angle over winter wheat fields, it was found negligible within a dense time series [72].…”

“…Furthermore, the estimated soil moisture still showed extreme low and high values due to the incidence angle still effect present in the backscatter time series. Crop-dominated stations showed significant variation in correlation and error between individual years due to the influence of incidence angle, which depends on plant geometry, plant phase, and vegetation water content, changing both between the year as well as within a year [72,84]. In contrast, the soil moisture estimation at the meadow station (RU_C_005) is stable over the years, with only slight changes in correlation and uRMSE.…”

Yield Prediction for Winter Wheat with Machine Learning Models Using Sentinel-1, Topography, and Weather Data

“…These results also strengthened the findings in [7,8], which showed that the VH/VV ratio is important in accessing soil moisture. Works [13,14] discuss the importance of correctly handling the variations in Sentinel-1 incident angles for any Radiative Transfer (RT)-based model. Similarly, Refs.…”

Comparative Analysis of the Sensitivity of SAR Data in C and L Bands for the Detection of Irrigation Events

“…and the SAR wavelength [24]. For certain crops, such as wheat and barley, several studies have demonstrated that the sensitivity of the C-band SAR signal (wavelength ~6 cm) to soil moisture is negligible in the presence of very well-developed vegetation cover [25][26][27][28][29][30]. El Hajj et al [25] showed that for wheat crops, the C-band S1-SAR signal in VV and VH polarizations becomes insensitive to surface soil moisture for LAI (Leaf Area Index) values beyond 1.5 m 2 /m 2 (vegetation height of approximately 70 cm).…”