scholarly journals Estimating Vegetation Water Content and Soil Surface Roughness Using Physical Models of L-Band Radar Scattering for Soil Moisture Retrieval

2018 ◽  
Vol 10 (4) ◽  
pp. 556 ◽  
Author(s):  
Seung-Bum Kim ◽  
Huanting Huang ◽  
Tien-Hao Liao ◽  
Andreas Colliander
Keyword(s):  
Surface Roughness ◽  
Soil Moisture ◽  
Water Content ◽  
Soil Surface ◽  
Physical Models ◽  
Vegetation Water Content ◽  
Radar Scattering ◽  
Soil Surface Roughness ◽  
L Band ◽  
Vegetation Water

Inferring the effects of surface canopy water on VOD estimation from L-band backscatter 

2021 ◽  
Author(s):  
Saeed Khabbazan ◽  
Paul.C. Vermunt ◽  
Susan.C. Steele Dunne ◽  
Ge Gao ◽  
Mariette Vreugdenhil ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Linear Relationship ◽  
Water Content ◽  
Growing Season ◽  
Empirical Parameter ◽  
Vegetation Water Content ◽  
L Band ◽  
Vegetation Water ◽  
Canopy Water ◽  
Effect Of Surface

<p>Quantification of vegetation parameters such as Vegetation Optical Depth (VOD) and Vegetation Water Content (VWC) can be used for better irrigation management, yield forecasting, and soil moisture estimation. Since VOD is directly related to vegetation water content and canopy structure, it can be used as an indicator for VWC. Over the past few decades, optical and passive microwave satellite data have mostly been used to monitor VWC. However, recent research is using active data to monitor VOD and VWC benefitting from their high spatial and temporal resolution.</p><p>Attenuation of the microwave signal through the vegetation layer is parametrized by the VOD. VOD is assumed to be linearly related to VWC with the proportionality constant being an empirical parameter b. For a given wavelength and polarization, b is assumed static and only parametrized as a function of vegetation type. The hypothesis of this study is that the VOD is not similar for dry and wet vegetation and the static linear relationship between attenuation and vegetation water content is a simplification of reality.</p><p>The aim of this research is to understand the effect of surface canopy water on VOD estimation and the relationship between VOD and vegetation water content during the growing season of a corn canopy. In addition to studying the dependence of VOD on bulk VWC for dry and wet vegetation, the effect of different factors, such as different growth stages and internal vegetation water content is investigated using time series analysis.</p><p>A field experiment was conducted in Florida, USA, for a full growing season of sweet corn. The corn field was scanned every 30 minutes with a truck-mounted, fully polarimetric, L-band radar. Pre-dawn vegetation water content was measured using destructive sampling three times a week for a full growing season. VWC could therefore be analyzed by constituent (leaf, stem, ear) or by height. Meteorological data, surface canopy water (dew or interception), and soil moisture were measured every 15 minutes for the entire growing season.</p><p>The methodology of Vreugdenhil et al.  [1], developed by TU Wien for ASCAT data, was adapted to present a new technique to estimate VOD from single-incidence angle backscatter data in each polarization. The results showed that the effect of surface canopy water on the VOD estimation increased by vegetation biomass accumulation and the effect was higher in the VOD estimated from the co-pol compared with the VOD estimated from the cross-pol. Moreover, the surface canopy water considerably affected the regression coefficient values (b-factor) of the linear relationship between VOD and VWC from dry and wet vegetation. This finding suggests that considering a similar b-factor for the dry and the wet vegetation will introduce errors in soil moisture retrievals. Furthermore, it highlights the importance of considering canopy wetness conditions when using tau-omega.</p><ul><li>[1] Vreugdenhil,W. A. Dorigo,W.Wagner, R. A. De Jeu, S. Hahn, andM. J. VanMarle, “Analyzing the vegetation parameterization in the TU-Wien ASCAT soil moisture retrieval,” IEEE Transactions on Geoscience and Remote Sensing, vol. 54, pp. 3513–3531, 2016</li> </ul>

Accounting for soil surface roughness in the inversion of ultrawideband off-ground GPR signal for soil moisture retrieval

Geophysics ◽  
2012 ◽  
Vol 77 (1) ◽  
pp. H1-H7 ◽  
Author(s):  
François Jonard ◽  
Lutz Weihermüller ◽  
Harry Vereecken ◽  
Sébastien Lambot
Keyword(s):  
Surface Roughness ◽  
Soil Moisture ◽  
Water Content ◽  
Soil Surface ◽  
Waveform Analysis ◽  
Surface Reflection ◽  
Full Waveform ◽  
Roughness Model ◽  
Soil Surface Roughness ◽  
Roughness Amplitude

We combined a full-waveform ground-penetrating radar (GPR) model with a roughness model to retrieve surface soil moisture through signal inversion. The proposed approach was validated under laboratory conditions with measurements performed above a sand layer subjected to seven different water contents and four different surface roughness conditions. The radar measurements were performed in the frequency domain in the range of 1–3 GHz and the roughness amplitude standard deviation was varied from 0 to 1 cm. Two inversion strategies were investigated: (1) Full-waveform inversion using the correct model configuration, and (2) inversion focused on the surface reflection only. The roughness model provided a good description of the frequency-dependent roughness effect. For the full-waveform analysis, accounting for roughness permitted us to simultaneously retrieve water content and roughness amplitude. However, in this approach, information on soil layering was assumed to be known. For the surface reflection analysis, which is applicable under field conditions, accounting for roughness only enabled water content to be reconstructed, but with a root mean square error (RMS) in terms of water content of [Formula: see text] compared to an RMS of [Formula: see text] for an analysis where roughness is neglected. However, this inversion strategy required a priori information on soil surface roughness, estimated, e.g., from laser profiler measurements.

Sensitivity of Passive Microwave Observations to Soil Moisture and Vegetation Water Content: L-Band to W-Band

2011 ◽  
Vol 49 (4) ◽  
pp. 1190-1199 ◽  
Author(s):  
Jean-Christophe Calvet ◽  
Jean-Pierre Wigneron ◽  
Jeffrey Walker ◽  
Fatima Karbou ◽  
André Chanzy ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Water Content ◽  
Passive Microwave ◽  
Vegetation Water Content ◽  
W Band ◽  
L Band ◽  
Vegetation Water

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

2020 ◽  
Vol 12 (14) ◽  
pp. 2303 ◽  
Author(s):  
Chunfeng Ma ◽  
Xin Li ◽  
Matthew F. McCabe
Keyword(s):  
Surface Roughness ◽  
Soil Moisture ◽  
High Resolution ◽  
Water Content ◽  
Cloud Model ◽  
Cosmic Ray ◽  
Vegetation Water Content ◽  
Water Cloud ◽  
Vegetation Water ◽  
Sentinel 2

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.

Detecting changes in root zone soil moisture from radar vegetation backscatter

2020 ◽  
Author(s):  
Coleen Carranza ◽  
Tim van Emmerik ◽  
Martine van der Ploeg
Keyword(s):  
Soil Moisture ◽  
Water Content ◽  
Hydrological Cycle ◽  
Root Zone ◽  
Water Status ◽  
Roughness Parameter ◽  
Growing Season ◽  
Irrigation Scheduling ◽  
Vegetation Water Content ◽  
Vegetation Water

<p>Root zone soil moisture (θ<sub>rz</sub>) is a crucial component of the hydrological cycle and provides information for drought monitoring, irrigation scheduling, and carbon cycle modeling. During vegetation conditions, estimation of θ<sub>rz</sub> thru radar has so far only focused on retrieving surface soil moisture using the soil component of the total backscatter (σ<sub>soil</sub>), which is then assimilated into physical hydrological models. The utility of the vegetation component of the total backscatter (σ<sub>veg</sub>) has not been widely explored and is commonly corrected for in most soil moisture retrieval methods. However, σ<sub>veg </sub>provides information about vegetation water content. Furthermore, it has been known in agronomy that pre-dawn leaf water potential is in equilibrium with that of the soil. Therefore soil water status can be inferred by examining  the vegetation water status. In this study, our main goal is to determine whether changes in root zone soil moisture (Δθ<sub>rz</sub>) shows corresponding changes in vegetation backscatter (Δσ<sub>veg</sub>) at pre-dawn. We utilized Sentinel-1 (S1) descending pass and in situ soil moisture measurements from 2016-2018 at two soil moisture networks (Raam and Twente) in the Netherlands. We focused on corn and grass which are the most dominant crops at the sites and considered the depth-averaged θ<sub>rz</sub> up to 40 cm to capture the rooting depths for both crops. Dubois’ model formulation for VV-polarization was applied to estimate the surface roughness parameter (H<sub>rms</sub>) and σ<sub>soil </sub>during vegetated periods. Afterwards, the Water Cloud Model was used to derive σ<sub>veg</sub> by subtracting σ<sub>soil</sub> from S1 backscatter (σ<sub>tot</sub>). To ensure that S1 only measures vegetation water content, rainy days were excluded to remove the influence of intercepted rainfall on the backscatter. The slope of regression lines (β) fitted over plots of Δσ<sub>veg</sub> against Δθ<sub>rz</sub> were used investigate the dynamics over a growing season. Our main result indicates that Δσ<sub>veg </sub>- Δθ<sub>rz</sub> relation is influenced by crop growth stage and changes in water content in the root zone. For corn, changes in β’s over a growing season follow the trend in a crop coefficient (K<sub>c</sub>) curve, which is a measure of crop water requirements. Grasses, which are perennial crops, show trends corresponding to the mature crop stage. The correlation between soil moisture (Δθ) at specific soil depths (5, 10, 20, and 40 cm) and Δσ<sub>veg </sub> matches root growth for corn and known rooting depths for both corn and grass. Dry spells (e.g. July 2018) and a large increase in root zone water content in between two dry-day S1 overpass (e.g. from rainfall) result in a lower β, which indicates that Δσ<sub>veg</sub> does not match well with Δθ<sub>rz</sub>. The influence of vegetation on S1 backscatter is more pronounced for corn, which translated to a clearer Δσ<sub>veg</sub> - Δθ<sub>rz</sub> relation compared to grass. The sensitivity of Δσ<sub>veg</sub> to Δθ<sub>rz</sub> in corn means that the analysis may be applicable to other broad leaf crops or forested areas, with potential applications for monitoring  periods of water stress.</p>

Sign in / Sign up

Export Citation Format

Share Document