The spatiotemporally continuous data of normalized difference snow index (NDSI) are key to understanding the mechanisms of snow occurrence and development as well as the patterns of snow distribution changes. However, the presence of clouds, particularly prevalent in polar regions such as the Greenland Ice Sheet (GrIS), introduces a significant number of missing pixels in the MODIS NDSI daily data. To address this issue, this study proposes the utilization of a spatiotemporal extreme gradient boosting (STXGBoost) model generate a comprehensive NDSI dataset. In the proposed model, various input variables are carefully selected, encompassing terrain features, geometry-related parameters, and surface property variables. Moreover, the model incorporates spatiotemporal variation information, enhancing its capacity for reconstructing the NDSI dataset. Verification results demonstrate the efficacy of the STXGBoost model, with a coefficient of determination of 0.962, root mean square error of 0.030, mean absolute error of 0.011, and negligible bias (0.0001). Furthermore, simulation comparisons involving missing data and cross-validation with Landsat NDSI data illustrate the model’s capability to accurately reconstruct the spatial distribution of NDSI data. Notably, the proposed model surpasses the performance of traditional machine learning models, showcasing superior NDSI predictive capabilities. This study highlights the potential of leveraging auxiliary data to reconstruct NDSI in GrIS, with implications for broader applications in other regions. The findings offer valuable insights for the reconstruction of NDSI remote sensing data, contributing to the further understanding of spatiotemporal dynamics in snow-covered regions.

中文翻译：

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使用时空极端梯度提升模型重建格陵兰冰盖上的 MODIS 归一化差值雪指数产品


归一化差值积雪指数 （NDSI） 的时空连续数据是理解积雪发生和发展机制以及积雪分布变化模式的关键。然而，云的存在，特别是在格陵兰冰盖 （GrIS） 等极地地区，在 MODIS NDSI 每日数据中引入了大量缺失像素。为了解决这个问题，本研究提出利用时空极端梯度提升 （STXGBoost） 模型生成一个全面的 NDSI 数据集。在所提出的模型中，我们仔细选择了各种输入变量，包括地形特征、几何相关参数和表面属性变量。此外，该模型还整合了时空变化信息，增强了其重建 NDSI 数据集的能力。验证结果证明了 STXGBoost 模型的有效性，决定系数为 0.962，均方根误差为 0.030，平均绝对误差为 0.011，偏差可以忽略不计 （0.0001）。此外，涉及缺失数据的模拟比较以及与 Landsat NDSI 数据的交叉验证表明，该模型能够准确重建 NDSI 数据的空间分布。值得注意的是，所提出的模型超越了传统机器学习模型的性能，展示了卓越的 NDSI 预测能力。本研究强调了利用辅助数据在 GrIS 中重建 NDSI 的潜力，对其他地区的更广泛应用具有影响。这些发现为 NDSI 遥感数据的重建提供了有价值的见解，有助于进一步了解积雪地区的时空动态。