scholarly journals Retrieving the Soil Moisture in Bare Farmland Areas Using a Modified Dubois Model

2021 ◽  
Vol 9 ◽  
Author(s):  
Teng Ma ◽  
Ling Han ◽  
Quanming Liu
Keyword(s):  
Surface Roughness ◽  
Soil Moisture ◽  
Agricultural Research ◽  
Incidence Angle ◽  
High Accuracy ◽  
Observation Data ◽  
Modified Model ◽  
Environmental Evaluation ◽  
Retrieval Accuracy ◽  
Low Incidence

Soil moisture is an important parameter for global soil moisture transport, environmental evaluation, and precision agricultural research. The accurate retrieval of soil moisture in farmland areas using Synthetic Aperture Radar (SAR) depends on the accurate description of surface and SAR parameters. In these parameters, surface roughness and incidence angle are the key factors that affect the accuracy of the soil moisture retrieval model. This article proposes a modified Dubois model to retrieve soil moisture suitable for the bare surface of farmland area. The model eliminates the incidence angle parameters and uses polarization parameters to depict the surface roughness parameters in the Dubois model. To eliminate the incidence angle, the backscattering coefficients gamma0, which eliminates the effect of the incidence angles, are used to replace the sigma0. Under rain and no rain condition, the trend of backscattering coefficients (VH and VV) and cross-polarization ratio (VH-VV) of different soil texture with the soil moisture are compared. Then, the polarization parameter based on VH backscattering coefficients is used to describe surface roughness. The model is evaluated with time-series soil moisture observation data in situ of the study area. The results indicate that the modified model can retrieve soil moisture with high accuracy, and the total RMSE can reach 0.064 cm3cm−3 while the Dubois model is 0.124 cm3cm−3. Under rain and no rain condition, the retrieval accuracy of the modified model is 0.066 cm3cm−3 and 0.063 cm3cm−3. The retrieval accuracy is 0.060 cm3cm−3 and 0.067 cm3cm−3 under high and low incidence angles conditions, respectively. These results indicate that the modified Dubois model can retrieve soil moisture with high accuracy under different conditions.

Soil moisture and vegetation effects on the ASCAT backscatter-incidence angle dependence

2021 ◽  
Author(s):  
Isabella Pfeil ◽  
Wolfgang Wagner ◽  
Sebastian Hahn ◽  
Raphael Quast ◽  
Susan Steele-Dunne ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Radiative Transfer ◽  
Incidence Angle ◽  
Transfer Model ◽  
Study Region ◽  
Vegetation Effects ◽  
First Order ◽  
Vegetation Parameters ◽  
Low Incidence

<div> <p>Soil moisture (SM) datasets retrieved from the advanced scatterometer (ASCAT) sensor are well established and widely used for various hydro-meteorological, agricultural, and climate monitoring applications. Besides SM, ASCAT is sensitive to vegetation structure and vegetation water content, enabling the retrieval of vegetation optical depth (VOD; 1). The challenge in the retrieval of SM and vegetation products from ASCAT observations is to separate the two effects. As described by Wagner et al. (2), SM and vegetation affect the relation between backscatter and incidence angle differently.  At high incidence angles, the response from bare soil and thus the sensitivity to SM conditions is significantly weaker than at low incidence angles, leading to decreasing backscatter with increasing incidence angle. The presence of vegetation on the other hand decreases the backscatter dependence on the incidence angle. The dependence of backscatter on the incidence angle can be described by a second-order Taylor polynomial based on a slope and a curvature coefficient. It was found empirically that SM conditions have no significant effect on the steepness of the slope, and that therefore, SM and vegetation effects can be separated using the slope (2).  This is a major assumption in the TU Wien soil moisture retrieval algorithm used in several operational soil moisture products. However, recent findings by Quast et al. (3) using a first-order radiative transfer model for the inversion of soil and vegetation parameters from scatterometer observations indicate that SM may influence the slope, as the SM-induced backscatter increase is more pronounced at low incidence angles. </p> </div><div> <div> <p>The aim of this analysis is to revisit the assumption that SM does not affect the slope of the backscatter incidence angle relations by investigating if short-term variability, observed in ASCAT slope timeseries on top of the seasonal vegetation cycle, is caused by SM. We therefore compare timeseries and anomalies of the ASCAT slope to air temperature, rainfall and SM from the ERA5-Land dataset. We carry out the analysis in a humid continental climate (Austria) and a Mediterranean climate study region (Portugal). First results show significant negative correlations between slope and SM anomalies. However, correlations between temperature and slope anomalies are of a similar magnitude, albeit positive, which may reflect temperature-induced vegetation dynamics. The fact that temperature and SM are strongly correlated with each other complicates the interpretation of the results. Thus, our second approach is to investigate daily slope values and their change between dry and wet days. The results of this study shall help to quantify the uncertainties in ASCAT SM products caused by the potentially inadequate assumption of a SM-independent slope. </p> </div> <div> <p> </p> </div> <div> <p>(1) Vreugdenhil, Mariette, et al. "Analyzing the vegetation parameterization in the TU-Wien ASCAT soil moisture retrieval." IEEE Transactions on Geoscience and Remote Sensing 54.6 (2016): 3513-3531.</p> <p><span>(2) Wagner, Wolfgang, et al. "Monitoring soil moisture over the Canadian Prairies with the ERS scatterometer." IEEE Transactions on Geoscience and Remote Sensing 37.1 (1999): 206-216. </span></p> </div> <div> <p>(3) Quast, Raphael, et al. "A Generic First-Order Radiative Transfer Modelling Approach for the Inversion of Soil and Vegetation Parameters from Scatterometer Observations." Remote Sensing 11.3 (2019): 285.</p> </div> </div>

scholarly journals Calibrated Integral Equation Model for Bare Soil Moisture Retrieval of Synthetic Aperture Radar: A Case Study in Linze County

2020 ◽  
Vol 10 (21) ◽  
pp. 7921
Author(s):  
Ling Zhang ◽  
Hao Li ◽  
Zhaohui Xue
Keyword(s):  
Integral Equation ◽  
Surface Roughness ◽  
Soil Moisture ◽  
Synthetic Aperture Radar ◽  
Correlation Length ◽  
Empirical Model ◽  
Incidence Angle ◽  
Bare Soil ◽  
Equation Model ◽  
Synthetic Aperture

Soil moisture plays a significant role in surface energy balance and material exchange. Synthetic aperture radar (SAR) provides a promising data source to monitor soil moisture. However, soil surface roughness is a key difficulty in bare soil moisture retrieval. To reduce the measurement error of the correlation length and improve the inversion accuracy, we used the surface roughness (Hrms, root mean surface height) and empirical correlation length lopt as proposed by Baghdadi to introduce analytical equations of the backscattering coefficient using the calibrated integral equation model (CIEM). This empirical model was developed based on analytical equations to invert soil moisture for Hrms between 0.5 and 4 cm. Experimental results demonstrated that when the incidence angle varied from 33.5° to 26.3°, R2 of the retrieved and measured soil moisture decreased from 0.67 to 0.57, and RMSE increased from 2.53% to 5.4%. Similarly, when the incidence angle varied from 33.5° to 26.3°, R2 of the retrieved and measured Hrms decreased from 0.64 to 0.51, and RMSE increased from 0.33 to 0.4 cm. Therefore, it is feasible to use the empirical model to invert soil moisture and surface roughness for bare soils. In the inversion of the soil moisture and Hrms, using Hrms and the empirical correlation length lopt as the roughness parameters in the simulations is sufficient. The empirical model has favorable validity when the incidence angle is set to 33.5° and 26.3° at the C-band.

Assessing simultaneous mono- and bistatic airborne radar observations for soil moisture retrieval

2020 ◽  
Author(s):  
Emma Tronquo ◽  
Hans Lievens ◽  
Niko E.C. Verhoest
Keyword(s):  
Surface Roughness ◽  
Soil Moisture ◽  
Incidence Angle ◽  
Added Value ◽  
Theoretical Research ◽  
Initial Assessment ◽  
Soil Conditions ◽  
Airborne Measurements ◽  
L Band ◽  
The Impact

<div> </div><p>Current radar systems are generally monostatic. However, some theoretical research indicated the potential of bistatic radar measurements to improve applications. In the research presented, we explore the use of InSAR/PolInSAR mono- and bistatic measurements acquired in L-band for soil moisture monitoring. The main objective of this study is to compare the performance of soil moisture retrieval from monostatic with that obtained through bistatic observations.</p><p>The recent BelSAR campaign (in 2018) provided time series of airborne mono- and bistatic measurements at L-band, recorded during the growing season including bare soil conditions. In addition, in situ measurements of soil moisture and surface roughness were acquired concurrently with the airborne flights. Here, we provide an initial assessment of the sensitivity of the scatter observations with respect to soil moisture and surface roughness. The literature suggests that the impact of surface roughness on the retrieval of soil moisture decreases due to the simultaneous use of the mono- and bistatic measurements. However, our preliminary results show that the bistatic data do not provide substantial added value to reduce the impact of surface roughness on soil moisture retrieval. Further, we validate both mono- and bistatic scatter simulations from the Advanced Integral Equation Model (AIEM) using the airborne measurements. The AIEM allows additional investigations with respect to the sensitivity towards surface roughness and soil moisture of both mono- and bistatic scattering signals, as well as the impacts of sensor-related parameters such as the incidence angle, the bistatic configuration (e.g. the location of the second sensor), the frequency and the polarization.</p><p> </p><p> </p>

scholarly journals Single-Pass Soil Moisture Retrievals Using GNSS-R: Lessons Learned

2020 ◽  
Vol 12 (12) ◽  
pp. 2064 ◽  
Author(s):  
Adriano Camps ◽  
Hyuk Park ◽  
Jordi Castellví ◽  
Jordi Corbera ◽  
Emili Ascaso
Keyword(s):  
Surface Roughness ◽  
Soil Moisture ◽  
Ad Hoc ◽  
Surface Soil ◽  
Incidence Angle ◽  
High Surface ◽  
Satellite System ◽  
Lessons Learned ◽  
Surface Soil Moisture ◽  
Roughness Effects

In this paper, an algorithm to retrieve surface soil moisture from GNSS-R (Global Navigaton Satellite System Reflectometry) observations is presented. Surface roughness and vegetation effects are found to be the most critical ones to be corrected. On one side, the NASA SMAP (Soil Moisture Active and Passive) correction for vegetation opacity (multiplied by two to account for the descending and ascending passes) seems too high. Surface roughness effects cannot be compensated using in situ measurements, as they are not representative. An ad hoc correction for surface roughness, including the dependence with the incidence angle, and the actual reflectivity value is needed. With this correction, reasonable surface soil moisture values are obtained up to approximately a 30° incidence angle, beyond which the GNSS-R retrieved surface soil moisture spreads significantly.

scholarly journals Impacts of Radiometric Uncertainty and Weather-Related Surface Conditions on Soil Moisture Retrievals with Sentinel-1

2019 ◽  
Vol 11 (17) ◽  
pp. 2025 ◽  
Author(s):  
Harm-Jan Benninga ◽  
Rogier van der Velde ◽  
Zhongbo Su
Keyword(s):  
Surface Roughness ◽  
Soil Moisture ◽  
Surface Area ◽  
Incidence Angle ◽  
Surface Condition ◽  
Integral Equation Method ◽  
Surface Scattering ◽  
Small Surface ◽  
Surface Conditions ◽  
Surface Areas

The radiometric uncertainty of Synthetic Aperture Radar (SAR) observations and weather-related surface conditions caused by frozen conditions, snow and intercepted rain affect the backscatter ( σ 0 ) observations and limit the accuracy of soil moisture retrievals. This study estimates Sentinel-1’s radiometric uncertainty, identifies the effects of weather-related surface conditions on σ 0 and investigates their impact on soil moisture retrievals for various conditions regarding soil moisture, surface roughness and incidence angle. Masking rules for the surface conditions that disturb σ 0 were developed based on meteorological measurements and timeseries of Sentinel-1 observations collected over five forests, five meadows and five cultivated fields in the eastern part of the Netherlands. The Sentinel-1 σ 0 observations appear to be affected by frozen conditions below an air temperature of 1 ∘ C , snow during Sentinel-1’s morning overpasses on meadows and cultivated fields and interception after more than 1.8 m m of rain in the 12 h preceding a Sentinel-1 overpass, whereas dew was not found to be of influence. After the application of these masking rules, the radiometric uncertainty was estimated by the standard deviation of the seasonal anomalies timeseries of the Sentinel-1 forest σ 0 observations. By spatially averaging the σ 0 observations, the Sentinel-1 radiometric uncertainty improves from 0.85 dB for a surface area of 0.25 h a to 0.30 dB for 10 h a for the VV polarization and from 0.89 dB to 0.36 dB for the VH polarization, following approximately an inverse square root dependency on the surface area over which the σ 0 observations are averaged. Deviations in σ 0 were combined with the σ 0 sensitivity to soil moisture as simulated with the Integral Equation Method (IEM) surface scattering model, which demonstrated that both the disturbing effects by the weather-related surface conditions (if not masked) and radiometric uncertainty have a significant impact on the soil moisture retrievals from Sentinel-1. The soil moisture retrieval uncertainty due to radiometric uncertainty ranges from 0.01 m 3 m − 3 up to 0.17 m 3 m − 3 for wet soils and small surface areas. The impacts on soil moisture retrievals are found to be weakly dependent on the surface roughness and the incidence angle, and strongly dependent on the surface area (or the σ 0 disturbance caused by a weather-related surface condition for a specific land cover type) and the soil moisture itself.

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

2021 ◽  
Vol 13 (19) ◽  
pp. 3889
Author(s):  
Chunfeng Ma ◽  
Shuguo Wang ◽  
Zebin Zhao ◽  
Hanqing Ma
Keyword(s):  
Sensitivity Analysis ◽  
Surface Roughness ◽  
Soil Moisture ◽  
Cloud Model ◽  
Incidence Angle ◽  
Agricultural Field ◽  
Estimation Of Parameters ◽  
Spatiotemporal Resolution ◽  
Water Cloud ◽  
Vegetation Parameters

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.

A Semiempirical Modeling of Soil Moisture, Vegetation, and Surface Roughness Impact on CYGNSS Reflectometry Data

2020 ◽  
pp. 1-17
Author(s):  
Simon H. Yueh ◽  
Rashmi Shah ◽  
M. Julian Chaubell ◽  
Akiko Hayashi ◽  
Xiaolan Xu ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Soil Moisture

scholarly journals Towards Including Dynamic Vegetation Parameters in the EUMETSAT H SAF ASCAT Soil Moisture Products

2021 ◽  
Vol 13 (8) ◽  
pp. 1463
Author(s):  
Susan C. Steele-Dunne ◽  
Sebastian Hahn ◽  
Wolfgang Wagner ◽  
Mariette Vreugdenhil
Keyword(s):  
Soil Moisture ◽  
Interannual Variability ◽  
Incidence Angle ◽  
Pearson Correlation ◽  
The United States ◽  
Research Network ◽  
Window Width ◽  
High Frequency Variability ◽  
Vegetation Parameters ◽  
The Impact

The TU Wien Soil Moisture Retrieval (TUW SMR) approach is used to produce several operational soil moisture products from the Advanced Scatterometer (ASCAT) on the Metop series of satellites as part of the EUMETSAT Satellite Application Facility on Support to Operational Hydrology and Water Management (H SAF). The incidence angle dependence of backscatter is described by a second-order Taylor polynomial, the coefficients of which are used to normalize ASCAT observations to the reference incidence angle of 40∘ and for correcting vegetation effects. Recently, a kernel smoother was developed to estimate the coefficients dynamically, in order to account for interannual variability. In this study, we used the kernel smoother for estimating these coefficients, where we distinguished for the first time between their two uses, meaning that we used a short and fixed window width for the backscatter normalisation while we tested different window widths for optimizing the vegetation correction. In particular, we investigated the impact of using the dynamic vegetation parameters on soil moisture retrieval. We compared soil moisture retrievals based on the dynamic vegetation parameters to those estimated using the current operational approach by examining their agreement, in terms of the Pearson correlation coefficient, unbiased RMSE and bias with respect to in situ soil moisture. Data from the United States Climate Research Network were used to study the influence of climate class and land cover type on performance. The sensitivity to the kernel smoother half-width was also investigated. Results show that estimating the vegetation parameters with the kernel smoother can yield an improvement when there is interannual variability in vegetation due to a trend or a change in the amplitude or timing of the seasonal cycle. However, using the kernel smoother introduces high-frequency variability in the dynamic vegetation parameters, particularly for shorter kernel half-widths.

scholarly journals A GNSS-IR Method for Retrieving Soil Moisture Content from Integrated Multi-Satellite Data That Accounts for the Impact of Vegetation Moisture Content

2021 ◽  
Vol 13 (13) ◽  
pp. 2442
Author(s):  
Jichao Lv ◽  
Rui Zhang ◽  
Jinsheng Tu ◽  
Mingjie Liao ◽  
Jiatai Pang ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Satellite Data ◽  
Vegetation Index ◽  
Soil Moisture Content ◽  
Support Vector ◽  
Observation Data ◽  
Multipath Effect ◽  
Mars Model ◽  
The Impact

There are two problems with using global navigation satellite system-interferometric reflectometry (GNSS-IR) to retrieve the soil moisture content (SMC) from single-satellite data: the difference between the reflection regions, and the difficulty in circumventing the impact of seasonal vegetation growth on reflected microwave signals. This study presents a multivariate adaptive regression spline (MARS) SMC retrieval model based on integrated multi-satellite data on the impact of the vegetation moisture content (VMC). The normalized microwave reflection index (NMRI) calculated with the multipath effect is mapped to the normalized difference vegetation index (NDVI) to estimate and eliminate the impact of VMC. A MARS model for retrieving the SMC from multi-satellite data is established based on the phase shift. To examine its reliability, the MARS model was compared with a multiple linear regression (MLR) model, a backpropagation neural network (BPNN) model, and a support vector regression (SVR) model in terms of the retrieval accuracy with time-series observation data collected at a typical station. The MARS model proposed in this study effectively retrieved the SMC, with a correlation coefficient (R2) of 0.916 and a root-mean-square error (RMSE) of 0.021 cm3/cm3. The elimination of the vegetation impact led to 3.7%, 13.9%, 11.7%, and 16.6% increases in R2 and 31.3%, 79.7%, 49.0%, and 90.5% decreases in the RMSE for the SMC retrieved by the MLR, BPNN, SVR, and MARS model, respectively. The results demonstrated the feasibility of correcting the vegetation changes based on the multipath effect and the reliability of the MARS model in retrieving the SMC.

Sign in / Sign up

Export Citation Format

Share Document