Polarimetric analysis of radar backscatter from ground-based scatterometers and wheat biomass monitoring with advanced synthetic aperture radar images

He, Lei; Tong, Ling; Li, Yuxia; Chen, Yan; Tan, Longfei; Guo, Caizheng

doi:10.1117/1.jrs.10.026008

Cited by 7 publications

(6 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Since accurate soil moisture estimation highly depends on reliable information about the canopy, the fusion of optical (Sentinel-2) and microwave (Sentinel-1) time series [67][68][69] might provide useful phenology stage-based information in terms of LAI, NDVI, VWC, or biomass. The increase in soil moisture sensitivity of the radar signal for later vegetation stages is further related to the loss of plant water after the heading stage, which leads to a more transparent canopy layer and higher sensitivity of the radar waves to the soil surfaces [26,58,60,70]. These findings of high surface scattering during the end of the vegetation period are also supported by similar polarimetric entropy and scattering alpha values for the tillering and ripening stages (Figure 9).…”

Section: Discussionmentioning

confidence: 55%

“…Although the maximum height of the canopy is reached, the transmissivity increases and, therefore, the uncertainty of soil moisture estimations decreases. The higher transmissivity might be explained by the loss of water within the vegetation, whereby the SAR signal is less attenuated by the canopy, and therefore, the SAR signal provides more information about the soil [58][59][60]. Changes in incidence angles do not result in varying soil moisture uncertainties for the IEM_B model combination, whereas within the phenology stages stem elongation to fruit development, the Oh92 model combination exhibits differences in soil moisture uncertainties for varying incidence angles.…”

Section: Sensitivity To Soil Moisture Estimations Over Time For the Rt Modelmentioning

confidence: 96%

See 1 more Smart Citation

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

et al. 2021

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

confidence: 55%

Section: Sensitivity To Soil Moisture Estimations Over Time For the Rt Modelmentioning

confidence: 96%

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

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Therefore, almost no information about the increased biomass and the water loss due to ripening of the plants is given within this model configuration. Plant moisture reduction affects the attenuation of the radar signal by the canopy in a way that the canopy is more transparent for the radar wave [83]. Therefore, the sensitivity of the radar signal to the canopy should decrease, whereas the sensitivity to the surface increases.…”

Section: Static Empirical Parametersmentioning

confidence: 99%

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

et al. 2020

View full text Add to dashboard Cite

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.

show abstract

“…Until now, many modelling studies have been substantially conducted to quantify the radar responses of various crop canopies. Given the simplicity and physical background, semi-empirical models based on original water cloud model (WCM) [18] were often applied to wheat canopy scattering simulation [19][20][21][22]. To further interpret radar signals of crop canopies, physical models driven by datasets measured at crop fields were developed to better explain scattering mechanisms in the growing period.…”

Section: Introductionmentioning

confidence: 99%

A Microwave Scattering Model for Simulating the C-Band SAR Backscatter of Wheat Canopy

Yan¹,

Zhang²,

Yang³

et al. 2019

AJRS

View full text Add to dashboard Cite

Accurate simulation of microwave scattering characteristics of wheat canopy can provide valuable insights into the scattering mechanisms of wheat crops. In this study, a wheat canopy scattering model (WCSM) was developed on a basis of first-order microwave radiative transfer equation. Several WCSM inputs, including wheat canopy and soil parameters, were measured in situ at the time (or near the time) of the satellite observation. The backscattering coefficients of wheat fields were then simulated at various incident angles and polarization modes. Four C-band quad-polarized (Radarsat-2 and Gaofen-3) SAR data were used to evaluate the WCSM performance in four key growth stages of winter wheat from stem elongation to ripening in 2017. Results showed that the WCSM simulated backscattering coefficients of wheat fields with error lower than 1.8 dB. This study demonstrates that the proposed WCSM is effective in characterizing the C-band backscatter features of wheat crops for various growth phases. It also indicated that the operational potential of C-band satellite SAR systems such as the Radarsat-2 and the China Gaofen-3 SAR in monitoring wheat growth for food safety in important agricultural regions.

show abstract

Polarimetric analysis of radar backscatter from ground-based scatterometers and wheat biomass monitoring with advanced synthetic aperture radar images

Cited by 7 publications

References 31 publications

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

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

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

A Microwave Scattering Model for Simulating the C-Band SAR Backscatter of Wheat Canopy

Contact Info

Product

Resources

About