Machine Learning-Based Summer Crops Mapping Using Sentinel-1 and Sentinel-2 Images
Accurate crop type mapping using satellite imagery is crucial for food security, yet accurately distinguishing between crops with similar spectral signatures is challenging. This study assessed the performance of Sentinel-2 (S2) time series (spectral bands and vegetation indices), Sentinel-1 (S1) ti...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-12-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/16/23/4548 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850060164210622464 |
|---|---|
| author | Saeideh Maleki Nicolas Baghdadi Hassan Bazzi Cassio Fraga Dantas Dino Ienco Yasser Nasrallah Sami Najem |
| author_facet | Saeideh Maleki Nicolas Baghdadi Hassan Bazzi Cassio Fraga Dantas Dino Ienco Yasser Nasrallah Sami Najem |
| author_sort | Saeideh Maleki |
| collection | DOAJ |
| description | Accurate crop type mapping using satellite imagery is crucial for food security, yet accurately distinguishing between crops with similar spectral signatures is challenging. This study assessed the performance of Sentinel-2 (S2) time series (spectral bands and vegetation indices), Sentinel-1 (S1) time series (backscattering coefficients and polarimetric parameters), alongside phenological features derived from both S1 and S2 time series (harmonic coefficients and median features), for classifying sunflower, soybean, and maize. Random Forest (RF), Multi-Layer Perceptron (MLP), and XGBoost classifiers were applied across various dataset configurations and train-test splits over two study sites and years in France. Additionally, the InceptionTime classifier, specifically designed for time series data, was tested exclusively with time series datasets to compare its performance against the three general machine learning algorithms (RF, XGBoost, and MLP). The results showed that XGBoost outperformed RF and MLP in classifying the three crops. The optimal dataset for mapping all three crops combined S1 backscattering coefficients with S2 vegetation indices, with comparable results between phenological features and time series data (mean F1 scores of 89.9% for sunflower, 76.6% for soybean, and 91.1% for maize). However, when using individual satellite sensors, S1 phenological features and time series outperformed S2 for sunflower, while S2 was superior for soybean and maize. Both phenological features and time series data produced close mean F1 scores across spatial, temporal, and spatiotemporal transfer scenarios, though median features dataset was the best choice for spatiotemporal transfer. Polarimetric S1 data did not yield effective results. The InceptionTime classifier further improved classification accuracy over XGBoost for all crops, with the degree of improvement varying by crop and dataset (the highest mean F1 scores of 90.6% for sunflower, 86.0% for soybean, and 93.5% for maize). |
| format | Article |
| id | doaj-art-b7bb4eb67dbb40f19bee5ee7ac7742e2 |
| institution | DOAJ |
| issn | 2072-4292 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-b7bb4eb67dbb40f19bee5ee7ac7742e22025-08-20T02:50:40ZengMDPI AGRemote Sensing2072-42922024-12-011623454810.3390/rs16234548Machine Learning-Based Summer Crops Mapping Using Sentinel-1 and Sentinel-2 ImagesSaeideh Maleki0Nicolas Baghdadi1Hassan Bazzi2Cassio Fraga Dantas3Dino Ienco4Yasser Nasrallah5Sami Najem6Territoires, Environnement, Télédétection et Information Spatiale (TETIS), University of Montpellier, Paris Institute of Technology for Life, Food and Environmental Sciences (AgroParisTech), French Agricultural Research Centre for International Development (CIRAD)/French National Center for Scientific Research (CNRS)/French National Research Institute for Agriculture, Food, and the Environment (INRAE), 34093 Montpellier, FranceTerritoires, Environnement, Télédétection et Information Spatiale (TETIS), University of Montpellier, Paris Institute of Technology for Life, Food and Environmental Sciences (AgroParisTech), French Agricultural Research Centre for International Development (CIRAD)/French National Center for Scientific Research (CNRS)/French National Research Institute for Agriculture, Food, and the Environment (INRAE), 34093 Montpellier, FranceTerritoires, Environnement, Télédétection et Information Spatiale (TETIS), University of Montpellier, Paris Institute of Technology for Life, Food and Environmental Sciences (AgroParisTech), French Agricultural Research Centre for International Development (CIRAD)/French National Center for Scientific Research (CNRS)/French National Research Institute for Agriculture, Food, and the Environment (INRAE), 34093 Montpellier, FranceTerritoires, Environnement, Télédétection et Information Spatiale (TETIS), University of Montpellier, Paris Institute of Technology for Life, Food and Environmental Sciences (AgroParisTech), French Agricultural Research Centre for International Development (CIRAD)/French National Center for Scientific Research (CNRS)/French National Research Institute for Agriculture, Food, and the Environment (INRAE), 34093 Montpellier, FranceTerritoires, Environnement, Télédétection et Information Spatiale (TETIS), University of Montpellier, Paris Institute of Technology for Life, Food and Environmental Sciences (AgroParisTech), French Agricultural Research Centre for International Development (CIRAD)/French National Center for Scientific Research (CNRS)/French National Research Institute for Agriculture, Food, and the Environment (INRAE), 34093 Montpellier, FranceTerritoires, Environnement, Télédétection et Information Spatiale (TETIS), University of Montpellier, Paris Institute of Technology for Life, Food and Environmental Sciences (AgroParisTech), French Agricultural Research Centre for International Development (CIRAD)/French National Center for Scientific Research (CNRS)/French National Research Institute for Agriculture, Food, and the Environment (INRAE), 34093 Montpellier, FranceTerritoires, Environnement, Télédétection et Information Spatiale (TETIS), University of Montpellier, Paris Institute of Technology for Life, Food and Environmental Sciences (AgroParisTech), French Agricultural Research Centre for International Development (CIRAD)/French National Center for Scientific Research (CNRS)/French National Research Institute for Agriculture, Food, and the Environment (INRAE), 34093 Montpellier, FranceAccurate crop type mapping using satellite imagery is crucial for food security, yet accurately distinguishing between crops with similar spectral signatures is challenging. This study assessed the performance of Sentinel-2 (S2) time series (spectral bands and vegetation indices), Sentinel-1 (S1) time series (backscattering coefficients and polarimetric parameters), alongside phenological features derived from both S1 and S2 time series (harmonic coefficients and median features), for classifying sunflower, soybean, and maize. Random Forest (RF), Multi-Layer Perceptron (MLP), and XGBoost classifiers were applied across various dataset configurations and train-test splits over two study sites and years in France. Additionally, the InceptionTime classifier, specifically designed for time series data, was tested exclusively with time series datasets to compare its performance against the three general machine learning algorithms (RF, XGBoost, and MLP). The results showed that XGBoost outperformed RF and MLP in classifying the three crops. The optimal dataset for mapping all three crops combined S1 backscattering coefficients with S2 vegetation indices, with comparable results between phenological features and time series data (mean F1 scores of 89.9% for sunflower, 76.6% for soybean, and 91.1% for maize). However, when using individual satellite sensors, S1 phenological features and time series outperformed S2 for sunflower, while S2 was superior for soybean and maize. Both phenological features and time series data produced close mean F1 scores across spatial, temporal, and spatiotemporal transfer scenarios, though median features dataset was the best choice for spatiotemporal transfer. Polarimetric S1 data did not yield effective results. The InceptionTime classifier further improved classification accuracy over XGBoost for all crops, with the degree of improvement varying by crop and dataset (the highest mean F1 scores of 90.6% for sunflower, 86.0% for soybean, and 93.5% for maize).https://www.mdpi.com/2072-4292/16/23/4548Random ForestMulti-Layer PerceptronXGBoostInceptionTimeS1 backscattering coefficientS1 polarimetric parameters |
| spellingShingle | Saeideh Maleki Nicolas Baghdadi Hassan Bazzi Cassio Fraga Dantas Dino Ienco Yasser Nasrallah Sami Najem Machine Learning-Based Summer Crops Mapping Using Sentinel-1 and Sentinel-2 Images Remote Sensing Random Forest Multi-Layer Perceptron XGBoost InceptionTime S1 backscattering coefficient S1 polarimetric parameters |
| title | Machine Learning-Based Summer Crops Mapping Using Sentinel-1 and Sentinel-2 Images |
| title_full | Machine Learning-Based Summer Crops Mapping Using Sentinel-1 and Sentinel-2 Images |
| title_fullStr | Machine Learning-Based Summer Crops Mapping Using Sentinel-1 and Sentinel-2 Images |
| title_full_unstemmed | Machine Learning-Based Summer Crops Mapping Using Sentinel-1 and Sentinel-2 Images |
| title_short | Machine Learning-Based Summer Crops Mapping Using Sentinel-1 and Sentinel-2 Images |
| title_sort | machine learning based summer crops mapping using sentinel 1 and sentinel 2 images |
| topic | Random Forest Multi-Layer Perceptron XGBoost InceptionTime S1 backscattering coefficient S1 polarimetric parameters |
| url | https://www.mdpi.com/2072-4292/16/23/4548 |
| work_keys_str_mv | AT saeidehmaleki machinelearningbasedsummercropsmappingusingsentinel1andsentinel2images AT nicolasbaghdadi machinelearningbasedsummercropsmappingusingsentinel1andsentinel2images AT hassanbazzi machinelearningbasedsummercropsmappingusingsentinel1andsentinel2images AT cassiofragadantas machinelearningbasedsummercropsmappingusingsentinel1andsentinel2images AT dinoienco machinelearningbasedsummercropsmappingusingsentinel1andsentinel2images AT yassernasrallah machinelearningbasedsummercropsmappingusingsentinel1andsentinel2images AT saminajem machinelearningbasedsummercropsmappingusingsentinel1andsentinel2images |