Update README.md

README.md

@@ -2,7 +2,7 @@
 license: mit
 ---
 
-# Models to estimate SAR-based Vegetation indices and biophysical variables 
+# 🌲🌳 ML Models to estimate SAR-based Vegetation indices and biophysical variables for forest ecosystems [in Central Europe]
 
 This study presents a machine learning-based approach to estimate optical vegetation indices and biophysical variables (hereafter referred to as VIs) using synthetic aperture radar (SAR) and ancillary data for forest monitoring. 
 The best-performing models were Random Forest Regressor (RFR) for LAI and FAPAR and XGBoost (XGB) for EVI and NDVI - these models are available in this repository. These models were trained on temporally and spatially aligned time series (TS) datasets, containing Sentinel-1 SAR data, Sentinel-2 multispectral data, DEM-based features and meteorological variables. It provides an accurate and timely alternative to optical-based VIs.
@@ -13,7 +13,7 @@ These models are part of the paper
 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6798c936ece6b7910c55d1e5/3rueSUVk9bOqsFy4fsD-7.png)
 Figure 1. Methodology used in the paper.
 
-## Model Details
+## ⚙️ Model Details
 ### Model Description
 
 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.
@@ -22,10 +22,10 @@ Using ML, particularly RFR and XGB, the study demonstrates that SAR-based VIs ca
 - **Developed by:** Daniel Paluba & Bertrand Le Saux
 - **License:** CC BY 4.0
 
-### Model Sources
+### 🔗 Model Sources
 
 **Repositories:**
-- [GitHub SAR-based-VIs](https://github.com/palubad/SAR-based-VIs) for data and for data generation.
+- [GitHub SAR-based-VIs](https://github.com/palubad/SAR-based-VIs) for data and for forest data generation.
 - [MMTS-GEE](https://github.com/palubad/MMTS-GEE) to generate multi-modal and time series datasets with spatially and temporally aligned data.
 
 **Paper:** 
@@ -34,7 +34,7 @@ Paluba et al. 2025: Estimating vegetation indices and biophysical parameters for
 **Demo:**
   Will be provided soon.
 
-## How to Get Started with the Model
+## 🛠 How to Get Started with the Model
 
 To implement this model:
 - Prepare input datasets using the [MMTS-GEE tool](https://github.com/palubad/MMTS-GEE): Collect Sentinel-1 SAR data, DEM-based features, and meteorological variables.
@@ -48,7 +48,7 @@ To implement this model:
 
 **Demo codes will be provided soon**
 
-## Training Details
+## 📊 Training Details
 
 ### Training Data
 
@@ -94,7 +94,7 @@ For detailed information on hyperparameter optimization, performances, speeds, p
 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6798c936ece6b7910c55d1e5/9Kvzn0XgLB0oL6tFduyUb.png)
 Figure 4. Areas used to test the transferability of the models in Central Europe.
 
-### Results
+### 🚀 Results
 
 Best models:
 - RFR performed best for LAI (MAE ~0.06) and FAPAR.
@@ -107,22 +107,22 @@ 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
+#### ❗ 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
+#### ❗ 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
+#### ❗ 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
+## ⭐ 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).