Joint use of Sentinel-2 and Sentinel-1 data for rapid mapping of volcanic eruption deposits in Southeast Asia

Orynbaikyzy, Aiym; Plank, Simon; Vetrita, Yenni; Martinis, Sandro; Santoso, I; Ismanto, Rido Dwi; Chusnayah, Farikhotul; Tjahjaningsih, A; Suwarsono,; Genzano, Nicola; Marchese, Francesco; Khomarudin, M. Rokhis; Strunz, Günter

doi:10.1016/j.jag.2022.103166

Cited by 6 publications

(2 citation statements)

References 40 publications

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“…The C-band synthetic aperture radar (SAR) sensor onboard the satellites has a spatial resolution of 5-20 m and captures backscatter information from multiple polarization channels on the Earth's surface. SAR is highly sensitive to surface roughness and is not affected by cloud cover or water vapor; thus, it has been used as complementary data to optical imagery to establish SOC mapping models [18,19]. Additionally, the relief significantly affects soil formation, resulting in spatial heterogeneity in soil properties at the regional scale [20].…”

Section: Introductionmentioning

confidence: 99%

Collaborative Utilization of Sentinel-1/2 and DEM Data for Mapping the Soil Organic Carbon in Forested Areas Based on the Random Forest

Wang,

Zhang,

et al. 2024

Forests

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Optical remote sensing data are widely used for constructing soil organic carbon (SOC) mapping models. However, it is challenging to map SOC in forested areas because atmospheric water vapor affects the results derived from optical remote sensing data. To address this issue, we utilized Sentinel-1, Sentinel-2, and digital elevation model (DEM) data to obtain a comprehensive feature set (including S1-based textural indices, S2-based spectral indices, and DEM-derived indices) to map the SOC content in forested areas. The features set were the predictor variables, and the measured SOC content was the dependent variable. The random forest algorithm was used to establish the SOC model. The ratio of performance to inter-quartile range (RPIQ) was 2.92 when the S2-based spectral indices were used as predictor variables. When the comprehensive feature set was utilized as the model input, the model achieved an RPIQ of 4.13 (R2 = 0.91, root mean square error (RMSE) = 9.18), representing a 41.44% improvement in model accuracy. The average SOC content in the Greater Khingan Mountains was 43.75 g kg−1. The northern and southwestern parts had higher SOC contents (>54.93 g kg−1), while the southeastern and northwestern parts had lower contents (<39.83 g kg−1). This discrepancy was primarily attributed to agricultural activities. The results indicate that using a comprehensive feature set and the random forest algorithm is a reliable approach for estimating the spatial distribution of the SOC content in forested areas and is suitable for forest ecology and carbon management studies.

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Section: Introductionmentioning

confidence: 99%

Collaborative Utilization of Sentinel-1/2 and DEM Data for Mapping the Soil Organic Carbon in Forested Areas Based on the Random Forest

Wang,

Zhang,

et al. 2024

Forests

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“…Some of these studies used both amplitude and phase information [4] in their characterization of surface textures and materials. Additionally, it has been argued that a combination of optical and SAR products can have beneficial impact on volcanic flow mapping [22], which can be applied in the areas recently impacted by volcanic hazards, as outlined by recent studies using such approaches for rapid volcanic hazard assessment [23]. Unlike ASAR instruments, which are deployed onboard aircraft at high altitudes, unoccupied aircraft systems (UAS) can be deployed at low altitudes to yield centimeter-scale digital surface models (DSMs) using Structure from Motion (SfM) photogrammetry [24].…”

Section: Introductionmentioning

confidence: 99%

Characterizing and Mapping Volcanic Flow Deposits on Mount St. Helens via Dual-Band SAR Imagery

Rogic,

Charbonnier,

Garin

et al. 2023

Remote Sensing

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Mapping volcanic flow deposits can be achieved by considering backscattering characteristics as a metric of surface roughness. In this study, we developed an approach to extract a measure of surface roughness from dual-band airborne Synthetic Aperture Radar (ASAR) backscattering data to characterize and map various volcanic flow deposits—namely, debris avalanches, lahars, lava flows, and pyroclastic density currents. We employed ASAR and Indian Space Research Organization (ISRO) airborne SAR datasets, from a joint project (ASAR-ISRO), acquired in December 2019 at 2 m spatial resolution, to assess the role and importance of incorporating dual-band data, i.e., L-band and S-band, into surface roughness models. Additionally, we derived and analyzed surface roughness from a digital surface model (DSM) generated from unoccupied aircraft systems (UAS) acquisitions using Structure from Motion (SfM) photogrammetry techniques. These UAS-derived surface roughness outputs served as meter-scale calibration products to validate the radar roughness data over targeted areas. Herein, we applied our method to a region in the United States over the Mount St. Helens volcano in the Cascade Range of Washington state. Our results showed that dual-band systems can be utilized to characterize different types of volcanic deposits and range of terrain roughness. Importantly, we found that a combination of radar wavelengths (i.e., 9 and 24 cm), in tandem with high-spatial-resolution backscatter measurements, yields improved surface roughness maps, compared to single-band, satellite-based approaches at coarser resolution. The L-band (24 cm) can effectively differentiate small, medium, and large-scale structures, namely, blocks/boulders from fine-grained lahar deposits and hummocks from debris avalanche deposits. Additionally, variation in the roughness estimates of lahar and debris avalanche deposits can be identified and quantified individually. In contrast, the S-band (9 cm) can distinguish different soil moisture conditions across variable terrain; for example, identify wet active channels. In principle, this dual-band approach can also be employed with time series of various other SAR data of higher coherence (such as satellite SAR), using different wavelengths and polarizations, encompassing a wider range of surface roughness, and ultimately enabling additional applications at other volcanoes worldwide and even beyond volcanology.

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Monitoring salinity in bare soil based on Sentinel-1/2 image fusion and machine learning

Zhang

Xiang

et al. 2023

Infrared Physics & Technology

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Joint use of Sentinel-2 and Sentinel-1 data for rapid mapping of volcanic eruption deposits in Southeast Asia

Cited by 6 publications

References 40 publications

Collaborative Utilization of Sentinel-1/2 and DEM Data for Mapping the Soil Organic Carbon in Forested Areas Based on the Random Forest

Collaborative Utilization of Sentinel-1/2 and DEM Data for Mapping the Soil Organic Carbon in Forested Areas Based on the Random Forest

Characterizing and Mapping Volcanic Flow Deposits on Mount St. Helens via Dual-Band SAR Imagery

Monitoring salinity in bare soil based on Sentinel-1/2 image fusion and machine learning

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