Daily cloud-gap-filled Terra–Aqua MODIS NDSI dataset over High Mountain Asia (2000-2023)
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https://zenodo.org/record/7341827
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资源简介:
1. The daily cloud-gap-filled (CGF) MODIS normalized difference snow index (NDSI) dataset over High Mountain Asia (HMA) (2000-2023) is generated by combining of the cubic spline interpolation (CSI) method and the Spatio-Temporal Weighted (STW) method. This dataset is derived from daily 500 m MOD10A1 (Terra) and MYD10A1 (Aqua) products.
2. The cloud persistence days (CPD) dataset is also provided. The CPD represents the number of consecutive days of cloud observed for a pixel from the last cloud-free observation to the next cloud-free observation. And the CPD is used to determine the combination of CSI and STW method, which is expressed as: when CPD < 8 d, the CSI method is used; when CPD ≥ 8 d, the STW is used.
3. The CGF MODIS NDSI dataset is provided in ENVI standard format (.img) and the CPD dataset is provided in Geotiff format. And they are all provided in a geographic projection using the WGS84 coordinate system at a 0.005° (about 500 m) resolution. The NDSI value ranges from 0~100 and the CPD value ranges from 0~366. The fill value of both dataset is set to 255 (outside the track coverage of MODIS product).
4. The CGF MODIS NDSI dataset contains 24 compressed packages (named after the normal year) of the daily CGF MODIS NDSI dataset over HMA (2000-2023), and after uncompressing the files are named as “YYYYDDD_HMA_MODIS_NDSI_0.5km.img”. The CPD dataset contains 24 compressed packages (named after the normal year) of the daily CPD dataset over HMA (2000-2023), and after uncompressing the files are named as “YYYYDDD_CPD.tif”. The YYYY represents the year and the DDD represents Julian day (001-365/366).
5. The accuracy of this dataset has been well evaluated based on in-situ snow depth (SD) observations and high-resolution snow cover maps derived from Landsat images. The detailed information can be found in the paper (Deng, G., Tang, Z., Dong, C., Shao, D., & Wang, X. (2024). Development and Evaluation of a Cloud-Gap-Filled MODIS Normalized Difference Snow Index Product over High Mountain Asia. Remote Sensing, 16(1), 192. https://doi.org/10.3390/rs16010192).
创建时间:
2024-08-09



