EVALUATING THE POTENTIAL OF 8 BAND PLANETSCOPE DATASET FOR CROP CLASSIFICATION USING RANDOM FOREST AND GRADIENT TREE BOOSTING BY GOOGLE EARTH ENGINE

7 citations as recorded by crossref.

1. Synergistic exploitation of localized spectral-spatial and temporal information with DNNs for multisensor-multitemporal image-based crop classification G. Phartiyal et al. https://doi.org/10.1016/j.jag.2023.103595
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3. A comparative analysis of PlanetScope 4-band and 8-band imageries for land use land cover classification S. Basheer et al. https://doi.org/10.1016/j.geomat.2024.100023
4. Evaluating the performance of random forest, support vector machine, gradient tree boost, and CART for improved crop-type monitoring using greenest pixel composite in Google Earth Engine C. Savitha & R. Talari https://doi.org/10.1007/s10661-025-13880-3
5. Land Use and Land Cover Change Associated with Coffee Production in Amazonas, Peru C. Chavez Cruz et al. https://doi.org/10.3390/land15030368
6. Comparison of PlanetScope and Sentinel-2 Spectral Channels and Their Alignment via Linear Regression for Enhanced Index Derivation C. Baldin & V. Casella https://doi.org/10.3390/geosciences15050184
7. Detection of Potato Fields Using Sentinel-2 and Landsat 8 Data-Based Machine Learning Models in Semi-arid Region of Central Anatolia, Türkiye E. Tokluoğlu et al. https://doi.org/10.1007/s11540-025-09985-4