Update README.md

README.md

@@ -5,7 +5,7 @@ license: mit
 # Models to estimate SAR-based Vegetation indices and biophysical variables 
 
 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 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.
+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.
 
 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