Sentinel-1 GRD Preprocessing Workflow

Filipponi, Federico

doi:10.3390/ecrs-3-06201

Cited by 356 publications

(223 citation statements)

References 1 publication

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“…The top-of-atmosphere reflectance of S-2 [39] was converted to the Level 2A bottom-of-atmospheric reflectance after geometric and atmospheric corrections using the ESA Sen2Cor algorithm [44]. The S-1 C-band image intensities at the VV and VH polarizations were processed in multiple steps, including thermal noise removal, calibration, despeckling, terrain correction, and finally conversion to the normalized radar σ 0 in decibel (dB) units [45]. Then, the S-2 data were co-registered with the S-1 and ALOS-2 PALSAR-2 data in the UTM/WGS84 coordinate system at the zone 48N projection.…”

Section: Satellite Image Processingmentioning

confidence: 99%

Comparison of Machine Learning Methods for Estimating Mangrove Above-Ground Biomass Using Multiple Source Remote Sensing Data in the Red River Delta Biosphere Reserve, Vietnam

et al. 2020

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This study proposes a hybrid intelligence approach based on an extreme gradient boosting regression and genetic algorithm, namely, the XGBR-GA model, incorporating Sentinel-2, Sentinel-1, and ALOS-2 PALSAR-2 data to estimate the mangrove above-ground biomass (AGB), including small and shrub mangrove patches in the Red River Delta biosphere reserve across the northern coast of Vietnam. We used the novel extreme gradient boosting decision tree (XGBR) technique together with genetic algorithm (GA) optimization for feature selection to construct and verify a mangrove AGB model using data from a field survey of 105 sampling plots conducted in November and December of 2018 and incorporated the dual polarimetric (HH and HV) data of the ALOS-2 PALSAR-2 L-band and the Sentinel-2 multispectral data combined with Sentinel-1 (C-band VV and VH) data. We employed the root-mean-square error (RMSE) and coefficient of determination (R 2 ) to evaluate the performance of the proposed model. The capability of the XGBR-GA model was assessed via a comparison with other machine-learning (ML) techniques, i.e., the CatBoost regression (CBR), gradient boosted regression tree (GBRT), support vector regression (SVR), and random forest regression (RFR) models. The XGBR-GA model yielded a promising result (R 2 = 0.683, RMSE = 25.08 Mg·ha −1 ) and outperformed the four other ML models. The XGBR-GA model retrieved a mangrove AGB ranging from 17 Mg·ha −1 to 142 Mg·ha −1 (with an average of 72.47 Mg·ha −1 ). Therefore, multisource optical and synthetic aperture radar (SAR) combined with the XGBR-GA model can be used to estimate the mangrove AGB in North Vietnam. The effectiveness of the proposed method needs to be further tested and compared to other mangrove ecosystems in the tropics.

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Section: Satellite Image Processingmentioning

confidence: 99%

Comparison of Machine Learning Methods for Estimating Mangrove Above-Ground Biomass Using Multiple Source Remote Sensing Data in the Red River Delta Biosphere Reserve, Vietnam

et al. 2020

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“…The precise orbits of SAR satellites are determined after many days and became available after the generation of the final product by days-to-weeks. SNAP software tools allow the SAR data users to update the orbit state vectors for each scene in its product metadata, provide velocity information, and give an accurate position for the satellite according to the radar tool [44]. Sentinel-specific processing facilities also host the relevant Level-1 instrument processor components [45].…”

Section: Data Processingmentioning

confidence: 99%

SAR Sentinel 1 Imaging and Detection of Palaeo-Landscape Features in the Mediterranean Area

et al. 2020

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The use of satellite radar in landscape archaeology offers great potential for manifold applications, such as the detection of ancient landscape features and anthropogenic transformations. Compared to optical data, the use and interpretation of radar imaging for archaeological investigations is more complex, due to many reasons including that: (i) ancient landscape features and anthropogenic transformations provide subtle signals, which are (ii) often covered by noise; and, (iii) only detectable in specific soil characteristics, moisture content, vegetation phenomenology, and meteorological parameters. In this paper, we assessed the capability of SAR Sentinel 1 in the imaging and detection of palaeo-landscape features in the Mediterranean area of Tavoliere delle Puglie. For the purpose of our investigations, a significant test site (larger than 200 km2) was selected in the Foggia Province (South of Italy) as this area has been characterized for millennia by human frequentation starting from (at least) the Neolithic. The results from the Sentinel 1 (S-1) data were successfully compared with independent data sets, and the comparison clearly showed an excellent match between the S-1 based outputs and ancient anthropogenic transformations and landscape features.

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“…Pre-processing of S-1A and S-2B involves in correction of the imageries to further process for specified applications. Pre-processing workflow consists of seven major methods followed as suggested here [Filipponi, 2019]:…”

Section: Fig 4 Main Methodology Workflowmentioning

confidence: 99%

A comparison of Sentinel-1 and Sentinel-2 in assessing flooded area and built-up land use: A case study of selected coastal districts in Andra Pradesh, India

Gonuguntla

Abdivaitov

Bose

et al. 2020

ICIGIS

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In tropical climatic conditions, floods occur during heavy rainfall. Floods during this thick cloud cover partially stops the optical imagery to pass through the atmosphere and record the surface reflectance. Another kind of satellite imagery that is available is microwave remote sensing data that can pass through the clouds. However, the exploration of this microwave remote sensing began recently for earth observation applications. So, the algorithms and methods available for exploiting advantages from microwave data is still under research. The current part of the work is to explore the methods available to differentiate between the microwave data (Sentinel-1) and Optical imagery (Sentinel-2) in flooded and built-up area estimation. The ultimate aim is to conclude with most suitable datasets and fast computing methods in estimating the built-up area and flooded area during the emergency disaster time. Two case studies taken up for the study are August 2019 East Godavari floods and October 2019 Titli cyclone. So, the adopted method to estimate the flooded areas and built-up areas from the Sentinel-1A and Sentinel-2B was RGB clustering (Red, Green and Blue clustering) using the derived RGB colour combinations in snap 7.0 software. The datasets were classified into built-up, flooded area and vegetation areas using Random Forest supervised classification, a machine learning technique Validation of estimated built-up and flooded areas estimated from Sentinel-1A and Sentinel-2B was done using the random pixel distribution technique. Since the de-centralisation of estimated flooded areas and built-up area helps in fast distribution of the response forces to the affected area, estimation of built-up and flooded area was also taken up for the sub-districts of East Godavari district, India. Finally, the study estimates the damaged built-up and vegetation due to August 2019 East Godavari floods from Sentinel-1A and Sentinel-2B. Flooded area due to ‘Titli’ cyclone 2018 was estimated in East Godavari, Visakhapatnam and Vijianagaram districts of Andhra Pradesh state.

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Sentinel-1 GRD Preprocessing Workflow

Cited by 356 publications

References 1 publication

Comparison of Machine Learning Methods for Estimating Mangrove Above-Ground Biomass Using Multiple Source Remote Sensing Data in the Red River Delta Biosphere Reserve, Vietnam

Comparison of Machine Learning Methods for Estimating Mangrove Above-Ground Biomass Using Multiple Source Remote Sensing Data in the Red River Delta Biosphere Reserve, Vietnam

SAR Sentinel 1 Imaging and Detection of Palaeo-Landscape Features in the Mediterranean Area

A comparison of Sentinel-1 and Sentinel-2 in assessing flooded area and built-up land use: A case study of selected coastal districts in Andra Pradesh, India

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