IEEE

Sentinel missions provide widespread opportunities of exploiting inter-sensor synergies to improve the operational monitoring of terrestrial photosynthetic activity and canopy structural variations using vegetation indices (VI). In this context, continuous and consistent temporal data are logically required to rapidly detect vegetation changes across sensors. Nonetheless, the existing temporal limitations inherent to satellite orbits, cloud occlusions, data degradation, and many other factors may severely constrain the availability of data involving multiple satellites. In response, this letter proposes a novel deep 3-D convolutional regression network (3CRN) for temporally enhancing Sentinel-3 (S3) VI by taking advantage of inter-sensor Sentinel-2 (S2) observations. Unlike existing regression and deep learning-based methods, the proposed approach allows convolutional kernels to slide across the temporal dimension to exploit not only the higher spatial resolution of the S2 instrument but also its own temporal evolution to better estimate time-resolved VI in S3. To validate the proposed approach, we built a database made of multiple day-synchronized S2 and S3 operational products from a study area in Extremadura (Spain). The conducted experimental comparison, including multiple state-of-the-art regression and deep learning models, shows the statistically significant advantages of the presented framework. The codes of this work will be made available at https://github.com/rufernan/3CRN

Sentinel-3/FLEX Biophysical Product Confidence Using Sentinel-2 Land-Cover Spatial Distributions

The estimation of biophysical variables from remote sensing data raises important challenges in terms of the acquisition technology and its limitations. In this way, some vegetation parameters, such as chlorophyll fluorescence, require sensors with a high spectral resolution that constrains the spatial resolution while significantly increasing the subpixel land-cover heterogeneity. Precisely, this spatial variability often makes that rather different canopy structures are aggregated together, which eventually generates important deviations in the corresponding parameter quantification. In the context of the Copernicus program (and other related Earth Explorer missions), this article proposes a new statistical methodology to manage the subpixel spatial heterogeneity problem in Sentinel-3 (S3) and FLuorescence EXplorer (FLEX) by taking advantage of the higher spatial resolution of Sentinel-2 (S2). Specifically, the proposed approach first characterizes the subpixel spatial patterns of S3/FLEX using inter-sensor data from S2. Then, a multivariate analysis is conducted to model the influence of these spatial patterns in the errors of the estimated biophysical variables related to chlorophyll which are used as fluorescence proxies. Finally, these modeled distributions are employed to predict the confidence of S3/FLEX products on demand. Our experiments, conducted using multiple operational S2 and simulated S3 data products, reveal the advantages of the proposed methodology to effectively measure the confidence and expected deviations of different vegetation parameters with respect to standard regression algorithms. The source codes of this work will be available at https://github.com/rufernan/PixelS3

iCOR Atmospheric Correction on Sentinel-3/OLCI over Land: Intercomparison with AERONET, RadCalNet, and SYN Level-2

To validate the iCOR atmospheric correction algorithm applied to the Sentinel-3 Ocean and Land Color Instrument (OLCI), Top-of-Atmosphere (TOA) observations over land, globally retrieved Aerosol Optical Thickness (AOT), Top-of-Canopy (TOC) reflectance, and Vegetation Indices (VIs) were intercompared with (i) AERONET AOT and AERONET-based TOC reflectance simulations, (ii) RadCalNet surface reflectance observations, and (iii) SYN Level 2 (L2) AOT, TOC reflectance, and VIs. The results reveal that, overall, iCOR's statistical and temporal consistency is high. iCOR AOT retrievals overestimate relative to AERONET, but less than SYN L2. iCOR and SYN L2 TOC reflectances exhibit a negative bias of ~−0.01 and −0.02, respectively, in the Blue bands compared to the simulations. This diminishes for RED and NIR, except for a +0.02 bias for SYN L2 in the NIR. The intercomparison with RadCalNet shows relative differences < ±6%, except for bands Oa02 (Blue) and Oa21 (NIR), which is likely related to the reported OLCI "excess of brightness". The intercomparison between iCOR and SYN L2 showed R2 = 0.80–0.93 and R2 = 0.92–0.96 for TOC reflectance and VIs, respectively. iCOR's higher temporal smoothness compared to SYN L2 does not propagate into a significantly higher smoothness for TOC reflectance and VIs. Altogether, we conclude that iCOR is well suitable to retrieve statistically and temporally consistent AOT, TOC reflectance, and VIs over land surfaces from Sentinel-3/OLCI observations

FloU-Net: An Optical Flow Network for Multi-modal Self-Supervised Image Registration

Image registration is an essential task in image processing, where the final objective is to geometrically align two or more images. In remote sensing, this process allows comparing, fusing or analyzing data, specially when multi-modal images are used. In addition, multi-modal image registration becomes fairly challenging when the images have a significant difference in scale and resolution, together with local small image deformations. For this purpose, this paper presents a novel optical flow-based image registration network, named the FloU-Net, which tries to further exploit inter-sensor synergies by means of deep learning. The proposed method is able to extract spatial information from resolution differences and through an U-Net backbone generate an optical flow field estimation to accurately register small local deformations of multi-modal images in a self-supervised fashion. For instance, the registration between Sentinel-2 (S2) and Sentinel-3 (S3) optical data is not trivial, as there are considerable spectral-spatial differences among their sensors. In this case, the higher spatial resolution of S2 result in S2 data being a convenient reference to spatially improve S3 products, as well as those of the forthcoming Fluorescence Explorer (FLEX) mission, since image registration is the initial requirement to obtain higher data processing level products. To validate our method, we compare the proposed FloU-Net with other state-of-the-art techniques using 21 coupled S2/S3 optical images from different locations of interest across Europe. The comparison is performed through different performance measures. Results show that proposed FloU-Net can outperform the compared methods. The code and dataset are available in https://github.com/ibanezfd/FloU-Net

Enhancing the usability of Satellite Earth Observations through Data Driven Models. An application to Sea Water Quality

Earth Observation from satellites has the potential to provide comprehensive, rapid and inexpensive information about land and water bodies. Marine monitoring could gain in effectiveness if integrated with approaches that are able to collect data from wide geographic areas, such as satellite observation. Integrated with in situ measurements, satellite observations enable to extend the punctual information of sampling campaigns to a synoptic view, increase the spatial and temporal coverage, and thus increase the representativeness of the natural diversity of the monitored water bodies, their inter-annual variability and water quality trends, providing information to support EU Member States’ action plans. Turbidity is one of the optically active water quality parameters that can be derived from satellite data, and is one of the environmental indicator considered by EU directives monitoring programmes. Turbidity is a visual property of water, related to the amount of light scattered by particles in water, and it can act as simple and convenient indirect measure of the concentration of suspended solids and other particulate material. A review of the state-of-the-art shows that most traditional methods to estimate turbidity from optical satellite images are based on semi-empirical models relying on few spectral bands. The choice of the most suitable bands to be used is often site and season specific, as it is related to the type and concentration of suspended particles. When investigating wide areas or long time series that include different optical water types, the application of machine learning algorithms seems to be promising due to their flexibility, responding to the need of a model that can adapt to varying water conditions with smooth transition, and their ability to exploit the wealth of spectral information. Moreover, machine learning models have shown to be less affected by atmospheric and other background factors. Atmospheric correction for water leaving reflectance, in fact, still remains one of the major challenges in aquatic remote sensing. The use of machine learning for remotely sensed water quality estimation has spread in recent years thanks to the advances in algorithm development, computing power, and availability of higher spatial resolution data. Among all existing algorithms, the choice of the complexity of the model derives from the nature and number of available data. The present study explores the use of Sentinel-2 MultiSpectral Instrument (MSI) Level-1C Top of Atmosphere spectral radiance to derive water turbidity, through application of a Polynomial Kernel Regularized Least Squares regression. This algorithms is characterized by a simple model structure, good generalization, global optimal solution, especially suitable for non-linear and high dimension problems. The study area is located in the North Tyrrhenian Sea (Italy), covering a coastline of about 100 km, characterized by a varied shoreline, embracing environments worthy of protection and valuable biodiversity, but also relevant ports, and three main river flow and sediment discharge. The coastal environment in this area has been monitored since 2001, according to the 2000/60/EC Water Framework Directive, and in 2008 EU Marine Strategy Framework Directive 2008/56/EC further strengthened the investigation in the area. A dataset of combination of turbidity measurements, expressed in nephelometric turbidity units (NTU), and values of the 13 spectral bands in the pixel corresponding to the sample location was used to calibrate and validate the model. The developed turbidity model shows good agreement of the estimated satellite-derived surface turbidity with the measured one, confirming that the use of ML techniques allows to reach a good accuracy in turbidity estimation from satellite Top of Atmosphere reflectance. Comparison between turbidity estimates obtained from the model with turbidity data from Copernicus CMEMS dataset named ’Mediterranean Sea, Bio-Geo-Chemical, L3, daily observation’, which was used as benchmark, produced consistent results. A band importance analysis revealed the contribution of the different spectral bands and the main role of the red-edge range. Finally, turbidity maps from satellite imagery were produced for the study area, showing the ability of the model to catch extreme events and, overall, how it represents an important tool to improve our understanding of the complex factors that influence water quality in our oceans

Remote Sensing of the Aquatic Environments

The book highlights recent research efforts in the monitoring of aquatic districts with remote sensing observations and proximal sensing technology integrated with laboratory measurements. Optical satellite imagery gathered at spatial resolutions down to few meters has been used for quantitative estimations of harmful algal bloom extent and Chl-a mapping, as well as winds and currents from SAR acquisitions. The knowledge and understanding gained from this book can be used for the sustainable management of bodies of water across our planet

Applying Independent Component Analysis on Sentinel-2 Imagery to Characterize Geomorphological Responses to an Extreme Flood Event near the Non-Vegetated Rio Colorado Terminus, Salar de Uyuni, Bolivia

In some internally-draining dryland basins, ephemeral river systems terminate at the margins of playas. Extreme floods can exert significant geomorphological impacts on the lower reaches of these river systems and the playas, including causing changes to flood extent, channel-floodplain morphology, and sediment dispersal. However, the characterization of these impacts using remote sensing approaches has been challenging owing to variable vegetation and cloud cover, as well as the commonly limited spatial and temporal resolution of data. Here, we use Sentinel-2 Multispectral Instrument (MSI) data to investigate the flood extent, flood patterns and channel-floodplain morphodynamics resulting from an extreme flood near the non-vegetated terminus of the R&iacute;o Colorado, located at the margins of the world&rsquo;s largest playa (Salar de Uyuni, Bolivia). Daily maximum precipitation frequency analysis based on a 42-year record of daily precipitation data (1976 through 2017) indicates that an approximately 40-year precipitation event (40.7 mm) occurred on 6 January 2017, and this was associated with an extreme flood. Sentinel-2 data acquired after this extreme flood were used to separate water bodies and land, first by using modified normalized difference water index (MNDWI), and then by subsequently applying independent component analysis (ICA) on the land section of the combined pre- and post-flood images to extract flooding areas. The area around the R&iacute;o Colorado terminus system was classified into three categories: water bodies, wet land, and dry land. The results are in agreement with visual assessment, with an overall accuracy of 96% and Kappa of 0.9 for water-land classification and an overall accuracy of 83% and Kappa of 0.65 for dry land-wet land classification. The flood extent mapping revealed preferential overbank flow paths on the floodplain, which were closely related to geomorphological changes. Changes included the formation and enlargement of crevasse splays, channel avulsion, and the development of erosion cells (floodplain scour-transport-fill features). These changes were visualized by Sentinel-2 images along with WorldView satellite images. In particular, flooding enlarged existing crevasse splays and formed new ones, while channel avulsion occurred near the river&rsquo;s terminus. Greater overbank flow on the floodplain led to rapid erosion cell development, with changes to channelized sections occurring as a result of adjustments in flow sources and intensity combined with the lack of vegetation on the fine-grained (predominantly silt, clay) sediments. This study has demonstrated how ICA can be implemented on Sentinel-2 imagery to characterize the impact of extreme floods on the lower R&iacute;o Colorado, and the method has potential application in similar contexts in many other drylands

Mapping intra- and inter-annual dynamics in wetlands with multispectral, thermal and SAR time series

Kartierung der intra- und interannuellen Dynamik von Feuchtgebieten mit multispektralen, thermischen und SAR-Zeitreihen Die Analyse der aktuellen räumlichen Verbreitung und der zeitlichen Entwicklung von Feuchtgebieten stellt eine äußerst komplexe Aufgabe dar, welche durch die Saisonalität, die schwierige Zugänglichkeit und die besonderen Eigenschaften als Ökoton bedingt ist. Erdbeobachtungssysteme sind somit das am besten geeignete Werkzeug, um zeitliche und räumliche Muster von Feuchtgebieten auf globaler Ebene zu beobachten (saisonale Veränderungen und Langzeit-Trends) und um den Einfluss der menschlichen Aktivitäten auf ihre physischen und biologischen Eigenschaften zu untersuchen. Zur Kartierung von raum-zeitlichen Mustern wurden Zeitreihen von Radar- (Sentinel-1), Multispektral- (Sentinel-2) und Thermal-Satellitendaten (MODIS) in fünf Untersuchungsgebieten, mit für Feuchtgebiete unterschiedlichen typischen Charakteristika, untersucht. In Kapitel 1 werden die Problematik in Bezug auf die Definition von Feuchtgebieten erläutert und allgemeine Degradations-Trends beschrieben. Die Kapitel 2 und 3 behandeln einen Algorithmus, der Veränderungen mithilfe von SAR-Zeitreihen feststellt, sowie die Vorteile des Cloud-Computings für das operationelle Monitoring saisonaler Muster und die Erkennung kurzfristig auftretender Veränderungen. In den Kapiteln 4 und 5 werden die zwei Hauptursachen für den Verlust von Feuchtgebieten betrachtet: der Staudammbau und die Ausdehnung landwirtschaftlicher Flächen. In Kapitel 4 werden dichte Zeitreihen multispektraler (Sentinel-2) und SAR-Daten (Sentinel-1) verwendet, um die Feuchtgebiete Albaniens – eines Landes in dem konträre Pläne zum Ausbau seines Wasserkraftpotentials und dem Schutz intakter Flussökosysteme zu Spannungen führen – landesweit zu kartieren. Die synergetischen Vorteile, die sich durch die Fusionierung von multispektralen und SAR-Daten für die Klassifikation ergeben, werden dabei herausgestellt. Kapitel 5 veranschaulicht, dass die Kilombero-Überschwemmungsebene in Tansania ein großes und bedeutendes Feuchtgebiet ist, das in den vergangenen Jahren infolge der weitgehend unkontrollierten Ausbreitung landwirtschaftlicher Flächen in seiner Ausdehnung und seiner Ökologie stark beeinträchtigt wurde. Um die Auswirkungen der Landnutzungsänderungen des Feuchtgebietes während der vergangenen 18 Jahre zu analysieren, wurden eine Zeitreihe (2000 bis 2017) thermaler Daten (MODIS) analysiert. Die drei für die Zeitreihenanalyse angewandten Modelle zeigen, wie landwirtschaftliche Praktiken die Landoberflächentemperatur in den landwirtschaftlich genutzten Gebieten sowie in den angrenzenden natürlichen Feuchtgebieten erhöht haben.Due to wetlands’ seasonality, their difficult access and ecotone character, determining their actual extension and trends over time is a complex task. Earth Observation systems are the most appropriate tool to monitor their spatio-temporal patterns (seasonal changes and long term trends) at global scales, and to study the effects that human activities have in their physical and biological properties. In this work I use time series of radar (Sentinel-1), multispectral (Sentinel-2) and thermal (MODIS) imagery to map the spatio-temporal patterns in 5 wetlands of different characteristics. First, I introduce in chapter 1 the problematic of wetlands’ definitions and their degradation trends. I continue with a brief introduction on remote sensing, time series analysis, and their applications on wetlands’ research and management. In chapters 2 and 3 I implement an algorithm for change detection of time series of Sentinel-1 images and demonstrate the advantages of cloud computation for operational monitoring. In chapters 4 and 5 I address two of the main causes of wetland degradation: dam building and agricultural expansion. In chapter 4 I use dense time series of Sentinel-1 and Sentinel-2 images map all the wetlands of Albania; a country struggling between developing its large hydropower potential or preserving its intact and valuable river ecosystems. I evaluate the synergic advantages of fusing multispectral and radar imagery in combination with knowledge-based rules to produce classification of higher thematic and spatial resolutions. In chapter 5 I present how the Kilombero Floodplain, in Tanzania, has been degraded during the last years due to uncontrolled farmland expansion. I use a time series of thermal imagery (MODIS) from 2000 until 2017 to analyze the effect of land use changes on the wetland. I compare three models for time series analysis and reveal how farming practices have increased the surface temperature of the farmed area, as well as in adjacent natural wetlands.Mapeo de las dinámicas inter- e intra-anuales en humedales con series temporales de imágenes multiespectrales, termales y de radar Debido a la estacionalidad de los humedales, su difícil acceso y sus características de ecotono, determinar su actual extensión y sus tendencias a lo largo del tiempo es una tarea compleja. Los sistemas de observación terrestres son la herramienta más apropiada para monitorear sus patrones espacio-temporales (estacionalidad y tendencias a largo plazo) a escalas globales, y para estudiar los efectos que las actividades humanas causan en sus propiedades físicas y biológicas. En esta tesis uso series temporales de imágenes radar (Sentinel-1), multiespectrales (Sentinel-2) y termales (MODIS) para mapear los patrones espacio-temporales de 5 humedales de diferentes características. En el capítulo 1 describo los retos que derivan de las diferentes definiciones que existen de los humedales. También presento las tendencias globales de degradación que la mayoría de los humedales continúan experimentando en los últimos años. Continúo con una breve introducción de los sistemas de teledetección remota, análisis de series temporales, y sus aplicaciones a la investigación y gestión de los humedales. En los capítulos 2 y 3 implemento un algoritmo de detección de cambios para series temporales de imágenes radar, y muestro las ventajas de usar sistemas de computación en la nube para monitorear cambios en la cobertura del suelo a corto plazo. En los capítulos 4 y 5 trato con dos de las causas más comunes de degradación de humedales: la construcción de presas y la expansión de la agricultura. En el capítulo 4 uso series temporales de imágenes multiespectrales (Sentinel-2) y radar (Sentinel-1) para mapear todos los humedales Albania; un país que se debate entre desarrollar su potencial hidroenergético o preservar sus valiosos e intactos ecosistemas de rivera. Mediante la fusión de imágenes radar y multiespectrales y el uso de reglas de decisión genero un mapa de suficiente resolución espacial y temática para que pueda ser usado por sectores interesados y gestores. En el capítulo 5 presento como las llanuras inundables de Kilombero, en Tanzania, han sido degradadas durante los últimos años debido a la expansión incontrolada de la agricultura. Usando series temporales de imágenes termales (MODIS) desde 2000 hasta 2017 y mapas de cambios de usos del suelo, determino los efectos que estos cambios han tenido en el humedal. Comparo 3 modelos diferentes de análisis de series temporales y muestro cómo la expansión de la agricultura ha incrementado la temperatura superficial terrestre, no solo de la zona cultivada, sino también de zonas adyacentes aún naturales

Remote sensing of phytoplankton biomass in oligotrophic and mesotrophic lakes: addressing estimation uncertainty through machine learning

Phytoplankton constitute the bottom of the aquatic food web, produce half of Earth’s oxygen and are part of the global carbon cycle. A measure of aquatic phytoplankton biomass therefore functions as a biological indicator of water status and quality. The abundance of phytoplankton in most lakes on Earth is low because they are weakly nourished (i.e., oligotrophic). It is practically infeasible to measure the millions of oligotrophic lakes on Earth through field sampling. Fortunately, phytoplankton universally contain the optically active pigment chlorophyll-a, which can be detected by optical sensors. Earth-orbiting satellite missions carry optical sensors that provide unparalleled high spatial coverage and temporal revisit frequency of lakes. However, when compared to waters with high nutrient loading (i.e., eutrophic), the remote sensing estimation of phytoplankton biomass in oligotrophic lakes is prone to high estimation uncertainties. Accurate retrieval of phytoplankton biomass is severely constrained by imperfect atmospheric correction, complicated inherent optical property (IOP) compositions, and limited model applicability. In order to address and reduce the current estimation uncertainties in phytoplankton remote sensing of low - moderate biomass lakes, machine learning is used in this thesis. In the first chapter the chlorophyll-a concentration (chla) estimation uncertainty from 13 chla algorithms is characterised. The uncertainty characterisation follows a two-step procedure: 1. estimation of chla from a representative dataset of field measurements and quantification of estimation uncertainty, 2. characterisation of chla estimation uncertainty. The results of this study show that estimation uncertainty across the dataset used in this chapter is high, whereby chla is both systematically under- and overestimated by the tested algorithms. Further, the characterisation reveals algorithm-specific causes of estimation uncertainty. The uncertainty sources for each of the tested algorithms are discussed and recommendations provided to improve the estimation capabilities. In the second chapter a novel machine learning algorithm for chla estimation is developed by combining Bayesian theory with Neural Networks (NNs). The resulting Bayesian Neural Networks (BNNs) are designed for the Ocean and Land Cover Instrument (OLCI) and MultiSpectral Imager (MSI) sensors aboard the Sentinel-3 and Sentinel-2 satellites, respectively. Unlike established chla algorithms, the BNNs provide a per-pixel uncertainty associated with estimated chla. Compared to reference chla algorithms, gains in chla estimation accuracy > 15% are achieved. Moreover, the quality of the provided BNN chla uncertainty is analysed. For most observations (> 75%) the BNN uncertainty estimate covers the reference in situ chla value, but the uncertainty calibration is not constantly accurate across several assessment strategies. The BNNs are applied to OLCI and MSI products to generate chla and uncertainty estimates in lakes from Africa, Canada, Europe and New Zealand. The BNN uncertainty estimate is furthermore used to deal with uncertainty introduced by prior atmospheric correction algorithms, adjacency affects and complex optical property compositions. The third chapter focuses on the estimation of lake biomass in terms of trophic status (TS). TS is conventionally estimated through chla. However, the remote sensing of chla, as shown in the two previous chapters, can be prone to high uncertainty. Therefore, in this chapter an algorithm for the direct classification of TS is designed. Instead of using a single algorithm for TS estimation, multiple individual algorithms are ensembled through stacking, whose estimates are evaluated by a higher-level meta-learner. The results of this ensemble scheme are compared to conventional switching of reference chla algorithms through optical water types (OWTs). The results show that estimation of TS is increased through direct classification rather than indirect estimation through chla. The designed meta-learning algorithm outperforms OWT switching of chla algorithms by 5-12%. Highest TS estimation accuracy is achieved for high biomass waters, whereas for low biomass waters extremely turbid waters produced high TS estimation uncertainty. Combining an ensemble of algorithms through a meta-learner represents a solution for the problem of algorithm selection across the large variation of global lake constituent concentrations and optical properties