Despeckling of Multitemporal Sentinel SAR Images and Its Impact on Agricultural Area Classification

Rubel, Oleksii; Kozhemiakin, Ruslan; Abramov, Sergey; Shelestov, Andrii; Lavreniuk, Mykola; Meretsky, M.; Vozel, Benôıt; Chehdi, Kacem

doi:10.5772/intechopen.72577

Cited by 9 publications

(3 citation statements)

References 41 publications

(58 reference statements)

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“…Speckle noise, due to constructive and deconstructive wave interference in the image, was minimized using the Refined Lee filter method, which was selected due to its reported superior performance in SNAP [14]. Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM) at 30 m resolution and 1 m DEM covering the study area derived from airborne photogrammetry in 2014 were combined for terrain correction.…”

Section: Satellite Data Collectionmentioning

confidence: 99%

Biomass Estimation for Semiarid Vegetation and Mine Rehabilitation Using Worldview-3 and Sentinel-1 SAR Imagery

Bao

et al. 2019

Remote Sensing

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The surface mining activities in grassland and rangeland zones directly affect the livestock production, forage quality, and regional grassland resources. Mine rehabilitation is necessary for accelerating the recovery of the grassland ecosystem. In this work, we investigate the integration of data obtained via a synthetic aperture radar (Sentinel-1 SAR) with data obtained by optical remote sensing (Worldview-3, WV-3) in order to monitor the conditions of a vegetation area rehabilitated after coal mining in North China. The above-ground biomass (AGB) is used as an indicator of the rehabilitated vegetation conditions and the success of mine rehabilitation. The wavelet principal component analysis is used for the fusion of the WV-3 and Sentinel-1 SAR images. Furthermore, a multiple linear regression model is applied based on the relationship between the remote sensing features and the AGB field measurements. Our results show that WV-3 enhanced vegetation indices (EVI), mean texture from band8 (near infrared band2, NIR2), the SAR vertical and horizon (VH) polarization, and band 8 (NIR2) from the fused image have higher correlation coefficient value with the field-measured AGB. The proposed AGB estimation model combining WV-3 and Sentinel 1A SAR imagery yields higher model accuracy (R 2 = 0.79 and RMSE = 22.82 g/m 2 ) compared to that obtained with any of the two datasets only. Besides improving AGB estimation, the proposed model can also reduce the uncertainty range by 7 g m −2 on average. These results demonstrate the potential of new multispectral high-resolution datasets, such as Sentinel-1 SAR and Worldview-3, in providing timely and accurate AGB estimation for mine rehabilitation planning and management.

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Section: Satellite Data Collectionmentioning

confidence: 99%

Biomass Estimation for Semiarid Vegetation and Mine Rehabilitation Using Worldview-3 and Sentinel-1 SAR Imagery

Bao

et al. 2019

Remote Sensing

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“…S1-Tiling uses the Quegan multi-temporal speckle filter to produce the final denoised images. One of the prospects for improving our methodology will be to compare the results with different advanced multi-temporal speckle filters [48,49], either a modified Quegan filter [50], or the Lee sigma based multi-temporal, which both give interesting results.…”

Section: Discussionmentioning

confidence: 99%

A Sentinel-1 Based Processing Chain for Detection of Cyclonic Flood Impacts

et al. 2020

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In the future, climate change will induce even more severe hurricanes. Not only should these be better understood, but there is also a necessity to improve the assessment of their impacts. Flooding is one of the most common powerful impacts of these storms. Analyzing the impacts of floods is essential in order to delineate damaged areas and study the economic cost of hurricane-related floods. This paper presents an automated processing chain for Sentinel-1 synthetic aperture radar (SAR) data. This processing chain is based on the S1-Tiling algorithm and the normalized difference ratio (NDR). It is able to download and clip S1 images on Sentinel-2 tiles footprints, perform multi-temporal filtering, and threshold NDR images to produce a mask of flooded areas. Applied to two different study zones, subject to hurricanes and cyclones, this chain is reliable and simple to implement. With the rapid mapping product of EMS Copernicus (Emergency Management Service) as reference, the method confers up to 95% accuracy and a Kappa value of 0.75.

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“…Shuttle Radar Topographic Mission Digital Elevation Model at 30 m resolution was used for the terrain correction. To reduce speckle noise, the Refined Lee speckle filtering algorithm (which adapts the window size to the local texture and edge information) was applied to the backscatter data which was selected due to its reported superior performance in SNAP (Lukin et al, 2018). To co‐registrate of S1 and S2 datasets at a spatial resolution of 10 m, the S1 dataset was chosen as the reference layer and the bilinear interpolation method was utilized (De Luca, Silva, Di Fazio, & Modica, 2022).…”

Section: Methodsmentioning

confidence: 99%

Above‐ground biomass retrieval with multi‐source data: Prediction and applicability analysis in Eastern Mongolia

Ji,

Dashpurev,

Phan

et al. 2024

Land Degrad Dev

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Grassland aboveground biomass (AGB) is a key variable to measure grassland productivity, and accurate assessment of AGB is important for optimizing grassland resource management and understanding carbon, water, and energy fluxes. Current approaches on large scales such as the Mongolian Steppe Ecosystem often combine field measurements with optical and/or synthetic aperture radar (SAR) data. Meanwhile, especially the representativeness of the field measurements for large‐scale analysis have seldom been accounted for. Therefore, we provide the first remotely sensed AGB product for central and Eastern Mongolia which (1) uses random forest (RF), (2) is fully validated against over 600 field samples, and (3) applies a novel method, dissimilarity index (DI), to derive the area of applicability of the model with respect to the training data. Therefore, different remote sensing data sources such as multi‐scale and multi‐temporal optical images—Worldview 2 (WV2), Sentinel 2 (S2), and Landsat 8 (L8) in combination with SAR data are tested for their suitability to provide an area‐wide estimation on large scale. The results showed that the AGB prediction by combining Sentinel 1 (S1) and S2 using RF had the highest accuracy. Furthermore, the model was applicable to at least 72.61% of the steppe area. Areas where the model was not applicable are mostly distributed along the edges of grassland. This study demonstrates the potential of combining Sentinel‐derived indices and machine learning to provide a reliable AGB prediction for grassland for extremely large ecosystems with strong climatic gradients.

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Despeckling of Multitemporal Sentinel SAR Images and Its Impact on Agricultural Area Classification

Cited by 9 publications

References 41 publications

Biomass Estimation for Semiarid Vegetation and Mine Rehabilitation Using Worldview-3 and Sentinel-1 SAR Imagery

Biomass Estimation for Semiarid Vegetation and Mine Rehabilitation Using Worldview-3 and Sentinel-1 SAR Imagery

A Sentinel-1 Based Processing Chain for Detection of Cyclonic Flood Impacts

Above‐ground biomass retrieval with multi‐source data: Prediction and applicability analysis in Eastern Mongolia

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