The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) mission, spanning the past five years, has collected extensive three-dimensional Earth observation data, facilitating the understanding of environmental changes on a global scale. Its key product, Land and Vegetation Height (ATL08), offers global land and vegetation height data for carbon budget and cycle modeling. Consistent measurement accuracy of ATL08 is crucial for reliable time series analysis. However, fluctuations in the temporal accuracy of ATL08 data have been ignored in previous studies, leading to unknown uncertainties in existing time-series analyses. To bridge the knowledge gap, this study analyzes 59 months of ATL08 version 006 data in Finland to assess terrain and surface height accuracy, with a focus on temporal fluctuations across six major land cover types. A random forest (RF) model is employed to quantify the relative importance of error factors affecting height accuracy. Moreover, the study assesses accuracy at two official spatial resolutions, i.e., 100 m × 11 m and 20 m × 11 m, to evaluate the capability of ATL08 for the high-resolution height retrieval. For the terrain, the 100 m segment shows a bias of 0.04 m, a mean absolute error (MAE) of 0.44 m, and a root mean square error (RMSE) of 0.66 m, while the 20 m segment exhibits a bias of 0.10 m, a MAE of 0.35 m, and an RMSE of 0.49 m. For the surface height, the 100 m segment shows a bias of −0.59 m, a MAE of 3.06 m, an RMSE of 4.52 m, a bias% of −3.45 %, a MAE% of 21.26 %, and an RMSE% of 31.40 %. The 20 m segment exhibits a bias of −0.72 m, a MAE of 3.51 m, an RMSE of 5.23 m, a bias% of −5.81 %, a MAE% of 28.52 %, and an RMSE% of 42.47 %. The results indicate that improving segment resolution enhances terrain accuracy but reduces surface height accuracy. According to the error factor analysis, surface coverage and beam type are crucial for terrain retrieval accuracy, with their effects varying over time. Seasonal changes, particularly the presence of snow, affect terrain retrieval accuracy, with the lowest accuracy observed around March each year. This study confirms the critical impact of surface height on its retrieval accuracy and suggests avoiding the use of ATL08 for retrieving low target surface heights, especially in steep terrains. Nevertheless, the analysis affirms the applicability of ATL08 for canopy height estimation in boreal forests, primarily composed of coniferous species, highlighting its potential for extensive spatial and temporal research. This contributes to bridging the gaps between accurate estimates and large area coverage in global carbon budget and cycle studies. Additionally, the findings reveal that similar issues may exist in other satellite laser altimetry missions, emphasizing the important impacts of temporal fluctuations in surface and terrain accuracy when utilizing satellite laser altimetry datasets.

中文翻译：

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ICESat-2 高度测量在时间序列中的精度波动


冰、云和陆地高程卫星 2 号 （ICESat-2） 任务跨越过去五年，收集了大量的三维地球观测数据，有助于了解全球范围内的环境变化。其主要产品 Land and Vegetation Height （ATL08） 为碳收支和循环建模提供全球土地和植被高度数据。ATL08 的一致测量精度对于可靠的时间序列分析至关重要。然而，在以前的研究中，ATL08 数据时间准确性的波动被忽略了，导致现有时间序列分析存在未知的不确定性。为了弥合知识差距，本研究分析了芬兰 59 个月的 ATL08 版本 006 数据，以评估地形和表面高度精度，重点关注六种主要土地覆被类型的时间波动。采用随机森林 （RF） 模型来量化影响高度精度的误差因子的相对重要性。此外，该研究还评估了两个官方空间分辨率的准确性，即 100 m × 11 m 和 20 m × 11 m，以评估 ATL08 的高分辨率高度检索能力。对于地形，100 m 路段的偏差为 0.04 m，平均绝对误差 （MAE） 为 0.44 m，均方根误差 （RMSE） 为 0.66 m，而 20 m 路段的偏差为 0.10 m，MAE 为 0.35 m，RMSE 为 0.49 m。对于表面高度，100 m 段显示 -0.59 m 的偏差，MAE 为 3.06 m，RMSE 为 4.52 m，偏差百分比为 -3.45 %，MAE % 为 21.26 %，RMSE% 为 31.40 %。20 m 段的偏差为 -0.72 m，MAE 为 3.51 m，RMSE 为 5.23 m，偏差百分比为 -5.81 %，MAE% 为 28.52 %，RMSE% 为 42.47 %。 结果表明，提高分段分辨率可以提高地形精度，但会降低表面高度精度。根据误差因子分析，表面覆盖和波束类型对地形反演精度至关重要，其影响随时间而变化。季节性变化（尤其是雪的存在）会影响地形检索精度，每年 3 月左右观察到的精度最低。本研究证实了表面高度对其检索精度的关键影响，并建议避免使用 ATL08 来检索低目标表面高度，尤其是在陡峭的地形中。尽管如此，该分析肯定了 ATL08 在主要由针叶树种组成的北方森林中树冠高度估计的适用性，突出了其在广泛的空间和时间研究中的潜力。这有助于弥合全球碳预算和循环研究中准确估计和大面积覆盖之间的差距。此外，研究结果显示，其他卫星激光测高任务中可能存在类似问题，强调了在利用卫星激光测高数据集时表面和地形精度的时间波动的重要影响。