The integral equation model and surface roughness signatures in soil moisture and tillage type determination

1998 ◽  
Vol 36 (3) ◽  
pp. 833-837 ◽  
Author(s):  
B.G. Colpitts
Keyword(s):  
Integral Equation ◽  
Surface Roughness ◽  
Soil Moisture ◽  
Equation Model

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.

The role of vegetation growth on the estimate of soil moisture in a grass field using Sentinel-1 and Sentinel-2 observations and data assimilation 

2021 ◽  
Author(s):  
Roberto Corona ◽  
Laura Fois ◽  
Nicola Montaldo
Keyword(s):  
Integral Equation ◽  
Surface Roughness ◽  
Soil Moisture ◽  
High Spatial Resolution ◽  
Equation Model ◽  
Empirical Models ◽  
Synthetic Aperture ◽  
Vegetation Growth ◽  
Semi Empirical ◽  
Sentinel 2

<p>The state of soil moisture is a key variable controlling surface water and energy balances. Nowadays remote sensors provide the unprecedented opportunity to monitor soil moisture at high time frequency on large spatial scales. The high spatial resolution of radar is a key element for soil moisture mapping of small hydrologic basins with strong spatial variability of physiographic and land cover properties, such as typical of Mediterranean basins. In addition, in the Mediterranean basins, soil moisture changes with strong dynamics, due to both interannual and seasonal rain variability, becoming a key term for water resources management and planning.</p><p>The new constellation of synthetic aperture radar (SAR) satellites, Sentinel-1 A and Sentinel-1B, provides images not only at the high spatial resolution (up to 10 m), typical of radar sensors, but also at high temporal resolutions (6-12 revisit days), with a major advance in the development of an operational soil moisture mapping at the plot.</p><p>Several models have been used for estimating soil moisture over bare soil surfaces from synthetic aperture radar satellites varying from physical models [e.g., the Integral Equation, the Advanced Integral Equation Model and the Integral Equation Model for Multiple Scattering, empirical models (e.g., Dubois model), and semi-empirical models. The main difficulty with SAR imagery is that soil moisture, surface roughness, and vegetation cover all have an important and nearly equal effect on radar backscatter.</p><p>In this work, the potentiality of Sentinel 1 for soil moisture retrieving in a water limited grass field have been tested using three common models for soil moisture retrieval from radar images: the empirical Change detection method, the semi-empirical Dubois model, and the physically based Fung model. For considering the growth vegetation effect on radar signal we propose an empirical model, which used simultaneously the optical Sentinel 2 images.</p><p>The case study is the Orroli site in Sardinia (Italy), a typical semi-arid Mediterranean ecosystem which is an experimental site for the ALTOS European project of the PRIMA MED program.</p><p>The 2016-2018 observation period was characterized by strong interannual rainfall variability, alternating wet and dry years, becoming an interesting opportunity for testing Sentinel 1 and 2 potentiality on soil moisture estimate in a wide range of climate conditions.</p><p>Using the Dubois model for soil moisture retrieval and the proposed model for accounting vegetation growth and surface roughness variability soil moisture was well estimated in both wet and dry conditions when compared with field observations</p><p>The unprecedented high temporal frequency of Sentinel 1 observations provides the opportunity to finally achieve operational procedures for soil moisture assimilation to guide ecohydrologic models. An operational procedure for assimilating soil moisture estimates from Sentinel 1 images in a land surface model using an Ensemble Kalman filter based assimilation scheme has been tested successfully, demonstrating the potentiality of the new generation of Satellite sensors for soil water balance predictions.</p>

scholarly journals Estimation of surface soil moisture and roughness from multi-angular ASAR imagery in the Watershed Allied Telemetry Experimental Research (WATER)

2011 ◽  
Vol 15 (5) ◽  
pp. 1415-1426 ◽  
Author(s):  
S. G. Wang ◽  
X. Li ◽  
X. J. Han ◽  
R. Jin
Keyword(s):  
Surface Roughness ◽  
Soil Moisture ◽  
Experimental Research ◽  
Empirical Relationship ◽  
Regional Scale ◽  
Equation Model ◽  
Heihe River ◽  
Roughness Parameters ◽  
Basin Scale ◽  
The Difference

Abstract. Radar remote sensing has demonstrated its applicability to the retrieval of basin-scale soil moisture. The mechanism of radar backscattering from soils is complicated and strongly influenced by surface roughness. Additionally, retrieval of soil moisture using AIEM (advanced integrated equation model)-like models is a classic example of underdetermined problem due to a lack of credible known soil roughness distributions at a regional scale. Characterization of this roughness is therefore crucial for an accurate derivation of soil moisture based on backscattering models. This study aims to simultaneously obtain surface roughness parameters (standard deviation of surface height σ and correlation length cl) along with soil moisture from multi-angular ASAR images by using a two-step retrieval scheme based on the AIEM. The method firstly used a semi-empirical relationship that relates the roughness slope, Zs (Zs = σ2/cl) and the difference in backscattering coefficient (Δσ) from two ASAR images acquired with different incidence angles. Meanwhile, by using an experimental statistical relationship between σ and cl, both these parameters can be estimated. Then, the deduced roughness parameters were used for the retrieval of soil moisture in association with the AIEM. An evaluation of the proposed method was performed in an experimental area in the middle stream of the Heihe River Basin, where the Watershed Allied Telemetry Experimental Research (WATER) was taken place. It is demonstrated that the proposed method is feasible to achieve reliable estimation of soil water content. The key challenge is the presence of vegetation cover, which significantly impacts the estimates of surface roughness and soil moisture.

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