scholarly journals Soil moisture retrieval through a merging of multi-temporal L-band SAR data and hydrologic modelling

2008 ◽  
Vol 5 (6) ◽  
pp. 3479-3515 ◽  
Author(s):  
F. Mattia ◽  
G. Satalino ◽  
V. R. N. Pauwels ◽  
A. Loew
Keyword(s):  
Soil Moisture ◽  
Soil Moisture Content ◽  
A Priori ◽  
Retrieval Algorithm ◽  
A Priori Information ◽  
Hydrologic Modelling ◽  
Multi Temporal ◽  
L Band ◽  
Sar Data ◽  
Priori Information

Abstract. The objective of the study is to investigate the potential of retrieving superficial soil moisture content (mv) from multi-temporal L-band synthetic aperture radar (SAR) data and hydrologic modelling. The study focuses on assessing the performances of an L-band SAR retrieval algorithm intended for agricultural areas and for watershed spatial scales (e.g. from 100 to 10 000 km2). The algorithm transforms temporal series of L-band SAR data into soil moisture contents by using a constrained minimization technique integrating a priori information on soil parameters. The rationale of the approach consists of exploiting soil moisture predictions, obtained at coarse spatial resolution (e.g. 15–30 km2) by point scale hydrologic models (or by simplified estimators), as a priori information for the SAR retrieval algorithm that provides soil moisture maps at high spatial resolution (e.g. 0.01 km2). In the present form, the retrieval algorithm applies to cereal fields and has been assessed on simulated and experimental data. The latter were acquired by the airborne E-SAR system during the AgriSAR campaign carried out over the Demmin site (Northern Germany) in 2006. Results indicate that the retrieval algorithm always improves the a priori information on soil moisture content though the improvement may be marginal when the accuracy of prior mv estimates is better than 5%.

scholarly journals Soil moisture retrieval through a merging of multi-temporal L-band SAR data and hydrologic modelling

2009 ◽  
Vol 13 (3) ◽  
pp. 343-356 ◽  
Author(s):  
F. Mattia ◽  
G. Satalino ◽  
V. R. N. Pauwels ◽  
A. Loew
Keyword(s):  
Soil Moisture ◽  
Soil Moisture Content ◽  
A Priori ◽  
Retrieval Algorithm ◽  
A Priori Information ◽  
Hydrologic Modelling ◽  
Multi Temporal ◽  
L Band ◽  
Sar Data ◽  
Priori Information

Abstract. The objective of the study is to investigate the potential of retrieving superficial soil moisture content (mv) from multi-temporal L-band synthetic aperture radar (SAR) data and hydrologic modelling. The study focuses on assessing the performances of an L-band SAR retrieval algorithm intended for agricultural areas and for watershed spatial scales (e.g. from 100 to 10 000 km2). The algorithm transforms temporal series of L-band SAR data into soil moisture contents by using a constrained minimization technique integrating a priori information on soil parameters. The rationale of the approach consists of exploiting soil moisture predictions, obtained at coarse spatial resolution (e.g. 15–30 km2) by point scale hydrologic models (or by simplified estimators), as a priori information for the SAR retrieval algorithm that provides soil moisture maps at high spatial resolution (e.g. 0.01 km2). In the present form, the retrieval algorithm applies to cereal fields and has been assessed on simulated and experimental data. The latter were acquired by the airborne E-SAR system during the AgriSAR campaign carried out over the Demmin site (Northern Germany) in 2006. Results indicate that the retrieval algorithm always improves the a priori information on soil moisture content though the improvement may be marginal when the accuracy of prior mv estimates is better than 5%.

scholarly journals Estimation of soil parameters over bare agriculture areas from C-band polarimetric SAR data using neural networks

2012 ◽  
Vol 16 (6) ◽  
pp. 1607-1621 ◽  
Author(s):  
N. Baghdadi ◽  
R. Cresson ◽  
M. El Hajj ◽  
R. Ludwig ◽  
I. La Jeunesse
Keyword(s):  
Neural Networks ◽  
Surface Roughness ◽  
Soil Moisture ◽  
A Priori ◽  
Soil Parameters ◽  
A Priori Information ◽  
Polarimetric Sar ◽  
The Neural Networks ◽  
Sar Data ◽  
Priori Information

Abstract. The purpose of this study was to develop an approach to estimate soil surface parameters from C-band polarimetric SAR data in the case of bare agricultural soils. An inversion technique based on multi-layer perceptron (MLP) neural networks was introduced. The neural networks were trained and validated on a noisy simulated dataset generated from the Integral Equation Model (IEM) on a wide range of surface roughness and soil moisture, as it is encountered in agricultural contexts for bare soils. The performances of neural networks in retrieving soil moisture and surface roughness were tested for several inversion cases using or not using a-priori knowledge on soil parameters. The inversion approach was then validated using RADARSAT-2 images in polarimetric mode. The introduction of expert knowledge on the soil moisture (dry to wet soils or very wet soils) improves the soil moisture estimates, whereas the precision on the surface roughness estimation remains unchanged. Moreover, the use of polarimetric parameters α1 and anisotropy were used to improve the soil parameters estimates. These parameters provide to neural networks the probable ranges of soil moisture (lower or higher than 0.30 cm3 cm−3) and surface roughness (root mean square surface height lower or higher than 1.0 cm). Soil moisture can be retrieved correctly from C-band SAR data by using the neural networks technique. Soil moisture errors were estimated at about 0.098 cm3 cm−3 without a-priori information on soil parameters and 0.065 cm3 cm−3 (RMSE) applying a-priori information on the soil moisture. The retrieval of surface roughness is possible only for low and medium values (lower than 2 cm). Results show that the precision on the soil roughness estimates was about 0.7 cm. For surface roughness lower than 2 cm, the precision on the soil roughness is better with an RMSE about 0.5 cm. The use of polarimetric parameters improves only slightly the soil parameters estimates.

scholarly journals Soil parameters estimation over bare agriculture areas from C-band polarimetric SAR data using neural networks

2012 ◽  
Vol 9 (3) ◽  
pp. 2897-2933 ◽  
Author(s):  
N. Baghdadi ◽  
R. Cresson ◽  
M. El Hajj ◽  
R. Ludwig ◽  
I. La Jeunesse
Keyword(s):  
Neural Networks ◽  
Surface Roughness ◽  
Soil Moisture ◽  
A Priori ◽  
Soil Parameters ◽  
A Priori Information ◽  
Polarimetric Sar ◽  
The Neural Networks ◽  
Sar Data ◽  
Priori Information

Abstract. The purpose of this study was to develop an approach to estimate soil surface parameters from C-band polarimetric SAR data in the case of bare agricultural soils. An inversion technique based on Multi-Layer Perceptron (MLP) neural networks was introduced. The neural networks were trained and validated on a noisy simulated dataset generated from the Integral Equation Model (IEM) on a wide range of surface roughness and soil moisture, as it is encountered in agricultural contexts for bare soils. The performances of neural networks in retrieving soil moisture and surface roughness were tested for several inversion cases in using or not a priori knowledge on soil parameters. The inversion approach was then validated in using RADARSAT-2 images in polarimetric mode. The introduction of expert knowledge on the soil moisture (dry to wet soils or very wet soils) improves the soil moisture estimates whereas the precision on the surface roughness estimation remains unchanged. Moreover, the use of polarimetric parameters α1 and anisotropy were used to improve the soil parameters estimates. These parameters provide to neural networks the probable ranges of soil moisture (lower or higher than 0.30 cm3 cm−3) and surface roughness (lower or higher than 1.5 cm). Soil moisture can be retrieved correctly from C-band SAR data by using the neural networks technique. Soil moisture errors were estimated at about 0.098 without a priori information on soil parameters and 0.065 cm3 cm−3 (RMSE) applying a priori information on the soil moisture. The retrieval of surface roughness is possible only for low and medium values (lower than 2 cm). Results show that the precision on the soil roughness estimates was about 0.7 cm. For surface roughness lower than 2 cm, the precision on the soil roughness is better with a RMSE about 0.5 cm. The use of polarimetric parameters improves only slightly the soil parameters estimates.

scholarly journals Towards the retrieval of tropospheric ozone with the Ozone Monitoring Instrument (OMI)

2015 ◽  
Vol 8 (2) ◽  
pp. 671-687 ◽  
Author(s):  
T. Mielonen ◽  
J. F. de Haan ◽  
J. C. A. van Peet ◽  
M. Eremenko ◽  
J. P. Veefkind
Keyword(s):  
Radiative Transfer ◽  
Covariance Matrix ◽  
Tropospheric Ozone ◽  
Surface Albedo ◽  
A Priori ◽  
Stray Light ◽  
Retrieval Algorithm ◽  
A Priori Information ◽  
Ozone Profile ◽  
Priori Information

Abstract. We have assessed the sensitivity of the operational Ozone Monitoring Instrument (OMI) ozone profile retrieval algorithm to a number of a priori and radiative transfer assumptions. We studied the effect of stray light correction, surface albedo assumptions and a priori ozone profiles on the retrieved ozone profile. Then, we studied how to modify the algorithm to improve the retrieval of tropospheric ozone. We found that stray light corrections have a significant effect on the retrieved ozone profile but mainly at high altitudes. Surface albedo assumptions, on the other hand, have the largest impact at the lowest layers. Choice of an ozone profile climatology which is used as a priori information has small effects on the retrievals at all altitudes. However, the usage of climatological a priori covariance matrix has a significant effect. Based on these sensitivity tests, we made several modifications to the retrieval algorithm: the a priori ozone climatology was replaced with a new tropopause-dependent climatology, the a priori covariance matrix was calculated from the climatological ozone variability values, and the surface albedo was assumed to be linearly dependent on wavelength in the 311.5–330 nm channel. As expected, we found that the a priori covariance matrix basically defines the vertical distribution of degrees of freedom for a retrieval. Moreover, our case study over Europe showed that the modified version produced over 10% smaller ozone abundances in the troposphere which reduced the systematic overestimation of ozone in the retrieval algorithm and improved correspondence with Infrared Atmospheric Sounding Instrument (IASI) retrievals. The comparison with ozonesonde measurements over North America showed that the operational retrieval performed better in the upper troposphere/lower stratosphere (UTLS), whereas the modified version improved the retrievals in the lower troposphere and upper stratosphere. These comparisons showed that the systematic biases in the OMI ozone profile retrievals are not caused by the a priori information but by some still unidentified problem in the radiative transfer modelling. Instead, the a priori information pushes the systematically wrong ozone profiles towards the true values. The smaller weight of the a priori information in the modified retrieval leads to better visibility of tropospheric ozone structures, because it has a smaller tendency to damp the variability of the retrievals in the troposphere. In summary, the modified retrieval unmasks systematic problems in the radiative transfer/instrument model and is more sensitive to tropospheric ozone variation; that is, it is able to capture the tropospheric ozone morphology better.

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