scholarly journals Comparison of Lake Optical Water Types Derived from Sentinel-2 and Sentinel-3

2019 ◽  
Vol 11 (23) ◽  
pp. 2883 ◽  
Author(s):  
Soomets ◽  
Uudeberg ◽  
Jakovels ◽  
Zagars ◽  
Reinart ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Lake Water ◽  
Natural Waters ◽  
Critical Role ◽  
Inland Waters ◽  
Water Types ◽  
Lake Võrtsjärv ◽  
Level 2 ◽  
Sentinel 2

Inland waters play a critical role in our drinking water supply. Additionally, they areimportant providers of food and recreation possibilities. Inland waters are known to be opticallycomplex and more diverse than marine or ocean waters. The optical properties of natural waters areinfluenced by three different and independent sources: phytoplankton, suspended matter, andcolored dissolved organic matter. Thus, the remote sensing of these waters is more challenging.Different types of waters need different approaches to obtain correct water quality products;therefore, the first step in remote sensing of lakes should be the classification of the water types. Theclassification of optical water types (OWTs) is based on the differences in the reflectance spectra ofthe lake water. This classification groups lake and coastal waters into five optical classes: Clear,Moderate, Turbid, Very Turbid, and Brown. We studied the OWTs in three different Latvian lakes:Burtnieks, Lubans, and Razna, and in a large Estonian lake, Lake Võrtsjärv. The primary goal of thisstudy was a comparison of two different Copernicus optical instrument data for opticalclassification in lakes: Ocean and Land Color Instrument (OLCI) on Sentinel-3 and MultispectralInstrument (MSI) on Sentinel-2. We found that both satellite OWT classifications in lakes werecomparable (R2 = 0.74). We were also able to study the spatial and temporal changes in the OWTs ofthe study lakes during 2017. The comparison between two satellites was carried out to understandif the classification of the OWTs with both satellites is compatible. Our results could give us not onlya better overview of the changes in the lake water by studying the temporal and spatial variabilityof the OWTs, but also possibly better retrieval of Level 2 satellite products when using OWT guidedapproach.

scholarly journals Validation and Comparison of Water Quality Products in Baltic Lakes Using Sentinel-2 MSI and Sentinel-3 OLCI Data

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 742 ◽  
Author(s):  
Tuuli Soomets ◽  
Kristi Uudeberg ◽  
Dainis Jakovels ◽  
Agris Brauns ◽  
Matiss Zagars ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Inland Waters ◽  
Water Type ◽  
Water Types ◽  
In Situ Data ◽  
Spatial Coverage ◽  
Level 2 ◽  
Sentinel 2

Inland waters, including lakes, are one of the key points of the carbon cycle. Using remote sensing data in lake monitoring has advantages in both temporal and spatial coverage over traditional in-situ methods that are time consuming and expensive. In this study, we compared two sensors on different Copernicus satellites: Multispectral Instrument (MSI) on Sentinel-2 and Ocean and Land Color Instrument (OLCI) on Sentinel-3 to validate several processors and methods to derive water quality products with best performing atmospheric correction processor applied. For validation we used in-situ data from 49 sampling points across four different lakes, collected during 2018. Level-2 optical water quality products, such as chlorophyll-a and the total suspended matter concentrations, water transparency, and the absorption coefficient of the colored dissolved organic matter were compared against in-situ data. Along with the water quality products, the optical water types were obtained, because in lakes one-method-to-all approach is not working well due to the optical complexity of the inland waters. The dynamics of the optical water types of the two sensors were generally in agreement. In most cases, the band ratio algorithms for both sensors with optical water type guidance gave the best results. The best algorithms to obtain the Level-2 water quality products were different for MSI and OLCI. MSI always outperformed OLCI, with R2 0.84–0.97 for different water quality products. Deriving the water quality parameters with optical water type classification should be the first step in estimating the ecological status of the lakes with remote sensing.

scholarly journals Testing Accuracy of Land Cover Classification Algorithms in the Qilian Mountains Based on GEE Cloud Platform

2021 ◽  
Vol 13 (24) ◽  
pp. 5064
Author(s):  
Yanpeng Yang ◽  
Dong Yang ◽  
Xufeng Wang ◽  
Zhao Zhang ◽  
Zain Nawaz
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Spatial Resolution ◽  
Qilian Mountains ◽  
Classification Algorithms ◽  
Cloud Platform ◽  
Remote Sensing Classification ◽  
The Qilian Mountains ◽  
Sentinel 2

The Qilian Mountains (QLM) are an important ecological barrier in western China. High-precision land cover data products are the basic data for accurately detecting and evaluating the ecological service functions of the QLM. In order to study the land cover in the QLM and performance of different remote sensing classification algorithms for land cover mapping based on the Google Earth Engine (GEE) cloud platform, the higher spatial resolution remote sensing images of Sentinel-1 and Sentinel-2; digital elevation data; and three remote sensing classification algorithms, including the support vector machine (SVM), the classification regression tree (CART), and the random forest (RF) algorithms, were used to perform supervised classification of Sentinel-2 images of the QLM. Furthermore, the results obtained from the classification process were compared and analyzed by using different remote sensing classification algorithms and feature-variable combinations. The results indicated that: (1) the accuracy of the classification results acquired by using different remote sensing classification algorithms were different, and the RF had the highest classification accuracy, followed by the CART and the SVM; (2) the different feature variable combinations had different effects on the overall accuracy (OA) of the classification results and the performance of the identification and classification of the different land cover types; and (3) compared with the existing land cover products for the QLM, the land cover maps obtained in this study had a higher spatial resolution and overall accuracy.

scholarly journals Towards user-adaptive remote sensing: Knowledge-driven automatic classification of Sentinel-2 time series

2021 ◽  
Vol 264 ◽  
pp. 112615
Author(s):  
Damien Arvor ◽  
Julie Betbeder ◽  
Felipe R.G. Daher ◽  
Tim Blossier ◽  
Renan Le Roux ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Automatic Classification ◽  
Sentinel 2

scholarly journals Potential for High Fidelity Global Mapping of Common Inland Water Quality Products at High Spatial and Temporal Resolutions Based on a Synthetic Data and Machine Learning Approach

2021 ◽  
Vol 9 ◽  
Author(s):  
Jeremy Kravitz ◽  
Mark Matthews ◽  
Lisl Lain ◽  
Sarah Fawcett ◽  
Stewart Bernard
Keyword(s):  
Machine Learning ◽  
Water Quality ◽  
Natural Waters ◽  
Dynamic Range ◽  
Synthetic Dataset ◽  
Inland Waters ◽  
Retrieval Models ◽  
Inland Water ◽  
Water Types

There is currently a scarcity of paired in-situ aquatic optical and biogeophysical data for productive inland waters, which critically hinders our capacity to develop and validate robust retrieval models for Earth Observation applications. This study aims to address this limitation through the development of a novel synthetic dataset of top-of-atmosphere and bottom-of-atmosphere reflectances, which is the first to encompass the immense natural optical variability present in inland waters. Novel aspects of the synthetic dataset include: 1) physics-based, two-layered, size- and type-specific phytoplankton inherent optical properties (IOPs) for mixed eukaryotic/cyanobacteria assemblages; 2) calculations of mixed assemblage chlorophyll-a (chl-a) fluorescence; 3) modeled phycocyanin concentration derived from assemblage-based phycocyanin absorption; 4) and paired sensor-specific top-of-atmosphere reflectances, including optically extreme cases and the contribution of green vegetation adjacency. The synthetic bottom-of-atmosphere reflectance spectra were compiled into 13 distinct optical water types similar to those discovered using in-situ data. Inspection showed similar relationships of concentrations and IOPs to those of natural waters. This dataset was used to calculate typical surviving water-leaving signal at top-of-atmosphere, and used to train and test four state-of-the-art machine learning architectures for multi-parameter retrieval and cross-sensor capability. Initial results provide reliable estimates of water quality parameters and IOPs over a highly dynamic range of water types, at various spectral and spatial sensor resolutions. The results of this work represent a significant leap forward in our capacity for routine, global monitoring of inland water quality.

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

PLoS ONE ◽  
2022 ◽  
Vol 17 (1) ◽  
pp. e0262247
Author(s):  
Katerina Kikaki ◽  
Ioannis Kakogeorgiou ◽  
Paraskevi Mikeli ◽  
Dionysios E. Raitsos ◽  
Konstantinos Karantzalos
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Semantic Segmentation ◽  
Marine Debris ◽  
Sea State ◽  
Water Types ◽  
Geographical Regions ◽  
Spectral Behaviour ◽  
Weakly Supervised ◽  
Sentinel 2

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.

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

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

<p>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.</p><p>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.</p><p>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.</p><p>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.</p><p>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.</p>

Monitoring cyanobacteria occurrence in freshwater reservoirs using semi-analytical algorithms and orbital remote sensing

2020 ◽  
Vol 71 (5) ◽  
pp. 569 ◽  
Author(s):  
Henrique Dantas Borges ◽  
Rejane Ennes Cicerelli ◽  
Tati de Almeida ◽  
Henrique L. Roig ◽  
Diogo Olivetti
Keyword(s):  
Remote Sensing ◽  
Atmospheric Correction ◽  
Cyanobacterial Blooms ◽  
Inland Waters ◽  
Strongly Correlated ◽  
Chl A ◽  
Analytical Algorithm ◽  
Remote Sensing Techniques ◽  
Better Than ◽  
Sentinel 2

Cyanobacterial blooms pose a serious threat to the multiple uses of inland waters because of their adverse effects on the environment and human health. Monitoring cyanobacteria concentrations using traditional methods can be expensive and impractical. Recently, alternative efforts using remote sensing techniques have been successful. In particular, semi-analytical modelling approaches have been used to successfully predict chlorophyll (Chl)-a concentrations from remote sensing reflectance. The aims of this study were to test the performance of different semi-analytical algorithms in the estimation of Chl-a concentrations and the applicability of Sentinel-2 multispectral instrument (MSI) imagery, and its atmospheric correction algorithms, in the estimation of Chl-a concentrations. For our dataset, phycocyanin concentration was strongly correlated with Chl-a concentration and the inversion model of inland waters (IIMIW) semi-analytical algorithm was the best performing model, achieving a root mean square error of 4.6mgm–3 in the prediction of Chl-a. When applying the IIMIW model to MSI data, the use of top-of-atmosphere reflectance performed better than the atmospheric correction algorithm tested. Overall, the results were satisfactory, demonstrating that even without an adequate atmospheric correction pipeline, the monitoring of cyanobacteria can be successfully achieved by applying a semi-analytical bio-optical model to MSI data.

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