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<p>Synthetic aperture radar remote sensing has long been recognized as a valuable tool for estimating aboveground biomass density in forest ecosystems. In this study, we specifically evaluated the sensitivity of the SAOCOM mission to forest biomass under conditions of high spatial uncertainty in reference data (± 1.6&#xa0;km). A complex single-look SAOCOM acquisition was processed to obtain a comprehensive set of polarimetric backscatter and decomposition metrics, which were used as predictors in Random Forest regression models calibrated with biomass estimates from the U.S. Forest Inventory and Analysis program. The optimized model achieved a concordance correlation coefficient of 0.50, with a mean square error of 59 Mg ha⁻¹ and a mean absolute error of 47 Mg ha⁻¹ on independent test data. Beyond predictive performance, the results demonstrate that SAOCOM observations retain significant sensitivity to forest structural variability, even when the spatial correspondence between satellite measurements and ground-based biomass references is substantially degraded. These findings highlight SAOCOM’s potential for biomass-oriented applications, even under challenging conditions.</p>

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Exploring the sensitivity of SAOCOM L-band SAR to forest biomass under severe spatial uncertainty

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月11日

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  • José Miguel Febles Díaz,
  • Zhaofei Fan

摘要

Synthetic aperture radar remote sensing has long been recognized as a valuable tool for estimating aboveground biomass density in forest ecosystems. In this study, we specifically evaluated the sensitivity of the SAOCOM mission to forest biomass under conditions of high spatial uncertainty in reference data (± 1.6 km). A complex single-look SAOCOM acquisition was processed to obtain a comprehensive set of polarimetric backscatter and decomposition metrics, which were used as predictors in Random Forest regression models calibrated with biomass estimates from the U.S. Forest Inventory and Analysis program. The optimized model achieved a concordance correlation coefficient of 0.50, with a mean square error of 59 Mg ha⁻¹ and a mean absolute error of 47 Mg ha⁻¹ on independent test data. Beyond predictive performance, the results demonstrate that SAOCOM observations retain significant sensitivity to forest structural variability, even when the spatial correspondence between satellite measurements and ground-based biomass references is substantially degraded. These findings highlight SAOCOM’s potential for biomass-oriented applications, even under challenging conditions.