Airborne multi-temporal L-band polarimetric SAR data for biomass estimation in semi-arid forests

Mihai Tanase, R Panciera, Kim Lowell, Siyuan Tian, Jorg Hacker, Jeffrey Walker

    Research output: Contribution to journalArticlepeer-review

    53 Citations (Scopus)


    Using the airborne Polarimetric L-band Imaging Synthetic aperture radar (PLIS) the impact of high revisit cycle and full polarimetric acquisitions on biomass retrieval was investigated by means of backscatter-based multi-temporal methods. Parametric and non-parametric models were used to relate reference biomass levels obtained from field plot measurements and high point density lidar data to backscatter intensities or polarimetric target decomposition components. Single-date retrieval using multiple independent variables provided lower estimation errors when compared to models using one independent variable with errors decreasing by 2% to 15%. The multi-temporal aggregation of daily biomass estimates did not improve the overall retrieval accuracy but provided more reliable estimates with respect to single-date methods. Backscatter intensities improved estimation accuracies up to 10% compared to polarimetric target decomposition components. Using all four polarizations increased the estimation accuracy marginally (2%) when compared to a dual-polarized system. The biomass estimation error was considerably reduced (up to 30%) only by decreasing the spatial resolution and was related to decreasing forest variability with increasing pixel size. These results indicate that, at least in semi-arid areas, future L-band missions would not significantly improve biomass estimation accuracy using backscatter-based modeling approaches despite their better spatial resolution, higher revisit cycles and the availability of fully polarimetric information.

    Original languageEnglish
    Pages (from-to)93-104
    Number of pages12
    JournalRemote Sensing of Environment
    Publication statusPublished - 5 Apr 2014


    • Forest biomass
    • Forest variability
    • L-band radar
    • Multi-temporal
    • Polarimetric decomposition
    • Random forest


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