Sensitivity of C-Band SAR Polarimetric Variables to the Directionality of Surface Roughness Parameters

Alijani, Zohreh; Lindsay, John B.; Chabot, Mélanie; Berg, Aaron

doi:10.3390/rs13112210

Cited by 7 publications

(2 citation statements)

References 51 publications

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“…The two-dimensional methods for parameterizing SSR are much more accurate than traditional one-dimensional profile methods. They can accurately characterize the surface while meeting the basic assumptions of stationarity required by most backscatter models, which one-dimensional profile methods usually cannot achieve [12,13]. LiDAR technology uses laser scanners and distance sensors to obtain the surface morphology of the target.…”

Section: Introductionmentioning

confidence: 99%

Soil Surface Roughness Characteristics Under Different Agricultural Tillage Practices—A Case Study in the Black Soil Region of Northeast China

Feng,

Zheng,

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Soil surface roughness (SSR) is an important factor affecting soil erosion and soil nutrient transport. Human tillage leads to increased instability in SSR, and the characteristics of SSR caused by different tillage practices await further study. This research utilizes terrestrial laser scanning (TLS) to measure the SSR of six farmland plots (25 m × 25 m) and analyzes the characteristics of SSR under different tillage practices (ploughing, harrowing, ridging, crusting, etc.). The study results show: 1) Different agricultural tillage practices lead to significant differences in SSR. The ploughed and harrowed plot correspond to the maximum (2.49 cm) and minimum (1.5 cm) root mean square height (RMSH), respectively. Correlation length (CL) is more affected by different tillage practices than RMSH. The difference in CL between the ridged and harrowed plot is 2.6 times.2) Ridging and crusting caused significant directional variation in SSR. The SSR anisotropy of the harrowed plot can be disregarded.3) Under the condition of measuring soil profile in 12 directions and randomly sampling 70 times in each direction, the profile length must be at least 3 m to ensure that the measurement error of SSR is better than 5% compared to the "true" value. TLS can measure two-dimensional SSR. Therefore, it is only necessary to ensure that the measurement range is at least 3 m × 3 m. The study results provide a reference for the high-precision measurement of SSR (RMSH and CL) under different agricultural tillage practices.

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Section: Introductionmentioning

confidence: 99%

Soil Surface Roughness Characteristics Under Different Agricultural Tillage Practices—A Case Study in the Black Soil Region of Northeast China

Feng,

Zheng,

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…A pin-profiler measures the surface roughness by tracing the one-dimensional surface profile from the relative position of pins placed vertically on the ground. This method is economical and provides acceptable accuracy for microwave remote-sensing applications, but it is time-consuming and requires physical contact with the surface [14][15][16][17][18]. In the stereo-photogrammetry technique, the DEM is calculated from two digital images acquired with 70% overlap of the same area.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning to Estimate Surface Roughness from Satellite Images

et al. 2021

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We apply the Support Vector Regression (SVR) machine learning model to estimate surface roughness on a large alluvial fan of the Kosi River in the Himalayan Foreland from satellite images. To train the model, we used input features such as radar backscatter values in Vertical–Vertical (VV) and Vertical–Horizontal (VH) polarisation, incidence angle from Sentinel-1, Normalised Difference Vegetation Index (NDVI) from Sentinel-2, and surface elevation from Shuttle Radar Topographic Mission (SRTM). We generated additional features (VH/VV and VH–VV) through a linear data fusion of the existing features. For the training and validation of our model, we conducted a field campaign during 11–20 December 2019. We measured surface roughness at 78 different locations over the entire fan surface using an in-house-developed mechanical pin-profiler. We used the regression tree ensemble approach to assess the relative importance of individual input feature to predict the surface soil roughness from SVR model. We eliminated the irrelevant input features using an iterative backward elimination approach. We then performed feature sensitivity to evaluate the riskiness of the selected features. Finally, we applied the dimension reduction and scaling to minimise the data redundancy and bring them to a similar level. Based on these, we proposed five SVR methods (PCA-NS-SVR, PCA-CM-SVR, PCA-ZM-SVR, PCA-MM-SVR, and PCA-S-SVR). We trained and evaluated the performance of all variants of SVR with a 60:40 ratio using the input features and the in-situ surface roughness. We compared the performance of SVR models with six different benchmark machine learning models (i.e., Gaussian Process Regression (GPR), Generalised Regression Neural Network (GRNN), Binary Decision Tree (BDT), Bragging Ensemble Learning, Boosting Ensemble Learning, and Automated Machine Learning (AutoML)). We observed that the PCA-MM-SVR perform better with a coefficient of correlation (R = 0.74), Root Mean Square Error (RMSE = 0.16 cm), and Mean Square Error (MSE = 0.025 cm2). To ensure a fair selection of the machine learning model, we evaluated the Akaike’s Information Criterion (AIC), corrected AIC (AICc), and Bayesian Information Criterion (BIC). We observed that SVR exhibits the lowest values of AIC, corrected AIC, and BIC of all the other methods; this indicates the best goodness-of-fit. Eventually, we also compared the result of PCA-MM-SVR with the surface roughness estimated from different empirical and semi-empirical radar backscatter models. The accuracy of the PCA-MM-SVR model is better than the backscatter models. This study provides a robust approach to measure surface roughness at high spatial and temporal resolutions solely from the satellite data.

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Application of RiTiCE in understanding hydro-meteorological controls on ice break-up patterns in River Tornionjoki

Shahrood,

Ahrari,

Karjalainen

et al. 2024

Environ Monit Assess

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The Arctic region experiences significant annual hydrologic events, with the spring flood and ice break-up being the most prominent. River ice break-up, in particular, poses high socioeconomic and ecological expenses, including morphological changes and damage to riverine structures. This study aims to investigate the spatiotemporal patterns of river ice in the River Tornionjoki, including the timing of ice break-up at different latitudes. We utilized observation data and remote sensing techniques to track changes in ice patterns overtime on the River Tornionjoki. The study indicates that the ice break-up in the River Tornionjoki basin typically occurs during Apr-Jun based on the reach location in different latitudes; therefore, different stations behave according to their latitudinal location. We observed significant spatial variations in ice break-up timing across the basin, with an earlier break-up in the lower latitudes compared to the upper latitudes. The average ice break-up day in lower latitude stations ranges between 200–205, while in higher latitude stations the average ice break-up day ranges between 215–228.

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Sensitivity of C-Band SAR Polarimetric Variables to the Directionality of Surface Roughness Parameters

Cited by 7 publications

References 51 publications

Soil Surface Roughness Characteristics Under Different Agricultural Tillage Practices—A Case Study in the Black Soil Region of Northeast China

Soil Surface Roughness Characteristics Under Different Agricultural Tillage Practices—A Case Study in the Black Soil Region of Northeast China

Machine Learning to Estimate Surface Roughness from Satellite Images

Application of RiTiCE in understanding hydro-meteorological controls on ice break-up patterns in River Tornionjoki

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