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2025
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| Online Access: | https://doi.org/10.5281/zenodo.15013314 |
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| _version_ | 1866902309502451712 |
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| author | Pandžić, Miloš Pavlovic, Dejan Radulović, Mirjana Marko, Oskar Crnojevic, Vladimir |
| author_facet | Pandžić, Miloš Pavlovic, Dejan Radulović, Mirjana Marko, Oskar Crnojevic, Vladimir |
| contents | <p>Ensuring sufficient food for the rising global population is a critical focus, especially given the<br>current turbulent political circumstances. The first step for policymakers in addressing this issue<br>is acquiring accurate crop maps. Modern Earth observation missions and advancements in data<br>processing, particularly in machine learning, have enabled the successful generation of these<br>maps. Although considered a traditional method nowadays, the Random Forest algorithm has<br>consistently proven effective for crop classification tasks. Conversely, while synthetic aperture<br>radar (SAR) satellite data lags behind optical satellite missions in terms of practical usage, its<br>contribution remains significant. In this study, we combined Sentinel-1 data with Random<br>Forest to classify nine crop types in Vojvodina, Serbia. Specifically, we assessed how varying<br>amounts of information from radar satellites impact crop classification accuracy by analyzing<br>three data availability scenarios: 1) single monthly acquisitions, 2) bi-monthly acquisitions<br>(every 15 days), and 3) all available acquisitions. For the first two scenarios, bicubic<br>interpolation was used. Out of 2399 parcels, 80% were used for training and validation (5-fold<br>cross-validation), with the remaining 20% for testing. We calculated the mean value for each<br>parcel and considered data acquisitions between April 1st and September 30th, 2021. The<br>scenarios achieved overall accuracies of 90%, 94%, and 96%, respectively, with high precision<br>and recall across nearly all classes, despite class imbalance. These findings underscore the<br>potential of SAR data for classifying diverse crop types and highlight the possibility of reducing<br>inputs for regional or global mapping typically characterized with high computational demands,<br>while still achieving remarkable results.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_15013314 |
| institution | Zenodo |
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| publishDate | 2025 |
| publisher | Zenodo |
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| spellingShingle | EVALUATING THE IMPACT OF SATELLITE DATA AVAILABILITY ON CROP CLASSIFICATION ACCURACY USING SENTINEL-1 AND RANDOM FOREST Pandžić, Miloš Pavlovic, Dejan Radulović, Mirjana Marko, Oskar Crnojevic, Vladimir <p>Ensuring sufficient food for the rising global population is a critical focus, especially given the<br>current turbulent political circumstances. The first step for policymakers in addressing this issue<br>is acquiring accurate crop maps. Modern Earth observation missions and advancements in data<br>processing, particularly in machine learning, have enabled the successful generation of these<br>maps. Although considered a traditional method nowadays, the Random Forest algorithm has<br>consistently proven effective for crop classification tasks. Conversely, while synthetic aperture<br>radar (SAR) satellite data lags behind optical satellite missions in terms of practical usage, its<br>contribution remains significant. In this study, we combined Sentinel-1 data with Random<br>Forest to classify nine crop types in Vojvodina, Serbia. Specifically, we assessed how varying<br>amounts of information from radar satellites impact crop classification accuracy by analyzing<br>three data availability scenarios: 1) single monthly acquisitions, 2) bi-monthly acquisitions<br>(every 15 days), and 3) all available acquisitions. For the first two scenarios, bicubic<br>interpolation was used. Out of 2399 parcels, 80% were used for training and validation (5-fold<br>cross-validation), with the remaining 20% for testing. We calculated the mean value for each<br>parcel and considered data acquisitions between April 1st and September 30th, 2021. The<br>scenarios achieved overall accuracies of 90%, 94%, and 96%, respectively, with high precision<br>and recall across nearly all classes, despite class imbalance. These findings underscore the<br>potential of SAR data for classifying diverse crop types and highlight the possibility of reducing<br>inputs for regional or global mapping typically characterized with high computational demands,<br>while still achieving remarkable results.</p> |
| title | EVALUATING THE IMPACT OF SATELLITE DATA AVAILABILITY ON CROP CLASSIFICATION ACCURACY USING SENTINEL-1 AND RANDOM FOREST |
| url | https://doi.org/10.5281/zenodo.15013314 |