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 GEOPHYSICAL PARAMETER RETRIEVAL FOR MICROWAVE C BAND SYNTHETIC APERTURE RADAR (SAR) DATASET USING INTEGRAL EQUATION MODEL

2018 ◽  
Vol XLII-5 ◽  
pp. 689-692
Author(s):  
S. B. Sayyad ◽  
M. A. Shaikh ◽  
S. B. Kolhe ◽  
P. W. Khirade
Keyword(s):  
Remote Sensing ◽  
Integral Equation ◽  
Synthetic Aperture Radar ◽  
Statistical Parameter ◽  
Microwave Remote Sensing ◽  
Equation Model ◽  
Synthetic Aperture ◽  
Geophysical Parameter ◽  
Planetary Bodies ◽  
Aperture Radar

<p><strong>Abstract.</strong> The microwave remote sensing is highly useful, as it provides synoptic observation of the Earth’s surface or planetary bodies, regardless of day or night and the atmospheric conditions, propagation through ionosphere with minimum loss. One of the best microwave technology for imaging system is the Synthetic Aperture Radar (SAR) remote sensing. The microwave SAR currently represents the best approach for obtaining spatially distributed geophysical parameter present on the Earth’s surface or planetary bodies. In the present work, geophysical parameters <i>viz.</i>, Soil Moisture, Surface Roughness, Dielectric Constant (&amp;epsilon;) and Backscattering Coefficients (&amp;sigma;<sup>0</sup>) will be retrieved. The modelling makes the process of estimating information beyond the real observation range for data interpretation. In the present paper most widely used modelling techniques for the microwave SAR dataset is an Integral Equation Model (IEM) which is implemented for above said geophysical parameters retrieval. The aim of the present work is to estimate accurate, reliable and skillful measurements of geophysical parameters from the microwave SAR dataset. In the present study microwave C band SAR dataset is used. The overall processing was done by using PolSARPro Ver. 5.0 software. In the present work, geophysical parameters are measured with the help IEM modelling, the statistical parameter and occurrence plane, estimated from the microwave SAR image, which was very helpful for retrieving geophysical parameters. From the overall paper work, it was concluded that the IEM modelling is a one of the realistic modelling methods for retrieving geophysical parameters for microwave C band SAR dataset.</p>

scholarly journals PRELIMINARY RESULTS OF ESTIMATING SOIL MOISTURE OVER BARE SOIL USING FULL-POLARIMETRIC ALOS-2 DATA

2016 ◽  
Vol XLII-2/W1 ◽  
pp. 173-176 ◽  
Author(s):  
A. Sekertekin ◽  
A. M. Marangoz ◽  
S. Abdikan ◽  
M. T. Esetlili
Keyword(s):  
Soil Moisture ◽  
Synthetic Aperture Radar ◽  
Imaging System ◽  
Incidence Angle ◽  
Linear Regression Analysis ◽  
Gravimetric Method ◽  
Synthetic Aperture ◽  
Preliminary Results ◽  
Aperture Radar

Synthetic Aperture Radar (SAR) imaging system is one of the most effective way for Earth observation. The aim of this study is to present the preliminary results about estimating soil moisture using L-band Synthetic Aperture Radar (SAR) data. Full-polarimetric (HH, HV, VV, VH) ALOS-2 data, acquired on 22.04.2016 with the incidence angle of 30.4o, were used in the study. Simultaneously with the SAR acquisition, in-situ soil moisture samples over bare agricultural lands were collected and evaluated using gravimetric method. Backscattering coefficients for all polarizations were obtained and linear regression analysis was carried out with in situ moisture measurements. The best correlation coefficient was observed with VV polarization. Cross-polarized backscattering coefficients were not so sensitive to soil moisture content. In the study, it was observed that soil moisture maps can be retrieved with the accuracy about 14% (RMSE).

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