MARIDA: A benchmark for Marine Debris detection from Sentinel-2 remote sensing data

Currently, a significant amount of research is focused on detecting Marine Debris and assessing its spectral behaviour via remote sensing, ultimately aiming at new operational monitoring solutions. Here, we introduce a Marine Debris Archive (MARIDA), as a benchmark dataset for developing and evaluating Machine Learning (ML) algorithms capable of detecting Marine Debris. MARIDA is the first dataset based on the multispectral Sentinel-2 (S2) satellite data, which distinguishes Marine Debris from various marine features that co-exist, including Sargassum macroalgae, Ships, Natural Organic Material, Waves, Wakes, Foam, dissimilar water types (i.e., Clear, Turbid Water, Sediment-Laden Water, Shallow Water), and Clouds. We provide annotations (georeferenced polygons/ pixels) from verified plastic debris events in several geographical regions globally, during different seasons, years and sea state conditions. A detailed spectral and statistical analysis of the MARIDA dataset is presented along with well-established ML baselines for weakly supervised semantic segmentation and multi-label classification tasks. MARIDA is an open-access dataset which enables the research community to explore the spectral behaviour of certain floating materials, sea state features and water types, to develop and evaluate Marine Debris detection solutions based on artificial intelligence and deep learning architectures, as well as satellite pre-processing pipelines.

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Adaptive ML-Models for analysis of TSS, Chlorophyll-a, in mixed water type scenarios in Ca Mau peninsula, Vietnam

10.5194/egusphere-egu21-15059 ◽

2021 ◽

Author(s):

Long Vu Huu ◽

Andreas Schenk ◽

Stefan Hinz

Keyword(s):

Remote Sensing ◽

Water Quality ◽

Surface Water ◽

Remote Sensing Data ◽

Quality Parameters ◽

Water Quality Parameters ◽

Water Type ◽

Water Types ◽

Chl A ◽

Sentinel 2

The multispectral mission of Sentinel-2 enables reliable, affordable and continuous environmental monitoring systems in fields like agriculture, biodiversity, environmental hazards and surface water. Several studies have proven that main water quality parameters like total suspended solids (TSS) and chlorophyll (Chl-a) can be estimated from multispectral data using different methods and algorithms. However, independently of the specific approach, these algorithms are selected and optimized to work primarily for one of the main water types i.e. open water, coastal water or inland water. This is also shown by the fact that there is not a single universal algorithm, which can be applied to all water types with consistent and reliable performance at the same time.Ca Mau peninsula is a spacious area located in the southern part of the Mekong Delta, with an area of around 1.6 million hectares. This area has high growth rates of agricultural and aquaculture production, hence diverse water demands and water use types. In this study we use Sentinel-2 remote sensing data to monitor surface water quality using adaptive ML models to account for the different surface water types which occur in this area. Through using remote sensing data, we can provide a synoptic and sufficient view in spatial aspects about water quality parameters in the Ca Mau peninsula. Adapting the ML model will address the bio-optical model for a mixed water scenario.The study is based on Sentinel-2 satellite images acquired in 2019 and 2020, supplemented by field data, i.e. hyperspectral measurements using close range observations, in-situ measurements and water samples, with the aim to collect a comprehensive reference data set as biophysical parameters are closely connected with spectral parameters at close range as well as at high spectral resolution. Therefore, surface hyperspectral measurement has been used to simulate Sentinel 2 multispectral image data at the respective bands.We automatically assign the water type classes to observed surface water by integrating GIS data and remote sensing as the pre-processing step. For each class, the ML models are trained based on the experimental measurements with the multispectral and the simulated multispectral images on the respective water types. We devote special attention to water type boundaries to provide a smooth transition of estimated parameters.The outputs of this model are surface water quality distribution maps with turbidity, TSS, and Chl-a parameters for all areas in Ca Mau peninsula, independent of the actual water type. Through the acceptable accuracy of model testing, the consolidation model will contribute water quality parameters that are crucial and meaningful to the planning and use of water for domestic use and production, besides, it also supports the decision-making of sustainable water use.

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Some Peculiarities of Arable Soil Organic Matter Detection Using Optical Remote Sensing Data

Remote Sensing ◽

10.3390/rs13122313 ◽

2021 ◽

Vol 13 (12) ◽

pp. 2313

Author(s):

Elena Prudnikova ◽

Igor Savin

Keyword(s):

Remote Sensing ◽

Organic Matter ◽

Soil Organic Matter ◽

Spectral Reflectance ◽

Remote Sensing Data ◽

Soil Surface ◽

Optical Remote Sensing ◽

Test Field ◽

Sensing Data ◽

Sentinel 2

Optical remote sensing only provides information about the very thin surface layer of soil. Rainfall splash alters soil surface properties and its spectral reflectance. We analyzed the impact of rainfall on the success of soil organic matter (SOM) content (% by mass) detection and mapping based on optical remote sensing data. The subject of the study was the arable soils of a test field located in the Tula region (Russia), their spectral reflectance, and Sentinel-2 data. Our research demonstrated that rainfall negatively affects the accuracy of SOM predictions based on Sentinel-2 data. Depending on the average precipitation per day, the R2cv of models varied from 0.67 to 0.72, RMSEcv from 0.64 to 1.1% and RPIQ from 1.4 to 2.3. The incorporation of information on the soil surface state in the model resulted in an increase in accuracy of SOM content detection based on Sentinel-2 data: the R2cv of the models increased up to 0.78 to 0.84, the RMSEcv decreased to 0.61 to 0.71%, and the RPIQ increased to 2.1 to 2.4. Further studies are necessary to identify how the SOM content and composition of the soil surface change under the influence of rainfall for other soils, and to determine the relationships between rainfall-induced SOM changes and soil surface spectral reflectance.

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Estimation of Boreal Forest Growing Stock Volume in Russia from Sentinel-2 MSI and Land Cover Classification

Remote Sensing ◽

10.3390/rs13214483 ◽

2021 ◽

Vol 13 (21) ◽

pp. 4483

Author(s):

W. Gareth Rees ◽

Jack Tomaney ◽

Olga Tutubalina ◽

Vasily Zharko ◽

Sergey Bartalev

Keyword(s):

Remote Sensing ◽

Land Cover ◽

Boreal Forest ◽

Remote Sensing Data ◽

Land Cover Classification ◽

Fundamental Parameter ◽

Sensing Data ◽

Growing Stock ◽

Stock Volume ◽

Sentinel 2

Growing stock volume (GSV) is a fundamental parameter of forests, closely related to the above-ground biomass and hence to carbon storage. Estimation of GSV at regional to global scales depends on the use of satellite remote sensing data, although accuracies are generally lower over the sparse boreal forest. This is especially true of boreal forest in Russia, for which knowledge of GSV is currently poor despite its global importance. Here we develop a new empirical method in which the primary remote sensing data source is a single summer Sentinel-2 MSI image, augmented by land-cover classification based on the same MSI image trained using MODIS-derived data. In our work the method is calibrated and validated using an extensive set of field measurements from two contrasting regions of the Russian arctic. Results show that GSV can be estimated with an RMS uncertainty of approximately 35–55%, comparable to other spaceborne estimates of low-GSV forest areas, with 70% spatial correspondence between our GSV maps and existing products derived from MODIS data. Our empirical approach requires somewhat laborious data collection when used for upscaling from field data, but could also be used to downscale global data.

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Mapping the Unseen: Exploiting Super-Resolution for Semantic Segmentation in Low-Resolution Images

10.5753/sibgrapi.est.2020.12987 ◽

2020 ◽

Author(s):

Matheus B. Pereira ◽

Jefersson Alex Dos Santos

Keyword(s):

Remote Sensing ◽

Pattern Recognition ◽

Super Resolution ◽

Remote Sensing Data ◽

Semantic Segmentation ◽

The Other ◽

Aerial Imagery ◽

Aerial Images ◽

Remote Sensing Images ◽

Low Resolution

High-resolution aerial images are usually not accessible or affordable. On the other hand, low-resolution remote sensing data is easily found in public open repositories. The problem is that the low-resolution representation can compromise pattern recognition algorithms, especially semantic segmentation. In this M.Sc. dissertation1 , we design two frameworks in order to evaluate the effectiveness of super-resolution in the semantic segmentation of low-resolution remote sensing images. We carried out an extensive set of experiments on different remote sensing datasets. The results show that super-resolution is effective to improve semantic segmentation performance on low-resolution aerial imagery, outperforming unsupervised interpolation and achieving semantic segmentation results comparable to highresolution data.

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ESTIMATION OF SURFACE SNOW WETNESS USING SENTINEL-2 MULTISPECTRAL DATA

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-iv-5-223-2018 ◽

2018 ◽

Vol IV-5 ◽

pp. 223-228 ◽

Cited By ~ 7

Author(s):

D. Varade ◽

O. Dikshit

Keyword(s):

Remote Sensing ◽

Water Resource Management ◽

Remote Sensing Data ◽

Microwave Remote Sensing ◽

In Situ Measurements ◽

Physical Relationship ◽

Sensing Data ◽

Surface Snow ◽

Sentinel 2

Abstract. Snow cover characterization and estimation of snow geophysical parameters is a significant area of research in water resource management and surface hydrological processes. With advances in spaceborne remote sensing, much progress has been achieved in the qualitative and quantitative characterization of snow geophysical parameters. However, most of the methods available in the literature are based on the microwave backscatter response of snow. These methods are mostly based on the remote sensing data available from active microwave sensors. Moreover, in alpine terrains, such as in the Himalayas, due to the geometrical distortions, the missing data is significant in the active microwave remote sensing data. In this paper, we present a methodology utilizing the multispectral observations of Sentinel-2 satellite for the estimation of surface snow wetness. The proposed approach is based on the popular triangle method which is significantly utilized for the assessment of soil moisture. In this case, we develop a triangular feature space using the near infrared (NIR) reflectance and the normalized differenced snow index (NDSI). Based on the assumption that the NIR reflectance is linearly related to the liquid water content in the snow, we derive a physical relationship for the estimation of snow wetness. The modeled estimates of snow wetness from the proposed approach were compared with in-situ measurements of surface snow wetness. A high correlation determined by the coefficient of determination of 0.94 and an error of 0.535 was observed between the proposed estimates of snow wetness and in-situ measurements.

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Possibilities of milpa identification in Yucatan through remote sensing techniques and Sentinel-2 data

10.29007/hbs2 ◽

2019 ◽

Author(s):

Juan Carlos Valdiviezo-Navarro ◽

Adan Salazar-Garibay ◽

Karla Juliana Rodríguez-Robayo ◽

Lilián Juárez ◽

María Elena Méndez-López ◽

...

Keyword(s):

Remote Sensing ◽

Urban Areas ◽

Food Sovereignty ◽

Region Of Interest ◽

Remote Sensing Data ◽

Support Vector ◽

Multispectral Images ◽

Crop Fields ◽

Remote Sensing Techniques ◽

Sentinel 2

Maya milpa is one of the most important agrifood systems in Mesoamerica, not only because its ancient origin but also due to lead an increase in landscape diversity and to be a relevant source of families food security and food sovereignty. Nowadays, satellite remote sensing data, as the multispectral images of Sentinel-2 platforms, permit us the monitor- ing of different kinds of structures such as water bodies, urban areas, and particularly agricultural fields. Through its multispectral signatures, mono-crop fields or homogeneous vegetation zones like corn fields, barley fields, or other ones, have been successfully detected by using classification techniques with multispectral images. However, Maya milpa is a complex field which is conformed by different kinds of vegetables species and fragments of natural vegetation that in conjunction cannot be considered as a mono-crop field. In this work, we show some preliminary studies on the availability of monitoring this complex system in a region of interest in Yucatan, through a support vector machine (SVM) approach.

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ANALYSIS OF THE IMPACT OF OIL PRODUCT SPILLS ON NATURAL OBJECTS BASED ON SENTINEL-2 REMOTE SENSING DATA IN EASTERN SIBERIA

Interexpo GEO-Siberia ◽

10.33764/2618-981x-2021-6-24-31 ◽

2021 ◽

Vol 6 ◽

pp. 24-31

Author(s):

Dmitry A. Baikin

Keyword(s):

Remote Sensing ◽

Oil Spills ◽

Remote Sensing Data ◽

Oil Products ◽

Eastern Siberia ◽

Sensing Data ◽

Sensing System ◽

Natural Objects ◽

The Impact ◽

Sentinel 2

The article analyzes the impact of oil spills on natural objects according to the remote sensing system Sentinel-2 in Eastern Siberia. Remote sensing data analysis is used to detect traces of oil products in the accident area. Conclusions about the usage of Sentinel-2 data for detecting traces of oil products were made.

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Pantropical modelling of canopy functional traits using Sentinel-2 remote sensing data

Remote Sensing of Environment ◽

10.1016/j.rse.2020.112122 ◽

2021 ◽

Vol 252 ◽

pp. 112122 ◽

Cited By ~ 1

Author(s):

Jesús Aguirre-Gutiérrez ◽

Sami Rifai ◽

Alexander Shenkin ◽

Imma Oliveras ◽

Lisa Patrick Bentley ◽

...

Keyword(s):

Remote Sensing ◽

Functional Traits ◽

Remote Sensing Data ◽

Sensing Data ◽

Sentinel 2

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Predicting Microhabitat Suitability for an Endangered Small Mammal Using Sentinel-2 Data

Remote Sensing ◽

10.3390/rs12030562 ◽

2020 ◽

Vol 12 (3) ◽

pp. 562 ◽

Cited By ~ 1

Author(s):

Francesco Valerio ◽

Eduardo Ferreira ◽

Sérgio Godinho ◽

Ricardo Pita ◽

António Mira ◽

...

Keyword(s):

Remote Sensing ◽

Landscape Heterogeneity ◽

Remote Sensing Data ◽

Predictive Distribution ◽

Distribution Models ◽

Explanatory Variables ◽

Habitat Features ◽

Main Challenge ◽

Cabrera Vole ◽

Sentinel 2

Accurate mapping is a main challenge for endangered small-sized terrestrial species. Freely available spatio-temporal data at high resolution from multispectral satellite offer excellent opportunities for improving predictive distribution models of such species based on fine-scale habitat features, thus making it easier to achieve comprehensive biodiversity conservation goals. However, there are still few examples showing the utility of remote-sensing-based products in mapping microhabitat suitability for small species of conservation concern. Here, we address this issue using Sentinel-2 sensor-derived habitat variables, used in combination with more commonly used explanatory variables (e.g., topography), to predict the distribution of the endangered Cabrera vole (Microtus cabrerae) in agrosilvopastorial systems. Based on vole surveys conducted in two different seasons over a ~176,000 ha landscape in Southern Portugal, we assessed the significance of each predictor in explaining Cabrera vole occurrence using the Boruta algorithm, a novel Random forest variant for dealing with high dimensionality of explanatory variables. Overall, results showed a strong contribution of Sentinel-2-derived variables for predicting microhabitat suitability of Cabrera voles. In particular, we found that photosynthetic activity (NDI45), specific spectral signal (SWIR1), and landscape heterogeneity (Rao’s Q) were good proxies of Cabrera voles’ microhabitat, mostly during temporally greener and wetter conditions. In addition to remote-sensing-based variables, the presence of road verges was also an important driver of voles’ distribution, highlighting their potential role as refuges and/or corridors. Overall, our study supports the use of remote-sensing data to predict microhabitat suitability for endangered small-sized species in marginal areas that potentially hold most of the biodiversity found in human-dominated landscapes. We believe our approach can be widely applied to other species, for which detailed habitat mapping over large spatial extents is difficult to obtain using traditional descriptors. This would certainly contribute to improving conservation planning, thereby contributing to global conservation efforts in landscapes that are managed for multiple purposes.

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Preventive Archaeology Based on Open Remote Sensing Data and Tools: The Cases of Sant’Arsenio (SA) and Foggia (FG), Italy

Sustainability ◽

10.3390/su11154145 ◽

2019 ◽

Vol 11 (15) ◽

pp. 4145 ◽

Cited By ~ 7

Author(s):

Nicodemo Abate ◽

Rosa Lasaponara

Keyword(s):

Remote Sensing ◽

Cultural Heritage ◽

Urban Sprawl ◽

Southern Italy ◽

Open Data ◽

Remote Sensing Data ◽

Public Works ◽

Oil Pipelines ◽

Specific Reason ◽

Sentinel 2

Sentinel-2 data have been used in various fields of human activity. In cultural heritage, their potential is still to be fully explored. This paper aims to illustrate how remote sensing and open source tools are useful for archaeological investigations. The whole issue revolves around the application of satellite (Sentinel-2) and accessory tools for the identification, knowledge and protection of the cultural heritage of two areas of southern Italy: Sant’Arsenio (SA) and Foggia (FG). Both study cases were selected for a specific reason: to demonstrate the usefulness of open data and software for research and preservation of cultural heritage, as in the case of urban sprawl, development of public works (gas- and oil-pipelines, etc.) or intensive use of land for agricultural purposes. The results obtained are relevant for the knowledge improvement and very useful to operate in the field of preventive archaeology, for the evaluation and management of risk, the planning of city-expansion or infrastructures that could damage the buried heritage.

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