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引用本文:

DOI:

10.11834/jrs.20210169

收稿日期:

2020-05-21

修改日期:

2020-08-26

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MODIS NDSI产品去云算法及最优阈值选择研究
王晓艳1, 陈思勇2, 郭慧1, 谢佩瑶1, 王建3
1.兰州大学 资源环境学院;2.Lanzhou University;3.中国科学院西北生态与环境资源研究院
摘要:

归一化差值积雪指数NDSI是积雪识别中最常用的指数,但由于云的遮挡限制了MODIS NDSI产品的应用。本文提出了一种基于邻近相似像元的MODIS NDSI产品去云方法,并分析了无云NDSI序列在积雪识别中的最优阈值。对于NDSI影像上某一个云遮挡的目标像元,选取目标像元的n个邻近相似像元进行加权平均来预测该目标像元的NDSI值。以东北积雪区2017年10月1日至2018年4月31日一个积雪季的NDSI产品进行去云实验,并采用“云假设”的方法进行了检验,所预测到的云覆盖像元NDSI值与实际值的相关系数达到0.95,均方根误差为0.08。将逐日无云NDSI序列与气象站点测量的雪深序列进行对比,二者具有很好的一致性。气象站点的测量雪深大于等于1cm时,假定该站点所在的像元为有雪像元,并以此为真值,分析无云NDSI序列在积雪识别中的最优阈值。结果表明,非森林地区NDSI阈值为0.1时积雪提取的精度最高,可以达到95.6%;森林地区的NDSI最优阈值为0,对应的积雪提取精度为93.5%。

Research on cloud removal and optimal threshold selection of MODIS NDSI production
Abstract:

Objective: Normalized difference snow index (NDSI) is the most commonly used index in snow identification. However, the application of MODIS NDSI products is restricted due to cloud occlusion. The objective of this study is to produce daily cloud-free MODIS NDSI production with high accuracy, and try to find the optimal NDSI threshold in snow identification. Method: In this paper, a cloud removal method based on adjacent similar pixels is presented for MODIS NDSI products. Firstly, MOD10A1 and MYD10A1 on the same day are combined. The rule is that MOD10A1 is updated by MYD10A1 at the same location when MOD10A1 is mark by clouds but MYD10A1 is cloud-free. Secondly, adjacent temporal composite is performed. For a cloudy pixel, the mean of the nearest valid NDSI values for the adjacent two days to that location was assigned to it. Finally, the residual cloud pixels are processed based on removed adjacent similar pixels. For a cloudy target pixel on the NDSI image, a weighted cloud-free similar pixel function is established to predict it. The first n similar pixels in the w by w local window are selected and a weighting function can be constructed to compute the NDSI value for the target pixel. In this study, n = 20 and w = 15 are recommended in practice. The cloud removal experiment is carried out with the MODIS NDSI products in the northeast China, from 1 October, 2017 to 31 April, 2018. Then the optimal NDSI threshold of snow identification is determined based on the snow depth (SD) data of the meteorological stations. Result: The method effectiveness of cloud removal was validated by “cloud assumption”, the results show that the correlation coefficient r between the predicted NDSI value and the true value is 0.95, and the root mean square error is 0.08. The daily cloud free NDSI sequence has good agreement with the snow depth sequence measured by the meteorological stations. When the measured snow depth of a meteorological station is greater than or equal to 1 cm, the pixel where the station is located is a snow pixel; otherwise, the pixel is snow free. Therefore, the true value of the binary snow distribution can be obtained according to the snow depth measured by meteorological station. Then the true value is used to analyze the optimal threshold of cloud free NDSI sequence in snow identification. The results show that the accuracy of snow identification is the highest when the NDSI threshold is 0.1 in non-forest areas, which can reach 95.6%; the optimal threshold of NDSI in forest areas is 0, and the corresponding snow identification accuracy is 93.5%. Conclusion: (1) The cloud removal method based on adjacent similar pixels is effective for the generation of daily cloud free MODIS NDSI products. (2) The daily cloud free NDSI sequence has good agreement with the snow depth sequence measured by the meteorological stations. (3) The optimal threshold of cloud free NDSI sequence in snow identification is 0.1 in non-forest areas and 0 in forest areas.

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