Sentinel-2 Data for Land Cover/Use Map**: A Review
Abstract
:1. Introduction
2. Methods for Searching Literature
3. Results
3.1. Characteristics of the Reviewed Studies
3.2. Trends of Published Articles on Sentinel-2
4. Discussion
4.1. Background of ESA Copernicus Sentinel Programme
4.2. Overview of Sentinel-2 Mission
4.2.1. Properties of Sentinel-2 Data
4.2.2. Sentinel-2 Data Products
4.3. Pre-processing of Sentinel-2 Images
4.3.1. Geometric Correction
4.3.2. Atmospheric Correction
4.3.3. Cloud Cover Masking
4.4. Land Cover/Use Classification with Sentinel-2
4.4.1. Supervised and Unsupervised
4.4.2. Pixel-Based Image Analysis
4.4.3. Object-Based Image Analysis
4.4.4. Accuracy of Sentinel Land Cover/Use Map**
4.5. Integration of Sentinel-2 with Other Remotely Sensed Data
4.6. Opportunities and Challenges of Sentinel-2 Data
4.7. Best Practices for Optimal Classification Accuracy with Sentinel-2
4.8. Specific Applications of Sentinel-2 in Land Cover/Land Use Monitoring
4.8.1. Sentinel-2 for Forest Monitoring
4.8.2. Sentinel-2 for Agricultural Monitoring
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Criteria | Details |
---|---|
Keywords | “Sentinel-2” AND “land cover”, “Sentinel-2” AND “landcover”, “Sentinel-2” AND “Forest”, “Sentinel-2” AND “Agriculture”, and “Sentinel-2” AND “urban” |
Document type | Journal articles, book chapters and conference proceedings, reports |
Language | English |
Publication period | 2015 to 2020 |
Sentinel-2A | Sentinel-2B | ||||
---|---|---|---|---|---|
Spatial Resolution (m) | Bands | Central wavelength (nm) | Bandwidth (nm) | Central Wavelength (nm) | Bandwidth (nm) |
10 | Band 2—Blue | 492.4 | 66 | 492.1 | 66 |
Band 3—Green | 559.8 | 36 | 559 | 36 | |
Band 4—Red | 664.6 | 31 | 664.9 | 31 | |
Band 8—NIR | 832.8 | 106 | 832.9 | 106 | |
20 | Band 6—Red edge | 740.5 | 15 | 739.1 | 15 |
Band 7—Red edge | 782.8 | 20 | 779.7 | 20 | |
Band 8A—Narrow NIR | 864.7 | 21 | 864 | 22 | |
Band 11—SWIR | 1613.7 | 91 | 1610.4 | 94 | |
Band 12—SWIR | 2202.4 | 175 | 2185.7 | 185 | |
60 | Band 1—Coastal aerosol | 442.7 | 21 | 442.2 | 21 |
Band 9—Water vapour | 945.1 | 20 | 943.2 | 21 | |
Band 10—SWIR—Cirrus | 1373.5 | 31 | 1376.9 | 30 |
Study | Land Cover/Use | Classification Method | Classifier | Accuracy (%) |
---|---|---|---|---|
Clark [83] | Bareland, built-up area, vegetation, crops | Subpixel | MESMA, RF | 74–84 |
Colkesen, et al. [85] | forest, soil, water, corn, barren, impervious surfaces | pixel-based | CCF | 87–95 |
Degerickx, et al. [84] | Roof, pavement, soil, shrub, tree | Sub-pixel | MESMA | 57–90 |
Denize, et al. [7] | Crop residues, bare soil, winter crop, grassland | Supervised pixel-based | SVM, RF | 81 |
Forkuor, et al. [62] | Agriculture, urban | Supervised pixel-based | RF, SVM, ANN | 87–92 |
Fragoso-Campón, et al. [86] | Forest, shrubs, water, rocks | Supervised pixel-based | RF | 73–79 |
Gašparović, et al. [87] | Water, built-up, bare soil, forests | Supervised classification | MLC, ANN | 83–91 |
Glinskis, et al. [88] | Oil pam | Supervised pixel-based | MLC | 60–70 |
Immitzer, et al. [8] | Maize, onion, sunflower, sugar beet | pixel-based | RF | 65–76 |
Khaliq, et al. [89] | Water, Cabbage, Maize, built-up | Supervised pixel-based | RF | 91 |
Kussul, et al. [90] | Crops, bareland, water | Unsupervised pixel-based | ANN | 88–94 |
Miranda, et al. [31] | Water, forest, urban bareland | Supervised pixel-based | MLC | 100 |
Pesaresi, et al. [12] | Built-up area | Supervised pixel-based | SML | 60 |
Rujoiu-Mare, et al. [81] | Forest, waterbodies, built-up | Supervised pixel-based | MLC, SVM | 92–98 |
Sekertekin, et al. [71] | Waterbody, settlement, bareland, vegetation | Supervised pixel-based | MLC | 78–85 |
Steinhausen, et al. [91] | Cropland, forest grassland, urban areas, water | Supervised pixel-based | RF | 89–91 |
Thanh Noi, et al. [73] | Residential, impervious surface, agriculture, bareland, forest, water | Supervised pixel based | RF, SVM, KNN | 90–95 |
Vuolo, et al. [40] | Carrot, Maize, potato Pumpkin | Supervised pixel-based | RF | 91–95 |
Weinmann, et al. [92] | Forest, garden, Fields, settlements | Supervised pixel-based | SVM | 72–80 |
Study | Land Cover/Use | Classification Method | Classifier | Accuracy (%) |
---|---|---|---|---|
Dong, et al. [98] | Cropland | OBIA-Classifier | RF | 78–96 |
Clark [83] | Bareland, built-up area, vegetation, crops | OBIA-Classifier | RF | 75–84 |
Csillik, et al. [99] | Wheat, maize, rice, sunflower, forest, unclassified | OBIA-Rule based | Ruleset | 78–98 |
Delalay, et al. [100] | settlement, industry, water, forest | OBIA-classifier | RF, CT | 61–95 |
Derksen, et al. [80] | Crops, road, orchards | OBIA-Contexture | Contextual | 80–90 |
Gašparović, et al. [87] | Water, built-up, bare soil, forests | OBIA-Classifier | ANN | 83– 91 |
Gašparović, et al. [87] | Water, built-up, bare soil, low vegetation, forest | OBIA-Rule based | Ruleset | 84–91 |
Gómez, et al. [101] | Winter, wheat, Others, Built-up | OBIA-Classifier | RF | 84–98 |
Heryadi, et al. [102] | forest, water body, urban, bare land | OBIA-classifier | k-NN | 80–98 |
Immitzer, et al. [8] | Maize, onion, sunflower, sugar beet | OBIA-Classifier | RF | 65–76 |
Kaplan, et al. [103] | Water, Forest, wetland, urban, green field, dry fields | OBIA-Rule based | Ruleset | 89–90 |
Kaplan, et al. [72] | Water, Forest, wetland, urban, green field, dry fields | OBIA-Rule based | Ruleset | 88–90 |
Kolokoussis, et al. [104] | Land, seawater, oil spill, possibly dissolved oil spill | OBIA-Rule based | Ruleset | 72–91 |
Labib, et al. [105] | Built-up, water, vegetation, Shadow | OBIA-Rule based | Ruleset | 67–71 |
Laurent, et al. [106] | Canopy, brown leaves, green leaves | OBIA-Classifier | Bayesian | 96–98 |
Lu, et al. [107] | Plastic-mulched land-cover, crops | OBIA-Classifier | CT | 88–90 |
Marangoz, et al. [108] | forest, water body, urban | OBIA-Rule based | Ruleset | 80–88 |
Marangoz, et al. [109] | Bare land, forest, settlement, vegetation, water | OBIA-Rule based | Ruleset | 66–76 |
Mongus, et al. [110] | Agriculture, forest, Water, grassland | OBIA-Classifier | Naïve Bayes | 88–95 |
Novelli, et al. [39] | Greenhouse | OBIA-Classifier | RF | 89–93 |
Phiri, et al. [41] | Water, built-up area, forests | OBIA-Classifier | RF | 67–91 |
Popescu, et al. [111] | urban area, water, forest, agriculture | OBIA-Classifier | Latent Dirichlet Allocation (LDA) | - |
Weinmann, et al. [92] | Settlement, industry, water, forest | OBIA-Classifier | RF | 80–83 |
**ong, et al. [76] | Cropland | OBIA-Google Earth Engine | SVM, RF | 68–85 |
Zheng, et al. [93] | Roads, bareland, Forest | OBIA-Classifier | KNN, ANN, RF, SVM | 70–90 |
Application | Specific Application | Country | Methods | Accuracy | Reference |
---|---|---|---|---|---|
Forest | Forest extent | Poland, China, Burkina Faso, South Africa, Madagascar, Zimbabwe, Bulgaria | Machine-learning, cloud computing | 80–90% | Suresh, et al. [158], Wang, et al. [127], [126], Adjognon, et al. [131]; Nzimande, et al. [128]; Filchev [159] |
Forest types | Italy, Ghana, South Africa, Togo | Linear discrimination analysis, spectral indices, Machine learning | 88–90% | Laurin, et al. [132]; Konko, et al. [160]; Puletti, et al. [141], Laurin, et al. [132] | |
Species Identification | Germany, Italy | OBIA-RF, Stepwise regression | 65–76% | Immitzer, et al. [8], Laurin, et al. [132] | |
Forest productivity | Germany, South Africa, Southern Africa | Machine-learning (Random Forest), Invertible Forest Reflectance Model | 90–92% | Mutowo, et al. [161]; Ramoelo, et al. [162]; Darvishzadeh, et al. [163] | |
Growing stock | Norway, Greece, Italy, Finland | Fusion with UAV data, Linear regression | SE=3.4–5.8% | Puliti, et al. [146], Chrysafis, et al. [164], Mura, et al. [118] | |
Forest Inventory | Finland, Norway | Fusion with UAV data, multivariable models | SE=3.4–5.8% | Puliti, et al. [146], Astola, et al. [165], [166] | |
Wetland map** | China, Canada, South Africa, Senegal, Ghana | machine-learning, Google Earth Engine, OBIA | 83–90% | Yesou, et al. [167], Mahdianpari, et al. [168], Whyte, et al. [144]; Mondal, et al. [152] | |
Leaf Area Index (LAI) | Finland, Germany, South Africa, Bulgaria | Red-edge band with Partial Least Squares Regression (PLSR), Spectral Indices | R2 = 91% | Clasen, et al. [143], Korhonen, et al. [11], Sibanda, et al. [142]; Dimitrov, et al. [169] | |
Forest Fires/Wildfire | Madeira Island, Bulgaria, Congo DRC, Africa (continent) | Active fire products with SAR data fusion, New Algorithm | 80–89% | Verhegghen, et al. [42]; Roteta, et al. [149]; Navarro, et al. [150], Nedkov [170], Filchev [159] | |
Dryland map** | German, South Africa | Sub-pixel classification, BiomeBGC Simulations | 82% | Munyati [171], Dotzler, et al. [172] | |
Grassland map** | South Africa | Sparse Partial Least Squares Regression (SPLSR) | R2 = 59% | Shoko, et al. [156] | |
Canopy cover | Finland, German | Generalized additive models, Spectral Unmixing and UAVs | RMSE = 0.05–0.42 | Korhonen, et al. [11], Clasen, et al. [143] | |
Forest succession | Brazil, Poland | SVM, RF, OBIA | 90–97% | Sothe, et al. [173], Szostak, et al. [126] | |
Forest Degradation | Bulgaria, Tanzania | OBIA-RF | R2 = 0.97, 95% | Hojas-Gascon, et al. [174], Nedkov [170] | |
Forest healthy | Poland | Machine-learning | 75–78 | Hawryło, et al. [175] | |
Forest phenology | German | Correlation with ground sensor | R2 = 0.99 | Lange, et al. [176] | |
Biomass assessment | Above-ground biomass | Vietnam, Finland, South Africa, Zimbabwe, Italy | Machine-Learning, SPLSR, PARAS, Regression analysis | 80–91% | Pham, et al. [154], Pandit, et al. [177]; Shoko, et al. [156], Majasalmi, et al. [155], Laurin, et al. [132] |
Below grown biomass | Turkey | Regression analysis, Supervised classification | R2 = 97% | Bulut, et al. [130] | |
Carbon Assessment | Carbon assessment | Czech Republic, Turkey, South Africa | Bootstrapped Random Forest, Regression analysis, multivariate regression models | R2 = 30–75% | Bulut, et al. [130], Naidoo, et al. [129], Gholizadeh, et al. [157] |
Application | Country | Methods | Accuracy | Reference |
---|---|---|---|---|
Crop diseases | China, South Africa, Zimbabwe | Random Forest | 77–94% | Zheng, et al. [189], Dhau, et al. [186]; Chemura, et al. [191] |
Crop residue | Spain, Malawi | Maximum likelihood, OBIA, regression | 90–97% | Andersson, et al. [82], Estrada, et al. [138], Zheng, et al. [196] |
Crop type detection | Ukraine, France, Austria, Zimbabwe, Ethiopia, Bulgaria | Deep learning, Random Forest, Support vector regression | 77–96% | Kussul, et al. [184], Chemura, et al. [187]; Vogels, et al. [188], Vuolo, et al. [40], Veloso, et al. [197]; Dimitrov, et al. [198] |
Crop yield focusing/productivity | Belgium, Saudi Arabia, Ukraine, Zimbabwe, Mali | Deep learning, Random Forest, Maximum likelihood, Spectral indices | 35–96% | Lambert, et al. [182]; Hiestermann, et al. [77], Al-Gaadi, et al. [135], Delloye, et al. [199] |
Cropland extent | United Kingdom, Madagascar, Ukraine Global Dataset (Burkina Faso, South Africa, Morocco, Madagascar) | Cloud-based computing, Machine-learning, OBIA | 64.4–96% | Zhang, et al. [200], Bontemps, et al. [178]; Inglada, et al. [201], Lebourgeois, et al. [136], Kussul, et al. [184] |
Irrigation crop | Ethiopia, Global Dataset (Burkina Faso, South Africa, Morocco, Madagascar) | Object-based | 94% | Vogels, et al. [137]; Vogels, et al. [188] |
Nitrogen content | Belgium, Bulgaria | Multivariant regression | 65–90%, RMSE = 0.25 | Clevers, et al. [185], Dimitrov, et al. [169] |
Real-time crop monitoring | South Africa | Cloud-based computing (Google Earth Engine) | - | Hiestermann, et al. [77] |
Smallholder crop monitoring | Mali, Ethiopia | Supervised pixel-based, object-based | 80–94% | Lambert, et al. [182]; Vogels, et al. [137] |
Soil properties | Spain, France, USA | Multivariant analysis, Neural Network, TRApezoid Model | 64–88% | Gao, et al. [139], El Hajj, et al. [202], Sadeghi, et al. [203] |
Biophysical parameter estimates | France, Spain, Bulgaria | neural networks (NN), support vector regression (SVR), kernel ridge regression (KRR), and Gaussian processes regression (GPR) | RMSE = 0.1–0.2 | Upreti, et al. [193]; **e, et al. [192], Dimitrov, et al. [169] |
Application | Specific Application | Country | Methods | Accuracy | Reference |
---|---|---|---|---|---|
Urban | Urban expansion | Brazil, China, Tanzania, Kenya | Spectral Indices, RF | 75–92% | Gombe, et al. [212]; Ng, et al. [148]; Iannelli, et al. [216]; Tavares, et al. [204] |
Urban extent | China, Brazil | Fusion | 83% | Iannelli, et al. [216]; Tavares, et al. [204] | |
Rural-urban transition | Ghana | Principal Components Analysis (PCA) | - | Møller-Jensen [206] | |
Informal settlement | South Africa | Cloud-based computing (Google Earth Engine) | - | Gibson, et al. [207] | |
Urban surface water | China, Macedonia | Pixel-based/OBIA | 80–92% | Yang, et al. [217], Yang, et al. [208], Sekertekin, et al. [218] | |
Urban climate | France, Germany | Canonical Correlation Forests | 69–75% | Qiu, et al. [219] | |
Urban change | France | Convolutional Neural Networks (CNN) | 60–91 | Daudt, et al. [220] | |
Urban ecosystem/forest/green space | Slovakia, Switzerland | SVM, Maximum likelihood | 73–90% | Haas, et al. [140], Recanatesi, et al. [221] | |
Urban heat island | Lebanon, France, German | MLC, Neural Network | 82–84% | Kaloustian, et al. [222]; Qiu, et al. [223], Chun**, et al. [205] | |
Natural hazards | Floods | Spain, Mozambique | Spectral indices and OBIA | 64–85% | Caballero, et al. [134], Phiri, et al. [41] |
Droughts | Germany, South Africa | Spectral Mixture Analysis, Biome-BGC Simulations | 73–82% | Munyati [171], Dotzler, et al. [172] | |
Earthquakes | New Zealand, France | cross-correlation | RMSE= 0.025–0.20 | Kääb, et al. [213], Jelének, et al. [215], Stumpf, et al. [224] | |
Volcanic eruption | Saunders Island, Germany | Correlation, visual assessment, Convolutional neural network (CNN) | RMSE = 0.03 | Gray, et al. [214], Valade, et al. [225] |
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Phiri, D.; Simwanda, M.; Salekin, S.; Nyirenda, V.R.; Murayama, Y.; Ranagalage, M. Sentinel-2 Data for Land Cover/Use Map**: A Review. Remote Sens. 2020, 12, 2291. https://doi.org/10.3390/rs12142291
Phiri D, Simwanda M, Salekin S, Nyirenda VR, Murayama Y, Ranagalage M. Sentinel-2 Data for Land Cover/Use Map**: A Review. Remote Sensing. 2020; 12(14):2291. https://doi.org/10.3390/rs12142291
Chicago/Turabian StylePhiri, Darius, Matamyo Simwanda, Serajis Salekin, Vincent R. Nyirenda, Yuji Murayama, and Manjula Ranagalage. 2020. "Sentinel-2 Data for Land Cover/Use Map**: A Review" Remote Sensing 12, no. 14: 2291. https://doi.org/10.3390/rs12142291