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Designing a European-Wide Crop Type Mapping Approach Based on Machine Learning Algorithms Using LUCAS Field Survey and Sentinel-2 Data
| Content Provider | MDPI |
|---|---|
| Author | Mateusz, Żółtak Ghassemi, Babak Dujakovic, Aleksandar Immitzer, Markus Atzberger, Clement Vuolo, Francesco |
| Copyright Year | 2022 |
| Description | One of the most challenging aspects of obtaining detailed and accurate land-use and land-cover (LULC) maps is the availability of representative field data for training and validation. In this manuscript, we evaluate the use of the Eurostat Land Use and Coverage Area frame Survey (LUCAS) 2018 data to generate a detailed LULC map with 19 crop type classes and two broad categories for woodland and shrubland, and grassland. The field data were used in combination with Copernicus Sentinel-2 (S2) satellite data covering Europe. First, spatially and temporally consistent S2 image composites of (1) spectral reflectances, (2) a selection of spectral indices, and (3) several bio-geophysical indicators were created for the year 2018. From the large number of features, the most important were selected for classification using two machine-learning algorithms (support vector machine and random forest). Results indicated that the 19 crop type classes and the two broad categories could be classified with an overall accuracy (OA) of 77.6%, using independent data for validation. Our analysis of three methods to select optimum training data showed that by selecting the most spectrally different pixels for training data, the best OA could be achieved, and this already using only 11% of the total training data. Comparing our results to a similar study using Sentinel-1 (S1) data indicated that S2 can achieve slightly better results, although the spatial coverage was slightly reduced due to gaps in S2 data. Further analysis is ongoing to leverage synergies between optical and microwave data. |
| Starting Page | 541 |
| e-ISSN | 20724292 |
| DOI | 10.3390/rs14030541 |
| Journal | Remote Sensing |
| Issue Number | 3 |
| Volume Number | 14 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2022-01-23 |
| Access Restriction | Open |
| Subject Keyword | Remote Sensing Crop Type Classification Random Forest Support Vector Machine Lucas 2018 |
| Content Type | Text |
| Resource Type | Article |
