Exploring optimal combinations of multi-frequency polarimetric SAR observations to estimate forest above-ground biomass

The penetration capability of electromagnetic wave signal into forest increases with increasing wavelength. SAR data at each frequency senses different components of forest structure. Therefore the biomass distributed at various tree components could be estimated using different radar frequencies. Additionally, the synthesis of multiple SAR frequencies could improve the accuracy in retrieving forest above-ground biomass (AGB). Taking advantage of available X-, C-, L-, and P-band quad-polarimetric SAR images of airborne or spaceborne for the test site located at Genhe national forest scientific field station, we used a Genetic Algorithm and Support Vector Regression optimization algorithm (GA-SVR) to explore the sensitivity of polarimetric observations at various frequencies to forest AGB and effectiveness of AGB retrievals using single-frequency, dual-frequency, triple-frequency, and quad-frequency SAR observation combinations. We found that: (i) Most of the polarimetric observations are sensitive to forest AGB, (ii) GA-SVR performed well in forest AGB retrieval using the single frequency SAR observations or combinations of multi-frequency observations; the highest Acc. value for single-frequency-retrieved results is 75.13% acquired at P-band, with multi-frequency, the highest Acc. values is 77.34% acquired by combining C- and P-band. (iii) For forest AGB retrievals, the single-frequency P-band accuracy is comparable to the combined C- and P-band retrieval accuracy, indicating that the long-wavelength single-frequency P-band is sufficient for an accurate forest AGB retrieval. The findings reconfirmed potential of P-band for forest AGB retrievals, they also demonstrated that the optimal combination of multi-frequency SAR for AGB retrievals is by using a short-wavelength (X/C-) and a long-wavelength (L/P-).