The Discrepancy Between Backscattering Model Simulations and Radar Observations Caused by Scaling Issues: An Uncertainty Analysis

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Estimating soil moisture based on synthetic aperture radar (SAR) data remains challenging due to the influences of vegetation and surface roughness. Here we present an algorithm that simultaneously retrieves soil moisture, surface roughness and vegetation water content by jointly using high-resolution Sentinel-1 SAR and Sentinel-2 multispectral imagery, with an application directed towards the provision of information at the precision agricultural scale. Sentinel-2-derived vegetation water indices are investigated and used to quantify the backscatter resulting from the vegetation canopy. The proposed algorithm then inverts the water cloud model to simultaneously estimate soil moisture and surface roughness by minimizing a cost function constructed by model simulations and SAR observations. To examine the performance of VV- and VH-polarized backscatters on soil moisture retrievals, three retrieval schemes are explored: a single channel algorithm using VV (SCA-VV) and VH (SCA-VH) polarizations and a dual channel algorithm using both VV and VH polarizations (DCA-VVVH). An evaluation of the approach using a combination of a cosmic-ray soil moisture observing system (COSMOS) and Soil Climate Analysis Network measurements over Nebraska shows that the SCA-VV scheme yields good agreement at both the COSMOS footprint and single-site scales. The features of the algorithms that have the most impact on the retrieval accuracy include the vegetation water content estimation scheme, parameters of the water cloud model and the specification of initial ranges of soil moisture and roughness, all of which are comprehensively analyzed and discussed. Through careful consideration and selection of these factors, we demonstrate that the proposed SCA-VV approach can provide reasonable soil moisture retrievals, with RMSE ranging from 0.039 to 0.078 m3/m3 and R2 ranging from 0.472 to 0.665, highlighting the utility of SAR for application at the precision agricultural scale.

Section: Discussionmentioning

confidence: 45%

Section: Evaluating Response Of Sentinel-1 To Surface Parametersmentioning

confidence: 57%

Retrieval of High-Resolution Soil Moisture through Combination of Sentinel-1 and Sentinel-2 Data

McCabe

2020

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“…(2) In order to provide information for optimal selection of vegetation description, we respectively conducted SA experiments under the Park scheme and Bindlish scheme, hence suggesting an optimal descriptor towards the retrieval of parameters in future work. (3) In order to address the influence of parameter ranges on their SIs and ranks, an experiment was performed to see the parameter SIs and their importance rank under various VWC ranges, which was expected to provide an implication as to the feasibility of soil moisture retrieval at various surface conditions; the full range of VWC (0-6.0 kg/m 2 ) was artificially divided into four sub-ranges, 0-1.5 kg/m 2 , 1.5-3.0 kg/m 2 , 3.0-4.5 kg/m 2 , 4.5-6.0 kg/m 2 , and SA tests were conducted under each subrange to see the variation of the parameter SIs. (4) To address the issue of selecting an optimal SAR configuration for parameter estimation, an experiment was designed to search the optimal incidence angle and polarization for key parameters retrieval through analyzing parameter SIs under different polarizations and incidence angles; through changing the incidence angle from 20 to 46 degrees (note this range is set according to the configuration of extra wide swath mode, which is slightly larger than the range of 29 to 46 degrees of interferometric wide mode) with a step of 1 degree, we computed parameter SIs at each incidence angle and observed the changes in them.…”

Section: Design Of Experimentsmentioning

confidence: 99%

“…This unique scientific and practical prospect has gradually become a reality, especially since the launch of Sentinel-1 satellites [2]. However, insufficient understanding of the microwave backscattering mechanism is one of the most challenging issues for retrieving highly accurate soil moisture from SAR [3]. A comprehensive understanding of the response of SAR observations to the surface permittivity and geometric properties is key to estimating soil moisture accurately because of the complicity of the interaction between radar observations and soil surface variables, that is, the SAR observations are jointly determined by various surface properties, such as soil moisture, surface roughness, Remote Sens.…”

Section: Introductionmentioning

confidence: 99%

Global Sensitivity Analysis of a Water Cloud Model toward Soil Moisture Retrieval over Vegetated Agricultural Fields

Wang

Zhang

et al. 2021

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The release of high-spatiotemporal-resolution Sentinel-1 Synthetic Aperture Radar (SAR) data to the public has provided an unprecedented opportunity to map soil moisture at watershed and agricultural field scales. However, the existing retrieval algorithms fail to derive soil moisture with expected accuracy. Insufficient understanding of the effects of soil and vegetation parameters on the backscatters is an important reason for this failure. To this end, we present a Sensitivity Analysis (SA) to quantify the effects of parameters on the dual-polarized backscatters of Sentinel-1 based on a Water Cloud Model (WCM) and multiple global SA methods. The identification of the incidence angle and polarization of Sentinel-1 and the description scheme of vegetation parameters (A, B and α) in WCM are especially emphasized in this analysis towards an optimal estimation of parameters. Multiple SA methods derive identical parameter importance ranks, indicating that a highly reasonable and reliable SA is performed. Comparison between two existing vegetation description schemes shows that the scheme using Vegetation Water Content (VWC) outperforms the scheme combing particle moisture content and VWC. Surface roughness, soil moisture, VWC, and B, are most sensitive on the backscatters. Variation of parameter sensitivity indices with incidence angle at different polarizations indicates that VV- and VH- polarized backscatters at small incidence angles are the optimal options for soil moisture and surface roughness estimation, respectively, while VV-polarized backscatter at larger incidence angles is well-suited for VWC and B estimation and HH-polarized backscatter is well suited for roughness estimation. This analysis improves the understanding of the effects of vegetated surface parameters on multi-angle and multi-polarized backscatters of Sentinel-1 SAR, informing improvement in SAR-based soil moisture retrieval.

“…Due to high spatiotemporal continuity, modeled and RS SM products have been widely used as alternative reference data [12][13][14][15][16][17]. However, the scale dependence of input parameters in the simulation and retrieval SM leads to difficulty in quantifying the uncertainties of modeled and RS SM products [18,19], which is caused by the imprecise expression of subgrid heterogeneity information. To capture the spatial heterogeneity of SM within a large-scale grid, RS ancillary data related to SM with a high spatial resolution relative to RS SM products, e.g., terrain data [20] and optical RS data [21][22][23][24][25], are employed; therefore, they have the potential to estimate a reliable reference dataset for decomposing the errors of RS SM products.…”

Section: Introductionmentioning

confidence: 99%

Error Decomposition of Remote Sensing Soil Moisture Products Based on the Triple-Collocation Method Introducing an Unbiased Reference Dataset: A Case Study on the Tibetan Plateau

Kang

Jin

et al. 2020

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Remote sensing (RS) soil moisture (SM) products have been widely used in various environmental studies. Understanding the error structure of data is necessary to properly apply RS SM products in trend and variation analysis and data fusion. However, a spatially continuous assessment of RS SM datasets is impeded by the limited spatial distribution of ground-based observations. As an alternative, the RS apparent thermal inertia (ATI) data related to the SM are transformed into SM values to expand the validation space. To obtain error components, the ATI-based SM along with the Soil Moisture Active Passive Mission (SMAP) and Advanced Microwave Scanning Radiometer 2 (AMSR2) SM are applied with the triple-collocation (TC) method to evaluate the RS SM data regarding random errors and amplitude variances at the regional scale. When the ATI-based SM is regarded as the reference data, the amplitude biases of the other two datasets are determined. The mean bias is also estimated by calculating the mean value difference between the ATI-based and validated RS SM. The results show that the ATI-based SM is a reliable source of reference data that, when combined with the TC method, can correctly estimate the error structure of RS SM datasets in wide space, promoting the reasonable application and calibration of RS SM datasets.