Complete analysis toolkit for NISAR L3 Soil Moisture (SME2) products with 15+ analysis types, 10+ export formats, and ML-ready pipelines.

pip install h5py numpy matplotlib scipy pandas seaborn scikit-learn

# For GeoTIFF export
pip install rasterio

# For NetCDF export
pip install netCDF4

# For geospatial analysis
pip install geopandas shapely

# For machine learning
pip install tensorflow torch

from nisar_comprehensive_analysis import NISARSoilMoistureAnalyzer

# Initialize analyzer
analyzer = NISARSoilMoistureAnalyzer("your_file.h5")

# Load data
analyzer.load_data()

# Run analyses
stats = analyzer.basic_statistics()
spatial = analyzer.spatial_analysis()
hydro = analyzer.hydrological_indices()
drought = analyzer.drought_monitoring()

# Generate visualizations
fig = analyzer.create_visualizations()
fig.savefig('results.png', dpi=300)

# Export data
analyzer.export_geotiff("output.tif")
analyzer.generate_report("report.txt")

python nisar_comprehensive_analysis.py

• Descriptive statistics (mean, median, std, CV, skewness, kurtosis)
• Distribution testing (normality tests)
• Quartile analysis
• Outlier detection

Output: Statistical summary with interpretation

• Regional statistics by quadrants
• Gradient analysis (spatial variability)
• Hotspot/coldspot detection
• Spatial clustering patterns

Output: Spatial pattern maps and metrics

• Soil moisture classification (dry/optimal/saturated)
• Soil Water Deficit Index (SWDI)
• Plant Available Water (PAW)
• Water Stress Index (WSI)

Use Cases:

• Drought monitoring
• Irrigation scheduling
• Crop water stress assessment

• Crop-specific suitability analysis
  • Wheat, Rice, Cotton, Soybean, Maize
• Irrigation requirement zones
• Growing season indicators
• Germination suitability

Output: Actionable agricultural recommendations

• Multi-level drought classification
  • None / Moderate / Severe / Extreme
• Percentile-based thresholds (P5, P10, P20)
• Overall drought index
• Severity assessment

Use Cases:

• Early warning systems
• Disaster management
• Policy planning

• Z-score based statistical anomalies
• Local spatial outliers
• Extreme value identification
• Pattern deviation analysis

• K-means clustering (5 zones by default)
• Zone characterization
• Spatial segmentation
• Moisture regime mapping

• Multi-threshold trigger analysis
• Area-based payout calculations
• Risk level assessment
• Activation status monitoring

Thresholds:

• Critical: <0.10 m³/m³
• Severe: <0.12 m³/m³
• Moderate: <0.15 m³/m³
• Mild: <0.18 m³/m³