基于modis的全北极地区1公里空间分辨率FSC日数据集(2000-2019)

Yuan Ma, Jian Wang, Hongyu Zhao, Donghang Shao, Weiguo Wang, Haojie Li, Hongyi Li
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引用次数: 0

摘要

分数积雪(Fractional snow cover, FSC)是对每个图像元素的积雪面积(SCA)与图像元素的空间范围之比的定量描述。本数据集以MODIS全球地表反射率产品MOD09GA为源数据,利用谷歌Earth Engine (GEE)平台,建立了FSC与归一化差雪指数(NDVI)、归一化差雪指数(NDSI)之间关系的基于NDVI的二元线性回归模型(BV-BLRM)。与MOD10A1 V6数据的均方根误差(RMST)相比,BV-BLRM制备的FSC数据的RMST提高了45%。基于该模型,我们获得了全北极地区(45°N ~ 90°N)基于modis的1 km空间分辨率的FSC日时间序列数据集。该数据集的时间序列为2000年2月24日至2019年11月18日,时间分辨率为1天,空间分辨率为1公里。该数据集有望为区域气候模拟、水文模型等提供定量的积雪分布信息。
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A dataset of MODIS-based daily FSC time-series data with one kilo-meter spatial resolution in the Holarctic region (2000–2019)
Fractional snow cover (FSC) is a quantitative description of the ratio of snow cover area (SCA) per image element to the spatial extent of the image element. Using the MODIS global surface reflectance product MOD09GA as the source data, this dataset takes advantage of the Google Earth Engine (GEE) platform to establish the Based NDVI Bivariate Linear Regression Model (BV-BLRM) showing the relation between the FSC and the Normalized Difference Snow Index (NDVI), and the Normalized Difference Snow Index (NDSI). Compared with the Root Mean Square Error (RMST) of MOD10A1 V6 data, the RMST of the FSC data prepared by the BV-BLRM has increased by 45%. Based on the model, we obtained a dataset of MODIS-based daily FSC time-series data with one kilo-meter spatial resolution in the Holarctic region (45°N to 90°N). The time series of this dataset is from February 24, 2000 to November 18, 2019, with a temporal resolution of one day and a spatial resolution of one km. The dataset is expected to provide quantitative information of snow distribution for regional climate simulation, hydrological models, etc.
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