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

https://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=1056&context=civilengfacpub

Landsat-8: Science and Product Vision for Terrestrial Global Change Research

Deep learning (DL) is a powerful state-of-the-art technique for image processing including remote sensing (RS) images. This letter describes a multilevel DL architecture that targets land cover and crop type classification from multitemporal multisource satellite imagery. The pillars of the architecture are unsupervised neural network (NN) that is used for optical imagery segmentation and missing data restoration due to clouds and shadows, and an ensemble of supervised NNs. As basic supervised NN architecture, we use a traditional fully connected multilayer perceptron (MLP) and the most commonly used approach in RS community random forest, and compare them with convolutional NNs (CNNs). Experiments are carried out for the joint experiment of crop assessment and monitoring test site in Ukraine for classification of crops in a heterogeneous environment using nineteen multitemporal scenes acquired by Landsat-8 and Sentinel-1A RS satellites. The architecture with an ensemble of CNNs outperforms the one with MLPs allowing us to better discriminate certain summer crop types, in particular maize and soybeans, and yielding the target accuracies more than 85% for all major crops (wheat, maize, sunflower, soybeans, and sugar beet).

Deep Learning Classification of Land Cover and Crop Types Using Remote Sensing Data

Improvement and expansion of the Fmask algorithm: cloud, cloud shadow, and snow detection for Landsats 4–7, 8, and Sentinel 2 images

https://ntrs.nasa.gov/api/citations/20170002673/downloads/20170002673.pdf

Preliminary analysis of the performance of the Landsat 8/OLI land surface reflectance product

The Copernicus programme is a European initiative for the implementation of information services dealing with environment and security, based on observation data received from Earth Observation (EO) satellites and ground based information. Within this context, ESA is responsible in particular, for the implementation of the Copernicus Sentinel missions, feeding the Copernicus services with operational EO data. The Sentinel-2 optical high-resolution imaging mission will be devoted to the operational and systematic monitoring of land and coastal areas. To maximize the products suitability and readiness to downstream usage for the majority of applications, the Sentinel-2 Payload Data Ground Segment (PDGS) will systematically generate, archive and distribute Level-1C products, which will provide Top-of-Atmosphere (TOA) reflectance images, orthorectified using a global Digital Elevation Model (DEM) and projected on Universal Transverse Mercator (UTM) coordinate system. A Level-1B product will also be available for expert users, providing radiance images in sensor geometry together with an appended geometric model. Additionally, a complementary atmospheric correction and enhanced cloud screening algorithm is being prototyped. This processor will allow converting the Level-1C TOA reflectance image into Bottom-of-Atmosphere (BOA) reflectance. The processor will be provided as plug-in software of the Sentinel-2 Toolbox that will run on user side. During the operational phase, the Sentinel-2 Mission Performance Centre (MPC), as integrating part of the mission ground segment, will be in charge of ensuring that mission performances are met in terms of data quality through the calibration and validation activities.

Copernicus Sentinel-2 mission: products, algorithms and Cal/Val

GMES is a joint initiative of the European Commission (EC) and the European Space Agency (ESA), designed to establish a European capacity for the provision and use of operational monitoring information for environment and security applications. ESA's role in GMES is to provide the definition and the development of the space- and ground-related system elements. GMES Sentinel-2 mission provides continuity to services relying on multi-spectral high-resolution optical observations over global terrestrial surfaces. The key mission objectives for Sentinel-2 are: (1) to provide systematic global acquisitions of high-resolution multi-spectral imagery with a high revisit frequency, (2) to provide enhanced continuity of multi-spectral imagery provided by the SPOT series of satellites, and (3) to provide observations for the next generation of operational products such as land-cover maps, land change detection maps, and geophysical variables. Consequently, Sentinel-2 will directly contribute to the Land Monitoring, Emergency Response, and Security services. The corresponding user requirements have driven the design towards a dependable multi-spectral Earth-observation system featuring the MSI with 13 spectral bands spanning from the visible and the near infrared to the short wave infrared. The spatial resolution varies from 10 m to 60 m depending on the spectral band with a 290 km field of view. This unique combination of high spatial resolution, wide field of view and large spectral coverage will represent a major step forward compared to current multi-spectral missions. The mission foresees a series of satellites, each having a 7.25-year lifetime (extendable to 12 years) over a 20-year period starting with the launch of Sentinel-2A foreseen by mid-2014. During full operations two identical satellites will be maintained in the same sun synchronous orbit with a phase delay of 180° providing a revisit time of five days at the equator.

Overview Of Sentinel-2

Landsat is a joint USGS and NASA space program for Earth Observation (EO), which represents the world’s longest running system of satellites for moderate-resolution. The European Space Agency (ESA) has acquired Landsat data over Europe, Northern Africa and the Middle East during the last 40 years. A new ESA Landsat Multi-Spectral Scanner (MSS), Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) processor has been developed. This enhanced processor aligns historical Landsat products to the highest quality standards that can be achieved with the current knowledge of the instruments. The updated processor is mainly based on the USGS algorithm; however it has some different features that are detailed in this paper. Current achievements include the processing and availability of approximately 860,000 new TM/ETM+ high-quality products between 1983 and 2011 from the Kiruna (S), Maspalomas (E) and Matera (I) archives; Matera includes data from the Fucino (I), Neustrelitz (D), O’Higgins (Antarctica), Malindi (Kenya), Libreville (Gabon) and Bishkek (Kyrgyzstan) ground stations. The products are freely available for immediate download to the users through a very fast and simple dissemination service (at: https://landsat-ds.eo.esa.int/app/) and through ESA’s browsing system, EOLI. The remaining MSS data, dating back more than 40 years, will gradually become available during 2015 and 2016. The ESA Landsat processor algorithm enhancement, together with the results of the ESA archive bulk-processing data regarding production, quality control and data validation are herein presented.

Bulk Processing of the Landsat MSS/TM/ETM+ Archive of the European Space Agency

Landsat is a joint USGS and NASA space program for Earth Observation ( EO ), which represents the world’s longest running system of satellites for moderate-resolution. The European Space Agency ( ESA ) has acquired Landsat data over Europe, Northern Africa and the Middle East during the last 40 years. A new ESA Landsat Multi-Spectral Scanner ( MSS ), Thematic Mapper ( TM ) and Enhanced Thematic Mapper Plus ( ETM+ ) processor has been developed. This enhanced processor aligns historical Landsat products to the highest quality standards that can be achieved with the current knowledge of the instruments. The updated processor is mainly based on the USGS algorithm; however it has some different features that are detailed in this paper. Current achievements include the processing and availability of approximately 860,000 new TM/ETM+ high-quality products between 1983 and 2011 from the Kiruna (S), Maspalomas (E) and Matera (I) archives; Matera includes data from the Fucino (I), Neustrelitz (D), O’Higgins (Antarctica), Malindi (Kenya), Libreville (Gabon) and Bishkek (Kyrgyzstan) ground stations. The products are freely available for immediate download to the users through a very fast and simple dissemination service (at: https://landsat-ds.eo.esa.int/app/) and through ESA’s browsing system, EOLI. The remaining MSS data, dating back more than 40 years, will gradually become available during 2015 and 2016. The ESA Landsat processor algorithm enhancement, together with the results of the ESA archive bulk-processing data regarding production, quality control and data validation are herein presented.

Bianca Hoersch

Papers

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

Copernicus Sentinel-2 mission: products, algorithms and Cal/Val

Overview Of Sentinel-2

Bulk Processing of the Landsat MSS/TM/ETM+ Archive of the European Space Agency

Bulk processing of the Landsat MSS/TM/ETM+ archive of the European Space Agency