A Generic First-Order Radiative Transfer Modelling Approach for the Inversion of Soil and Vegetation Parameters from Scatterometer Observations

Quast, Raphael; Albergel, Clément; Calvet, Jean‐Christophe; Wagner, Wolfgang

doi:10.3390/rs11030285

Cited by 28 publications

(22 citation statements)

References 51 publications

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“…A previous study has tried characterizing σ and σ as time series based on a kernel smoother showing promising results [62], however, further work is needed to combine this method with the new selection of cross-over angles investigated in this study. In addition, the assumptions behind the TU Wien soil moisture retrieval algorithm have been tested and evaluated against a newly developed radiative transfer model (RT1) [63], which has been recently applied to ASCAT [64]. RT1 has shown a very similar functional behavior, but also differences e.g.…”

Section: Discussionmentioning

confidence: 99%

Improving ASCAT Soil Moisture Retrievals With an Enhanced Spatially Variable Vegetation Parameterization

Hahn

Wagner

Steele‐Dunne

et al. 2021

IEEE Trans. Geosci. Remote Sensing

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This study investigates the performance of the TU Wien soil moisture retrieval algorithm (TUW-SMR) by adapting the strength of the vegetation correction. The semiempirical change detection method TUW-SMR exploits the multi-angle backscatter observations from spaceborne fan-beam scatterometer systems in order to derive surface soil moisture information expressed in degree of saturation. The vegetation parameterization of TUW-SMR is controlled by the dry and wet cross-over angles that are used to determine the dry and wet backscatter reference. Backscatter observations from the Advanced Scatterometer (ASCAT) are used to produce four soil moisture data sets based on different dry and wet crossover angles describing: (i) a static respectively no vegetation correction, (ii) the currently used seasonal vegetation correction (iii) a stronger seasonal vegetation correction and (iv) a spatiallyvariable seasonal vegetation correction with the stronger vegetation correction over vegetated areas and no vegetation correction over bare land. All four ASCAT soil moisture data sets are evaluated against soil moisture estimates from GLDAS-2.1 Noah land surface model and the ESA CCI Passive v04.5 soil moisture product using the triple collocation method and a traditional correlation analysis. The results show that the spatially-variable vegetation correction overall improves soil moisture estimates in both more densely vegetated areas, e.g. in large parts of North America and Europe, and more sparsely-vegetated, e.g. Western Africa. Nonetheless, the experiment also provides insight into challenging retrieval conditions where the TUW-SMR fails to take all relevant backscatter processes into account, e.g. wetlands and bare soils with sub-surface scattering.

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Section: Discussionmentioning

confidence: 99%

Improving ASCAT Soil Moisture Retrievals With an Enhanced Spatially Variable Vegetation Parameterization

Hahn

Wagner

Steele‐Dunne

et al. 2021

IEEE Trans. Geosci. Remote Sensing

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“…These model simulations of radar backscatter from agricultural fields are based on sensor and platform configurations (e.g., incidence angle, azimuth angle, frequency, and polarization), soil properties (e.g., soil moisture, texture, and surface roughness), and vegetation parameters (e.g., Leaf Area Index (LAI), Normalized Difference Vegetation Index (NDVI), Vegetation Water Content (VWC), and biomass) [19,29]. In recent decades, several complex models such as the Michigan Canopy Scattering Model (MIMICS) [31], the Tor Vergata model [32], a first-order radiative transfer model from Quast [33,34], and a Wheat Canopy Scattering Model (WCSM) [27] have been developed for modeling the electromagnetic scattering of different vegetation types by using the first or second order of the RT equation. For precisely modeling the backscatter characteristics that occur during different phenology stages of winter wheat, complex models with detailed information, such as their canopy element size and distribution (length; diameter; thickness; and water content fraction of the stem, leaf, and ears) are needed to account for a multi-layer volume canopy [27].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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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.

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“…A possible explanation for the mismatch between VOD and LAI time series was that VOD at C-band may be sensitive to non-photosynthetic vegetation parts (e.g., branches in the case of forests) in addition to leaves. Finally, the WCM may not be able to represent all components of scattering within vegetation canopies [10].…”

Section: Introductionmentioning

confidence: 99%

Interpretation of ASCAT Radar Scatterometer Observations Over Land: A Case Study Over Southwestern France

et al. 2019

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This paper investigates to what extent soil moisture and vegetation density information can be extracted from the Advanced Scatterometer (ASCAT) satellite-derived radar backscatter (σ°) in a data assimilation context. The impact of independent estimates of the surface soil moisture (SSM) and leaf area index (LAI) of diverse vegetation types on ASCAT σ° observations is simulated over southwestern France using the water cloud model (WCM). The LAI and SSM variables used by the WCM are derived from satellite observations and from the Interactions between Soil, Biosphere, and Atmosphere (ISBA) land surface model, respectively. They permit the calibration of the four parameters of the WCM describing static soil and vegetation characteristics. A seasonal analysis of the model scores shows that the WCM has shortcomings over karstic areas and wheat croplands. In the studied area, the Klaus windstorm in January 2009 damaged a large fraction of the Landes forest. The ability of the WCM to represent the impact of Klaus and to simulate ASCAT σ° observations in contrasting land-cover conditions is explored. The difference in σ° observations between the forest zone affected by the storm and the bordering agricultural areas presents a marked seasonality before the storm. The difference is small in the springtime (from March to May) and large in the autumn (September to November) and wintertime (December to February). After the storm, hardly any seasonality was observed over four years. This study shows that the WCM is able to simulate this extreme event. It is concluded that the WCM could be used as an observation operator for the assimilation of ASCAT σ° observations into the ISBA land surface model.

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A Generic First-Order Radiative Transfer Modelling Approach for the Inversion of Soil and Vegetation Parameters from Scatterometer Observations

Cited by 28 publications

References 51 publications

Improving ASCAT Soil Moisture Retrievals With an Enhanced Spatially Variable Vegetation Parameterization

Improving ASCAT Soil Moisture Retrievals With an Enhanced Spatially Variable Vegetation Parameterization

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

Interpretation of ASCAT Radar Scatterometer Observations Over Land: A Case Study Over Southwestern France

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