سابِق|تال

Cloud removal and optimal threshold selection of MODIS NDSI production

  • role： First author第一作者
  • Affiliation：

    College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China

  • Email：wangxiaoy@lzu.edu.cn
  • Introduction：王晓艳，研究方向为定量遥感、积雪遥感。E-mail：wangxiaoy@lzu.edu.cn
WANG Xiaoyan1,  
  • Affiliation：

    School of Geography and Ocean Science, Nanjing University, Nanjing 210008, China

CHEN Siyong2,  
  • Affiliation：

    College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China

GUO Hui1,  
  • Affiliation：

    College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China

XIE Peiyao1,  
  • Affiliation：

    Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China

    Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China

WANG Jian34,  
  • Affiliation：

    Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China

HAO Xiaohua3

ملخص

The 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. This study aims to produce daily cloud-free MODIS NDSI production with high accuracy and determine the optimal NDSI threshold in snow identification.In this work, a cloud removal method based on adjacent similar pixels is presented for MODIS NDSI products. First, 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 marked by clouds. However, MYD10A1 is cloud-free. Second, an adjacent temporal composite is created. The mean of the nearest valid NDSI values for the adjacent 2 days to that location was assigned to a cloudy pixel. Finally, the residual cloud pixels are processed based on the removed adjacent similar pixels. A weighted cloud-free similar pixel function is established to predict the cloudy target pixel on the NDSI image. 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 October 1, 2017 to April 31, 2018. The optimal NDSI threshold of snow identification is then determined based on the Snow Depth (SD) data of the meteorological stations.The effectiveness of cloud removal was validated through “cloud assumption”. Results showed 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 SD sequence measured by the meteorological stations. When the measured SD 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. Accordingly, the true value of the binary snow distribution can be obtained according to the SD measured by meteorological station. Thereafter, 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%.(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 SD sequence measured by the meteorological stations. (3) The optimal threshold of the cloud free NDSI sequence in snow identification is 0.1 in non-forest areas and 0 in forest areas.

مفهوم

remote sensing;Snow;cloud removal;MODIS;NDSI;Northeast China;forest

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