Application of Sentinel-2A/B satellites to retrieve turbidity in the Guadalquivir estuary (Southern Spain)

2021 ◽  
Author(s):  
Masuma Chowdhury ◽  
César Vilas ◽  
Stef VanBergeijk ◽  
Gabriel Navarro ◽  
Irene Laiz ◽  
...  
Keyword(s):  
Climate Change ◽  
Remote Sensing ◽  
Water Quality ◽  
Coastal Waters ◽  
Atmospheric Correction ◽  
Southern Spain ◽  
Guadalquivir Estuary ◽  
Sensing Applications ◽  
Sentinel 2A ◽  
Sentinel 2

<p>Application of Sentinel-2A/B satellites to retrieve turbidity in the Guadalquivir estuary (Southern Spain)</p><p>Due to climate change, contamination, and diverse anthropogenic effects, water quality monitoring is intensifying its importance nowadays. Remote sensing techniques are becoming an important tool, in parallel with fieldwork, for supporting the cost-effective accomplishment of water quality mapping and management. In the recent years, Sentinel-2A/B twin satellites of the European Commission Earth Observation Copernicus programme emerged as a promising way to monitor complex coastal waters with higher spatial, spectral and temporal resolution. However, atmospheric and sunglint correction for the Sentinel-2 data over the coastal and inland waters is one of the major challenges in terms of accurate water quality retrieval. This study aimed at evaluating the ACOLITE atmospheric correction processor in order to develop a regional turbidity model for the Guadalquivir estuary (southern Spain) and its adjacent coastal region using Sentinel-2 imagery at a 10 m spatial resolution. Two settings for the atmospheric correction algorithm within the ACOLITE software were applied: the standard dark spectrum fitting (DSF) and the DSF with an additional option for sunglint correction. Turbidity field data were collected for calibration/validation purposes from the monthly Guadalquivir Estuary-LTER programme by Andalusian Institute of Agricultural and Fisheries Research and Training (IFAPA) using a YSI-EXO2 multiparametric sonde for the period 2017-2020 at 2 fixed stations (Bonanza and Tarfia) sampling 4 different water masses along the estuary salinity gradient. Several regional models were evaluated using the red band (665 nm) and the red-edge bands (i.e. 704, 740, 783 nm) of the Sentinel-2 satellites. The results revealed that DSF with glint correction performs better than without glint correction, especially for this region where sunglint is a major concern during summer, affecting most of the satellite scenes. This study demonstrates the invaluable potential of the Sentinel-2A/B mission to monitor complex coastal waters even though they were not designed for aquatic remote sensing applications. This improved knowledge will be a helpful guideline and tool for the coastal managers, policy-makers, stakeholders and the scientific community for ensuring sustainable ecosystem-based coastal resource management under a global climate change scenario.</p>

Use of Sentinel-2A/B satellites and Google Earth Engine for monitoring estuarine systems: a study case in the Western Mediterranean

2021 ◽  
Author(s):  
Mar Roca Mora ◽  
Gabriel Navarro Almendros ◽  
Javier García Sanabria ◽  
Isabel Caballero de Frutos
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Western Mediterranean ◽  
Coastal Areas ◽  
Google Earth ◽  
Local Data ◽  
Sensing Applications ◽  
Google Earth Engine ◽  
Sentinel 2A ◽  
Sentinel 2

<p>Coastal areas are being rapidly transformed in the last 50 years due to anthropogenic causes. New infrastructures and intensive activities have changed the natural behaviour of coastal ecosystems, promoting problems related to water quality, eutrophication and coastal erosion. This situation increases the vulnerability to climate change, requiring important efforts in monitoring and defining protocols for optimizing operational decision-making and strategic management. Remote sensing techniques are becoming a key tool for coastal mapping in terms of resolution, effectiveness and cost reduction. In the last decade, the European Commission launched the Copernicus programme for Earth Observation as a way of improving coastal monitoring with higher resolution. Sentinel-2A/B twin satellites are part of this free and open policy programme available since 2015, but atmospheric corrections or cloud cover are still challenges to face. In order to process this data, cloud computing platforms such as Google Earth Engine (GEE) have revolutionized the way satellite images are processed, without the need to download and store local data. The present study aimed at developing a GEE-based technique for selecting cloud-free Sentinel-2 Level-2A images in the Guadiaro estuary in the Western Mediterranean (Spain) during the last four years (2017-2020).  It has been used to analyse the evolution of the sand bar and to identify hotspots in its sedimentary variation along the coast, at 10 m and 5 days spatial and temporal resolution respectively. NDWI index was evaluated using 0.05 to 0.15 threshold, revealing 0.1 as the best threshold to be used for land/water mapping, easily incorporated in the GEE platform. In addition to Sentinel-2 potential, this study also demonstrates the power of GEE, computing more than 400 images for statistical analysis in terms of seconds, which enabled the automatic filtering method developed for cloud-free images selection with a 95% of effectiveness. Moreover, ACOLITE processor has been used on Sentinel-2 L1A images for atmospheric and sunglint correction to generate Level-2 data and for analysing turbidity and water quality patterns during extreme rainfall events, providing key information as early-warning indicators development. This improvement will be useful for near future implementation of remote sensing applications for coastal managers, ensuring a continuous and detailed monitoring and helping to support an ecosystem-based approach for coastal areas.</p>

scholarly journals Using Optical Water Types to Monitor Changes in Optically Complex Inland and Coastal Waters

2019 ◽  
Vol 11 (19) ◽  
pp. 2297 ◽  
Author(s):  
Kristi Uudeberg ◽  
Ilmar Ansko ◽  
Getter Põru ◽  
Ave Ansper ◽  
Anu Reinart
Keyword(s):  
Remote Sensing ◽  
Coastal Waters ◽  
Atmospheric Correction ◽  
Suspended Sediments ◽  
Reflectance Spectra ◽  
Water Type ◽  
Colored Dissolved Organic Matter ◽  
Water Color ◽  
Sentinel 2

The European Space Agency’s Copernicus satellites Sentinel-2 and Sentinel-3 provide observations with high spectral, spatial, and temporal resolution which can be used to monitor inland and coastal waters. Such waters are optically complex, and the water color may vary from completely clear to dark brown. The main factors influencing water color are colored dissolved organic matter, phytoplankton, and suspended sediments. Recently, there has been a growing interest in the use of the optical water type (OWT) classification in the remote sensing of ocean color. Such classification helps to clarify relationships between different properties inside a certain class and quantify variation between classes. In this study, we present a new OWT classification based on the in situ measurements of reflectance spectra for boreal region lakes and coastal areas without extreme optical conditions. This classification divides waters into five OWT (Clear, Moderate, Turbid, Very Turbid, and Brown) and shows that different OWTs have different remote sensing reflectance spectra and that each OWT is associated with a specific bio-optical condition. Developed OWTs are distinguishable by both the MultiSpectral Instrument (MSI) and the Ocean and Land Color Instrument (OLCI) sensors, and the accuracy of the OWT assignment was 95% for both the MSI and OLCI bands. To determine OWT from MSI images, we tested different atmospheric correction (AC) processors, namely ACOLITE, C2RCC, POLYMER, and Sen2Cor and for OLCI images, we tested AC processors ALTNNA, C2RCC, and L2. The C2RCC AC processor was the most accurate and reliable for use with MSI and OLCI images to estimate OWTs.

scholarly journals Deep learning para la clasificación de usos de suelo agrícola con Sentinel-2

2020 ◽  
pp. 35
Author(s):  
M. Campos-Taberner ◽  
F.J. García-Haro ◽  
B. Martínez ◽  
M.A. Gilabert
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Near Infrared ◽  
Spatial Information ◽  
Learning Algorithm ◽  
Support Vector ◽  
Sensing Applications ◽  
Learning Techniques ◽  
Remote Sensing Applications ◽  
Sentinel 2

<p class="p1">The use of deep learning techniques for remote sensing applications has recently increased. These algorithms have proven to be successful in estimation of parameters and classification of images. However, little effort has been made to make them understandable, leading to their implementation as “black boxes”. This work aims to evaluate the performance and clarify the operation of a deep learning algorithm, based on a bi-directional recurrent network of long short-term memory (2-BiLSTM). The land use classification in the Valencian Community based on Sentinel-2 image time series in the framework of the common agricultural policy (CAP) is used as an example. It is verified that the accuracy of the deep learning techniques is superior (98.6 % overall success) to that other algorithms such as decision trees (DT), k-nearest neighbors (k-NN), neural networks (NN), support vector machines (SVM) and random forests (RF). The performance of the classifier has been studied as a function of time and of the predictors used. It is concluded that, in the study area, the most relevant information used by the network in the classification are the images corresponding to summer and the spectral and spatial information derived from the red and near infrared bands. These results open the door to new studies in the field of the explainable deep learning in remote sensing applications.</p>

scholarly journals Retrieving Total and Inorganic Suspended Sediments in Amazon Floodplain Lakes: A Multisensor Approach

2019 ◽  
Vol 11 (15) ◽  
pp. 1744 ◽  
Author(s):  
Daniel Maciel ◽  
Evlyn Novo ◽  
Lino Sander de Carvalho ◽  
Cláudio Barbosa ◽  
Rogério Flores Júnior ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Atmospheric Correction ◽  
Suspended Sediments ◽  
Floodplain Lakes ◽  
Empirical Modeling ◽  
Landsat 8 ◽  
Amazon Floodplain ◽  
The Impact ◽  
Sentinel 2

Remote sensing imagery are fundamental to increasing the knowledge about sediment dynamics in the middle-lower Amazon floodplains. Moreover, they can help to understand both how climate change and how land use and land cover changes impact the sediment exchange between the Amazon River and floodplain lakes in this important and complex ecosystem. This study investigates the suitability of Landsat-8 and Sentinel-2 spectral characteristics in retrieving total (TSS) and inorganic (TSI) suspended sediments on a set of Amazon floodplain lakes in the middle-lower Amazon basin using in situ Remote Sensing Reflectance (Rrs) measurements to simulate Landsat 8/OLI (Operational Land Imager) and Sentinel 2/MSI (Multispectral Instrument) bands and to calibrate/validate several TSS and TSI empirical algorithms. The calibration was based on the Monte Carlo Simulation carried out for the following datasets: (1) All-Dataset, consisting of all the data acquired during four field campaigns at five lakes spread over the lower Amazon floodplain (n = 94); (2) Campaign-Dataset including samples acquired in a specific hydrograph phase (season) in all lakes. As sample size varied from one season to the other, n varied from 18 to 31; (3) Lake-Dataset including samples acquired in all seasons at a given lake with n also varying from 17 to 67 for each lake. The calibrated models were, then, applied to OLI and MSI scenes acquired in August 2017. The performance of three atmospheric correction algorithms was also assessed for both OLI (6S, ACOLITE, and L8SR) and MSI (6S, ACOLITE, and Sen2Cor) images. The impact of glint correction on atmosphere-corrected image performance was assessed against in situ glint-corrected Rrs measurements. After glint correction, the L8SR and 6S atmospheric correction performed better with the OLI and MSI sensors, respectively (Mean Absolute Percentage Error (MAPE) = 16.68% and 14.38%) considering the entire set of bands. However, for a given single band, different methods have different performances. The validated TSI and TSS satellite estimates showed that both in situ TSI and TSS algorithms provided reliable estimates, having the best results for the green OLI band (561 nm) and MSI red-edge band (705 nm) (MAPE < 21%). Moreover, the findings indicate that the OLI and MSI models provided similar errors, which support the use of both sensors as a virtual constellation for the TSS and TSI estimate over an Amazon floodplain. These results demonstrate the applicability of the calibration/validation techniques developed for the empirical modeling of suspended sediments in lower Amazon floodplain lakes using medium-resolution sensors.

scholarly journals Remote Sensing Approaches for Monitoring Mangrove Species, Structure, and Biomass: Opportunities and Challenges

2019 ◽  
Vol 11 (3) ◽  
pp. 230 ◽  
Author(s):  
Tien Pham ◽  
Naoto Yokoya ◽  
Dieu Bui ◽  
Kunihiko Yoshino ◽  
Daniel Friess
Keyword(s):  
Climate Change ◽  
Remote Sensing ◽  
Mangrove Ecosystem ◽  
Carbon Stocks ◽  
Machine Learning Techniques ◽  
Species Structure ◽  
Mangrove Species ◽  
Mitigation And Adaptation ◽  
Sensing Applications ◽  
Wide Range

The mangrove ecosystem plays a vital role in the global carbon cycle, by reducing greenhouse gas emissions and mitigating the impacts of climate change. However, mangroves have been lost worldwide, resulting in substantial carbon stock losses. Additionally, some aspects of the mangrove ecosystem remain poorly characterized compared to other forest ecosystems due to practical difficulties in measuring and monitoring mangrove biomass and their carbon stocks. Without a quantitative method for effectively monitoring biophysical parameters and carbon stocks in mangroves, robust policies and actions for sustainably conserving mangroves in the context of climate change mitigation and adaptation are more difficult. In this context, remote sensing provides an important tool for monitoring mangroves and identifying attributes such as species, biomass, and carbon stocks. A wide range of studies is based on optical imagery (aerial photography, multispectral, and hyperspectral) and synthetic aperture radar (SAR) data. Remote sensing approaches have been proven effective for mapping mangrove species, estimating their biomass, and assessing changes in their extent. This review provides an overview of the techniques that are currently being used to map various attributes of mangroves, summarizes the studies that have been undertaken since 2010 on a variety of remote sensing applications for monitoring mangroves, and addresses the limitations of these studies. We see several key future directions for the potential use of remote sensing techniques combined with machine learning techniques for mapping mangrove areas and species, and evaluating their biomass and carbon stocks.

In-Situ Validation of Water Quality Algorithms and Monitoring of Irish Lakes using Sentinel 2 Imagery

2020 ◽  
Author(s):  
Sita Karki ◽  
Kevin French ◽  
Valerie McCarthy ◽  
Jennifer Hanafin ◽  
Eleanor Jennings ◽  
...  
Keyword(s):  
Water Quality ◽  
Time Window ◽  
Atmospheric Correction ◽  
European Space Agency ◽  
Weather Condition ◽  
Water Bodies ◽  
Observation Data ◽  
Field Validation ◽  
Field Samples ◽  
Sentinel 2

&lt;p&gt;Through Remote Sensing of Irish Surface Water (INFER) project, we are validating the algorithms to measure the &amp;#160;water quality using Sentinel 2 imagery, which comprises of two European Space Agency (ESA) terrestrial satellites with combined temporal resolution of 5 days. The project is focused on selection of optimal algorithms that will be applicable in Irish context in relation to the high cloud cover and relatively small sizes of the water bodies. The current procedure entails collection of reflectance data from the lakes during the Sentinel overpass as it helps to identify the correct atmospheric correction algorithm. Field radiometry tasks were carried out using TRIOS RAMSES radiometers. Standard field procedures were employed for acquiring glint free reflectance from the water bodies.&lt;/p&gt;&lt;p&gt;Historical data collected from the 11 lakes, which had field bathymetry survey data, were analysed in order to determine the influence of environmental conditions on the quality of samples. Based on the analysis, recommendations to collect field samples from areas deeper than 10 m and 30 m away from the shoreline were provided in order to avoid the reflectance from the bottom and the surrounding topography. A site selection process was undertaken during the spring of 2019 to shortlist appropriate sites for field validation of satellite-derived products. A total of fifteen lakes were identified for field validation based on several criteria so as to ensure lakes with varying size, depth, trophic status and Water Framework Directive (WFD) status . In addition, a timetable for proposed sampling was established by drawing up a timetable of satellite passes starting from summer of 2019. C2RCC and Acolite processors are being used to compute the chlorophyll and turbidity from identified lakes. Considering the fast changing weather condition of Ireland, it was difficult to obtain the exact overlap between the sentinel overpass and the field sampling. In order to address this issue, the field samples collected within 10 days from the sensor overpass were considered for the field validation. Study of the satellite derived water chemistry data showed that the data collected outside of that time window may not represent the natural fluctuation that occurs in the water bodies.&lt;/p&gt;&lt;p&gt;The end product of this project is the web platform with the access to Sentinel 2 MSI data products where users can visualize the water quality products for Ireland. This platform will promote the use of earth observation data for inland water quality monitoring and would enable sustainable utilization of the water resources.&lt;/p&gt;

Mapping seagrass and water depths using Sentinel-2

2020 ◽  
Author(s):  
Katja Kuhwald ◽  
Philipp Held ◽  
Florian Gausepohl ◽  
Jens Schneider von Deimling ◽  
Natascha Oppelt
Keyword(s):  
Remote Sensing ◽  
Baltic Sea ◽  
Atmospheric Correction ◽  
The Baltic Sea ◽  
Seagrass Meadows ◽  
Spatial Coverage ◽  
Acoustic Methods ◽  
The Baltic ◽  
Water Depths ◽  
Sentinel 2

&lt;p&gt;Seagrass meadows cover large benthic areas of the Baltic Sea, but eutrophication and climate change imply declining seagrass coverage. Apart from acoustic methods and traditional diver mappings, optical remote sensing techniques allow for mapping seagrass. Optical satellite analyses of seagrass mapping may supplement acoustic methods in shallow coastal waters with observations that are more frequent and have a larger spatial coverage.&lt;/p&gt;&lt;p&gt;In the clear Greek Mediterranean Sea, Sentinel-2 was already applied successfully to detect bathymetry and seagrass meadows. We are now testing whether Sentinel-2 data are also suitable for analysing the sublittoral in the turbid waters of the Baltic Sea. We focus on an extensive shallow water area near Kiel/Germany. Based on Sentinel-2 data, we analyse water depth and differentiate between seagrass covered and bare sandy ground. We derive these parameters using empirical and process-based models. First results show that Sentinel-2 allows to determine water depths up to 4 m (RMSE ~ 0.2 m). Comparisons with LiDAR water depths show that inaccuracies increase in overgrown areas. Our study also shows that the atmospheric correction algorithm influences sublittoral ground mappings with Sentinel-2 data. For instance, the absolute water depths of the process-based modelling differ up to 2.5 m on average depending on the atmospheric correction algorithm (ACOLITE, Sen2Cor, iCOR).&lt;/p&gt;&lt;p&gt;Comparing Sentinel-2 seagrass classifications with diver mappings and aerial imagery emphasises that empiric approaches provide plausible sublittoral ground classifications up to approximately 4 m water depth. Combining these results with seagrass mappings based on acoustic measurements (deeper than 4 m water) provides a synthesised sublittoral classification map of the study area up to the present growth limit of seagrass (~ 7 m in the study area).&lt;/p&gt;&lt;p&gt;The Baltic Sea is considered as a very&amp;#160;turbid environment, nevertheless we show that satellite-based remote sensing has a great potential for shedding light into the&amp;#160; &quot;white ribbon&quot;. The spatial coverage and temporal resolution of the analysed Sentinel-2 data increases the knowledge about the occurrence of seagrass and its spatio-temporal dynamics. Nevertheless, the influence of the selected atmospheric correction approach on the results shows that further research in remote sensing is necessary to assess seagrass meadows reliably.&lt;/p&gt;

scholarly journals Estudio multitemporal de calidad del agua del embalse de Sitjar (Castelló, España) utilizando imágenes Sentinel-2

2020 ◽  
pp. 117
Author(s):  
C. Radin ◽  
X. Sòria-Perpinyà ◽  
J. Delegido
Keyword(s):  
Water Quality ◽  
Atmospheric Correction ◽  
Ecological Status ◽  
Water Structure ◽  
Chl A ◽  
Reservoir Conditions ◽  
Parameter Values ◽  
Reflectance Data ◽  
Control Water ◽  
Sentinel 2

<p class="p1">Water quality is a subject of intense scientific inquiry because of its repercussion in human’s life, agriculture or even energy generation. Remote sensing can be used to control water masses by analyzing biophysical variables. Chlorophyll-a (Chl-a) and Total Suspended Solids (SS) are a well-known feature of water quality. These variables have been measured in Sitjar reservoir (Castelló, Spain) as a part of the project Ecological Status of Aquatic Systems with Sentinel Satellites (ESAQS), in order to compare the results with satellite reflectance data. Two processes were compared to correct atmospherically the level 1C Sentinel 2 (S2) images. The results show that Case 2 Regional Coast Colour (C2RCC) method, with a Root Mean Square Error of 2.4 mg/m<span class="s1">3 </span>(Chl-a) and 3.9 g/m<span class="s1">3 </span>(SS) is a better tool for atmospheric correction in this scenario due to the low turbidity levels of water. Besides, in this paper we study the Chl-a and SS variability through April 2017 to March 2019 with fourteen S2 images with the automatic products from C2RCC correction, finding correlations between them and the climate and reservoir conditions. Chl-a increase from 0.4 mg/m<span class="s1">3 </span>to 9.5 mg/m<span class="s1">3 </span>while SS rise 18 g/m<span class="s1">3 </span>in this period, which makes Sitjar as an oligotrophic-mesotrophic system. The correlation results demonstrate an excellent correspondence between them (R<span class="s1">2</span>=0.9). Sitjar reservoir lost almost 40 hm<span class="s1">3 </span>at the beginning of the study, which it had a possible relationship with the increasing parameter values. Also discussed was the role played by the climatology in the reservoir conditions due to the changes in the water structure with seasons, which explains the ariability through the year.</p><p class="p1"> </p>

scholarly journals Potential of Multiway PLS (N-PLS) Regression Method to Analyse Time-Series of Multispectral Images: A Case Study in Agriculture

2022 ◽  
Vol 14 (1) ◽  
pp. 216
Author(s):  
Eva Lopez-Fornieles ◽  
Guilhem Brunel ◽  
Florian Rancon ◽  
Belal Gaci ◽  
Maxime Metz ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Heat Wave ◽  
Large Scale ◽  
Regional Scale ◽  
Pls Regression ◽  
Wave Impact ◽  
Sensing Applications ◽  
Sentinel 2

Recent literature reflects the substantial progress in combining spatial, temporal and spectral capacities for remote sensing applications. As a result, new issues are arising, such as the need for methodologies that can process simultaneously the different dimensions of satellite information. This paper presents PLS regression extended to three-way data in order to integrate multiwavelengths as variables measured at several dates (time-series) and locations with Sentinel-2 at a regional scale. Considering that the multi-collinearity problem is present in remote sensing time-series to estimate one response variable and that the dataset is multidimensional, a multiway partial least squares (N-PLS) regression approach may be relevant to relate image information to ground variables of interest. N-PLS is an extension of the ordinary PLS regression algorithm where the bilinear model of predictors is replaced by a multilinear model. This paper presents a case study within the context of agriculture, conducted on a time-series of Sentinel-2 images covering regional scale scenes of southern France impacted by the heat wave episode that occurred on 28 June 2019. The model has been developed based on available heat wave impact data for 107 vineyard blocks in the Languedoc-Roussillon region and multispectral time-series predictor data for the period May to August 2019. The results validated the effectiveness of the proposed N-PLS method in estimating yield loss from spectral and temporal attributes. The performance of the model was evaluated by the R2 obtained on the prediction set (0.661), and the root mean square of error (RMSE), which was 10.7%. Limitations of the approach when dealing with time-series of large-scale images which represent a source of challenges are discussed; however, the N–PLS regression seems to be a suitable choice for analysing complex multispectral imagery data with different spectral domains and with a clear temporal evolution, such as an extreme weather event.

Sign in / Sign up

Export Citation Format

Share Document