From parcel to continental scale – A first European crop type map based on Sentinel-1 and LUCAS Copernicus in-situ observations
Raphaël d'Andrimont,Astrid Verhegghen,Guido Lemoine,Pieter Kempeneers,Michele Meroni,Marijn van der Velde +5 more
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TLDR
In this paper, the authors presented the first continental crop type map at 10m spatial resolution for the EU based on S1A and S1B Synthetic Aperture Radar observations for the year 2018.About:
This article is published in Remote Sensing of Environment.The article was published on 2021-12-01 and is currently open access. It has received 48 citations till now. The article focuses on the topics: European union.read more
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Designing a European-Wide Crop Type Mapping Approach Based on Machine Learning Algorithms Using LUCAS Field Survey and Sentinel-2 Data
Babak Ghassemi,Aleksandar Dujakovic,Mateusz Zoltak,Markus Immitzer,Clement Atzberger,Francesco Vuolo +5 more
TL;DR: In this article , the use of the Eurostat Land Use and Coverage Area frame Survey (LUCAS) 2018 data to generate a detailed LULC map with 19 crop type classes and two broad categories for woodland and shrubland, and grassland.
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Environmental and human health at risk - Scenarios to achieve the Farm to Fork 50% pesticide reduction goals.
TL;DR: In this article , the characteristics of all 230 EU-approved, synthetic, open-field use active substances (AS) used as herbicides, fungicides and insecticides, and explored the potential of seven Farm to Fork-inspired pesticide use reduction scenarios to achieve the 50% reduction goals.
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Mapping Crop Types of Germany by Combining Temporal Statistical Metrics of Sentinel-1 and Sentinel-2 Time Series with LPIS Data
Sarah Asam,Ursula Gessner,Roger Almengor González,Martina Wenzl,Jennifer Kriese,Claudia Kuenzer +5 more
TL;DR: In this article , an approach combining multispectral and Synthetic Aperture Radar (SAR) time series for the classification of 17 crop classes at 10 m spatial resolution for Germany in the year 2018 was presented.
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Spatial Transferability of Random Forest Models for Crop Type Classification Using Sentinel-1 and Sentinel-2
TL;DR: In this article , the effects of different input datasets, i.e., only optical, only Synthetic Aperture Radar (SAR), and optical-SAR data combination, and the impact of spatial feature selection were systematically tested to identify the optimal approach that shows the highest accuracy in the transfer region.
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Performance and the Optimal Integration of Sentinel-1/2 Time-Series Features for Crop Classification in Northern Mongolia
Battsetseg Tuvdendorj,Hongwei Zeng,Bingfang Wu,Abdelrazek Elnashar,Miao Zhang,Fuyou Tian,Mohsen Nabil,Lkhagvadorj Nanzad,Amanjol Bulkhbai,Natsagsuren Natsagdorj +9 more
TL;DR: In this article , a crop-type map with acceptable accuracy and spatial resolution in northern Mongolia by optimizing the combination of Sentinel-1 (S1) and Sentinel-2 (S2) images with the Google Earth Engine (GEE) environment is presented.
References
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Journal ArticleDOI
Random Forests
TL;DR: Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the forest, and are also applicable to regression.
Journal Article
Scikit-learn: Machine Learning in Python
Fabian Pedregosa,Gaël Varoquaux,Alexandre Gramfort,Vincent Michel,Bertrand Thirion,Olivier Grisel,Mathieu Blondel,Peter Prettenhofer,Ron Weiss,Vincent Dubourg,Jake Vanderplas,Alexandre Passos,David Cournapeau,Matthieu Brucher,Matthieu Perrot,Edouard Duchesnay +15 more
TL;DR: Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems, focusing on bringing machine learning to non-specialists using a general-purpose high-level language.
Journal ArticleDOI
Google Earth Engine: Planetary-scale geospatial analysis for everyone
TL;DR: Google Earth Engine is a cloud-based platform for planetary-scale geospatial analysis that brings Google's massive computational capabilities to bear on a variety of high-impact societal issues including deforestation, drought, disaster, disease, food security, water management, climate monitoring and environmental protection.
Journal ArticleDOI
Random forest in remote sensing: A review of applications and future directions
Mariana Belgiu,Lucian Drăguţ +1 more
TL;DR: This review has revealed that RF classifier can successfully handle high data dimensionality and multicolinearity, being both fast and insensitive to overfitting.
Journal ArticleDOI
High-resolution mapping of global surface water and its long-term changes
TL;DR: Using three million Landsat satellite images, this globally consistent, validated data set shows that impacts of climate change and climate oscillations on surface water occurrence can be measured and that evidence can be gathered to show how surface water is altered by human activities.