MODIS daily cloud-gap-filled fractional snow cover dataset of the Asian Water Tower region (2000–2022)

IF 11.2 1区地球科学 Q1 GEOSCIENCES, MULTIDISCIPLINARY

Earth System Science Data Pub Date : 2024-05-29 DOI:10.5194/essd-16-2501-2024

Fangbo Pan, Lingmei Jiang, Gongxue Wang, Jinmei Pan, Jinyu Huang, Cheng Zhang, Huizhen Cui, Jianwei Yang, Zhaojun Zheng, Shengli Wu, Jiancheng Shi

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引用次数: 0

Abstract

Abstract. Accurate long-term daily cloud-gap-filled fractional snow cover products are essential for climate change and snow hydrological studies in the Asian Water Tower (AWT) region, but existing Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover products are not sufficient. In this study, the multiple-endmember spectral mixture analysis algorithm based on automatic endmember extraction (MESMA-AGE) and the multistep spatiotemporal interpolation algorithm (MSTI) are used to produce the MODIS daily cloud-gap-filled fractional snow cover product over the AWT region (AWT MODIS FSC). The AWT MODIS FSC products have a spatial resolution of 0.005° and span from 2000 to 2022. The 2745 scenes of Landsat-8 images are used for the areal-scale accuracy assessment. The fractional snow cover accuracy metrics, including the coefficient of determination (R2), root mean squared error (RMSE) and mean absolute error (MAE), are 0.80, 0.16 and 0.10, respectively. The binarized identification accuracy metrics, including overall accuracy (OA), producer's accuracy (PA) and user's accuracy (UA), are 95.17 %, 97.34 % and 97.59 %, respectively. Snow depth data observed at 175 meteorological stations are used to evaluate accuracy at the point scale, yielding the following accuracy metrics: an OA of 93.26 %, a PA of 84.41 %, a UA of 82.14 % and a Cohen kappa (CK) value of 0.79. Snow depth observations from meteorological stations are also used to assess the fractional snow cover resulting from different weather conditions, with an OA of 95.36 % (88.96 %), a PA of 87.75 % (82.26 %), a UA of 86.86 % (78.86 %) and a CK of 0.84 (0.72) under the MODIS clear-sky observations (spatiotemporal reconstruction based on the MSTI algorithm). The AWT MODIS FSC product can provide quantitative spatial distribution information on snowpacks for mountain hydrological models, land surface models and numerical weather prediction in the Asian Water Tower region. This dataset is freely available from the National Tibetan Plateau Data Center at https://doi.org/10.11888/Cryos.tpdc.272503 (Jiang et al., 2022) or from the Zenodo platform at https://doi.org/10.5281/zenodo.10005826 (Jiang et al., 2023a).

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亚洲水塔地区的 MODIS 日云隙积雪数据集（2000-2022 年）

摘要。精确的长期日云隙填充分数雪盖产品对于亚洲水塔（AWT）地区的气候变化和雪水文研究至关重要，但现有的中分辨率成像分光仪（MODIS）雪盖产品并不充分。本研究采用基于内含物自动提取的多内含物光谱混合分析算法（MESMA-AG）和多步骤时空插值算法（MSTI）来生成亚洲水塔地区的 MODIS 日云隙填充积雪覆盖率产品（AWT MODIS FSC）。AWT MODIS FSC 产品的空间分辨率为 0.005°，时间跨度为 2000 年至 2022 年。大地遥感卫星-8 图像的 2745 个场景被用于面积尺度精度评估。包括判定系数（R2）、均方根误差（RMSE）和平均绝对误差（MAE）在内的分数雪覆盖精度指标分别为 0.80、0.16 和 0.10。二值化识别精度指标，包括总体精度（OA）、生产者精度（PA）和用户精度（UA），分别为 95.17 %、97.34 % 和 97.59 %。175 个气象站观测到的雪深数据用于评估点尺度的准确性，得出以下准确性指标：OA 为 93.26 %，PA 为 84.41 %，UA 为 82.14 %，Cohen kappa (CK) 值为 0.79。在 MODIS 晴空观测（基于 MSTI 算法的时空重建）下，气象站的积雪深度观测数据也用于评估不同天气条件下的积雪覆盖率，OA 为 95.36 %（88.96 %），PA 为 87.75 %（82.26 %），UA 为 86.86 %（78.86 %），CK 为 0.84（0.72）。AWT MODIS FSC 产品可为亚洲水塔地区的山区水文模型、地表模型和数值天气预报提供定量的积雪空间分布信息。该数据集可从国家青藏高原数据中心免费获取，网址为 https://doi.org/10.11888/Cryos.tpdc.272503（蒋等，2022 年），也可从 Zenodo 平台获取，网址为 https://doi.org/10.5281/zenodo.10005826（蒋等，2023a）。

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来源期刊

Earth System Science Data GEOSCIENCES, MULTIDISCIPLINARYMETEOROLOGY-METEOROLOGY & ATMOSPHERIC SCIENCES

CiteScore

18.00

自引率

5.30%

发文量

231

审稿时长

35 weeks

期刊介绍： Earth System Science Data (ESSD) is an international, interdisciplinary journal that publishes articles on original research data in order to promote the reuse of high-quality data in the field of Earth system sciences. The journal welcomes submissions of original data or data collections that meet the required quality standards and have the potential to contribute to the goals of the journal. It includes sections dedicated to regular-length articles, brief communications (such as updates to existing data sets), commentaries, review articles, and special issues. ESSD is abstracted and indexed in several databases, including Science Citation Index Expanded, Current Contents/PCE, Scopus, ADS, CLOCKSS, CNKI, DOAJ, EBSCO, Gale/Cengage, GoOA (CAS), and Google Scholar, among others.