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Normalized Difference Vegetation Index (NDVI) has been widely used for monitoring vegetation. This dataset employed all available Landsat 5/7/8 data on the Qinghai-Tibetan Plateau (QTP) (> 100,000 scenes), and reconstructed high spatiotemporal NDVI time-series data (30-m and 8-d) during 2000-2020 on the TP (QTP-NDVI30) by using the MODIS-Landsat fusion algorithm (gap filling and Savitzky–Golay filtering；GF-SG). For the details of GF-SG, please refer to Chen et al. (2021).

This dataset has been evaluated carefully. The quantitative assessments show that the reconstructed NDVI images have an average MAE value of 0.02, correlation coefficient of 0.96, and SSIM value of 0.94. We compared the reconstructed images in some typical areas with the PlanetScope 3-m images and found that the spatial details were well preserved by QTP-NDVI30.

The geographic coordinate system of this dataset is GCS_WGS_84. The spatial range covers the vegetation area of the QTP, which is defined as the areas with average NDVI during July- September larger than 0.15.

File naming and required software

-The data of the same year was stored in the same folder, and the folder was named QTP_NDVI_XXXX(Year). The data were named by QTP_NDVI_XXXX(Year)_XX(Index).tiff, and the Index order corresponds to the date of the MOD09Q1 time series data of the current year.The data were in .tiff format, which can be read by software such as ENVI and ArcGIS. The NDVI time-series data in 2012 were not reconstructed due to the failure of Landsat 5. We will update the data after 2020 in the future.
For the use of this dataset, please refer to the following citations:

“The QTP-NDVI30 dataset (8-day and 30-m spatiotemporal resolution) was collected from the National Tibetan Plateau Data Center (TPDC) (Cao et al., 2022a). This dataset was generated through the fusion of MODIS (250 m) and Landsat (30 m) NDVI time-series data using the Gap Filling and Savitzky–Golay (GF-SG) fusion method (Chen et al., 2021), and showed satisfactory accuracy with visual inspection and quantitative assessments (Cao et al., 2022b)”.

Cao, R., Xu, Z., Chen, Y., Shen, M., Chen, J. (2022a). QTP-NDVI30: High spatiotemporal resolution (30-m and 8-d) NDVI time-series data during 2000-2020 for the Qinghai-Tibetan Plateau. National Tibetan Plateau Data Center, DOI: 10.11888/Terre.tpdc.272681. CSTR: 18406.11.Terre.tpdc.272681.
Chen, Y., Cao, R., Chen, J., Liu, L., & Matsushita, B. (2021). A practical approach to reconstruct high-quality Landsat NDVI time-series data by gap filling and the Savitzky–Golay filter. ISPRS journal of Photogrammetry and Remote Sensing, 180, 174-190.
Cao, R., Xu, Z., Chen, Y., Chen, J., Shen, M. (2022b). Reconstructing High-Spatiotemporal-Resolution (30 m and 8-Days) NDVI Time-Series Data for the Qinghai–Tibetan Plateau from 2000–2020. Remote Sensing, 14, 3648. https://doi.org/10.3390/rs14153648.

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Cao, R., Xu, Z., Chen, Y., Shen, M., Chen, J. (2022). QTP-NDVI30: High spatiotemporal resolution (30-m and 8-d) NDVI time-series data during 2000-2020 for the Qinghai-Tibetan Plateau. A Big Earth Data Platform for Three Poles, DOI: 10.11888/Terre.tpdc.272681. CSTR: 18406.11.Terre.tpdc.272681. (Download the reference： RIS | Bibtex )

1. Cao, R., Xu, Z., Chen, Y., Chen, J., Shen, M., 2022. Reconstructing High-Spatiotemporal-Resolution (30 m and 8-Days) NDVI Time-Series Data for the Qinghai–Tibetan Plateau from 2000–2020. Remote Sensing, 14, 3648. https://doi.org/10.3390/rs14153648.( View Details | Bibtex)

2. Chen, Y., Cao, R., Chen, J., Liu, L., & Matsushita, B. (2021). A practical approach to reconstruct high-quality Landsat NDVI time-series data by gap filling and the Savitzky–Golay filter. ISPRS journal of Photogrammetry and Remote Sensing, 180, 174-190.( View Details | Bibtex)

Using this data, the data citation is required to be referenced and the related literatures are suggested to be cited.

Support Program

Second Tibetan Plateau Scientific Expedition Program

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