Forest Canopy Height Estimation based on InSAR Coherence

Interferometric Synthetic Aperture Radar (InSAR) has been recognized as an effective remote sensing tool for Earth Observation (EO) thanks to its capacity to penetrate clouds and forests. In the field of InSAR deformation monitoring, coherence is used as an indicator for the stability of the interferometric phase. Actually, interferometric coherence itself contains valuable information about land surface and can be used to classify land cover types. Currently, there are few studies about the possibility of retrieving forest canopy height from interferometric coherence, especially its multitemporal variation. In this study, we emphasize the relationship between multi-temporal InSAR coherence and forest canopy height by investigating the InSAR coherence variation of multi-temporal Sentinel-1 data regarding the tree height measured by the NASA Global Ecosystem Dynamics Investigation (GEDI). Results show that forest canopy height can be estimated by using the Random Forest (RF) regression model trained by samples of GEDI height and InSAR coherence variation. InSAR coherence performs better in estimating the forest canopy height than using other remote sensing data, such as multispectral reflectance from Sentinel2 and SAR backscatter intensity.