Synergetic Exploitation of the Sentinel-2 Missions for Validating the Sentinel-3 Ocean and Land Color Instrument Terrestrial Chlorophyll Index Over a Vineyard Dominated Mediterranean Environment

[EN] Continuity to the Medium Resolution Imaging Spectrometer (MERIS) Terrestrial Chlorophyll Index (MTCI) will be provided by the Ocean and Land Color Instrument (OLCI) on-board the Sentinel-3 missions. To ensure its utility in a wide range of scientific and operational applications, validation efforts are required. In the past, direct validation has been constrained by the need for costly airborne hyperspectral data acquisitions, due to the lack of freely available high spatial resolution imagery incorporating appropriate spectral bands. The Multispectral Instrument (MSI) on-board the Sentinel-2 missions now offers a promising alternative. We explored the synergetic use of MSI data for validation of the OLCI Terrestrial Chlorophyll Index (OTCI) over the Valencia Anchor Station, a large agricultural site in the Valencian Community, Spain. Using empirical and machine learning techniques applied to MSI data, in situ measurements were upscaled to the moderate spatial resolution of the OTCI. An RMSECV of 0.09 g.m(-2) (NRMSECV = 20.93%) was achieved, highlighting the valuable information MSI data can provide when used in synergy with OLCI data for land product validation. Good agreement between the OTCI and upscaled in situ measurements was observed (r = 0.77, p < 0.01), providing increased confidence to users of the product over vineyard dominated Mediterranean environments.This work was supported in part by the European Space Agency and European Commission through the Sentinel-3 Mission Performance Centre.Brown, LA.; Dash, J.; Lidón, A.; Lopez-Baeza, E.; Dransfeld, S. (2019). Synergetic Exploitation of the Sentinel-2 Missions for Validating the Sentinel-3 Ocean and Land Color Instrument Terrestrial Chlorophyll Index Over a Vineyard Dominated Mediterranean Environment. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 12(7):2244-2251. https://doi.org/10.1109/JSTARS.2019.28999982244225112

Use of the Sentinel‐2 and Landsat‐8 Satellites for Water Quality Monitoring: An Early Warning Tool in the Mar Menor Coastal Lagoon

During recent years, several eutrophication processes and subsequent environmental crises have occurred in Mar Menor, the largest hypersaline coastal lagoon in the Western Mediterranean Sea. In this study, the Landsat‐8 and Sentinel‐2 satellites are jointly used to examine the evolution of the main water quality descriptors during the latest ecological crisis in 2021, resulting in an important loss of benthic vegetation and unusual mortality events affecting different aquatic species. Several field campaigns were carried out in March, July, August, and November 2021 to measure water quality variables over 10 control points. The validation of satellite biogeochemical variables against on‐site measurements indicates precise results of the water quality algorithms with median errors of 0.41 mg/m3 and 2.04 FNU for chlorophyll‐a and turbidity, respectively. The satellite preprocessing scheme shows consistent performance for both satellites; therefore, using them in tandem can improve mapping strategies. The findings demonstrate the suitability of the methodology to capture the spatiotemporal distribution of turbidity and chlorophyll‐a concentration at 10– 30 m spatial resolution on a systematic basis and in a cost‐effective way. The multitemporal products allow the identification of the main critical areas close to the mouth of the Albujon watercourse and the beginning of the eutrophication process with chlorophyll‐a concentration above 3 mg/m3. These innovative tools can support decision makers in improving current monitoring strategies as early warning systems for timely assistance during these ecological disasters, thus preventing detrimental conditions in the lagoon.0,64

Copernicus Cal/Val Solution - D3.2 - Recommendations for R&D on Cal/Val Methods

This document presents a gap analysis of the methods used in the calibration and validation of Earth Observation satellites relevant to the Copernicus programme and suggests recommendations for the research and developments required to fulfil this gap when/where possible. The document identifies the gaps and limitations of the CalVal methods, used for calibration and validation (CalVal) activities for the current Copernicus missions. It will also address the development needs for future Copernicus missions. Four types of missions are covered based on the division used in the rest of the CCVS project: optical, altimetry, radar and microwave and atmospheric composition. Finally, it will give a prioritized list of recommendations for R&D activities on the CalVal methods. The information included is mainly collected from the deliverables of work packages 1 and 2 in the CCVS project and from the consortium experts in CalVal activities

Copernicus Cal/Val Solution - D1.1 - Optical Missions Cal/Val requirements

This document intends to identify the requirements applicable to the calibration and validation (Cal/Val) activities for the optical missions of the Copernicus Space Component. These optical missions can be further classified as Surface Colour and Surface Temperature missions. Missions dedicated to atmosphere composition are addressed in a specific document and as such excluded from the present document, even if based on optical remote sensing techniques. Copernicus Space Component Surface Colour missions are: ▪ Sentinel2/MSI ▪ Sentinel3/OLCI ▪ Sentinel3/SYN ▪ CHIME Copernicus Space Component Surface Temperature missions are: ▪ Sentinel3/SLSTR ▪ LST

Copernicus Cal/Val Solution - D3.1 Recommendations for R&D activities on Instrumentation Technologies

The Document identifies the gaps in instrumentation technologies for pre-flight characterisation, onboard calibration and Fiducial Reference Measurements (FRM) used for calibration and validation (Cal/Val) activities for the current Copernicus missions. It also addresses the measurement needs for future Copernicus missions and gives a prioritised list of recommendations for R&D activities on instrumentation technologies. Four types of missions are covered based on the division used in the rest of the CCVS project: optical, altimetry, radar and microwave and atmospheric composition. It also gives an overview of some promising instrumentation technologies in each measurement field for FRM that could fill the gaps for requirements not yet met for the current and future Copernicus missions and identifies the research and development (R&D) activities needed to mature these example technologies. The Document does not provide an exhaustive list of all the new technologies being developed but will give a few examples for each field to show what efforts are being made to fill the gaps. None of the examples is promoted as the best possible solutions. The selection is based on the authors' knowledge during the preparation of the Document. The information included is mainly collected from the deliverables of work packages 1 and 2 in the CCVS project. The new technologies are primarily from the interviews with various measurement networks and campaigns carried out in tasks 2.4 and 2.5. Reference documents can be found in section 1.3

Detection of Change Points in Pseudo-Invariant Calibration Sites Time Series Using Multi-Sensor Satellite Imagery

The remote sensing community has extensively used Pseudo-Invariant Calibration Sites (PICS) to monitor the long-term in-flight radiometric calibration of Earth-observing satellites. The use of the PICS has an underlying assumption that these sites are invariant over time. However, the site’s temporal stability has not been assured in the past. This work evaluates the temporal stability of PICS by not only detecting the trend but also locating significant shifts (change points) lying behind the time series. A single time series was formed using the virtual constellation approach in which multiple sensors data were combined for each site to achieve denser temporal coverage and overcome the limitation of dependence related to a specific sensor. The sensors used for this work were selected based on radiometric calibration uncertainty and availability of the data: operational land imager (Landsat-8), enhanced thematic mapper (Landsat-7), moderate resolution imaging spectroradiometer (Terra and Aqua), and multispectral instrument (Sentinel-2A). An inverse variance weighting method was applied to the Top-of- Atmosphere (TOA) reflectance time series to reveal the underlying trend. The sequential Mann–Kendall test was employed upon the weighted TOA reflectance time-series recorded over 20 years to detect abrupt changes for six reflective bands. Statistically significant trends and abrupt changes have been detected for all sites, but the magnitude of the trends (maximum of 0.215% change in TOA reflectance per year) suggest that these sites are not changing substantially over time. Hence, it can be stated that despite minor changes in all evaluated PICS, they can be used for radiometric calibration of optical remote sensing sensors. The new approach provides useful results by revealing underlying trends and providing a better understanding of PICS\u27 stability

Multitemporal mapping of post-fire land cover using multiplatform PRISMA hyperspectral and Sentinel-UAV multispectral data: Insights from case studies in Portugal and Italy

Wildfires have affected global forests and the Mediterranean area with increasing recurrency and intensity in the last years, with climate change resulting in reduced precipitations and higher temperatures. To assess the impact of wildfires on the environment, burned area mapping has become progressively more relevant. Initially carried out via field sketches, the advent of satellite remote sensing opened new possibilities, reducing the cost uncertainty and safety of the previous techniques. In the present study an experimental methodology was adopted to test the potential of advanced remote sensing techniques such as multispectral Sentinel-2, PRISMA hyperspectral satellite, and UAV (unmanned aerial vehicle) remotely-sensed data for the multitemporal mapping of burned areas by soil–vegetation recovery analysis in two test sites in Portugal and Italy. In case study one, innovative multiplatform data classification was performed with the correlation between Sentinel-2 RBR (relativized burn ratio) fire severity classes and the scene hyperspectral signature, performed with a pixel-by-pixel comparison leading to a converging classification. In the adopted methodology, RBR burned area analysis and vegetation recovery was tested for accordance with biophysical vegetation parameters (LAI, fCover, and fAPAR). In case study two, a UAV-sensed NDVI index was adopted for high-resolution mapping data collection. At a large scale, the Sentinel-2 RBR index proved to be efficient for burned area analysis, from both fire severity and vegetation recovery phenomena perspectives. Despite the elapsed time between the event and the acquisition, PRISMA hyperspectral converging classification based on Sentinel-2 was able to detect and discriminate different spectral signatures corresponding to different fire severity classes. At a slope scale, the UAV platform proved to be an effective tool for mapping and characterizing the burned area, giving clear advantage with respect to filed GPS mapping. Results highlighted that UAV platforms, if equipped with a hyperspectral sensor and used in a synergistic approach with PRISMA, would create a useful tool for satellite acquired data scene classification, allowing for the acquisition of a ground truth

Novel Satellite-Based Methodologies for Multi-Sensor and Multi-Scale Environmental Monitoring to Preserve Natural Capital

Global warming, as the biggest manifestation of climate change, has changed the distribution of water in the hydrological cycle by increasing the evapotranspiration rate resulting in anthropogenic and natural hazards adversely affecting modern and past human properties and heritage in different parts of the world. The comprehension of environmental issues is critical for ensuring our existence on Earth and environmental sustainability. Environmental modeling can be described as a simplified form of a real system that enhances our knowledge of how a system operates. Such models represent the functioning of various processes of the environment, such as processes related to the atmosphere, hydrology, land surface, and vegetation. The environmental models can be applied on a wide range of spatiotemporal scales (i.e. from local to global and from daily to decadal levels); and they can employ various types of models (e.g. process-driven, empirical or data-driven, deterministic, stochastic, etc.). Satellite remote sensing and Earth Observation techniques can be utilized as a powerful tool for flood mapping and monitoring. By increasing the number of satellites orbiting around the Earth, the spatial and temporal coverage of environmental phenomenon on the planet has in-creased. However, handling such a massive amount of data was a challenge for researchers in terms of data curation and pre-processing as well as required computational power. The advent of cloud computing platforms has eliminated such steps and created a great opportunity for rapid response to environmental crises. The purpose of this study was to gather state-of-the-art remote sensing and/or earth observation techniques and to further the knowledge concerned with any aspect of the use of remote sensing and/or big data in the field of geospatial analysis. In order to achieve the goals of this study, some of the water-related climate-change phenomena were studied via different mathematical, statistical, geomorphological and physical models using different satellite and in-situ data on different centralized and decentralized computational platforms. The structure of this study was divided into three chapters with their own materials, methodologies and results including: (1) flood monitoring; (2) soil water balance modeling; and (3) vegetation monitoring. The results of this part of the study can be summarize in: 1) presenting innovative procedures for fast and semi-automatic flood mapping and monitoring based on geomorphic methods, change detection techniques and remote sensing data; 2) modeling soil moisture and water balance components in the root zone layer using in-situ, drone and satellite data; incorporating downscaling techniques; 3) combining statistical methods with the remote sensing data for detecting inner anomalies in the vegetation covers such as pest emergence; 4) stablishing and disseminating the use of cloud computation platforms such as Google Earth Engine in order to eliminate the unnecessary steps for data curation and pre-processing as well as required computational power to handle the massive amount of RS data. As a conclusion, this study resulted in provision of useful information and methodologies for setting up strategies to mitigate damage and support the preservation of areas and landscape rich in cultural and natural heritage

Long-Term Spatiotemporal Variability of Whitings in Lake Geneva from Multispectral Remote Sensing and Machine Learning

Whiting events are massive calcite precipitation events turning hardwater lake waters to a milky turquoise color. Herein, we use a multispectral remote sensing approach to describe the spatial and temporal occurrences of whitings in Lake Geneva from 2013 to 2021. Landsat-8, Sentinel-2, and Sentinel-3 sensors are combined to derive the AreaBGR index and identify whitings using appropriate filters. 95% of the detected whitings are located in the northeastern part of the lake and occur in a highly reproducible environmental setting. An extended time series of whitings in the last 60 years is reconstructed from a random forest algorithm and analyzed through a Bayesian decomposition for annual and seasonal trends. The annual number of whiting days between 1958 and 2021 does not follow any particular monotonic trend. The inter-annual changes of whiting occurrences significantly correlate to the Western Mediterranean Oscillation Index. Spring whitings have increased since 2000 and significantly follow the Atlantic Multidecadal Oscillation index. Future climate change in the Mediterranean Sea and the Atlantic Ocean could induce more variable and earlier whiting events in Lake Geneva