Update README.md

README.md

@@ -10,7 +10,6 @@ The best-performing models were Random Forest Regressor (RFR) for LAI and FAPAR
 These models are part of the paper 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
 
 ## 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.
@@ -18,8 +17,7 @@ Using ML, particularly RFR and XGB, the study demonstrates that SAR-based VIs ca
 
 
 - **Developed by:** Daniel Paluba, Bertrand Le Saux
-- **Funded by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
+- **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
 - **License:** CC BY 4.0
 
 ### Model Sources
@@ -53,72 +51,59 @@ Paluba et al. 2025: Estimating vegetation indices and biophysical parameters for
 ## How to Get Started with the Model
 
 To implement this model:
-- Prepare input datasets using the MMTS-GEE tool: Collect Sentinel-1 SAR data, DEM-based features, and meteorological variables.
-- Preprocess data: Use the MMTS-GEE tool for temporal and spatial alignment.
-- Train the model: Implement RFR for LAI/FAPAR and XGB for EVI/NDVI using optimized hyperparameters.
-- Evaluate performance: Compare model outputs with Sentinel-2-based VIs to validate accuracy.
+- 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.
+- The models were trained using the following input features:
+  - SAR features: VV, VH, incidence angle (angle), VV/VH, VH/VV
+  - DEM-based features: Local Incidence Angle (LIA), elevation and slope
+  - Meteorological features: sum of precipitation 12 hours prior to SAR acquisition (prec.12h) and temperature at the time of SAR acquisition;
+  - Land cover category: the forest type as a differentiating feature between coniferous and broad-leaved forests
+  - Temporal features: DOYsin and DOYcos containing information about the time of the corresponding SAR acquisition.
 - Deploy for inference: Apply trained models to monitor vegetation indices in new regions or for near real-time applications.
 
-[More Information Needed]
+**Demo codes will be provided soon**
 
 ## Training Details
 
 ### Training Data
 
-The model was trained on:
-- Sentinel-1 SAR time series (VH and VV polarizations).
-- Sentinel-2 optical vegetation indices (LAI, FAPAR, EVI, NDVI) as ground truth.
-- Digital Elevation Model (DEM)-based features (elevation, slope, LIA).
-- Meteorological variables (temperature, precipitation).
-- Forest type maps (broad-leaved vs. coniferous).
-- Geographic scope: Czechia for training, validated on Central European forests.
+The training data is available from the [SAR-based-VIs GitHub repository](https://github.com/palubad/SAR-based-VIs).
 
 ### Training Procedure
 
-Feature Selection: Using permutation feature importance analysis to identify key predictors.
-Data Splitting: Training and validation sets created with a balanced representation of healthy and disturbed forests.
-Hyperparameter Optimization:
-RFR: Fine-tuned for maximum depth, number of trees, and minimum samples per split.
-XGB: Optimized learning rate, tree depth, and number of boosting rounds.
-Model Training: Using scikit-learn and XGBoost libraries with MAE loss function.
-Computational Requirements:
-XGB: Faster training with built-in early stopping (~30-70x faster than RFR).
-RFR: Slower but slightly better performance for LAI/FAPAR.
-
-
-#### Training Hyperparameters
+- Feature Selection: Using permutation feature importance analysis to identify key predictors.
+- Data Splitting: Training and validation sets created with a balanced representation of healthy and disturbed forests.
+- Hyperparameter Optimization:
+  - RFR: Fine-tuned for maximum depth, number of trees, and minimum samples per split.
+  - XGB: Optimized learning rate, tree depth, and number of boosting rounds.
+- Model Training: Using scikit-learn and XGBoost libraries with MAE loss function.
+- Computational Requirements:
+  - XGB: Faster training with built-in early stopping (~30-70x faster than RFR).
+  - RFR: Slower but slightly better performance for LAI and FAPAR.
 
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+#### Used computation infrastructure
 
-#### Speeds, Sizes, Times [optional]
+12th Gen Intel(R) Core(TM) i7-12700 with 2.10 GHz, 64 Gigabyte of RAM and 20 CPU cores.
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+#### Training Hyperparameters
 
-[More Information Needed]
+For detailed information on hyperparameter optimization, performances, speeds, please see the article Paluba et al. (2025).
 
-## Evaluation
+## Evaluation metrics
 
-Mean Absolute Error (MAE): Primary metric for accuracy.
-R² Score: To assess correlation with Sentinel-2 VIs.
-Transferability Test: Applied to different Central European forests.
+- Mean Absolute Error (MAE): Primary metric for accuracy.
+- Mean Squared Error (MSE): Secondary metric for accuracy.
+- R² Score: To assess correlation with Sentinel-2 VIs.
+- Transferability Test: Applied to different Central European forests.
 
 ### Results
 
 Best models:
-RFR performed best for LAI (MAE ~0.06) and FAPAR.
-XGB performed best for EVI and NDVI.
-SAR-based VIs successfully replicated optical VIs, with clear seasonal and forest-type differentiation.
-Higher MAEs observed in NDVI estimation (~0.48), attributed to forest type inaccuracies and change detection errors.
-SAR-based VIs detected forest changes up to 4 days earlier than Sentinel-2 VIs, significantly improving change detection capabilities.
-Adding DEM and meteorological features improved R² by 3-4%.
-
-
-#### Summary
-
-
-### Used computation infrastructure
-
-12th Gen Intel(R) Core(TM) i7-12700 with 2.10 GHz, 64 Gigabyte of RAM and 20 CPU cores.
+- RFR performed best for LAI (MAE ~0.06) and FAPAR.
+- XGB performed best for EVI and NDVI.
+- SAR-based VIs successfully replicated optical VIs, with clear seasonal and forest-type differentiation.
+- Higher MAEs observed in NDVI estimation (~0.48), attributed to forest type inaccuracies and change detection errors.
+- SAR-based VIs detected forest changes up to 4 days earlier than Sentinel-2 VIs, significantly improving change detection capabilities.
+- Adding DEM and meteorological features improved R² by 3-4%.
 
 
 ## Citation [optional]
@@ -127,26 +112,8 @@ Adding DEM and meteorological features improved R² by 3-4%.
 
 **BibTeX:**
 
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+Will be added soon.
 
 **APA:**
 
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-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
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-## More Information [optional]
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-## Model Card Authors [optional]
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-## Model Card Contact
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+Will be added soon.