Penetration effect on TanDEM-X InSAR heights and its compensation in different forest types

The potential of X-band interferometric synthetic aperture radar (InSAR) heights (e.g. from the TanDEM-X mission) for vegetation canopy height estimation has long been recognized, e.g. for the estimation of aboveground biomass. This is based on the assumption that short wavelengths penetrate only marginally into the forest canopy and thus are an approximation of the canopy surface. However, substantial penetration was frequently observed, which affects this approximation. In addition, penetration depth differs between acquisitions of different geometry and in relation to the structure of the forest stands. Thus, the canopy height and AGB retrieval from InSAR are typically biased and cannot be compared directly to estimates from other data sources. Consequently, an assessment and a correction of the penetration depth is necessary when comparing InSAR height models with other height models (e.g. LiDAR) or in multi-temporal analysis of InSAR height models. We applied a semi-empirical penetration depth model on different TanDEM-X data in order to assess their penetration resulting in the aforementioned height bias. The penetration depth model is based on the fact that the penetration is related to the phase normalized coherence. The height bias was retrieved from the coherence magnitude due to the unique relationship of coherence magnitude and phase. We extracted the volume coherence from different TanDEM-X data and modeled the height bias. The outcome of the model was used to compensate for the penetration in InSAR heights resulting in comparable quantities (i.e. canopy heights) with a LiDAR model considered as true surface. These results can be also used to understand and quantify InSAR height differences due to different penetration depths. We evaluated the estimation of the height bias by compensating it in highly accurate TanDEM-X DEMs and compared the original InSAR height and the height bias compensated InSAR height with a LiDAR digital surface model. In general, the compensation of the height bias based on penetration from the original InSAR height resulted in heights with a lower root mean squared error (RMSE) and mean error (ME) close to 0 compared to the original InSAR heights. The RMSE was 6 m to 13 m before the compensation and decreased to about 7 m to 5 m. The ME was -12 m to -5 m indicating a substantial height bias due to the penetration and improved to about ±1 m in all studied forests. This suggested that the height bias was accurately modeled, which can be of high relevance when InSAR heights are used for canopy height estimation or used in multi-temporal analysis in order to detect deforestation. Furthermore, the resulting canopy height estimates are subsequently converted to AGB estimates using regression models. The uncorrected InSAR heights of the forest canopy are biased due to the penetration of the signal into the canopy and differ substantially to LiDAR canopy height estimates. The application of the penetration depth compensation results in unbiased forest canopy height estimates and AGB regression models, that are comparable between InSAR and LiDAR. These results indicate that TanDEM-X InSAR and LiDAR technologies can be used to estimate AGB in complex tropical forests suggesting a synergistic use of these fundamentally different observation concepts