Evaluation of the Penetration Depth of L- and S-Band (NISAR mission) Microwave SAR Signals into Ground

Singh, Aviral; Meena, Ganesh Kumar; Kumar, Shashi; Gaurav, Kumar

doi:10.23919/ursiap-rasc.2019.8738217

Cited by 13 publications

(6 citation statements)

References 3 publications

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“…Sentinel-1A and 1B operates at a frequency of 5.405 GHz. At this frequency, microwave signals can penetrate up to 5 cm below the dry soil column [66][67][68] .…”

Section: Datasetsmentioning

confidence: 99%

“…1) and randomly selected the grids for measurements. To ensure the same moisture content is illuminated and recorded by the Sentinel-1 SAR pulses, we measured the surface soil moisture at 5 cm depth from the topsoil layer [66][67][68] . To minimise the spatial heterogeneity, we collect 7-10 in-situ measurements over the footprint of the satellite pixel (i.e., 60 m).…”

Section: Field Measurementmentioning

confidence: 99%

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Deep learning and data fusion to estimate surface soil moisture from multi-sensor satellite images

Singh

Gaurav

2023

Sci Rep

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We propose a new architecture based on a fully connected feed-forward Artificial Neural Network (ANN) model to estimate surface soil moisture from satellite images on a large alluvial fan of the Kosi River in the Himalayan Foreland. We have extracted nine different features from Sentinel-1 (dual-polarised radar backscatter), Sentinel-2 (red and near-infrared bands), and Shuttle Radar Topographic Mission (digital elevation model) satellite products by leveraging the linear data fusion and graphical indicators. We performed a feature importance analysis by using the regression ensemble tree approach and also feature sensitivity to evaluate the impact of each feature on the response variable. For training and assessing the model performance, we conducted two field campaigns on the Kosi Fan in December 11–19, 2019 and March 01–06, 2022. We used a calibrated TDR probe to measure surface soil moisture at 224 different locations distributed throughout the fan surface. We used input features to train, validate, and test the performance of the feed-forward ANN model in a 60:10:30 ratio, respectively. We compared the performance of ANN model with ten different machine learning algorithms [i.e., Generalised Regression Neural Network (GRNN), Radial Basis Network (RBN), Exact RBN (ERBN), Gaussian Process Regression (GPR), Support Vector Regression (SVR), Random Forest (RF), Boosting Ensemble Learning (Boosting EL), Recurrent Neural Network (RNN), Binary Decision Tree (BDT), and Automated Machine Learning (AutoML)]. We observed that the ANN model accurately predicts the soil moisture and outperforms all the benchmark algorithms with correlation coefficient (R = 0.80), Root Mean Square Error (RMSE = 0.040 $$\mathrm {m^3/m^3}$$ m 3 / m 3 ), and bias = 0.004 $$\mathrm {m^3/m^3}$$ m 3 / m 3 . Finally, for an unbiased and robust conclusion, we performed spatial distribution analysis by creating thirty different sets of training-validation-testing datasets. We observed that the performance remains consistent in all thirty scenarios. The outcomes of this study will foster new and existing applications of soil moisture.

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“…Sentinel-1A and 1B operates at a frequency of 5.405 GHz. At this frequency, microwave signals can penetrate up to 5 cm below the dry soil column [66][67][68] .…”

Section: Datasetsmentioning

confidence: 99%

Section: Field Measurementmentioning

confidence: 99%

Deep learning and data fusion to estimate surface soil moisture from multi-sensor satellite images

Singh

Gaurav

2023

Sci Rep

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“…They operate at a frequency of 5.405 GHz and measure the uninterrupted backscattered signals from the earth's surface in all weather conditions. Depending on the soil type and moisture conditions, at this frequency, the SAR signals can penetrate up to 5 cm of the topsoil surface [54,55]. Sentinel-1 satellites have a temporal resolution of 12 days, that jointly (1A and 1B) result in a 6-day repeat pass over the equator [56,57].…”

Section: Satellite Datamentioning

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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“…It has a temporal resolution of 12 days (in combination with Sentinel-1B the temporal resolution is 6 days). Microwave signals at C-band can penetrate up to 5 cm deep below the soil surface [51,52]. The Sentinel-1A level-1 data is categorised into two product types: Ground Range Detected (GRD) and Single Look Complex (SLC).…”

Section: Satellite Datamentioning

confidence: 99%

Estimation of Soil Moisture Applying Modified Dubois Model to Sentinel-1; A Regional Study from Central India

et al. 2020

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Surface soil moisture has a wide application in climate change, agronomy, water resources, and in many other domain of science and engineering. Measurement of soil moisture at high spatial and temporal resolution at regional and global scale is needed for the prediction of flood, drought, planning and management of agricultural productivity to ensure food security. Recent advancement in microwave remote sensing, especially after the launch of Sentinel operational satellites has enabled the scientific community to estimate soil moisture at higher spatial and temporal resolution with greater accuracy. This study evaluates the potential of Sentinel-1A satellite images to estimate soil moisture in a semi-arid region. Exactly at the time when satellite passes over the study area, we have collected soil samples at 37 different locations and measured the soil moisture from 5 cm below the ground surface using ML3 theta probe. We processed the soil samples in laboratory to obtain volumetric soil moisture using the oven dry method. We found soil moisture measured from calibrated theta probe and oven dry method are in good agreement with Root Mean Square Error (RMSE) 0.025 m 3 /m 3 and coefficient of determination (R 2 ) 0.85. We then processed Sentinel-1A images and applied modified Dubois model to calculate relative permittivity of the soil from the backscatter values ( σ ∘ ). The volumetric soil moisture at each pixel is then calculated by applying the universal Topp’s model. Finally, we masked the pixels whose Normalised Difference Vegetation Index (NDVI) value is greater than 0.4 to generate soil moisture map as per the Dubois NDVI criterion. Our modelled soil moisture accord with the measured values with RMSE = 0.035 and R 2 = 0.75. We found a small bias in the modelled soil moisture ( 0.02 m 3 / m 3 ). However, this has reduced significantly ( 0.001 m 3 / m 3 ) after applying a bias correction based on Cumulative Distribution Function (CDF) matching. Our approach provides a first-order estimate of soil moisture from Sentinel-1A images in sparsely vegetated agricultural land.

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Evaluation of the Penetration Depth of L- and S-Band (NISAR mission) Microwave SAR Signals into Ground

Cited by 13 publications

References 3 publications

Deep learning and data fusion to estimate surface soil moisture from multi-sensor satellite images

Deep learning and data fusion to estimate surface soil moisture from multi-sensor satellite images

Machine Learning to Estimate Surface Roughness from Satellite Images

Estimation of Soil Moisture Applying Modified Dubois Model to Sentinel-1; A Regional Study from Central India

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