Create README.md

README.md

@@ -0,0 +1,73 @@
+---
+tags:
+- soil-organic-carbon
+- remote-sensing
+- xgboost
+- sentinel-2
+- sentinel-1
+- geospatial
+- northeast-india
+library_name: xgboost
+license: mit
+metrics:
+- r2
+- rmse
+pipeline_tag: tabular-regression
+---
+
+# Soil Organic Carbon (SOC) Estimation Model for Northeast India
+
+## Model Description
+This is an **XGBoost Regressor** trained to estimate **Soil Organic Carbon (SOC)** values (g/kg) across **Northeast India**. 
+
+It uses multi-source remote sensing data (Optical, Radar, Topography, and Climate) representing the year **2022**. The model is designed to handle diverse landscapes, including forests, agricultural land, water bodies, and urban areas.
+
+* **Region:** Northeast India (89.4°E - 97.6°E).
+* **Time Period:** 2022 (Annual Median).
+* **Input Data:** Sentinel-2, Sentinel-1, SRTM Terrain, CHIRPS Rainfall, ERA5 Temperature.
+* **Training Samples:** ~10,000 points (Random stratified sampling).
+
+## Intended Use
+* **Regional Soil Health Monitoring:** rapid assessment of SOC without lab tests.
+* **Carbon Baseline Studies:** Establishing 2022 baselines for carbon projects.
+* **Land Use Analysis:** Distinguishing between high-carbon forests and low-carbon degraded lands.
+
+## Feature Inputs (16 Columns)
+To use this model, your input dataframe must contain these **16 columns** in this exact order:
+
+| Category | Features |
+| :--- | :--- |
+| **Sentinel-2 (Optical)** | `S2_B2`, `S2_B3`, `S2_B4`, `S2_B8`, `S2_B11`, `S2_NDVI` |
+| **Sentinel-1 (Radar)** | `S1_VV`, `S1_VH` |
+| **Terrain** | `elevation`, `slope`, `aspect` |
+| **Climate** | `precip_annual`, `temp_mean` |
+| **Soil/Location** | `soil_texture`, `latitude`, `longitude` |
+
+## How to Use
+```python
+import xgboost as xgb
+from huggingface_hub import hf_hub_download
+import pandas as pd
+
+# 1. Download Model
+model_path = hf_hub_download(
+    repo_id="mona0125/soc-estimation-ne-india", 
+    filename="soc_estimation_model_ne_india.json"
+)
+
+# 2. Load Model
+model = xgb.XGBRegressor()
+model.load_model(model_path)
+
+# 3. Predict (Example Data)
+# Ensure columns match the Feature Inputs list above!
+data = pd.DataFrame({
+    'S2_B2': [0.03], 'S2_B3': [0.05], 'S2_B4': [0.04], 'S2_B8': [0.25], 'S2_B11': [0.15], 'S2_NDVI': [0.72],
+    'S1_VV': [-8.5], 'S1_VH': [-14.2],
+    'elevation': [150], 'slope': [5.5], 'aspect': [120],
+    'precip_annual': [1800], 'temp_mean': [24.5],
+    'soil_texture': [2], 'latitude': [26.1], 'longitude': [91.7]
+})
+
+prediction = model.predict(data)
+print(f"Predicted SOC: {prediction[0]:.2f} g/kg")