Update README.md

README.md

@@ -19,9 +19,7 @@ Figure 1. Methodology used in the paper.
 The study explores the feasibility of using SAR-based features in combination with additional datasets (e.g., DEM-based features and meteorological data) to estimate optical VIs, specifically, LAI, FAPAR, EVI and NDVI. Traditional optical remote sensing methods are often hindered by cloud cover, making it difficult to obtain continuous and reliable vegetation monitoring data. This research addresses this challenge by applying SAR data, which is unaffected by atmospheric conditions.
 Using ML, particularly RFR and XGB, the study demonstrates that SAR-based VIs can replicate the patterns of optical-based VIs, while also offering advantages such as higher temporal resolution and all-year monitoring. The inclusion of ancillary data improves model accuracy, particularly in differentiating forest types and seasonal variations. The transferability tests confirm that the methodology generalizes well across Central European forests and shows potential for large-scale monitoring applications.
 
-
-- **Developed by:** Daniel Paluba, Bertrand Le Saux
-- **Funded by:** Charles University Grant Agency – Grantová Agentura Univerzity Karlovy (GAUK) Grant No. 412722; the European Union’s Caroline Herschel Framework Partnership Agreement on Copernicus User Uptake under grant agreement No. FPA 275/G/GRO/COPE/17/10042, project FPCUP (Framework Partnership Agreement on Copernicus User Uptake) and the Spatial Data Analyst project (NPO_UK_MSMT-16602/2022) funded by the European Union – NextGenerationEU
+- **Developed by:** Daniel Paluba & Bertrand Le Saux
 - **License:** CC BY 4.0
 
 ### Model Sources
@@ -36,22 +34,6 @@ Paluba et al. 2025: Estimating vegetation indices and biophysical parameters for
 **Demo:**
   Will be provided soon.
 
-
-#### Scenarios Where the Model May Not Work Well
-- Forest areas significantly different from those in the training data (e.g., tropical rainforests, drylands).
-- Extreme weather conditions (e.g., snow, heavy rain) affecting SAR signal interpretation.
-- Recently disturbed areas with high structural variability, leading to noisier results.
-
-#### Known limitations
-- Reliance on forest type classification: Errors in input forest type maps can propagate into the VI estimations.
-- The model's effectiveness in disturbed forests is lower than in healthy forests, which may affect early disturbance detection.
-- Seasonal variations introduce noise, particularly in winter, affecting model accuracy.
-
-#### Recommendations to overcome the limitations - future work
-- Ensure diverse training data covering different forest types and disturbance scenarios to improve generalizability.
-- Complement SAR-based estimations with optical data when available to enhance accuracy.
-- Improve noise reduction techniques and incorporate multi-band SAR data (L-, P-bands) in future studies for better vegetation characterization.
-
 ## How to Get Started with the Model
 
 To implement this model:
@@ -125,6 +107,21 @@ 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)
 
+#### Scenarios Where the Model May Not Work Well
+- Forest areas significantly different from those in the training data (e.g., tropical rainforests, drylands).
+- Extreme weather conditions (e.g., snow, heavy rain) affecting SAR signal interpretation.
+- Recently disturbed areas with high structural variability, leading to noisier results.
+
+#### Known limitations
+- Reliance on forest type classification: Errors in input forest type maps can propagate into the VI estimations.
+- The model's effectiveness in disturbed forests is lower than in healthy forests, which may affect early disturbance detection.
+- Seasonal variations introduce noise, particularly in winter, affecting model accuracy.
+
+#### Recommendations to overcome the limitations - future work
+- Ensure diverse training data covering different forest types and disturbance scenarios to improve generalizability.
+- Complement SAR-based estimations with optical data when available to enhance accuracy.
+- Improve noise reduction techniques and incorporate multi-band SAR data (L-, P-bands) in future studies for better vegetation characterization.
+
 ## 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
@@ -136,4 +133,7 @@ Will be added soon.
 
 **APA:**
 
-Will be added soon.
+Will be added soon.
+
+
+- **Funded by:** Charles University Grant Agency – Grantová Agentura Univerzity Karlovy (GAUK) Grant No. 412722; the European Union’s Caroline Herschel Framework Partnership Agreement on Copernicus User Uptake under grant agreement No. FPA 275/G/GRO/COPE/17/10042, project FPCUP (Framework Partnership Agreement on Copernicus User Uptake) and the Spatial Data Analyst project (NPO_UK_MSMT-16602/2022) funded by the European Union – NextGenerationEU