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Journal ArticleDOI

The Global Monitoring for Environment and Security (GMES) Sentinel-3 mission

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TLDR
Sentinel-3 as mentioned in this paper is an Earth observation satellite mission specifically designed for Global Monitoring for Environment and Security (GMES) to ensure the long-term collection and operational delivery of high-quality measurements to GMES ocean, land, and atmospheric services, while contributing to the emergency and security services.
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This article is published in Remote Sensing of Environment.The article was published on 2012-05-15. It has received 546 citations till now. The article focuses on the topics: AATSR & Earth observation.

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Sentinel-2: ESA's Optical High-Resolution Mission for GMES Operational Services

TL;DR: An overview of the GMES Sentinel-2 mission including a technical system concept overview, image quality, Level 1 data processing and operational applications is provided.
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The Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) system

TL;DR: The Operational Sea Surface Temperature (SST) and Sea Ice Analysis (OSTIA) as discussed by the authors system uses satellite SST data provided by international agencies via the Group for High Resolution SST (GHRSST) Regional/Global Task Sharing (R/GTS) framework.
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Unsupervised Deep Feature Extraction for Remote Sensing Image Classification

TL;DR: The proposed algorithm clearly outperforms standard principal component analysis and its kernel counterpart (kPCA), as well as current state-of-the-art algorithms of aerial classification, while being extremely computationally efficient at learning representations of data.
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Remote estimation of crop and grass chlorophyll and nitrogen content using red-edge bands on Sentinel-2 and -3

TL;DR: Results confirm the importance of the red-edge bands on particularly Sentinel-2 for agricultural applications, because of the combination with its high spatial resolution of 20 m and linear estimators of canopy chlorophyll and N content.
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Unsupervised Deep Feature Extraction for Remote Sensing Image Classification

TL;DR: In this paper, a greedy layer-wise unsupervised pretraining coupled with a highly efficient algorithm for learning of sparse features is proposed for remote sensing data analysis, which is rooted on sparse representations and enforces both population and lifetime sparsity of the extracted features simultaneously.
References
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Journal ArticleDOI

A New Sea Surface Temperature and Sea Ice Boundary Dataset for the Community Atmosphere Model

TL;DR: In this paper, a new surface boundary forcing dataset for uncoupled simulations with the Community Atmosphere Model is described, based on the monthly mean Hadley Centre sea ice and SST dataset version 1 (HadISST1) and version 2 of the National Oceanic and Atmospheric Administration (NOAA) weekly optimum interpolation (OI) SST analysis.
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The International Laser Ranging Service

TL;DR: The ILRS works with new satellite missions in the design and building of retroreflector targets to maximize data quality and quantity, and science programs to optimize scientific data yield.
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A Global Database of Land Surface Parameters at 1-km Resolution in Meteorological and Climate Models

TL;DR: In this article, the authors presented a new complete surface parameter global dataset at a 1-km resolution, which is intended to initialize the soil-vegetation-atmosphere transfer schemes (SVATs) in meteorological and climate models (at all horizontal scales).
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Retrieval of biomass combustion rates and totals from fire radiative power observations: FRP derivation and calibration relationships between biomass consumption and fire radiative energy release

TL;DR: In this paper, the authors present results from a sensitivity analysis and from experimental fires conducted to investigate the relationship between fire radiative energy (FRE) and fuel mass combusted, and they conclude that FRE assessment offers a powerful tool for supplementing existing burned-area based fuel consumption measures.
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