Water quality estimation using RS (Coding-Persian)

🚀 Satellite-Based Water Quality Prediction in Python Sentinel-1 + Sentinel-2 + Machine Learning In this session, we build an end-to-end pipeline to predict key water quality parameters (pH, Dissolved Oxygen, Nitrate, Chlorophyll proxies) using multi-sensor satellite data. Designed for real-world conditions: small datasets, noise, and multi-source fusion. 🔍 What’s Inside 🟡 Data Quality (Block 5) GOLD / SILVER / BRONZE tiering based on cloud and quality flags. 🟡 Model Benchmarking (Block 8) Ridge, Huber, SVR, Random Forest, Extra Trees, Gradient Boosting, GPR → Evaluated with LOO & K-Fold CV for data-scarce settings. 🟡 Feature Engineering (Block 9) Band ratios, normalized indices, SAR-optical features, log transforms, Mutual Information selection, and controlled augmentation. 📊 Outputs R² heatmaps Observed vs predicted plots Residual diagnostics Permutation feature importance Taylor diagrams & violin plots ⚙️ Key Techniques Sentinel-1 + Sentinel-2 fusion Robust scaling & outlier-resistant models Leave-One-Out CV Physically meaningful feature design 📂 Code: https://drive.google.com/drive/folders/1nhGBMx1QaQdc69pU5nZ0ATTwwTkUhXnH?usp=sharing Timestamps: 00:00 - Data introduction 02:35 - Block 1 (Code foundations) 04:10 - Block 2 (Bird-View) 09:41 - Block 3 (Temporal Matching) 18:44 - Block 4 (Predicting QW Parameters) 28:56 - Block 5 (Feature Engineering) 35:22 - Summary --- 💡 Tools & Libraries: scikit-learn, pandas, seaborn, matplotlib, scipy #Python #MachineLearning #RemoteSensing #WaterQuality #Sentinel2 #Sentinel1 #DataScience #EnvironmentalScience #EarthObservation #Geospatial #Coding If you enjoy scientific coding and real-world environmental ML projects, hit LIKE and subscribe for more!