资源简介

摘要： 积雪面积比例（Fractional Snow Cover， FSC）是定量描述单位像元内积雪覆盖面积（Snow Cover Area， SCA）与像元空间范围的比值，可为区域气候模拟、水文模型等提供积雪分布的定量信息。MODIS FSC产品是根据经验模型计算得到，并没有考虑地形、植被和地表温度等环境因素的影响，在青藏高原的验证精度低。针对此问题，考虑青藏高原地区环境因素（地形、植被、地表温度）对FSC制备的影响，基于多元自适应回归模型（Multivariate Adaptive Regression Splines， MARS）和线性回归模型分别建立FSC制备的非参数回归模型和经验回归模型。用Landsat 8地表反射率的数据和SNOMAP算法制备FSC的参考数据集。选取一部分参考数据集作为模型的训练数据集，另一部分作为模型的检验数据集。研究结果表明：MARS方法估计FSC的精度明显高于线性回归模型和原有的MODIS FSC制备方法。MARS的总体R、RMSE、MAE分别为0.791、0.103、0.058。在线性回归模型中精度最高的总体R、RMSE、MAE分别为0.647、0.128、0.072。MODIS 原有FSC制图方法的总体R、RMSE、MAE分别为0.595、0.221、0.170。考虑了环境信息的MARS方法更加适用于青藏高原地区FSC制备。本研究为制备青藏高原地区更高精度的FSC数据提供了新思路。 关键词： 青藏高原;  线性回归模型;  积雪面积比例;  MODIS;  多元自适应回归模型     Abstract: Fractional Snow Cover （FSC） is the ratio of the Snow Cover Area （SCA） to the spatial area in a unit pixel， which can provide quantitative information of snow cover distribution for regional climate simulation and hydrological model. MODIS FSC products are calculated according to the empirical model， without considering the impact of environmental factors such as topography， vegetation and surface temperature. The accuracy in the Tibetan plateau is low. Therefore ， the effects of environmental factors （topography， vegetation， and surface temperature） on FSC preparation were taken into account in Tibetan plateau， based on Multivariate Adaptive Regression Splines （MARS） and linear regression model， and established a non-parametric regression model and an empirical regression model respectively based on Multivariate Adaptive Regression Splines （MARS） and linear regression model. The reference dataset of FSC was prepared with Landsat 8 surface reflectance data and SNOMAP algorithm. A part of reference dataset is selected as the training samples of the model， and the other part as the validation dataset of the model. The results show that the accuracy of the MARS method is significantly higher than that of the linear regression model and the original MODIS FSC preparation method. The total R， RMSE and MAE of MARS were 0.791， 0.103 and 0.058， respectively. In the linear regression model， the overall R， RMSE and MAE with the highest accuracy are 0.647， 0.128 and 0.072， respectively. The overall R， RMSE and MAE of the original MODIS FSC mapping method are 0.595， 0.221 and 0.170 respectively. MARS method with environmental information is more suitable for FSC preparation in Tibetan plateau. This study provides a new idea for preparing FSC data with higher accuracy in Tibetan plateau.

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