Obtaining accurate snow depth estimates under dense canopies using airborne lidar (light detection and ranging) techniques is challenging due to the under-sampling of ground and snow surfaces. Existing interpolation techniques do not adequately address this problem and they often result in an overestimation of under-canopy snow depths. To address this issue, we introduce and evaluate a new interpolation method that incorporates intra-canopy snow depth variability to provide more accurate estimations at unsampled locations. Four interpolation methods were tested, considering systematic trends (landscape trend, canopy vs. gap trend, and intra-canopy trend) along with spatial interpolation of the residuals. Our results show that spatial interpolation methods without consideration of trends are sufficient to capture and reconstruct the small-scale variability of snow depths below a separation distance of 1 m between sampled and unsampled locations, (i.e., ground surface point density > 1 pt. m⁻²). However, beyond a separation distance of 2.5–3 m (point density < 0.33–0.40 pt. m⁻²), spatial interpolation based on proximity alone becomes unreliable because point separation becomes larger than the snow depth spatial correlation scale. Within these limiting distances, the method that incorporates trends along with spatial interpolation techniques can resolve the small-scale variability and thereby reduce the likely overestimation of snow depths under the canopy.

由于地面和雪面采样不足，使用机载激光雷达（光探测和测距）技术在茂密的树冠下获得准确的雪深估计具有挑战性。现有的插值技术不能充分解决这个问题，并且常常导致冠层下积雪深度的高估。为了解决这个问题，我们引入并评估了一种新的插值方法，该方法结合了树冠内雪深的变化，以在未采样的位置提供更准确的估计。测试了四种插值方法，考虑系统趋势（景观趋势、冠层与间隙趋势和冠层内趋势）以及残差的空间插值。我们的结果表明，不考虑趋势的空间插值方法足以捕获和重建采样位置和未采样位置之间间隔距离 1 m 以下的雪深小尺度变化（即地表点密度 > 1 pt.m）⁻² )。然而，超过 2.5-3 m 的间隔距离（点密度 < 0.33-0.40 pt. m ^-2），仅基于邻近度的空间插值变得不可靠，因为点间隔变得大于雪深空间相关尺度。在这些限制距离内，结合趋势和空间插值技术的方法可以解决小尺度的变化，从而减少对冠层下积雪深度的可能高估。