Machine Learning-Based Summer Crops Mapping Using Sentinel-1 and Sentinel-2 Images
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Site
2.2. Data
2.2.1. Ground Data
2.2.2. Sentinel-2 Data
2.2.3. Sentinel-1 Data
Sentinel-1 Backscattering Coefficients
- For the ascending orbit (asc), containing time series for VVasc, VHasc, and VV/VHasc;
- For the descending orbit (des), containing time series for VVdes, VHdes, and VV/VHdes;
- Both orbits, containing the two datasets mentioned above in addition to the differences between them: VVasc-VVdes, VHasc-VHdes, and VV/VHasc-VV/VHdes.
Sentinel-1 Polarimetric Data
- For the ascending orbit, containing five polarimetric parameters: g0, g1, Shannon_I, Shannon_P, and Shannon
- For the descending orbit with the same polarimetric parameters
- For both ascending and descending orbits as well as the differences between them (for example: g0asc-g0des, g1asc-g1des, etc.).
2.3. Methodology
2.3.1. Features
Harmonic Coefficients
Monthly and Seasonal Medians
2.3.2. Classification Algorithms
Random Forest (RF)
Multi-Layer Perceptron (MLP)
Extreme Gradient Boosting (XGBoost)
InceptionTime
2.3.3. Summer Crop Types Mapping
- S2 images
- Spectral bands time series
- Vegetation indices time series
- Both spectral bands and vegetation indices
- Harmonic coefficients extracted from S2 vegetation indices
- Median features extracted from S2 vegetation indices
- Both harmonic coefficients and median features from S2 vegetation indices
- S1 images
- S1 backscattering coefficients time series (separate orbits and combined orbits)
- Harmonic coefficients extracted from S1 backscattering coefficients time series (separate orbits and combined orbits)
- Median features extracted from S1 backscattering coefficients time series (separate orbits and combined orbits)
- Both harmonic coefficients and median features from S1 backscattering coefficients time series (combined orbits)
- Polarimetric parameters
- S1 polarimetric parameters time series (separate orbits and combined orbits)
- Harmonic coefficients extracted from S1 polarimetric parameters time series (separate orbits and combined orbits)
- Median features extracted from S1 polarimetric parameters time series (separate orbits and combined orbits)
- Both harmonic coefficients and median features from S1 polarimetric parameters time series (combined orbits)
- A combined dataset of S1 backscattering coefficients and polarimetric parameters time series
- S1 and S2 images
- S1 backscattering coefficients and S2 vegetation indices time series
- Harmonic coefficients and median features extracted from S1 backscattering coefficients and S2 vegetation indices
- Harmonic coefficients and median features extracted from S1 polarimetric parameters and S2 vegetation indices
- Harmonic coefficients and median features extracted from S1 backscattering coefficients, S1 polarimetric parameters, and S2 vegetation indices
- S2 vegetation indices time series
- S1 backscattering coefficients time series (separate orbits, and combined orbits)
- S1 polarimetric parameters time series (separate orbits and combined orbits)
- A combined dataset of S1 backscattering coefficients and S2 vegetation indices time series
- A combined dataset of S1 backscattering coefficients and polarimetric parameters time series
- Spatial transferability: Different study sites were used for training and testing within the same year (e.g., Tarbes 2020 for training and Dijon 2020 for testing, or Dijon 2020 for training and Tarbes 2020 for testing).
- Temporal transferability: The same study site was used for training and testing, but across different years (e.g., Tarbes 2020 for training and Tarbes 2021 for testing, or Tarbes 2021 for training and Tarbes 2020 for testing).
- Spatiotemporal transferability: Different study sites and years were used for training and testing (e.g., Tarbes 2021 for training and Dijon 2020 for testing, or Dijon 2020 for training and Tarbes 2021 for testing).
2.4. Evaluation Metrics
3. Results
3.1. Trends in S2 Vegetation Indices and S1 Data for Three Summer Crops Mapping
3.1.1. RENDVI and NDVI Trends for Sunflower, Soybean, and Maize
3.1.2. S1 Backscattering and Polarimetric Trends for Sunflower, Soybean, and Maize
3.2. Summer Crops Mapping
3.2.1. Using S2 Images
3.2.2. Using S1 Images
Using Backscattering Coefficients
Using Polarimetric Parameters
Using S1 Backscattering Coefficients and Polarimetric Parameters
3.2.3. Using S1 and S2 Images
3.2.4. Using Time Series Dataset with the IT Classifier
4. Discussion
4.1. Classification Performance Analysis
4.2. Classifiers Transferability Analysis
4.3. Misclassification in Crop Mapping
4.4. Implications and Constraints
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A. The Equations for the Accuracy Metrics
- Precision (P) measures the accuracy of positive predictions, indicating how many of the predicted positive cases are actually correct:
- Recall evaluates how well the model identifies positive cases, showing the proportion of actual positives that were correctly classified:
- F1-Score is the harmonic mean of Precision and Recall. It provides a single score that balances the two metrics. A high F1-Score suggests both high Precision and Recall:
Appendix B. RF Feature Importance Analysis for Mapping Sunflower, Soybean and Maize in Tarbes 2021
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Study Site/Year | Crop Type | Number of Reference Fields |
---|---|---|
Dijon 2020 | Sunflower | 853 |
Soybean | 4630 | |
Maize | 8344 | |
Tarbes 2020 | Sunflower | 4807 |
Soybean | 5293 | |
Maize | 47,015 | |
Tarbes 2021 | Sunflower | 5124 |
Soybean | 5554 | |
Maize | 53,256 |
Vegetation Index | Equation | Applications | Reference |
---|---|---|---|
Normalized Difference Vegetation Index (NDVI) | Biomass, yield, disease, soil moisture, water stress | [46] | |
Land Surface Water Index (LSWI) | Monitoring crop water stress | [47] | |
Normalized Difference Red Edge Index (NDRE) | Crop yield, biomass, disease | [48] | |
Red-Edge Spectral Indices (RESI) | Discriminating between different types of vegetation, monitoring crop stress, chlorophyll content, senescence | [49] | |
Normalized Difference Senescent Vegetation Index (NDSVI) | Monitor crop aging or stress | [50] | |
Modified Crop Residue Cover (MODCRC) | Crop residue, classify crops based on the level of residue cover | [51] | |
Chlorophyll Index Green (CIgreen) | Crop stress, growth anomalies | [49] | |
Chlorophyll Index Red Edge (CI red_edge) | Crop stress, growth anomalies | [49] | |
Normalized Difference Water Index (NDWI) | Vegetation water content | [52] | |
Red Edge Normalized Difference Vegetation Index (RENDVI) | Yield, irrigation management, disease | [53] | |
Green Normalized Difference Vegetation Index (GNDVI) | Water stress, yield, biomas, disease | [48] | |
Enhanced Vegetation Index (EVI) | Disease, biomass | [54] | |
Modified Soil Adjusted Vegetation Index (MSAVI) | Biomass, crop yield, chlorophyll content | [55] |
Satellite | Time Series | Study Site/Year | Study Period (per Year) |
---|---|---|---|
S2 | S2 bands time series S2 vegetation indices time series S1 backscattering coefficients time series | Dijon 2020, Tarbes 2020,Tarbes 2021 | From 1 April to 1 December |
S1 | S1 polarimetric parameters time series | Dijon 2020, Tarbes 2021 |
Satellite | Time Series | Details | Features | ||
---|---|---|---|---|---|
S2 | S2 spectral bands | 12 time series | B1, B2, B3, B4, B5, B6, B7, B8, B8A, B9, B11, B12 | ||
5 harmonic coefficients for each time series | a1, a2, b1, b2, c (Equation (4)) | ||||
7 median features for each time series | P1 | June | |||
P2 | July | ||||
P3 | August | ||||
P4 | September | ||||
S1 | April + May | ||||
S2 | June + July + August | ||||
S3 | September + October + November | ||||
S2 vegetation indices | 13 time series | LSWI, NDRE, RESI, NDSVI, MODCRC, CIgreen, CI_red_edge, NDWI, RENDVI, GNDVI, EVI, MSAVI, NDVI | |||
5 harmonic coefficients for each time series | a1, a2, b1, b2, c (Equation (4)) | ||||
7 median features for each time series | P1, P2, P3, P4, S1, S2, S3 | ||||
S1 | S1 backscattering coefficients | S1 ascending orbit | 3 time series | VVasc, VHasc, VV/VHasc | |
5 harmonic coefficients for each time series | a1, a2, b1, b2, c (Equation (4)) | ||||
7 median features for each time series | P1, P2, P3, P4, S1, S2, S3 | ||||
S1 descending orbit | 3 time series | VVdes, VHdes, VV/VHdes | |||
5 harmonic coefficients for each time series | a1, a2, b1, b2, c (Equation (4)) | ||||
7 median features for each time series | P1, P2, P3, P4, S1, S2, S3 | ||||
S1 both orbits | 9 time series | VVasc, VHasc, VV/VHasc, VVdes, VHdes, VV/VHdes, VVasc-VVdes, VHasc-VHdes, VV/VHasc-VV/VHdes | |||
5 harmonic coefficients for each time series | a1, a2, b1, b2, c (Equation (4)) | ||||
7 median features for each time series | P1, P2, P3, P4, S1, S2, S3 | ||||
S1 polarimetric parameters | S1 ascending orbit | 5 time series | g0asc, g1asc, Shasc, Sh_I asc, Sh_Pasc | ||
5 harmonic coefficients for each time series | a1, a2, b1, b2, c (Equation (4)) | ||||
7 median features for each time series | P1, P2, P3, P4, S1, S2, S3 | ||||
S1 descending orbit | 5 time series | g0des, g1des, Shdes, Sh_Ides, Sh_Pdec | |||
5 harmonic coefficients for each time series | a1, a2, b1, b2, c (Equation (4)) | ||||
7 median features for each time series | P1, P2, P3, P4, S1, S2, S3 | ||||
S1 both orbits | 15 time series | g0asc, g1asc, Shasc, Sh_I asc, Sh_Pasc, g0des, g1des, Shdes, Sh_Ides, Sh_Pdec, g0asc-g0des, g1asc-g1des, Shasc- Shdes, Sh_I asc- Sh_Ides, Sh_Pasc- Sh_Pdec | |||
5 harmonic coefficients for each time series | a1, a2, b1, b2, c (Equation (4)) | ||||
7 median features for each time series | P1, P2, P3, P4, S1, S2, S3 |
Transfer Scenario | Train Site/Year | Test Site/Year | Number of Agricultural Plots in Train Dataset | Number of Agricultural Plots in Test Dataset |
---|---|---|---|---|
Spatial transferability | Tarbes 2020 | Dijon 2020 | 76,153 | 43,670 |
Dijon 2020 | Tarbes 2020 | 43,670 | 76,153 | |
Temporal transferability | Tarbes 2020 | Tarbes 2021 | 76,153 | 93,654 |
Tarbes 2021 | Tarbes 2020 | 93,654 | 76,153 | |
Spatiotemporal transferability | Tarbes 2021 | Dijon 2020 | 93,654 | 43,670 |
Dijon 2020 | Tarbes 2021 | 43,670 | 93,654 |
Crop Type | Classifier | S2_Ind TS | S2_Ind Med | S1_Back TS | S1_Back Med | S1_Polar Med | S2_Ind TS & S1_Back TS | S2_Ind Med & S1_Back Med | S2_Ind Med & S1_Polar Med |
---|---|---|---|---|---|---|---|---|---|
Sunflower | RF | 69.4 | 70.0 | 82.7 | 84.7 | 73.6 | 85.7 | 87.1 | 82.9 |
XGBoost | 75.3 | 77.1 | 83.2 | 85.6 | 74.1 | 87.4 | 89.9 | 83.7 | |
MLP | 74.9 | 75.9 | 82.3 | 82.7 | 67.2 | 84.9 | 89.2 | 78.6 | |
InceptionTime | 82.7 | --- | 87.2 | --- | --- | 90.6 | --- | --- | |
Soybean | RF | 60.4 | 63.1 | 46.3 | 51.6 | 25.3 | 61.6 | 65.1 | 52.3 |
XGBoost | 67.8 | 72.8 | 46.9 | 58.3 | 30.9 | 72.0 | 69.8 | 65.0 | |
MLP | 71.1 | 69.4 | 49.3 | 62.7 | 28.7 | 71.2 | 75.0 | 66.0 | |
InceptionTime | 83.5 | --- | 63.6 | --- | --- | 86.1 | --- | --- | |
Maize | RF | 87.9 | 87.1 | 69.0 | 65.4 | 65.7 | 88.0 | 88.1 | 81.6 |
XGBoost | 90.0 | 90.1 | 66.0 | 71.2 | 65.0 | 89.7 | 91.1 | 87.6 | |
MLP | 89.4 | 88.4 | 58.3 | 68.8 | 56.7 | 89.6 | 89.7 | 81.8 | |
InceptionTime | 91.9 | --- | 81.7 | --- | --- | 93.5 | --- | --- |
Precision | Recall | ||||
---|---|---|---|---|---|
Crop Type | Classifier | S2_Ind TS & S1_Back TS | S2_Ind Med & S1_Back Med | S2_Ind TS & S1_Back TS | S2_Ind Med & S1_Back Med |
Sunflower | RF | 93.84 | 92.97 | 80.17 | 83.18 |
XGBoost | 95.05 | 93.32 | 81.71 | 87.10 | |
MLP | 87.72 | 90.92 | 82.64 | 87.87 | |
InceptionTime | 95.10 | --- | 84.18 | --- | |
Soybean | RF | 75.90 | 78.79 | 53.35 | 57.52 |
XGBoost | 88.68 | 82.52 | 61.37 | 61.69 | |
MLP | 78.49 | 83.80 | 66.67 | 70.18 | |
InceptionTime | 91.80 | --- | 79.02 | --- | |
Maize | RF | 88.16 | 88.88 | 88.97 | 88.35 |
XGBoost | 91.29 | 94.65 | 87.58 | 88.29 | |
MLP | 93.88 | 92.29 | 86.18 | 88.03 | |
InceptionTime | 91.60 | --- | 92.80 | --- |
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Maleki, S.; Baghdadi, N.; Bazzi, H.; Dantas, C.F.; Ienco, D.; Nasrallah, Y.; Najem, S. Machine Learning-Based Summer Crops Mapping Using Sentinel-1 and Sentinel-2 Images. Remote Sens. 2024, 16, 4548. https://doi.org/10.3390/rs16234548
Maleki S, Baghdadi N, Bazzi H, Dantas CF, Ienco D, Nasrallah Y, Najem S. Machine Learning-Based Summer Crops Mapping Using Sentinel-1 and Sentinel-2 Images. Remote Sensing. 2024; 16(23):4548. https://doi.org/10.3390/rs16234548
Chicago/Turabian StyleMaleki, Saeideh, Nicolas Baghdadi, Hassan Bazzi, Cassio Fraga Dantas, Dino Ienco, Yasser Nasrallah, and Sami Najem. 2024. "Machine Learning-Based Summer Crops Mapping Using Sentinel-1 and Sentinel-2 Images" Remote Sensing 16, no. 23: 4548. https://doi.org/10.3390/rs16234548
APA StyleMaleki, S., Baghdadi, N., Bazzi, H., Dantas, C. F., Ienco, D., Nasrallah, Y., & Najem, S. (2024). Machine Learning-Based Summer Crops Mapping Using Sentinel-1 and Sentinel-2 Images. Remote Sensing, 16(23), 4548. https://doi.org/10.3390/rs16234548