The spectral signature of recent climate change

Spectrally resolved measurements of the Earth’s reflected shortwave (RSW) and outgoing longwave radiation (OLR) at the top of the atmosphere intrinsically contain the imprints of a multitude of climate relevant parameters. Here, we review the progress made in directly using such observations to diagnose and attribute change within the Earth system over the past four decades. We show how changes associated with perturbations such as increasing greenhouse gases are expected to be manifested across the spectrum and illustrate the enhanced discriminatory power that spectral resolution provides over broadband radiation measurements. Advances in formal detection and attribution techniques and in the design of climate model evaluation exercises employing spectrally resolved data are highlighted. We illustrate how spectral observations have been used to provide insight into key climate feedback processes and quantify multi-year variability but also indicate potential barriers to further progress. Suggestions for future research priorities in this area are provided

Quantitative Comparison of the Variability in Observed and Simulated Shortwave Reflectance

The Climate Absolute Radiance and Refractivity Observatory (CLARREO) is a climate observation system that has been designed to monitor the Earth's climate with unprecedented absolute radiometric accuracy and SI traceability. Climate Observation System Simulation Experiments (OSSEs) have been generated to simulate CLARREO hyperspectral shortwave imager measurements to help define the measurement characteristics needed for CLARREO to achieve its objectives. To evaluate how well the OSSE-simulated reflectance spectra reproduce the Earth s climate variability at the beginning of the 21st century, we compared the variability of the OSSE reflectance spectra to that of the reflectance spectra measured by the Scanning Imaging Absorption Spectrometer for Atmospheric Cartography (SCIAMACHY). Principal component analysis (PCA) is a multivariate decomposition technique used to represent and study the variability of hyperspectral radiation measurements. Using PCA, between 99.7%and 99.9%of the total variance the OSSE and SCIAMACHY data sets can be explained by subspaces defined by six principal components (PCs). To quantify how much information is shared between the simulated and observed data sets, we spectrally decomposed the intersection of the two data set subspaces. The results from four cases in 2004 showed that the two data sets share eight (January and October) and seven (April and July) dimensions, which correspond to about 99.9% of the total SCIAMACHY variance for each month. The spectral nature of these shared spaces, understood by examining the transformed eigenvectors calculated from the subspace intersections, exhibit similar physical characteristics to the original PCs calculated from each data set, such as water vapor absorption, vegetation reflectance, and cloud reflectance

Segmentation of multispectral images and prediction of ChI-a concentration for effective ocean colour remote sensing

With the development of new sensors and data processing techniques, ocean colour remote sensing has undergone rapid development in more accurately measurement of coastal shelf classification and concentration of chlorophyll. In this paper, multispectral images are employed to achieve these targets, using techniques including region-growing based segmentation for pixel classification and support vector regression for ChI-a prediction. Interesting results are reported to show the great potential in using state-of-the-art data analysis techniques for effective ocean colour remote sensing

Hyperspectral Imaging for Fine to Medium Scale Applications in Environmental Sciences

The aim of the Special Issue “Hyperspectral Imaging for Fine to Medium Scale Applications in Environmental Sciences” was to present a selection of innovative studies using hyperspectral imaging (HSI) in different thematic fields. This intention reflects the technical developments in the last three decades, which have brought the capacity of HSI to provide spectrally, spatially and temporally detailed data, favoured by e.g., hyperspectral snapshot technologies, miniaturized hyperspectral sensors and hyperspectral microscopy imaging. The present book comprises a suite of papers in various fields of environmental sciences—geology/mineral exploration, digital soil mapping, mapping and characterization of vegetation, and sensing of water bodies (including under-ice and underwater applications). In addition, there are two rather methodically/technically-oriented contributions dealing with the optimized processing of UAV data and on the design and test of a multi-channel optical receiver for ground-based applications. All in all, this compilation documents that HSI is a multi-faceted research topic and will remain so in the future

Sathyendranath S., Bracher A., Brockmann C., Platt T., Ramon D., Regner P. (2017) Colour and Light in the Ocean (CLEO) 2016: A Scientific Roadmap from the CLEO Workshop Organised by ESA and PML. Held at ESRIN, Frascati, Italy on 6 - 8 September, 2016.

The Colour and Light in the Ocean (CLEO) Workshop, organized by the European Space Agency (ESA) and the Plymouth Marine Laboratory (PML) was held on the ESRIN, the ESA Centre for Earth Observations, at Frascati, Italy on 6-8 September 2016. The workshop is sponsored through selected SEOM (Scientific Exploitation of Operational Missions) projects, including: Pools of Carbon in the Ocean (POCO), Photosynthetically Active Radiation and Primary Production (PPP), Synergistic Exploitation of Hyper- and Multispectral Sentinel-Measurements to Determine Phytoplankton Functional Types (PFT) (SynSenPFT), and Extreme Case-2 Waters (C2X). Additional partner projects of ESA are: Marine Photosynthesis Parameters from Space (MAPPS), a Pathfinder STSE (Support to Science Element) project; and Ocean Colour Climate Change Initiative (OC-CCI) through the CCI (Climate Change Initiative). The objectives of the workshop were to: Evaluate state-of-art Exchange information with other relevant projects and activities Bring together remote sensing community, in situ data providers, modellers and other users Explore applications in marine ecosystem models Plan for the future: Identify challenge areas and research priorities for future EO data exploitation activities Discuss key science issues and make recommendations to strengthen community engagement Shape ideas for potential new ocean-colour products to be developed in the era of the Sentinel-3 mission The workshop was organized in five themes, developed around the activities of the sponsoring projects. Each t heme had oral, poster and discussion sessions. The workshop attracted some 160 registered participants. The workshop served an important need to connect the community, to provide a forum for lively exchange of ideas, and to recommend priorities for future activities in a collective manner. The workshop brought together scientists working on development of novel products from ocean-colour data and the user community, including, notably, the modeling community. One of the key outputs of the workshop is this report, which provides the Scientific Roadmap for future activities. Another planned outcome is a Special Issue on Colour and Light in the Oceans, to be published in the Journal, which will highlight the major scientific results presented at the workshop. Each section of the report, dealing with one of the themes of the workshop, is self-contained, but cross-references to other sections are provided where appropriate. Some recommendations found common resonance across sections, such as the need for continuous, consistent, ocean-colour data streams from satellites for long-term monitoring of the marine ecosystem; the need for an integrated approach, bringing together the remote-sensing community, the in situ data providers and the modeling community; the need to promote development of novel products and advanced sensors; and the importance of providing high-quality and uninterrupted support to the user community, through easy and free access to data and products. Each section discusses the current state of the art, identifies user requirements and gaps, and priorities for research in the short and medium terms. The workshop served the important function of sounding the community’s aspirations, and presenting them in a concise manner for ESA, through this Scientific Roadmap. One of the recommendations from the participants was that CLEO workshops be organized on a regular basis in the future, to develop the ocean-colour community , to promote exchange of new results and ideas, and to plan future activities. We thank all workshop participants, keynote speakers, authors of the oral presentations and the posters, the Scientific Committee and the Organising Committee, and the Session Chairs for all their contributions to the workshop. For the logistical support and local organization and hospitality, we thank the ESRIN Graphics Bureau, Administration, Catering Service and the Events Office, especially Irene Renis, Anne Lisa Pichler and Giulia Vinicola

Sensor capability and atmospheric correction in ocean colour remote sensing

© 2015 by the authors; licensee MDPI, Basel, Switzerland. Accurate correction of the corrupting effects of the atmosphere and the water's surface are essential in order to obtain the optical, biological and biogeochemical properties of the water from satellite-based multi-and hyper-spectral sensors. The major challenges now for atmospheric correction are the conditions of turbid coastal and inland waters and areas in which there are strongly-absorbing aerosols. Here, we outline how these issues can be addressed, with a focus on the potential of new sensor technologies and the opportunities for the development of novel algorithms and aerosol models. We review hardware developments, which will provide qualitative and quantitative increases in spectral, spatial, radiometric and temporal data of the Earth, as well as measurements from other sources, such as the Aerosol Robotic Network for Ocean Color (AERONET-OC) stations, bio-optical sensors on Argo (Bio-Argo) floats and polarimeters. We provide an overview of the state of the art in atmospheric correction algorithms, highlight recent advances and discuss the possible potential for hyperspectral data to address the current challenges

Cirrus cloud identification from airborne far-infrared and mid-infrared spectra

Airborne interferometric data, obtained from the Cirrus Coupled Cloud-Radiation Experiment (CIRCCREX) and from the PiknMix-F field campaign, are used to test the ability of a machine learning cloud identification and classification algorithm (CIC). Data comprise a set of spectral radiances measured by the Tropospheric Airborne Fourier Transform Spectrometer (TAFTS) and the Airborne Research Interferometer Evaluation System (ARIES). Co-located measurements of the two sensors allow observations of the upwelling radiance for clear and cloudy conditions across the far-and mid-infrared part of the spectrum. Theoretical sensitivity studies show that the performance of the CIC algorithm improves with cloud altitude. These tests also suggest that, for conditions encompassing those sampled by the flight campaigns, the additional information contained within the far-infrared improves the algorithm's performance compared to using mid-infrared data only. When the CIC is applied to the airborne radiance measurements, the classification performance of the algorithm is very high. However, in this case, the limited temporal and spatial variability in the measured spectra results in a less obvious advantage being apparent when using both mid-and far-infrared radiances compared to using mid-infrared information only. These results suggest that the CIC algorithm will be a useful addition to existing cloud classification tools but that further analyses of nadir radiance observations spanning the infrared and sampling a wider range of atmospheric and cloud conditions are required to fully probe its capabilities. This will be realised with the launch of the Far-infrared Outgoing Radiation Understanding and Monitoring (FORUM) mission, ESA's 9th Earth Explorer

Deep learning-based change detection in remote sensing images:a review

Images gathered from different satellites are vastly available these days due to the fast development of remote sensing (RS) technology. These images significantly enhance the data sources of change detection (CD). CD is a technique of recognizing the dissimilarities in the images acquired at distinct intervals and are used for numerous applications, such as urban area development, disaster management, land cover object identification, etc. In recent years, deep learning (DL) techniques have been used tremendously in change detection processes, where it has achieved great success because of their practical applications. Some researchers have even claimed that DL approaches outperform traditional approaches and enhance change detection accuracy. Therefore, this review focuses on deep learning techniques, such as supervised, unsupervised, and semi-supervised for different change detection datasets, such as SAR, multispectral, hyperspectral, VHR, and heterogeneous images, and their advantages and disadvantages will be highlighted. In the end, some significant challenges are discussed to understand the context of improvements in change detection datasets and deep learning models. Overall, this review will be beneficial for the future development of CD methods