Remote sensing of surface roughness using polarimetric SAR data

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.

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Modeling Surface Roughness to Estimate Surface Moisture Using Radarsat-2 Quad Polarimetric SAR Data

Journal of Physics Conference Series ◽

10.1088/1742-6596/739/1/012105 ◽

2016 ◽

Vol 739 ◽

pp. 012105

Author(s):

R. Nurtyawan ◽

A. Saepuloh ◽

A. Budiharto ◽

K. Wikantika

Keyword(s):

Surface Roughness ◽

Polarimetric Sar ◽

Surface Moisture ◽

Sar Data

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Road Surface Roughness Estimation Using Polarimetric SAR Data

2020 21st International Radar Symposium (IRS) ◽

10.23919/irs48640.2020.9253934 ◽

2020 ◽

Author(s):

Arun Babu ◽

Stefan V. Baumgartner

Keyword(s):

Surface Roughness ◽

Road Surface ◽

Polarimetric Sar ◽

Sar Data ◽

Road Surface Roughness ◽

Surface Roughness Estimation

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

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-9-2897-2012 ◽

2012 ◽

Vol 9 (3) ◽

pp. 2897-2933 ◽

Cited By ~ 2

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.

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Neural Network-Based Urban Change Monitoring with Deep-Temporal Multispectral and SAR Remote Sensing Data

Remote Sensing ◽

10.3390/rs13153000 ◽

2021 ◽

Vol 13 (15) ◽

pp. 3000

Author(s):

Georg Zitzlsberger ◽

Michal Podhorányi ◽

Václav Svatoň ◽

Milan Lazecký ◽

Jan Martinovič

Keyword(s):

Neural Network ◽

Remote Sensing ◽

Urban Change ◽

Small Scale ◽

Atmospheric Effects ◽

Wide Field ◽

Ground Truth Data ◽

Automated Method ◽

Urban Monitoring ◽

Sar Data

Remote-sensing-driven urban change detection has been studied in many ways for decades for a wide field of applications, such as understanding socio-economic impacts, identifying new settlements, or analyzing trends of urban sprawl. Such kinds of analyses are usually carried out manually by selecting high-quality samples that binds them to small-scale scenarios, either temporarily limited or with low spatial or temporal resolution. We propose a fully automated method that uses a large amount of available remote sensing observations for a selected period without the need to manually select samples. This enables continuous urban monitoring in a fully automated process. Furthermore, we combine multispectral optical and synthetic aperture radar (SAR) data from two eras as two mission pairs with synthetic labeling to train a neural network for detecting urban changes and activities. As pairs, we consider European Remote Sensing (ERS-1/2) and Landsat 5 Thematic Mapper (TM) for 1991–2011 and Sentinel 1 and 2 for 2017–2021. For every era, we use three different urban sites—Limassol, Rotterdam, and Liège—with at least 500km2 each, and deep observation time series with hundreds and up to over a thousand of samples. These sites were selected to represent different challenges in training a common neural network due to atmospheric effects, different geographies, and observation coverage. We train one model for each of the two eras using synthetic but noisy labels, which are created automatically by combining state-of-the-art methods, without the availability of existing ground truth data. To combine the benefit of both remote sensing types, the network models are ensembles of optical- and SAR-specialized sub-networks. We study the sensitivity of urban and impervious changes and the contribution of optical and SAR data to the overall solution. Our implementation and trained models are available publicly to enable others to utilize fully automated continuous urban monitoring.

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