Gao Yang, Wang Xuetao, Mou Naixia, Dai Yufeng, Che Tao, Yao Tandong
State Key Laboratory of Tibetan Plateau Earth System, Environment and Resources (TPESER), Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China.
State Key Laboratory of Tibetan Plateau Earth System, Environment and Resources (TPESER), Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China; College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China.
Sci Total Environ. 2024 Nov 1;949:175245. doi: 10.1016/j.scitotenv.2024.175245. Epub 2024 Aug 3.
Accurate snow cover data is crucial for understanding climate change, managing water resources, and calibrating models. The MODIS (Moderate-resolution Imaging Spectroradiometer) and its cloud-free snow cover datasets are widely used, but they have not been systematically evaluated due to different benchmark data and evaluation parameters. Conventional methods using station observations as a ground truth suffer from underrepresentation and mismatches in temporal and spatial scales. This study established a scale-matched spatial benchmark dataset, compiling from 18,433 Landsat series and 11,172 Sentinel-2 images over two decades, totaling ∼1.86 billion samples and ∼320 million snow samples. We evaluated seven MODIS cloud-free snow cover datasets for seasons, elevation zones, land covers and subregions using this benchmark data. For the clear-sky part, NIEER_MODIS_SCE (MODIS snow cover extent product over China) performs best due to its use of optimal NDSI thresholds suitable for each land use type. This highlights the importance of regional customization in snow mapping algorithms, and it can be further improved in spring, forests and zone 1 by combining it with M*D10A1GL06. For the cloud removed part, one-step integrated spatiotemporal cloud removal datasets perform better than any other approach does. The second-best dataset is obtained from a simple but effective single temporal cloud removal method using nearby time information. For the whole dataset, the best NIEER_MODIS_SCE has an overall accuracy of 0.82 and snow retrieval accuracy of 84.56 %. It performs excellently in most settings but weakest in forests thus requiring more efficient strategies. This research provides new perspectives and methods for objectively assessing MODIS snow cover products and other relevant datasets. These methods can be readily extended to other regions and adapted to future satellite missions. And such findings may guide the selection of more valid snow cover data and the developing of even better snow detecting strategies.
准确的积雪数据对于理解气候变化、管理水资源以及校准模型至关重要。中分辨率成像光谱仪（MODIS）及其无云积雪数据集被广泛使用，但由于基准数据和评估参数不同，尚未得到系统评估。使用地面观测站数据作为地面真值的传统方法存在时间和空间尺度代表性不足以及不匹配的问题。本研究建立了一个尺度匹配的空间基准数据集，该数据集由二十年间的18433景陆地卫星系列图像和11172景哨兵 - 2图像汇编而成，共有约18.6亿个样本和约3.2亿个积雪样本。我们使用该基准数据对七个MODIS无云积雪数据集在季节、海拔区域、土地覆盖类型和次区域方面进行了评估。对于晴空部分，NIEER_MODIS_SCE（中国区域的MODIS积雪范围产品）表现最佳，因为它使用了适用于每种土地利用类型的最佳归一化差异雪指数（NDSI）阈值。这突出了积雪制图算法中区域定制的重要性，并且通过将其与M*D10A1GL06相结合，在春季、森林区域和1区可以进一步改进。对于去云部分，一步集成时空去云数据集的表现优于任何其他方法。第二好的数据集来自一种简单但有效的利用附近时间信息的单时相去云方法。对于整个数据集，最佳的NIEER_MODIS_SCE总体精度为0.82，积雪反演精度为84.56%。它在大多数情况下表现出色，但在森林区域最弱，因此需要更有效的策略。本研究为客观评估MODIS积雪产品和其他相关数据集提供了新的视角和方法。这些方法可以很容易地扩展到其他地区，并适用于未来的卫星任务。这样的研究结果可能会指导选择更有效的积雪数据以及开发更好的积雪检测策略。
