Sentinel-2 Data for Land Cover/Use Mapping: A Review

The advancement in satellite remote sensing technology has revolutionised the approaches to monitoring the Earth’s surface. The development of the Copernicus Programme by the European Space Agency (ESA) and the European Union (EU) has contributed to the effective monitoring of the Earth’s surface by producing the Sentinel-2 multispectral products. Sentinel-2 satellites are the second constellation of the ESA Sentinel missions and carry onboard multispectral scanners. The primary objective of the Sentinel-2 mission is to provide high resolution satellite data for land cover/use monitoring, climate change and disaster monitoring, as well as complementing the other satellite missions such as Landsat. Since the launch of Sentinel-2 multispectral instruments in 2015, there have been many studies on land cover/use classification which use Sentinel-2 images. However, no review studies have been dedicated to the application of ESA Sentinel-2 land cover/use monitoring. Therefore, this review focuses on two aspects: (1) assessing the contribution of ESA Sentinel-2 to land cover/use classification, and (2) exploring the performance of Sentinel-2 data in different applications (e.g., forest, urban area and natural hazard monitoring). The present review shows that Sentinel-2 has a positive impact on land cover/use monitoring, specifically in monitoring of crop, forests, urban areas, and water resources. The contemporary high adoption and application of Sentinel-2 can be attributed to the higher spatial resolution (10 m) than other medium spatial resolution images, the high temporal resolution of 5 days and the availability of the red-edge bands with multiple applications. The ability to integrate Sentinel-2 data with other remotely sensed data, as part of data analysis, improves the overall accuracy (OA) when working with Sentinel-2 images. The free access policy drives the increasing use of Sentinel-2 data, especially in developing countries where financial resources for the acquisition of remotely sensed data are limited. The literature also shows that the use of Sentinel-2 data produces high accuracies (>80%) with machine-learning classifiers such as support vector machine (SVM) and Random forest (RF). However, other classifiers such as maximum likelihood analysis are also common. Although Sentinel-2 offers many opportunities for land cover/use classification, there are challenges which include mismatching with Landsat OLI-8 data, a lack of thermal bands, and the differences in spatial resolution among the bands of Sentinel-2. Sentinel-2 data show promise and have the potential to contribute significantly towards land cover/use monitoring.

[1]  Run Wang,et al.  Evaluating the Performance of Sentinel-2, Landsat 8 and Pléiades-1 in Mapping Mangrove Extent and Species , 2018, Remote. Sens..

[2]  Sinan Bulut,et al.  Determination of Total Carbon Storage using Sentinel-2 and Geographic Information Systems in Mixed Forests , 2019 .

[3]  Elia Quirós,et al.  Accuracy Enhancement for Land Cover Classification Using LiDAR and Multitemporal Sentinel 2 Images in a Forested Watershed , 2018, Proceedings.

[4]  Ichio Asanuma,et al.  Quantification of Annual Urban Growth of Dar es Salaam Tanzania from Landsat Time Series Data , 2017 .

[5]  Chris Roelfsema,et al.  Tropical cyclone disaster management using remote sensing and spatial analysis: A review , 2017 .

[6]  Uwe Weidner,et al.  Land-Cover and Land-Use Classification Based on Multitemporal Sentinel-2 Data , 2018, IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium.

[7]  Jie Wang,et al.  Comparison of Classification Algorithms and Training Sample Sizes in Urban Land Classification with Landsat Thematic Mapper Imagery , 2014, Remote. Sens..

[8]  Jin Chen,et al.  Global land cover mapping at 30 m resolution: A POK-based operational approach , 2015 .

[9]  Bryan A. Franz,et al.  Sentinel-2 MultiSpectral Instrument (MSI) data processing for aquatic science applications: Demonstrations and validations , 2017 .

[10]  Fabio Recanatesi,et al.  Monitoring Mediterranean Oak Decline in a Peri-Urban Protected Area Using the NDVI and Sentinel-2 Images: The Case Study of Castelporziano State Natural Reserve , 2018, Sustainability.

[11]  Xiao Xiang Zhu,et al.  Local climate zone-based urban land cover classification from multi-seasonal Sentinel-2 images with a recurrent residual network , 2019, ISPRS journal of photogrammetry and remote sensing : official publication of the International Society for Photogrammetry and Remote Sensing.

[12]  M. Gašparović,et al.  The effect of fusing Sentinel-2 bands on land-cover classification , 2018 .

[13]  André Stumpf,et al.  Improved Co-Registration of Sentinel-2 and Landsat-8 Imagery for Earth Surface Motion Measurements , 2018, Remote. Sens..

[14]  S. Jones,et al.  The optical trapezoid model: A novel approach to remote sensing of soil moisture applied to Sentinel-2 and Landsat-8 observations , 2017 .

[15]  Anatoly A. Gitelson,et al.  Remote estimation of nitrogen and chlorophyll contents in maize at leaf and canopy levels , 2013, Int. J. Appl. Earth Obs. Geoinformation.

[16]  Marco Heurich,et al.  Mapping leaf chlorophyll content from Sentinel-2 and RapidEye data in spruce stands using the invertible forest reflectance model , 2019, Int. J. Appl. Earth Obs. Geoinformation.

[17]  J. Townshend,et al.  Global land cover classi(cid:142) cation at 1 km spatial resolution using a classi(cid:142) cation tree approach , 2004 .

[18]  C. Munyati.  The potential for integrating Sentinel 2 MSI with SPOT 5 HRG and Landsat 8 OLI imagery for monitoring semi-arid savannah woody cover , 2017 .

[19]  Corinna Rebmann,et al.  Validating MODIS and Sentinel-2 NDVI Products at a Temperate Deciduous Forest Site Using Two Independent Ground-Based Sensors , 2017, Sensors.

[20]  Yady Tatiana Solano Correa,et al.  Analysis of multitemporal Sentinel-2 images in the framework of the ESA Scientific Exploitation of Operational Missions , 2017, 2017 9th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp).

[21]  Xuehong Chen,et al.  Mapping Winter Wheat in North China Using Sentinel 2A/B Data: A Method Based on Phenology-Time Weighted Dynamic Time Warping , 2020, Remote. Sens..

[22]  Ugur Avdan,et al.  Object-based water body extraction model using Sentinel-2 satellite imagery , 2017 .

[23]  Michael Berger,et al.  Sentinel-2 optical high resolution mission for GMES operational services , 2007, 2007 IEEE International Geoscience and Remote Sensing Symposium.

[24]  Anthony G. Vorster,et al.  A survival guide to Landsat preprocessing. , 2017, Ecology.

[25]  Nektarios Chrysoulakis,et al.  Comparison of physically and image based atmospheric correction methods for Sentinel-2 satellite imagery , 2016, International Conference on Remote Sensing and Geoinformation of Environment.

[26]  J. Hiestermann,et al.  CLOUD-BASED AGRICULTURAL SOLUTION: A CASE STUDY OF NEAR REAL-TIME REGIONAL AGRICULTURAL CROP GROWTH INFORMATION IN SOUTH AFRICA , 2017 .

[27]  Pierre Soille,et al.  Assessment of the Added-Value of Sentinel-2 for Detecting Built-up Areas , 2016, Remote. Sens..

[28]  E. Næsset,et al.  Combining UAV and Sentinel-2 auxiliary data for forest growing stock volume estimation through hierarchical model-based inference , 2018 .

[29]  Stéphane Dupuy,et al.  A Combined Random Forest and OBIA Classification Scheme for Mapping Smallholder Agriculture at Different Nomenclature Levels Using Multisource Data (Simulated Sentinel-2 Time Series, VHRS and DEM) , 2017, Remote. Sens..

[30]  Mark Huxham,et al.  Evaluating, predicting and mapping belowground carbon stores in Kenyan mangroves , 2017, Global change biology.

[31]  Edward T. A. Mitchard,et al.  More Than Meets the Eye: Using Sentinel-2 to Map Small Plantations in Complex Forest Landscapes , 2018, Remote. Sens..

[32]  Alexandre Bouvet,et al.  Understanding the temporal behavior of crops using Sentinel-1 and Sentinel-2-like data for agricultural applications , 2017 .

[33]  Gerald Forkuor,et al.  Landsat-8 vs. Sentinel-2: examining the added value of sentinel-2’s red-edge bands to land-use and land-cover mapping in Burkina Faso , 2018 .

[34]  C. Woodcock,et al.  Improvement and expansion of the Fmask algorithm: cloud, cloud shadow, and snow detection for Landsats 4–7, 8, and Sentinel 2 images , 2015 .

[35]  Matthieu Molinier,et al.  Comparison of Sentinel-2 and Landsat 8 imagery for forest variable prediction in boreal region , 2019, Remote Sensing of Environment.

[36]  Eka Miranda,et al.  Classification of Land Cover from Sentinel-2 Imagery Using Supervised Classification Technique (Preliminary Study) , 2018, 2018 International Conference on Information Management and Technology (ICIMTech).

[37]  T. Kavzoglu,et al.  Ensemble-based canonical correlation forest (CCF) for land use and land cover classification using sentinel-2 and Landsat OLI imagery , 2017 .

[38]  Qi Gao,et al.  Synergetic Use of Sentinel-1 and Sentinel-2 Data for Soil Moisture Mapping at 100 m Resolution , 2017, Sensors.

[39]  V. Gutierrez-Velez,et al.  Quantifying and understanding land cover changes by large and small oil palm expansion regimes in the Peruvian Amazon , 2019, Land Use Policy.

[40]  Clement Atzberger,et al.  How much does multi-temporal Sentinel-2 data improve crop type classification? , 2018, Int. J. Appl. Earth Obs. Geoinformation.

[41]  Georg S. Ruppert,et al.  Accuracy Assessment of Satellite Image Classification Depending on Training Sample , 2016 .

[42]  R. DeFries,et al.  Deforestation driven by urban population growth and agricultural trade in the twenty-first century , 2010 .

[43]  Andrew K. Skidmore,et al.  Potential of Sentinel-2 spectral configuration to assess rangeland quality , 2015 .

[44]  Leonhard Blesius,et al.  The use of the Minnaert correction for land‐cover classification in mountainous terrain , 2005 .

[45]  Peijun Du,et al.  A review of supervised object-based land-cover image classification , 2017 .

[46]  Wenjiang Huang,et al.  New Spectral Index for Detecting Wheat Yellow Rust Using Sentinel-2 Multispectral Imagery , 2018, Sensors.

[47]  O. Mutanga,et al.  Estimating LAI and mapping canopy storage capacity for hydrological applications in wattle infested ecosystems using Sentinel-2 MSI derived red edge bands , 2018, GIScience & Remote Sensing.

[48]  Xiao Xiang Zhu,et al.  EFFECT OF THE TRAINING SET CONFIGURATION ON SENTINEL-2-BASED URBAN LOCAL CLIMATE ZONE CLASSIFICATION , 2018, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences.

[49]  Darius Phiri,et al.  Long-term land cover change in Zambia: An assessment of driving factors. , 2019, The Science of the total environment.

[50]  D. van Soest,et al.  Satellite-based tree cover mapping for forest conservation in the drylands of Sub Saharan Africa (SSA): Application to Burkina Faso gazetted forests , 2019, Development Engineering.

[51]  Aliihsan Sekertekin,et al.  Pixel-Based Classification Analysis of Land Use Land Cover Using SENTINEL-2 and LANDSAT-8 Data , 2017 .

[52]  Yuji Murayama,et al.  Integrating Geospatial Techniques for Urban Land Use Classification in the Developing Sub-Saharan African City of Lusaka, Zambia , 2017, ISPRS Int. J. Geo Inf..

[53]  Matamyo Simwanda,et al.  Mapping the impacts of cyclone Idai in Mozambique using Sentinel-2 and OBIA approach , 2020 .

[54]  Alfonso Fernández-Sarría,et al.  Assessing the Accuracy of Automatically Extracted Shorelines on Microtidal Beaches from Landsat 7, Landsat 8 and Sentinel-2 Imagery , 2018, Remote. Sens..

[55]  Mariana Belgiu,et al.  Cropland mapping from Sentinel-2 time series data using object-based image analysis , 2017 .

[56]  Kenton Lee,et al.  Landsat 8 Operational Land Imager On-Orbit Geometric Calibration and Performance , 2014, Remote. Sens..

[57]  David Rush,et al.  Detecting historic informal settlement fires with Sentinel 1 and 2 satellite data - Two case studies in Cape Town , 2019, Fire Safety Journal.

[58]  Domen Mongus,et al.  Segmentation schema for enhancing land cover identification: A case study using Sentinel 2 data , 2018, Int. J. Appl. Earth Obs. Geoinformation.

[59]  Gary R. Watmough,et al.  Evaluating the capabilities of Sentinel-2 for quantitative estimation of biophysical variables in vegetation , 2013 .

[60]  B. Mihai,et al.  Land cover classification in Romanian Carpathians and Subcarpathians using multi-date Sentinel-2 remote sensing imagery , 2017 .

[61]  Le Yu,et al.  Comparisons of three recent moderate resolution African land cover datasets: CGLS-LC100, ESA-S2-LC20, and FROM-GLC-Africa30 , 2019, International Journal of Remote Sensing.

[62]  Varun Tiwari,et al.  Land-use and land-cover classification using Sentinel-2 data and machine-learning algorithms: operational method and its implementation for a mountainous area of Nepal , 2019, Journal of Applied Remote Sensing.

[63]  J. Campbell,et al.  Remote sensing of crop residue and tillage practices: Present capabilities and future prospects , 2014 .

[64]  M. Rautiainen,et al.  The potential of Sentinel-2 data for estimating biophysical variables in a boreal forest: a simulation study , 2016 .

[65]  Wenzhong Shi,et al.  Fusion of Sentinel-2 images , 2016 .

[66]  Gérard Dedieu,et al.  “Sentinel-2 for agriculture”: Supporting global agriculture monitoring , 2015, 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

[67]  Matthew L. Clark,et al.  Comparison of simulated hyperspectral HyspIRI and multispectral Landsat 8 and Sentinel-2 imagery for multi-seasonal, regional land-cover mapping , 2017 .

[68]  Nataliia Kussul,et al.  Deep Learning Classification of Land Cover and Crop Types Using Remote Sensing Data , 2017, IEEE Geoscience and Remote Sensing Letters.

[69]  S. Sloan,et al.  Forest Resources Assessment of 2015 shows positive global trends but forest loss and degradation persist in poor tropical countries , 2015 .

[70]  W. Verhoef,et al.  Bayesian object-based estimation of LAI and chlorophyll from a simulated Sentinel-2 top-of-atmosphere radiance image , 2014 .

[71]  Paul D. Wagner,et al.  Combining Sentinel-1 and Sentinel-2 data for improved land use and land cover mapping of monsoon regions , 2018, Int. J. Appl. Earth Obs. Geoinformation.

[72]  Elif Sertel,et al.  ASSESSMENT OF CLASSIFICATION ACCURACIES OF SENTINEL-2 AND LANDSAT-8 DATA FOR LAND COVER / USE MAPPING , 2016 .

[73]  Andreas Dengel,et al.  Towards a Sentinel-2 Based Human Settlement Layer , 2019, IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium.

[74]  Marcello Chiaberge,et al.  Land cover and crop classification using multitemporal sentinel-2 images based on crops phenological cycle , 2018, 2018 IEEE Workshop on Environmental, Energy, and Structural Monitoring Systems (EESMS).

[75]  Yingying Dong,et al.  Retrieval of crop biophysical parameters from Sentinel-2 remote sensing imagery , 2019, Int. J. Appl. Earth Obs. Geoinformation.

[76]  Peter Strobl,et al.  Copernicus Sentinel-2 Calibration and Validation , 2019, European Journal of Remote Sensing.

[77]  Liping Di,et al.  Object-Based Plastic-Mulched Landcover Extraction Using Integrated Sentinel-1 and Sentinel-2 Data , 2018, Remote. Sens..

[78]  Herman Eerens,et al.  Sub-Pixel Crop Type Classification Using PROBA-V 100 m NDVI Time Series and Reference Data from Sentinel-2 Classifications , 2019, Remote. Sens..

[79]  Clement Atzberger,et al.  Assessing the Potential of Sentinel-2 and Pléiades Data for the Detection of Prosopis and Vachellia spp. in Kenya , 2017, Remote. Sens..

[80]  Michael Förster,et al.  UAV data as alternative to field sampling to map woody invasive species based on combined Sentinel-1 and Sentinel-2 data , 2019, Remote Sensing of Environment.

[81]  George P. Petropoulos,et al.  A new synergistic approach for monitoring wetlands using Sentinels -1 and 2 data with object-based machine learning algorithms , 2018, Environ. Model. Softw..

[82]  Michael Hovenbitzer,et al.  Quantification of forest extent in Germany by combining multi-temporal stacks of Sentinel-1 and Sentinel-2 images , 2018, International Conference on Remote Sensing and Geoinformation of Environment.

[83]  Mark C. Vanderwel,et al.  Allometric equations for integrating remote sensing imagery into forest monitoring programmes , 2016, Global change biology.

[84]  Gérard Dedieu,et al.  Benchmarking of algorithms for crop type land-cover maps using Sentinel-2 image time series , 2015, 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

[85]  Michael Schmitt,et al.  Urban Local Climate Zone Classification with a Residual Convolutional Neural Network and Multi-Seasonal Sentinel-2 Images , 2018, 2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS).

[86]  Eric Pottier,et al.  Identification of Winter Land Use in Temperate Agricultural Landscapes based on Sentinel-1 and 2 Times-Series , 2018, IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium.

[87]  Li Chen,et al.  Evaluation of automated urban surface water extraction from Sentinel-2A imagery using different water indices , 2017 .

[88]  Ioannis Papoutsis,et al.  Scalable Parcel-Based Crop Identification Scheme Using Sentinel-2 Data Time-Series for the Monitoring of the Common Agricultural Policy , 2018, Remote. Sens..

[89]  Onisimo Mutanga,et al.  Mapping the spatial distribution of the yellowwood tree (Podocarpus henkelii) in the Weza-Ngele forest using the newly launched Sentinel-2 multispectral imager data , 2020 .

[90]  E. Chuvieco,et al.  Development of a Sentinel-2 burned area algorithm: Generation of a small fire database for sub-Saharan Africa , 2019, Remote Sensing of Environment.

[91]  Gabriel Navarro,et al.  Evaluation of forest fire on Madeira Island using Sentinel-2A MSI imagery , 2017, Int. J. Appl. Earth Obs. Geoinformation.

[92]  Veronika Kopačková,et al.  Post-Earthquake Landslide Distribution Assessment Using Sentinel-1 and -2 Data: The Example of the 2016 Mw 7.8 Earthquake in New Zealand , 2018 .

[93]  Saeid Homayouni,et al.  The First Wetland Inventory Map of Newfoundland at a Spatial Resolution of 10 m Using Sentinel-1 and Sentinel-2 Data on the Google Earth Engine Cloud Computing Platform , 2018, Remote. Sens..

[94]  Xavier Blaes,et al.  Estimating smallholder crops production at village level from Sentinel-2 time series in Mali's cotton belt , 2018, Remote Sensing of Environment.

[95]  Andrea Della Vecchia,et al.  PROBA-V Mission Exploitation Platform , 2016, Remote. Sens..

[96]  Lachezar Filchev,et al.  Estimation of biophysical and biochemical variables of winter wheat through Sentinel-2 vegetation indices , 2019 .

[97]  Vassilia Karathanassi,et al.  Oil Spill Detection and Mapping Using Sentinel 2 Imagery , 2018 .

[98]  Barry Haack.  Landsat: A tool for development , 1982 .

[99]  Vanessa Paredes Gomez,et al.  Accuracy Assessment of a 122 Classes Land Cover Map Based on Sentinel-2, Landsat 8 and Deimos-1 Images and Ancillary Data , 2018, IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium.

[100]  P. Wężyk,et al.  Estimating defoliation of Scots pine stands using machine learning methods and vegetation indices of Sentinel-2 , 2018 .

[101]  Craig Underwood,et al.  Radiometric inter-sensor cross-calibration uncertainty using a traceable high accuracy reference hyperspectral imager , 2017 .

[102]  Joachim Hill,et al.  The Potential of EnMAP and Sentinel-2 Data for Detecting Drought Stress Phenomena in Deciduous Forest Communities , 2015, Remote. Sens..

[103]  Thomas Blaschke,et al.  Object based image analysis for remote sensing , 2010 .

[104]  Jennifer L. Dungan,et al.  Harmonized Landsat/Sentinel-2 Products for Land Monitoring , 2018, IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium.

[105]  Luis Alonso,et al.  Evaluation of Sentinel-2 Red-Edge Bands for Empirical Estimation of Green LAI and Chlorophyll Content , 2011, Sensors.

[106]  Na Zhao,et al.  Mapping of Urban Surface Water Bodies from Sentinel-2 MSI Imagery at 10 m Resolution via NDWI-Based Image Sharpening , 2017, Remote. Sens..

[107]  Michael E. Schaepman,et al.  Sentinels for science: potential of Sentinel-1, -2, and -3 missions for scientific observations of ocean, cryosphere, and land , 2012 .

[108]  Joanne C. White,et al.  Optical remotely sensed time series data for land cover classification: A review , 2016 .

[110]  Gérard Dedieu,et al.  A Multi-Temporal and Multi-Spectral Method to Estimate Aerosol Optical Thickness over Land, for the Atmospheric Correction of FormoSat-2, LandSat, VENμS and Sentinel-2 Images , 2015, Remote. Sens..

[111]  Wenting Cai,et al.  Comparison of Different Crop Residue Indices for Estimating Crop Residue Cover Using Field Observation Data , 2018, 2018 7th International Conference on Agro-geoinformatics (Agro-geoinformatics).

[112]  Alexandre Boulch,et al.  Urban Change Detection for Multispectral Earth Observation Using Convolutional Neural Networks , 2018, IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium.

[113]  Gérard Dedieu,et al.  Production of a Dynamic Cropland Mask by Processing Remote Sensing Image Series at High Temporal and Spatial Resolutions , 2016, Remote. Sens..

[114]  S. M. Labib,et al.  The potentials of Sentinel-2 and LandSat-8 data in green infrastructure extraction, using object based image analysis (OBIA) method , 2018 .

[115]  Peter M. Atkinson,et al.  Spatio-temporal fusion for daily Sentinel-2 images , 2018 .

[116]  Onisimo Mutanga,et al.  Testing the capability of spectral resolution of the new multispectral sensors on detecting the severity of grey leaf spot disease in maize crop , 2018 .

[117]  Radoslaw Guzinski,et al.  Evaluating the feasibility of using Sentinel-2 and Sentinel-3 satellites for high-resolution evapotranspiration estimations , 2019, Remote Sensing of Environment.

[118]  Mingquan Wu,et al.  Monitoring cotton root rot by synthetic Sentinel-2 NDVI time series using improved spatial and temporal data fusion , 2018, Scientific Reports.

[119]  Wolfram Mauser,et al.  Optimal Exploitation of the Sentinel-2 Spectral Capabilities for Crop Leaf Area Index Mapping , 2012, Remote. Sens..

[120]  Julien Michel,et al.  Spatially Precise Contextual Features Based on Superpixel Neighborhoods for Land Cover Mapping with High Resolution Satellite Image Time Series , 2018, IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium.

[121]  Andreas Uhl,et al.  Improvement of the Fmask algorithm for Sentinel-2 images: Separating clouds from bright surfaces based on parallax effects , 2018, Remote Sensing of Environment.

[122]  Qihao Weng,et al.  A survey of image classification methods and techniques for improving classification performance , 2007 .

[123]  Jan Haas,et al.  Sentinel-1A SAR and sentinel-2A MSI data fusion for urban ecosystem service mapping , 2017 .

[124]  Cong Xu,et al.  Effects of pre-processing methods on Landsat OLI-8 land cover classification using OBIA and random forests classifier , 2018, Int. J. Appl. Earth Obs. Geoinformation.

[125]  R. Nedkov.  Quantitative Assessment of Forest Degradation after Fire Using Ortogonalized Satellite Images from Sentinel-2 , 2018 .

[126]  A. Kayad,et al.  Prediction of Potato Crop Yield Using Precision Agriculture Techniques , 2016, PloS one.

[127]  Eka Miranda,et al.  Land Cover Classification Based on Sentinel-2 Satellite Imagery Using Convolutional Neural Network Model: A Case Study in Semarang Area, Indonesia , 2019, ACIIDS.

[128]  Olaf Hellwich,et al.  Towards Global Volcano Monitoring Using Multisensor Sentinel Missions and Artificial Intelligence: The MOUNTS Monitoring System , 2019, Remote. Sens..

[129]  Mariana Belgiu,et al.  Sentinel-2 cropland mapping using pixel-based and object-based time-weighted dynamic time warping analysis , 2018 .

[130]  Onisimo Mutanga,et al.  Evaluating the potential of the red edge channel for C3 (Festuca spp.) grass discrimination using Sentinel-2 and Rapid Eye satellite image data , 2019 .

[131]  Luis Alonso,et al.  Machine learning regression algorithms for biophysical parameter retrieval: Opportunities for Sentinel-2 and -3 , 2012 .

[132]  I. Reusen,et al.  Atmospheric correction of Landsat-8/OLI and Sentinel-2/MSI data using iCOR algorithm: validation for coastal and inland waters , 2018 .

[133]  O. Mutanga,et al.  Transferability of species distribution models for the detection of an invasive alien bryophyte using imaging spectroscopy data , 2018, Int. J. Appl. Earth Obs. Geoinformation.

[134]  Pinki Mondal,et al.  Evaluating Combinations of Sentinel-2 Data and Machine-Learning Algorithms for Mangrove Mapping in West Africa , 2019, Remote. Sens..

[135]  Mihai Datcu,et al.  Enhanced classification of land cover through joint analysis of Sentinel 1 and Sentinel 2 data , 2016 .

[136]  Laurent Tits,et al.  Spectral Unmixing of Forest Crown Components at Close Range, Airborne and Simulated Sentinel-2 and EnMAP Spectral Imaging Scale , 2015, Remote. Sens..

[137]  Timothy Dube,et al.  Estimating Above-Ground Biomass in Sub-Tropical Buffer Zone Community Forests, Nepal, Using Sentinel 2 Data , 2018, Remote. Sens..

[138]  Matthias Drusch,et al.  Sentinel-2: ESA's Optical High-Resolution Mission for GMES Operational Services , 2012 .

[139]  F. Chianucci,et al.  Use of Sentinel-2 for forest classification in Mediterranean environments , 2018 .

[140]  Cong Xu,et al.  Four decades of land cover and forest connectivity study in Zambia - An object-based image analysis approach , 2019, Int. J. Appl. Earth Obs. Geoinformation.

[141]  Nataliia Kussul,et al.  Sentinel-2 for agriculture national demonstration in ukraine: Results and further steps , 2017, 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

[142]  Claire Jarvis,et al.  Synergistic application of Sentinel 1 and Sentinel 2 derivatives for terrestrial oil spill impact mapping , 2018, Remote Sensing.

[143]  Ian W. Housman,et al.  Empirical cross sensor comparison of Sentinel-2A and 2B MSI, Landsat-8 OLI, and Landsat-7 ETM+ top of atmosphere spectral characteristics over the conterminous United States , 2019, Remote Sensing of Environment.

[144]  Jan Haas,et al.  Urban Land Cover and Ecosystem Service Changes based on Sentinel-2A MSI and Landsat TM Data , 2018, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[145]  Frédéric Achard,et al.  The Potential of Sentinel Satellites for Burnt Area Mapping and Monitoring in the Congo Basin Forests , 2016, Remote. Sens..

[146]  Onisimo Mutanga,et al.  Empirical Modeling of Leaf Chlorophyll Content in Coffee (Coffea Arabica) Plantations With Sentinel-2 MSI Data: Effects of Spectral Settings, Spatial Resolution, and Crop Canopy Cover , 2017, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[147]  M. Herold,et al.  An assessment of deforestation and forest degradation drivers in developing countries , 2012 .

[148]  A. Marangoz.  ANALYSIS OF LAND USE/LAND COVER CLASSIFICATION RESULTS DERIVED FROM SENTINEL-2 IMAGE , 2017 .

[149]  Martin Kappas,et al.  Comparison of Random Forest, k-Nearest Neighbor, and Support Vector Machine Classifiers for Land Cover Classification Using Sentinel-2 Imagery , 2017, Sensors.

[150]  J. Mascaro,et al.  Coseismic displacements of the 14 November 2016 M w 7.8 Kaikoura, New Zealand, earthquake using the Planet optical cubesat constellation , 2017 .

[151]  Renaud Mathieu,et al.  Estimating above ground biomass as an indicator of carbon storage in vegetated wetlands of the grassland biome of South Africa , 2019, Int. J. Appl. Earth Obs. Geoinformation.

[152]  Lênio Soares Galvão,et al.  Use of MSI/Sentinel-2 and airborne LiDAR data for mapping vegetation and studying the relationships with soil attributes in the Brazilian semi-arid region , 2018, Int. J. Appl. Earth Obs. Geoinformation.

[153]  Paolo Gamba,et al.  Jointly Exploiting Sentinel-1 and Sentinel-2 for Urban Mapping , 2018, IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium.

[154]  Ana C. Teodoro,et al.  Integration of Sentinel-1 and Sentinel-2 for Classification and LULC Mapping in the Urban Area of Belém, Eastern Brazilian Amazon , 2019, Sensors.

[155]  Marjolein F. A. Vogels,et al.  Spatio-Temporal Patterns of Smallholder Irrigated Agriculture in the Horn of Africa Using GEOBIA and Sentinel-2 Imagery , 2019, Remote. Sens..

[156]  O. Mutanga,et al.  Examining the potential of Sentinel-2 MSI spectral resolution in quantifying above ground biomass across different fertilizer treatments , 2015 .

[157]  M. Rautiainen,et al.  Comparison of Sentinel-2 and Landsat 8 in the estimation of boreal forest canopy cover and leaf area index , 2017 .

[158]  C. Justice,et al.  The Harmonized Landsat and Sentinel-2 surface reflectance data set , 2018, Remote Sensing of Environment.

[159]  Golshah Naghdy,et al.  Unsupervised Classification of Images: A Review , 2014 .

[160]  Cláudia Maria de Almeida,et al.  Evaluating Sentinel-2 and Landsat-8 Data to Map Sucessional Forest Stages in a Subtropical Forest in Southern Brazil , 2017, Remote. Sens..

[161]  Wenjiang Huang,et al.  A Comparison of Hybrid Machine Learning Algorithms for the Retrieval of Wheat Biophysical Variables from Sentinel-2 , 2019, Remote. Sens..

[162]  Gérard Dedieu,et al.  Building a Data Set over 12 Globally Distributed Sites to Support the Development of Agriculture Monitoring Applications with Sentinel-2 , 2015, Remote. Sens..

[163]  Abderrahim Nemmaoui,et al.  Performance evaluation of object based greenhouse detection from Sentinel-2 MSI and Landsat 8 OLI data: A case study from Almería (Spain) , 2016, Int. J. Appl. Earth Obs. Geoinformation.

[164]  Dumitru Roman,et al.  Enabling the Use of Sentinel-2 and LiDAR Data for Common Agriculture Policy Funds Assignment , 2017, ISPRS Int. J. Geo Inf..

[165]  Onisimo Mutanga,et al.  Mapping foliar N in miombo woodlands using sentinel-2 derived chlorophyll and structural indices , 2018, Journal of Applied Remote Sensing.

[166]  Luis Alonso,et al.  Optimizing LUT-Based RTM Inversion for Semiautomatic Mapping of Crop Biophysical Parameters from Sentinel-2 and -3 Data: Role of Cost Functions , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[167]  Mohammadmehdi Saberioon,et al.  Soil organic carbon and texture retrieving and mapping using proximal, airborne and Sentinel-2 spectral imaging , 2018, Remote Sensing of Environment.

[168]  Ferran Gascon,et al.  Sen2Cor for Sentinel-2 , 2017, Remote Sensing.

[169]  Simon Plank,et al.  Mapping burn scars, fire severity and soil erosion susceptibility in Southern France using multisensoral satellite data , 2017, 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

[170]  Quinten Vanhellemont,et al.  Adaptation of the dark spectrum fitting atmospheric correction for aquatic applications of the Landsat and Sentinel-2 archives , 2019, Remote Sensing of Environment.

[171]  David P. Roy,et al.  A note on the temporary misregistration of Landsat-8 Operational Land Imager (OLI) and Sentinel-2 Multi Spectral Instrument (MSI) imagery , 2016 .

[172]  Naoto Yokoya,et al.  Performance Evaluation of Downscaling Sentinel-2 Imagery for Land Use and Land Cover Classification by Spectral-Spatial Features , 2017, Remote. Sens..

[173]  Hugh Eva,et al.  Potential improvement for forest cover and forest degradation mapping with the forthcoming Sentinel-2 program , 2015 .

[174]  Martha C. Anderson,et al.  Free Access to Landsat Imagery , 2008, Science.

[175]  Paweł Hawryło,et al.  Using of Sentinel-2 images for automation of the forest succession detection , 2018 .

[176]  Ewa Gromny,et al.  Creation of training dataset for Sentinel-2 land cover classification , 2019, Symposium on Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments (WILGA).

[177]  Carsten Brockmann,et al.  A Radiometric Uncertainty Tool for the Sentinel 2 Mission , 2017, Remote. Sens..

[178]  Gabriel Navarro,et al.  Sentinel-2 Satellites Provide Near-Real Time Evaluation of Catastrophic Floods in the West Mediterranean , 2019, Water.

[179]  Onisimo Mutanga,et al.  Remote sensing of crop health for food security in Africa: Potentials and constraints , 2017 .

[180]  Tiziana Simoniello,et al.  A first assessment of the Sentinel-2 Level 1-C cloud mask product to support informed surface analyses , 2018, Remote Sensing of Environment.

[181]  Benjamin Bechtel,et al.  Local Climatic Zoning and Urban Heat Island in Beirut , 2016 .

[182]  Pierre Defourny,et al.  Retrieval of the canopy chlorophyll content from Sentinel-2 spectral bands to estimate nitrogen uptake in intensive winter wheat cropping systems , 2018, Remote Sensing of Environment.

[183]  Thomas R. Loveland,et al.  A review of large area monitoring of land cover change using Landsat data , 2012 .

[184]  Justin Morgenroth,et al.  Developments in Landsat Land Cover Classification Methods: A Review , 2017, Remote. Sens..

[185]  Russell G. Congalton,et al.  Nominal 30-m Cropland Extent Map of Continental Africa by Integrating Pixel-Based and Object-Based Algorithms Using Sentinel-2 and Landsat-8 Data on Google Earth Engine , 2017, Remote. Sens..

[186]  Damir Medak,et al.  Support Vector Machine Accuracy Assessment for Extracting Green Urban Areas in Towns , 2019, Remote. Sens..

[187]  Eric Pottier,et al.  Synergy of Sentinel-1 and Sentinel-2 imagery for wetland monitoring information extraction from continuous flow of sentinel images applied to water bodies and vegetation mapping and monitoring , 2016, 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

[188]  Kouami Kokou,et al.  Spatio-Temporal Distribution of Southeastern Community Forests in Togo (West Africa) , 2018 .

[189]  Kamalika Das,et al.  Cloud detection algorithm for multi-modal satellite imagery using convolutional neural-networks (CNN) , 2020 .

[190]  Naoto Yokoya,et al.  Mangrove Species Mapping Using Sentinel-1 and Sentinel-2 Data in North Vietnam , 2019, IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium.

[191]  Jan G. P. W. Clevers,et al.  Remote estimation of crop and grass chlorophyll and nitrogen content using red-edge bands on Sentinel-2 and -3 , 2013, Int. J. Appl. Earth Obs. Geoinformation.

[192]  C. Justice,et al.  High-Resolution Global Maps of 21st-Century Forest Cover Change , 2013, Science.

[193]  Peter T. Fretwell,et al.  Evidence for a lava lake on Mt. Michael volcano, Saunders Island (South Sandwich Islands) from Landsat, Sentinel-2 and ASTER satellite imagery , 2019, Journal of Volcanology and Geothermal Research.

[194]  Juha Hyyppä,et al.  Comparison of two-dimensional multitemporal Sentinel-2 data with three-dimensional remote sensing data sources for forest inventory parameter estimation over a boreal forest , 2019, Int. J. Appl. Earth Obs. Geoinformation.

[195]  Pierre Grussenmeyer,et al.  Urban surface water body detection with suppressed built-up noise based on water indices from Sentinel-2 MSI imagery , 2018, Remote Sensing of Environment.

[196]  R. Valentini,et al.  Discrimination of tropical forest types, dominant species, and mapping of functional guilds by hyperspectral and simulated multispectral Sentinel-2 data , 2016 .

[197]  G. Mallinis,et al.  Assessing the relationships between growing stock volume and Sentinel-2 imagery in a Mediterranean forest ecosystem , 2017 .

[198]  Remo Bertani,et al.  Exploiting the capabilities of the Sentinel-2 multi spectral instrument for predicting growing stock volume in forest ecosystems , 2018, Int. J. Appl. Earth Obs. Geoinformation.

[199]  C. Schröder,et al.  Land use and land cover mapping in wetlands one step closer to the ground: Sentinel-2 versus landsat 8. , 2019, Journal of environmental management.

[200]  Iryna Dronova,et al.  Object-Based Image Analysis in Wetland Research: A Review , 2015, Remote. Sens..

[201]  Clement Atzberger,et al.  First Experience with Sentinel-2 Data for Crop and Tree Species Classifications in Central Europe , 2016, Remote. Sens..

[202]  Lin Yan,et al.  An Automated Approach for Sub-Pixel Registration of Landsat-8 Operational Land Imager (OLI) and Sentinel-2 Multi Spectral Instrument (MSI) Imagery , 2016, Remote. Sens..

[203]  G. Kaplan,et al.  MAPPING AND MONITORING WETLANDS USING SENTINEL-2 SATELLITE IMAGERY , 2017 .

[204]  Mehrez Zribi,et al.  Synergic Use of Sentinel-1 and Sentinel-2 Images for Operational Soil Moisture Mapping at High Spatial Resolution over Agricultural Areas , 2017, Remote. Sens..

[205]  Lian Pin Koh,et al.  Free and open-access satellite data are key to biodiversity conservation , 2015 .

[206]  Onisimo Mutanga,et al.  Separability of coffee leaf rust infection levels with machine learning methods at Sentinel-2 MSI spectral resolutions , 2017, Precision Agriculture.

[207]  Guohai Liu,et al.  Band selection in sentinel-2 satellite for agriculture applications , 2017, 2017 23rd International Conference on Automation and Computing (ICAC).

[208]  Gabriele Bitelli,et al.  Preliminary Comparison of Sentinel-2 and Landsat 8 Imagery for a Combined Use , 2016, Remote. Sens..

[209]  Ben Somers,et al.  Enhancing the performance of Multiple Endmember Spectral Mixture Analysis (MESMA) for urban land cover mapping using airborne lidar data and band selection , 2019, Remote Sensing of Environment.

[210]  Nkanyiso Mbatha,et al.  Characterisation of aerosol constituents from wildfires using satellites and model data: a case study in Knysna, South Africa , 2019, International Journal of Remote Sensing.