{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,13]],"date-time":"2026-06-13T18:39:39Z","timestamp":1781375979032,"version":"3.54.1"},"reference-count":66,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2020,8,27]],"date-time":"2020-08-27T00:00:00Z","timestamp":1598486400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Crop type classification using Earth Observation (EO) data is challenging, particularly for crop types with similar phenological growth stages. In this regard, the synergy of optical and Synthetic-Aperture Radar (SAR) data enables a broad representation of biophysical and structural information on target objects, enhancing crop type mapping. However, the fusion of multi-sensor dense time-series data often comes with the challenge of high dimensional feature space. In this study, we (1) evaluate how the usage of only optical, only SAR, and their fusion affect the classification accuracy; (2) identify the combination of which time-steps and feature-sets lead to peak accuracy; (3) analyze misclassifications based on the parcel size, optical data availability, and crops\u2019 temporal profiles. Two fusion approaches were considered and compared in this study: feature stacking and decision fusion. To distinguish the most relevant feature subsets time- and variable-wise, grouped forward feature selection (gFFS) was used. gFFS allows focusing analysis and interpretation on feature sets of interest like spectral bands, vegetation indices (VIs), or data sensing time rather than on single features. This feature selection strategy leads to better interpretability of results while substantially reducing computational expenses. The results showed that, in contrast to most other studies, SAR datasets outperform optical datasets. Similar to most other studies, the optical-SAR combination outperformed single sensor predictions. No significant difference was recorded between feature stacking and decision fusion. Random Forest (RF) appears to be robust to high feature space dimensionality. The feature selection did not improve the accuracies even for the optical-SAR feature stack with 320 features. Nevertheless, the combination of RF feature importance and time- and variable-wise gFFS rankings in one visualization enhances interpretability and understanding of the features\u2019 relevance for specific classification tasks. For example, by enabling the identification of features that have high RF feature importance values but are, in their information content, correlated with other features. This study contributes to the growing domain of interpretable machine learning.<\/jats:p>","DOI":"10.3390\/rs12172779","type":"journal-article","created":{"date-parts":[[2020,8,27]],"date-time":"2020-08-27T08:05:18Z","timestamp":1598515518000},"page":"2779","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":125,"title":["Crop Type Classification Using Fusion of Sentinel-1 and Sentinel-2 Data: Assessing the Impact of Feature Selection, Optical Data Availability, and Parcel Sizes on the Accuracies"],"prefix":"10.3390","volume":"12","author":[{"given":"Aiym","family":"Orynbaikyzy","sequence":"first","affiliation":[{"name":"German Remote Sensing Data Center (DFD), German Aerospace Center (DLR), Muenchner Strasse 20, D-82234 Wessling, Germany"},{"name":"Institute of Geosciences and Geography, Martin-Luther-University of Halle-Wittenberg, Von-Seckendorff-Platz 4, 06120 Halle, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ursula","family":"Gessner","sequence":"additional","affiliation":[{"name":"German Remote Sensing Data Center (DFD), German Aerospace Center (DLR), Muenchner Strasse 20, D-82234 Wessling, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Benjamin","family":"Mack","sequence":"additional","affiliation":[{"name":"Independent scholar, 80331 Munich, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0807-7059","authenticated-orcid":false,"given":"Christopher","family":"Conrad","sequence":"additional","affiliation":[{"name":"Institute of Geosciences and Geography, Martin-Luther-University of Halle-Wittenberg, Von-Seckendorff-Platz 4, 06120 Halle, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,8,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1016\/j.rse.2017.10.005","article-title":"Sentinel-2 cropland mapping using pixel-based and object-based time-weighted dynamic time warping analysis","volume":"204","author":"Belgiu","year":"2018","journal-title":"Remote Sens. 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