Comparison of Masking Algorithms for Sentinel-2 Imagery

Zekoll, Viktoria; Main-Knorn, Magdalena; Alonso, Kevin; Louis, Jérôme; Frantz, David; Richter, Rudolf; Pflug, Bringfried

doi:10.3390/rs13010137

Cited by 41 publications

(26 citation statements)

References 27 publications

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“…shows a close-up to a region of one of the classified images and shows a visual comparison between the results of the current approach and those of Sen2Cor. An illustration of shadow omission by Sen2Cor can be clearly viewed and is consistent with literature reporting low detection reaching lower than 30% of cloud shadows in imagery[65]. Furthermore, the cloud commission error Sen2Cor can be recognized through the river pattern classified as "cloud medium probability" (Figure13c).…”

supporting

confidence: 86%

Cloud and Cloud-Shadow Detection for Applications in Mapping Small-Scale Mining in Colombia Using Sentinel-2 Imagery

Ibrahim

Jiang

Lema³

et al. 2021

Remote Sensing

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Small-scale placer mining in Colombia takes place in rural areas and involves excavations resulting in large footprints of bare soil and water ponds. Such excavated areas comprise a mosaic of challenging terrains for cloud and cloud-shadow detection of Sentinel-2 (S2A and S2B) data used to identify, map, and monitor these highly dynamic activities. This paper uses an efficient two-step machine-learning approach using freely available tools to detect clouds and shadows in the context of mapping small-scale mining areas, one which places an emphasis on the reduction of misclassification of mining sites as clouds or shadows. The first step is comprised of a supervised support-vector-machine classification identifying clouds, cloud shadows, and clear pixels. The second step is a geometry-based improvement of cloud-shadow detection where solar-cloud-shadow-sensor geometry is used to exclude commission errors in cloud shadows. The geometry-based approach makes use of sun angles and sensor view angles available in Sentinel-2 metadata to identify potential directions of cloud shadow for each cloud projection. The approach does not require supplementary data on cloud-top or bottom heights nor cloud-top ruggedness. It assumes that the location of dense clouds is mainly impacted by meteorological conditions and that cloud-top and cloud-base heights vary in a predefined manner. The methodology has been tested over an intensively excavated and well-studied pilot site and shows 50% more detection of clouds and shadows than Sen2Cor. Furthermore, it has reached a Specificity of 1 in the correct detection of mining sites and water ponds, proving itself to be a reliable approach for further related studies on the mapping of small-scale mining in the area. Although the methodology was tailored to the context of small-scale mining in the region of Antioquia, it is a scalable approach and can be adapted to other areas and conditions.

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supporting

confidence: 86%

Cloud and Cloud-Shadow Detection for Applications in Mapping Small-Scale Mining in Colombia Using Sentinel-2 Imagery

Ibrahim

Jiang

Lema³

et al. 2021

Remote Sensing

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“…The results excluding thin clouds from the ground truth in Section 5.3 showed significant differences between the developed machine learning models and the algorithms that include a spatial buffer to over-mask cloud borders. This issue was also highlighted in [24], where the authors verified that the analyzed cloud detection methods yielded different results because of different definitions of the dilation buffer size for the cloud masks.…”

Section: Impact Of Cloud Borders On the Cloud Detection Accuracymentioning

confidence: 89%

“…Recent studies have compared the performance of different cloud detection algorithms for both Landsat [18] and Sentinel-2 [23,24]. Our work differs from those in several aspects: they are mainly focused on rule-based methods; they do not explicitly include the characteristics of the employed datasets in the validation analysis; and they do not consider cloud detection from a cross-sensor perspective, where developments from both satellites can help each other.…”

Section: Introductionmentioning

confidence: 99%

Benchmarking Deep Learning Models for Cloud Detection in Landsat-8 and Sentinel-2 Images

2021

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The systematic monitoring of the Earth using optical satellites is limited by the presence of clouds. Accurately detecting these clouds is necessary to exploit satellite image archives in remote sensing applications. Despite many developments, cloud detection remains an unsolved problem with room for improvement, especially over bright surfaces and thin clouds. Recently, advances in cloud masking using deep learning have shown significant boosts in cloud detection accuracy. However, these works are validated in heterogeneous manners, and the comparison with operational threshold-based schemes is not consistent among many of them. In this work, we systematically compare deep learning models trained on Landsat-8 images on different Landsat-8 and Sentinel-2 publicly available datasets. Overall, we show that deep learning models exhibit a high detection accuracy when trained and tested on independent images from the same Landsat-8 dataset (intra-dataset validation), outperforming operational algorithms. However, the performance of deep learning models is similar to operational threshold-based ones when they are tested on different datasets of Landsat-8 images (inter-dataset validation) or datasets from a different sensor with similar radiometric characteristics such as Sentinel-2 (cross-sensor validation). The results suggest that (i) the development of cloud detection methods for new satellites can be based on deep learning models trained on data from similar sensors and (ii) there is a strong dependence of deep learning models on the dataset used for training and testing, which highlights the necessity of standardized datasets and procedures for benchmarking cloud detection models in the future.

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“…All images with cloud cover 10% or less were considered suitable and were included in the analysis. From the ≤10% threshold filtered data, cloudy pixels found on the area of interest (farms) were masked out using the Fmask algorithm [62][63][64].…”

Section: Extraction Of Remote Sensing Data and Derivation Of Vegetation Indicesmentioning

confidence: 99%

Assessing the Potential of Sentinel-2 Derived Vegetation Indices to Retrieve Phenological Stages of Mango in Ghana

et al. 2021

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In 2020, mango (Mangifera indica) exports contributed over 40 million tons, worth around US$20 billion, to the global economy. Only 10% of this contribution was made from African countries including Ghana, largely due to lower investment in the sector and general paucity of research into the mango value chain, especially production, quality and volume. Considering the global economic importance of mango coupled with the gap in the use of the remote sensing technology in the sector, this study tested the hypothesis that phenological stages of mango can be retrieved from Sentinel-2 (S2) derived time series vegetation indices (VIs) data. The study was conducted on four mango farms in the Yilo Krobo Municipal Area of Ghana. Seasonal (temporal) growth curves using four VIs (NDVI, GNDVI, EVI and SAVI) for the period from 2017 to 2020 were derived for each of the selected orchards and then aligned with five known phenology stages: Flowering/Fruitset (F/FS), Fruit Development (FRD), Maturity/Harvesting (M/H), Flushing (FLU) and Dormancy (D). The significance of the variation “within” and “between” farms obtained from the VI metrics of the S2 data were tested using single-factor and two-factor analysis of variance (ANOVA). Furthermore, to identify which specific variable pairs (phenology stages) were significantly different, a Tukey honest significant difference (HSD) post-hoc test was conducted, following the results of the ANOVA. Whilst it was possible to differentiate the phenological stages using all the four VIs, EVI was found to be the best related with p < 0.05 for most of the studied farms. A distinct annual trend was identified with a peak in June/July and troughs in December/January. The derivation of remote sensing based ‘time series’ growth profiles for commercial mango orchards supports the ‘benchmarking’ of annual and seasonal orchard performance and therefore offers a near ‘real time’ technology for identifying significant variations resulting from pest and disease incursions and the potential impacts of seasonal weather variations.

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Comparison of Masking Algorithms for Sentinel-2 Imagery

Cited by 41 publications

References 27 publications

Cloud and Cloud-Shadow Detection for Applications in Mapping Small-Scale Mining in Colombia Using Sentinel-2 Imagery

Cloud and Cloud-Shadow Detection for Applications in Mapping Small-Scale Mining in Colombia Using Sentinel-2 Imagery

Benchmarking Deep Learning Models for Cloud Detection in Landsat-8 and Sentinel-2 Images

Assessing the Potential of Sentinel-2 Derived Vegetation Indices to Retrieve Phenological Stages of Mango in Ghana

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