A Copernicus Sentinel-1 and Sentinel-2 Classification Framework for the 2020+ European Common Agricultural Policy: A Case Study in València (Spain)
Abstract
:1. Introduction
2. Data Collection
2.1. Study Area
2.2. Sentinel-1 and Sentinel-2 Data
2.3. Sistema de Información Geográfica de Parcelas Agrícolas (SIGPAC)
2.4. Ground Truth
3. Methodology
3.1. Feature Selection
3.2. Classifiers
3.3. Agreement Map
4. Results and Analysis
4.1. Sentinel-2 Spectral Separability Analysis
4.2. Accuracy Assessment
4.3. Classification, Class Probability and Agreement Maps
4.4. Utility of the Derived Agreement Map
5. Discussion
6. Conclusions
Supplementary Materials
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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2017 | 2018 | |
---|---|---|
Sentinel-1 | 12 April, 24 April, 6 May, 18 May, 30 May, 5 June, 17 June, 29 June, 11 July, 23 July, 4 August, 16 August, 28 August, 9 September, 21 September, 3 October, 15 October, 27 October, 8 November, 20 November, 2 December, 14 December, 26 December. | 7 January, 31 January, 19 January, 12 February, 24 February, 20 March, 8 March. |
Sentinel-2 | 6 May, 26 May, 15 June, 13 September, 13 October, 2 December, 17 December. | 21 January, 26 May 26, 15 February, 7 March, 27 March. |
Class | Shrubs | Dried Fruit | Fruits | Pasture | Rice | Forest | Vineyard | Pasture with Trees | Olive Groove | Citrus |
---|---|---|---|---|---|---|---|---|---|---|
No. Pixels | 20,382 | 20,703 | 19,800 | 20,058 | 20,157 | 20,478 | 20,295 | 18,846 | 19,434 | 20,385 |
Vegetation Index | Equation |
---|---|
NDVI | |
OSAVI | |
NDVI705 | |
OSAVI705 | |
MCARI | |
PSRI |
Classification Map and SIGPAC | Classification Confidence | Level of Agreement | |
---|---|---|---|
Same class | AND | >95% | Very high agreement |
Same class | AND | 70–95% | High agreement |
Same class | AND | 50–70% | Significant agreement |
Same class | AND | <50% | Low agreement |
Different classes | AND | <50% | Low discrepancy |
Different classes | AND | 50–70% | Significant discrepancy |
Different classes | AND | 50–70% | High discrepancy |
Different classes | AND | >95% | Very high discrepancy |
JM | SH | DFR | FR | PA | RI | FO | VI | PAT | OL | CI | |
---|---|---|---|---|---|---|---|---|---|---|---|
BH | |||||||||||
SH | 1.750 | 1.597 | 1.508 | 1.996 | 0.636 | 1.870 | 0.253 | 1.497 | 1.879 | ||
DFR | 2.078 | 0.392 | 0.698 | 1.983 | 1.863 | 0.466 | 1.717 | 0.402 | 1.439 | ||
FR | 1.603 | 0.218 | 0.621 | 1.985 | 1.760 | 0.672 | 1.538 | 0.250 | 1.472 | ||
PA | 1.403 | 0.429 | 0.371 | 1.986 | 1.653 | 1.202 | 1.410 | 0.505 | 1.123 | ||
RI | 6.102 | 4.776 | 4.880 | 4.939 | 1.989 | 1.989 | 1.986 | 1.988 | 1.995 | ||
FO | 0.382 | 2.679 | 2.120 | 1.752 | 5.228 | 1.951 | 0.296 | 1.713 | 1.899 | ||
VI | 2.731 | 0.265 | 0.409 | 0.919 | 5.249 | 3.714 | 1.872 | 0.792 | 1.692 | ||
PAT | 0.135 | 1.955 | 1.465 | 1.221 | 4.991 | 0.160 | 2.752 | 1.446 | 1.837 | ||
OL | 1.381 | 0.225 | 0.133 | 0.291 | 5.135 | 1.942 | 0.504 | 1.283 | 1.382 | ||
CI | 2.802 | 1.271 | 1.331 | 0.825 | 6.033 | 2.988 | 1.870 | 2.509 | 1.174 |
JM | SH | DFR | FR | PA | RI | FO | VI | PAT | OL | CI | |
---|---|---|---|---|---|---|---|---|---|---|---|
BH | |||||||||||
SH | 1.750 | 1.597 | 1.508 | 1.996 | 0.636 | 1.870 | 0.253 | 1.497 | 1.879 | ||
DFR | 2.078 | 0.392 | 0.698 | 1.983 | 1.863 | 0.466 | 1.717 | 0.402 | 1.439 | ||
FR | 1.603 | 0.218 | 0.621 | 1.985 | 1.760 | 0.672 | 1.538 | 0.250 | 1.472 | ||
PA | 1.403 | 0.429 | 0.371 | 1.986 | 1.653 | 1.202 | 1.410 | 0.505 | 1.123 | ||
RI | 6.102 | 4.776 | 4.880 | 4.939 | 1.989 | 1.989 | 1.986 | 1.988 | 1.995 | ||
FO | 0.382 | 2.679 | 2.120 | 1.752 | 5.228 | 1.951 | 0.296 | 1.713 | 1.899 | ||
VI | 2.731 | 0.265 | 0.409 | 0.919 | 5.249 | 3.714 | 1.872 | 0.792 | 1.692 | ||
PAT | 0.135 | 1.955 | 1.465 | 1.221 | 4.991 | 0.160 | 2.752 | 1.446 | 1.837 | ||
OL | 1.381 | 0.225 | 0.133 | 0.291 | 5.135 | 1.942 | 0.504 | 1.283 | 1.382 | ||
CI | 2.802 | 1.271 | 1.331 | 0.825 | 6.033 | 2.988 | 1.870 | 2.509 | 1.174 |
Multitemporal Features (Optical + SAR) | Overall Accuracy (%) (κ) | ||||||
---|---|---|---|---|---|---|---|
LDA | QDA | k-NN | SVM | RF | Bagging Trees | Boosting Trees | |
6 × Sentinel-2 (12 bands) + NDVI 30 × Sentinel-1 (3 bands) | 69.82 (0.68) | 80.36 (0.79) | 84.93 (0.83) | 86.80 (0.85) | 88.69 (0.87) | 88.30 (0.87) | 92.66 (0.90) |
6 × Sentinel-2 (12 bands) + OSAVI 30 × Sentinel-1 (3 bands) | 70.58 (0.69) | 80.45 (0.79) | 85.68 (0.84) | 86.97 (0.85) | 88.86 (0.87) | 88.48 (0.87) | 93.58 (0.91) |
6 × Sentinel-2 (12 bands) + NDVI705 30 × Sentinel-1 (3 bands) | 67.54 (0.66) | 76.41 (0.75) | 83.29 (0.82) | 85.29 (0.84) | 87.64 (0.86) | 87.23 (0.86) | 90.75 (0.89) |
6 × Sentinel-2 (12 bands) + OSAVI705 30 × Sentinel-1 (3 bands) | 70.95 (0.70) | 80.50 (0.79) | 85.94 (0.85) | 86.99 (0.84) | 89.05 (0.88) | 88.76 (0.87) | 93.96 (0.91) |
6 × Sentinel-2 (12 bands) + MCARI 30 × Sentinel-1 (3 bands) | 68.93 (0.68) | 77.83 (0.76) | 83.15 (0.82) | 85.04 (0.84) | 88.15 (0.87) | 87.51 (0.86) | 90.15 (0.89) |
6 × Sentinel-2 (12 bands) + PSRI 30 × Sentinel-1 (3 bands) | 66.94 (0.66) | 71.60 (0.69) | 76.83 (0.75) | 84.07 (0.83) | 87.10 (0.86) | 87.08 (0.86) | 88.96 (0.88) |
6 × Sentinel-2 (12 bands) 30 × Sentinel-1 (3 bands) | 66.73 (0.65) | 70.23 (0.69) | 75.84 (0.74) | 85.59 (0.84) | 86.80 (0.85) | 86.25 (0.85) | 88.19 (0.87) |
Ground Truth | Total | UA (%) | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
SH | DFR | FR | PA | RI | FO | VI | PAT | OL | CI | ||||
Classified | SH | 5560 | 19 | 19 | 1 | 0 | 259 | 1 | 329 | 69 | 7 | 6264 | 88.8 |
DFR | 24 | 6619 | 10 | 1 | 0 | 7 | 5 | 12 | 72 | 12 | 6762 | 97.9 | |
FR | 70 | 63 | 6450 | 4 | 1 | 36 | 20 | 36 | 87 | 16 | 6783 | 95.1 | |
PA | 3 | 8 | 1 | 6661 | 0 | 3 | 2 | 1 | 10 | 3 | 6692 | 99.5 | |
RI | 1 | 1 | 0 | 2 | 6717 | 1 | 0 | 1 | 1 | 0 | 6724 | 99.9 | |
FO | 455 | 11 | 15 | 5 | 0 | 5845 | 1 | 349 | 38 | 12 | 6731 | 86.8 | |
VI | 3 | 11 | 18 | 1 | 0 | 0 | 6719 | 1 | 35 | 3 | 6791 | 98.9 | |
PAT | 500 | 36 | 27 | 2 | 1 | 569 | 0 | 5443 | 50 | 10 | 6638 | 82.0 | |
OL | 175 | 118 | 53 | 7 | 0 | 105 | 14 | 104 | 6101 | 36 | 6713 | 90.9 | |
CI | 3 | 15 | 7 | 2 | 0 | 1 | 3 | 6 | 15 | 6696 | 6748 | 99.2 | |
Total PA (%) | 6794 | 6901 | 6600 | 6686 | 6719 | 6826 | 6765 | 6282 | 6478 | 6795 | OA = 93.96% κ = 0.91 | ||
81.8 | 95.9 | 97.7 | 99.6 | 99.9 | 85.6 | 99.3 | 86.6 | 94.2 | 98.5 |
Multitemporal Features (Optical + SAR) | Overall Accuracy (%) (κ) | ||||||
---|---|---|---|---|---|---|---|
LDA | QDA | k-NN | SVM | RF | Bagging Trees | Boosting Trees | |
6 × Sentinel-2 (12 bands) + NDVI 30 × Sentinel-1 (3 bands) | 69.82 (0.68) | 80.36 (0.79) | 84.93 (0.83) | 86.80 (0.85) | 88.69 (0.87) | 88.30 (0.87) | 92.66 (0.90) |
6 × Sentinel-2 (12 bands) + OSAVI 30 × Sentinel-1 (3 bands) | 70.58 (0.69) | 80.45 (0.79) | 85.68 (0.84) | 86.97 (0.85) | 88.86 (0.87) | 88.48 (0.87) | 93.58 (0.91) |
6 × Sentinel-2 (12 bands) + NDVI705 30 × Sentinel-1 (3 bands) | 67.54 (0.66) | 76.41 (0.75) | 83.29 (0.82) | 85.29 (0.84) | 87.64 (0.86) | 87.23 (0.86) | 90.75 (0.89) |
6 × Sentinel-2 (12 bands) + OSAVI705 30 × Sentinel-1 (3 bands) | 70.95 (0.70) | 80.50 (0.79) | 85.94 (0.85) | 86.99 (0.84) | 89.05 (0.88) | 88.76 (0.87) | 93.96 (0.91) |
6 × Sentinel-2 (12 bands) + MCARI 30 × Sentinel-1 (3 bands) | 68.93 (0.68) | 77.83 (0.76) | 83.15 (0.82) | 85.04 (0.84) | 88.15 (0.87) | 87.51 (0.86) | 90.15 (0.89) |
6 × Sentinel-2 (12 bands) + PSRI 30 × Sentinel-1 (3 bands) | 66.94 (0.66) | 71.60 (0.69) | 76.83 (0.75) | 84.07 (0.83) | 87.10 (0.86) | 87.08 (0.86) | 88.96 (0.88) |
6 × Sentinel-2 (12 bands) 30 × Sentinel-1 (3 bands) | 66.73 (0.65) | 70.23 (0.69) | 75.84 (0.74) | 85.59 (0.84) | 86.80 (0.85) | 86.25 (0.85) | 88.19 (0.87) |
Ground Truth | Total | UA(%) | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
SH | DFR | FR | PA | RI | FO | VI | PAT | OL | CI | ||||
Classified | SH | 5050 | 20 | 32 | 2 | 0 | 409 | 2 | 475 | 67 | 9 | 6066 | 83.3 |
DFR | 16 | 6519 | 9 | 16 | 0 | 27 | 19 | 16 | 72 | 30 | 6724 | 97.0 | |
FR | 101 | 111 | 6349 | 17 | 3 | 40 | 70 | 25 | 87 | 40 | 6843 | 92.8 | |
PA | 3 | 8 | 1 | 6566 | 0 | 21 | 2 | 4 | 37 | 4 | 6646 | 98.8 | |
RI | 2 | 3 | 0 | 3 | 6714 | 2 | 0 | 3 | 2 | 0 | 6729 | 99.8 | |
FO | 955 | 11 | 17 | 31 | 0 | 5515 | 1 | 474 | 39 | 14 | 7057 | 78.1 | |
VI | 13 | 20 | 55 | 2 | 0 | 0 | 6593 | 8 | 97 | 4 | 6792 | 97.1 | |
PAT | 519 | 34 | 51 | 14 | 2 | 689 | 1 | 5160 | 99 | 11 | 6580 | 78.4 | |
OL | 129 | 155 | 78 | 32 | 0 | 109 | 62 | 103 | 5962 | 160 | 6790 | 87.8 | |
CI | 6 | 20 | 8 | 3 | 0 | 14 | 15 | 14 | 16 | 6523 | 6619 | 98.5 | |
Total PA (%) | 6794 | 6901 | 6600 | 6686 | 6719 | 6826 | 6765 | 6282 | 6478 | 6795 | OA = 91.18% κ = 0.88 | ||
74.3 | 94.5 | 96.2 | 98.2 | 99.9 | 80.8 | 97.5 | 82.1 | 92.0 | 96.0 |
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Campos-Taberner, M.; García-Haro, F.J.; Martínez, B.; Sánchez-Ruíz, S.; Gilabert, M.A. A Copernicus Sentinel-1 and Sentinel-2 Classification Framework for the 2020+ European Common Agricultural Policy: A Case Study in València (Spain). Agronomy 2019, 9, 556. https://doi.org/10.3390/agronomy9090556
Campos-Taberner M, García-Haro FJ, Martínez B, Sánchez-Ruíz S, Gilabert MA. A Copernicus Sentinel-1 and Sentinel-2 Classification Framework for the 2020+ European Common Agricultural Policy: A Case Study in València (Spain). Agronomy. 2019; 9(9):556. https://doi.org/10.3390/agronomy9090556
Chicago/Turabian StyleCampos-Taberner, Manuel, Francisco Javier García-Haro, Beatriz Martínez, Sergio Sánchez-Ruíz, and María Amparo Gilabert. 2019. "A Copernicus Sentinel-1 and Sentinel-2 Classification Framework for the 2020+ European Common Agricultural Policy: A Case Study in València (Spain)" Agronomy 9, no. 9: 556. https://doi.org/10.3390/agronomy9090556