Synergistic Use of SAR for Biomass Estimation

Vegetation Structure and Biomass Estimation Using Multi-Band Radar Remote Sensing

Introduction

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Estimating vegetation structure and biomass is crucial for understanding global carbon cycles, assessing forest health, and monitoring environmental changes. Synthetic Aperture Radar (SAR) remote sensing is particularly useful for this purpose due to its ability to penetrate vegetation and operate in all weather conditions. Using multiple radar frequency bands—X, C, L, and P—together improves biomass estimation by capturing different structural aspects of forest canopies and underlying vegetation.

Radar Bands and Their Role in Vegetation Monitoring

X- and C-Band: High-Resolution Canopy Structure

X-band (3.1–3.3 cm) and C-band (3.8–7.5 cm) interact primarily with the uppermost layers of vegetation, such as leaves and small branches. These bands provide high spatial resolution and are used to map canopy structure, detect deforestation, and monitor short-term vegetation changes. Common satellites for these applications include Sentinel-1 (C-band) and TerraSAR-X (X-band).

L- and P-Band: Deep Vegetation and Trunk Penetration

L-band (15–30 cm) and P-band (30–100 cm) penetrate deeper into vegetation, interacting with larger tree branches and trunks. These longer wavelengths help estimate biomass more accurately by reducing interference from leaves and smaller twigs. P-band is particularly valuable for measuring forest structure in dense forests, aiding carbon stock assessments. Missions such as ALOS-2 (L-band) and ESA’s BIOMASS (P-band) are designed to take advantage of these properties.

Methods for Biomass Estimation

Several key techniques are used to estimate biomass using SAR:

• Backscatter Analysis: The intensity of radar backscatter varies with vegetation density and structure, enabling biomass estimation through empirical models.
• Polarimetric SAR (PolSAR): Different polarization combinations (e.g., HH, HV, VV) enhance the characterization of canopy structure and biomass distribution.
• Interferometric SAR (InSAR): By analyzing the phase difference between two SAR images taken at different times or angles, tree height estimation becomes possible, which correlates with biomass.
• Tomographic SAR (TomoSAR): Multiple SAR acquisitions from different angles reconstruct 3D vegetation structures, improving biomass assessments.
• Machine Learning and Data Fusion: Integrating SAR with LiDAR, optical data, and field measurements using machine learning improves biomass prediction accuracy.

Key Sensors and Missions for Biomass Estimation

Several spaceborne and airborne SAR missions provide valuable biomass data:

• Sentinel-1 (C-band, ESA): Regular global coverage for land cover classification and biomass trend monitoring.
• ALOS-2 PALSAR (L-band, JAXA): High-resolution forest monitoring and biomass estimation.
• ESA BIOMASS (P-band, ESA): Designed explicitly for global biomass mapping using long-wavelength radar.
• SAOCOM (L-band, CONAE): Complements other L-band missions for vegetation analysis.
• NISAR (L- and S-band, NASA-ISRO): Scheduled for launch, offering improved vegetation structure data.
• TerraSAR-X/TanDEM-X (X-band, DLR): High-resolution canopy structure data.
• Airborne UAVSAR (L-band, NASA): Detailed biomass mapping and calibration of spaceborne missions.

Synergistic Use of Multiple Radar Bands

Combining data from X, C, L, and P bands enhances biomass estimation by:

• Improving Above-Ground Biomass (AGB) Estimation: High-frequency bands (X, C) capture fine-scale canopy structure, while low-frequency bands (L, P) assess large-scale biomass distribution.
• Characterizing Vertical Forest Structure: Multi-band SAR data help distinguish tree heights, trunk thickness, and overall vegetation density.
• Reducing Saturation Effects in Dense Forests: High-biomass areas (>100 Mg/ha) often saturate in X and C bands, but L- and P-band can still capture meaningful information.

Application Example: Tropical Rainforest Biomass Mapping

Tropical rainforests store massive amounts of carbon, making their monitoring essential for climate change mitigation. Optical and LiDAR methods are often limited by cloud cover and inconsistent data availability. Multi-band SAR provides a robust alternative by offering continuous, high-resolution data across large regions.

For example, combining Sentinel-1 (C-band) with ALOS-2 (L-band) has improved biomass mapping in the Amazon rainforest. ESA’s BIOMASS mission, using P-band SAR, is expected to set new standards for global biomass estimation. These datasets support REDD+ (Reducing Emissions from Deforestation and Forest Degradation) initiatives and global climate modeling.

Conclusion

Multi-band SAR remote sensing is a powerful tool for vegetation structure and biomass estimation. Leveraging the strengths of different radar frequencies enables more precise forest monitoring and improved carbon cycle assessments. Future advancements in SAR missions and processing techniques will further enhance the role of radar in environmental and ecological studies.

Expand Your Knowledge – Dive Deeper

• Echoses in Space We particularly recommend topics Forest applications, Forest Tutorial
• Biomass estimation using SAR data
• Regression-based mapping of forest aboveground biomass

Further Reading

• Prakash, A. J., Behera, M. D., Ghosh, S. M., Das, A., & Mishra, D. R. (2022). A new synergistic approach for Sentinel-1 and PALSAR-2 in a machine learning framework to predict aboveground biomass of a dense mangrove forest. Ecological Informatics, 72, 101900.
• Englhart, S., Keuck, V., & Siegert, F. (2011). Aboveground biomass retrieval in tropical forests—The potential of combined X-and L-band SAR data use. Remote sensing of environment, 115(5), 1260-1271.
• Sinha, S., Mohan, S., Das, A. K., Sharma, L. K., Jeganathan, C., Santra, A., … & Nathawat, M. S. (2020). Multi-sensor approach integrating optical and multi-frequency synthetic aperture radar for carbon stock estimation over a tropical deciduous forest in India. Carbon Management, 11(1), 39-55.