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SAR-based-VIs-models

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palubad commited on Jan 30, 2025

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@@ -125,9 +125,10 @@ Best models:
 Table 2. Best results for RFR and XGB for each VI. Bolded results represent the best achieved results for the VI.
 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6798c936ece6b7910c55d1e5/1iLMhWwGJo6bbeb9COcSm.png)
-## Citation [optional]
 > Paluba, D., Le Saux, B., Sarti, F., Štych, P. (2025): Estimating vegetation indices and biophysical parameters for Central European temperate forests with Sentinel-1 SAR data and machine learning. Published in Big Earth Data
 **BibTeX:**

 Table 2. Best results for RFR and XGB for each VI. Bolded results represent the best achieved results for the VI.
 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6798c936ece6b7910c55d1e5/1iLMhWwGJo6bbeb9COcSm.png)
+## Citation
 > Paluba, D., Le Saux, B., Sarti, F., Štych, P. (2025): Estimating vegetation indices and biophysical parameters for Central European temperate forests with Sentinel-1 SAR data and machine learning. Published in Big Earth Data
+> Paluba, D., Le Saux, B., Sarti, F., Štych, P. (2024): Identification of Optimal Sentinel-1 SAR Polarimetric Parameters for Forest Monitoring in Czechia. AUC Geographica 59(2), 1–15, DOI: [10.14712/23361980.2024.18](https://doi.org/10.14712/23361980.2024.18).
 **BibTeX:**