• A GORT-based vertical sampling error compensation for ICESat-2. • Canopy height retrieved via reconstructed waveform and FPH matching. • Reduced height underestimation and robust to pulse strength and canopy cover. The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) photon-counting lidar provides abundant canopy height estimates for assessing forest structure and carbon stocks. However, the nonlinear response of photon-counting detectors introduces vertical sampling errors, leading to underestimation of derived canopy heights, especially for the needleleaf forests. To address this issue, a canopy height correction algorithm based on the geometric-optical and radiative-transfer (GORT) model is proposed. By extracting the surface roughness and the gap probability from the ICESat-2 photon data, the entire signal waveform including the ground and canopy return is recovered according to the assumed Gaussian ground echo and the GORT model. The first photon height (FPH) is reconstructed from the recovered signal waveform and compared with the measured FPH from the ICESat-2 product. Based on the criterion that the difference between the measured and reconstructed FPH is minimal, the canopy height correction can be derived. The proposed algorithm is implemented using ICESat-2 data and reference airborne lidar data over the Eldorado National Forest, California. The results show that ATL08-derived canopy heights are underestimated with the biases of −2.77 m, whereas our proposed algorithm can substantially reduce such biases to −0.24 m. Specifically, the effects of the pulse strength and canopy cover on the corrected canopy heights are quantitatively investigated. Our proposed algorithm can correct the underestimation of the canopy heights for different pulse strengths and canopy covers, which is physically applicable to improve the precision of the canopy height in needleleaf forests.
Zhang et al. (Thu,) studied this question.