An Improved Sar Radiometric Terrain Correction Method and Its Application in Polarimetric Sar Terrain Effect Reduction

Chen, Zhaohua

doi:10.2528/pierb13052021

Cited by 8 publications

(4 citation statements)

References 26 publications

(35 reference statements)

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“…Areas of shadow, layover, and foreshortening areas were identified for each orbit using the SNAP module "SAR Simulation Terrain Correction". Since no specific corrections have been made to correct or attenuate layover/foreshortening effects (see for instance methods proposed by [27,28]), shadow, layover, foreshortening areas have been excluded. Note that the use of such methods would correct the geometry of the scene as well as its radiometry by normalizing the backscatter to the local illuminated area as seen by the sensor without using an ellipsoid-model-based incidence angle as we get from terrain correction.…”

Section: Sentinel-1 Datamentioning

confidence: 99%

Monitoring Wet Snow Over an Alpine Region Using Sentinel-1 Observations

et al. 2021

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The main objective of this study was to monitor wet snow conditions from Sentinel-1 over a season, to examine its variation over time by cross-checking wet snow with independent snow and weather estimates, and to study its distribution taking into account terrain characteristics such as elevation, orientation, and slope. One of our motivations was to derive useful representations of daily or seasonal snow changes that would help to easily identify wet snow elevations and determine melt-out days in an area of interest. In this work, a well-known approach in the literature is used to estimate the extent of wet snow cover continuously over a season and an analysis of the influence of complex mountain topography on snow distribution is proposed taking into account altitude, slope, and aspect of the terrain. The Sentinel-1 wet snow extent product was compared with Sentinel-2 snow products for cloud free scenes. We show that while there are good agreements between the two satellite products, differences exist, especially in areas of forests and glaciers where snow is underestimated. This underestimation must be considered alongside the areas of geometric distortion that were excluded from our study. We analysed retrievals at the scale of our study area by examining wet snow Altitude–Orientation diagrams for different classes of slopes and also wet snow Altitude–Time diagrams for different classes of orientations. We have shown that this type of representation is very useful to get an overview of the snow distribution as it allows to identify very easily wet snow lines for different orientations. For an orientation of interest, the Altitude–Time diagrams can be used to track the evolution of snow to locate altitudes and dates of snow loss. We also show that ascending/descending Sentinel-1 image time series are complementary to monitor wet snow over the French alpine areas to highlight wet snow altitude ranges and identify melt-out days. Links have also been made between Sentinel-1 responses (wet snow) and snow/meteorological events carefully listed over the entire 2017–2018 season.

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Section: Sentinel-1 Datamentioning

confidence: 99%

Monitoring Wet Snow Over an Alpine Region Using Sentinel-1 Observations

et al. 2021

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“…Terrain correction helps improve the geometric representation of the real-world surface. This is needed because during image capture, topographical variations and off-nadir distortion unsettles the image (Wang et al, 2013). A bilinear interpolation resampling method was used for the correction.…”

Section: Pre-processing and Secondary Derivativesmentioning

confidence: 99%

A new synergistic approach for monitoring wetlands using Sentinels -1 and 2 data with object-based machine learning algorithms

Whyte

Ferentinos

Petropoulos

2018

Environmental Modelling & Software

152

106

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In this work the synergistic use of Sentinel-1 and 2 combined with the System for Automated Geoscientific Analyses (SAGA) Wetness Index in the content of land use/cover (LULC) mapping with emphasis in wetlands is evaluated. A further objective has been to a new Object-based Image Analysis (OBIA) approach for mapping wetland areas using Sentinel-1 and 2 data, where the latter is also tested against two popular machine learning algorithms (Support Vector Machines -SVMs and Random Forests -RFs). The highly vulnerable iSimangaliso Wetland Park was used as the study site. Results showed that two-part image segmentation could efficiently create object features across the study area. For both classification algorithms, an increase in overall accuracy was observed when the full synergistic combination of available datasets. A statistically significant difference in classification accuracy at all levels between SVMs and RFs was also reported, with the latter being up to 2.4% higher. SAGA wetness index showed promising ability to distinguish wetland environments, and in combination with Sentinel-1 and 2 synergies can successfully produce a land use and land cover classification in a location where both wetland and non-wetland classes exist.

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“…Among them, the estimation method based on circular polarization covariance matrix has the best adaptability, a simple calculation process, and an optimal comprehensive effect [49]. Subsequently, correction factors constructed by POA are used to compensate for different forms of polarization information [48,50,51], such as the complex Sinclair scatter matrix (S2), the three-dimensional polarization coherency matrix (T3), or the three-dimensional polarization covariance matrix (C3). It is worth noting that the compensation for POA is only for full-polarization SAR data [48].…”

Section: Introductionmentioning

confidence: 99%

“…Compared to a process that does not consider or does not adequately consider topographic correction [38,61], radiative terrain correction for PolSAR data is a combination of three aspects of terrain correction [50,59,60,62,63], of which the most comprehensive radiometric terrain correction (RTC) process is one that includes all three aspects (POAC, ESAC, and AVEC) [43,64,65]. However, in terms of ESAC, the cross-sectional area of radar scattering is only applicable to a single scattering target (the target object is smaller than the irradiation range), and the scattered signal of the resolution unit for distributed scatterers (such as forests) is the result of coherent superposition of the scattered signal of each single scatterer, so the backscattering coefficient is commonly used to describe the scattering ability of ground objects based on statistical methods.…”

Section: Introductionmentioning

confidence: 99%

Inversion of Forest above Ground Biomass in Mountainous Region Based on PolSAR Data after Terrain Correction: A Case Study from Saihanba, China

Nie,

Hu,

et al. 2024

Remote Sensing

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Accurate retrieval of forest above ground biomass (AGB) based on full-polarization synthetic aperture radar (PolSAR) data is still challenging for complex surface regions with fluctuating terrain. In this study, the three-step process of radiometric terrain correction (RTC), which includes polarization orientation angle correction (POAC), effective scattering area correction (ESAC), and angular variation effect correction (AVEC), is adopted as the technical framework. In the ESAC stage, a normalized correction factor is introduced based on local incidence angle and radar incidence angle to achieve accurate correction of PolSAR data information and improve the inversion accuracy of forest AGB. In order to verify the validity and robustness of this research method, the full-polarization SAR data of ALOS-2 and the ground measured AGB data collected in the Saihanba research area in 2020 were used for experiments. Our findings revealed that in the ESAC phase, the introduction of the normalized correction factor can effectively eliminate the ESA phenomenon and improve the correlation coefficients of the backscatter coefficient and AGB. Taking the data of 25 July 2020 as an example, ESAC increases the correlation coefficients between AGB and the backscattering coefficients of HH, HV, and VV polarization channels by 0.343, 0.296, and 0.382, respectively. In addition, the RTC process has strong robustness in different AGB statistical models and different date PolSAR data.

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An Improved Sar Radiometric Terrain Correction Method and Its Application in Polarimetric Sar Terrain Effect Reduction

Cited by 8 publications

References 26 publications

Monitoring Wet Snow Over an Alpine Region Using Sentinel-1 Observations

Monitoring Wet Snow Over an Alpine Region Using Sentinel-1 Observations

A new synergistic approach for monitoring wetlands using Sentinels -1 and 2 data with object-based machine learning algorithms

Inversion of Forest above Ground Biomass in Mountainous Region Based on PolSAR Data after Terrain Correction: A Case Study from Saihanba, China

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