Dataset List

Vol.|Area

No.04 Vol.11,2024
No.03 Vol.11,2024
No.02 Vol.11,2024
No.01 Vol.11,2024
No.12 Vol.10,2023
No.11 Vol.10,2023
No.10 Vol.10,2023
No.09 Vol.10,2023
No.08 Vol.10,2023
No.07 Vol.10,2023
No.06 Vol.10,2023
No.05 Vol.10,2023
No.04 Vol.10,2023
No.03 Vol.10,2023
No.02 Vol.10,2023
No.01 Vol.10,2023
No.12 Vol.9,2022
No.11 Vol.9,2022
No.10 Vol.9,2022
No.09 Vol.9,2022
No.08 Vol.9,2022
No.07 Vol.9,2022
No.06 Vol.9,2022
No.05 Vol.9,2022
No.04 Vol.9,2022
No.03 Vol.9,2022
No.02 Vol.9,2022
No.01 Vol.9,2022
No.12 Vol.8,2021
No.11 Vol.8,2021
No.10 Vol.8,2021
No.09 Vol.8,2021
No.08 Vol.8,2021
No.07 Vol.8,2021
No.06 Vol.8,2021
No.05 Vol.8,2021
No.04 Vol.8,2021
No.03 Vol.8,2021
No.03 Vol.8,2021
No.02 Vol.8,2021
No.01 Vol.8,2021
No.09 Vol.7,2020
No.08 Vol.7,2020
No.07 Vol.7,2020
No.06 Vol.7,2020
No.05 Vol.7,2020
No.04 Vol.7,2020
No.03 Vol.7,2020
No.02 Vol.7,2020
No.01 Vol.7,2020
No.06 Vol.6,2019
No.05 Vol.6,2019
No.04 Vol.6,2019
No.03 Vol.6,2019
No.02 Vol.6,2019
No.01 Vol.6,2019
No.08 Vol.5,2018
No.07 Vol.5,2018
No.06 Vol.5,2018
No.05 Vol.5,2018
No.04 Vol.5,2018
No.03 Vol.5,2018
No.02 Vol.5,2018
No.01 Vol.5,2018
No.04 Vol.4,2017
No.03 Vol.4,2017
No.02 Vol.4,2017
No.01 Vol.4,2017
No.09 Vol.3,2016
No.08 Vol.3,2016
No.07 Vol.3,2016
No.06 Vol.3,2016
No.05 Vol.3,2016
No.04 Vol.3,2016
No.03 Vol.3,2016
No.02 Vol.3,2016
No.01 Vol.3,2016
No.02 Vol.2,2015
No.01 Vol.2,2015
No.02 Vol.1,2014
No.01 Vol.1,2014
China
Asia
Oceanic region
African
European
North American
South American
Arctic
Global Scale
Data Details

1-km/Daily Land Surface Temperature Optimized Dataset for the Qinghai-Tibet Plateau Based on MODIS Data (2000-2020)


XU Xunpeng1,2,3ZHANG Yu1,2,3JI Luyan1,2TANG Hairong*1,2,3
1 Aerospace Information Research Institute，Chinese Academy of Sciences，Beijing 100094，China2 the Key Laboratory of Technology in Geo-Spatial information Processing and Application System，Chinese Academy of Sciences，Beijing 100190，China3 the School of Electronic，Electrical and Communication Engineering，University of Chinese Academy of Sciences，Beijing 101408，China

DOI:10.3974/geodb.2023.10.02.V1

Published:Oct. 2023

Visitors：2091       Data Files Downloaded：87      
Data Downloaded：589869.60 MB      Citations：

Key Words：

Qinghai-Tibet Plateau,Daily Land Surface Temperature,1 KM,2000-2020,MODIS

Abstract：

Remote sensing data has strong correlation and continuity in space and time, so time series remote sensing images have low-rank property. In this dataset, we repaired images using low-rank tensor complementation. Firstly, we preprocessed the MODIS land surface temperature data and employed spatio-temporal interpolation to initially fill in the missing values caused by cloud cover. Secondly, we treated the land surface temperature time series data as a third-order spatio-temporal tensor and introduced Fourier transform on the time dimension to convert it into a space-frequency tensor. By performing singular value decomposition and Gaussian low-pass filtering on this tensor, followed by inverse Fourier transform, we obtained a space-time tensor. Lastly, we further optimized the missing tensor using the alternating direction method of multipliers. The data accuracy using the method was validated through simulation experiments, where artificial masks were added and subsequently recovered. The resulting mean absolute error (MAE) falls within the range of 2.1℃ to 4.9℃. This dataset includes the following data for the Tibetan Plateau on a daily basis for the years 2000-2020: (1) the optimized surface temperature data for the cloud-shaded regions of the MOD11A1, MYD11A1 products (MOD11A1_QTP_PART, MYD11A1_QTP_PART); (2) optimized MOD11A1/MYD11A1 data (MOD11A1_QTP_TEMP, MYD11A1_QTP_TEMP); and (3) original MOD11A1 and MYD11A1 products (MOD11A1_QTP_ORIGIN, MOD11A1_QTP_ORIGIN). All data have a spatial resolution of 1 km and are stored in an integer data format, with pixel value representing the thermodynamic temperature of the surface with a scale factor of 0.02 in Kelvin. The dataset is archived in .tif format, and consists of 43833 data files with data size of 143 GB (compressed into 21 files with 138 GB).

Foundation Item：

Ministry of Science and Technology of P. R. China (2019QZKK0206, 31400)

Data Citation：

XU Xunpeng, ZHANG Yu, JI Luyan, TANG Hairong*. 1-km/Daily Land Surface Temperature Optimized Dataset for the Qinghai-Tibet Plateau Based on MODIS Data (2000-2020)[J/DB/OL]. Digital Journal of Global Change Data Repository, 2023. https://doi.org/10.3974/geodb.2023.10.02.V1.

References：


     [1] Wang, A. H., Zeng, X. B.. Development of global hourly 0.5° land surface air temperature datasets [J]. Journal of Climate, 2013, 26(19): 7676-7691.
     [2] Mostovoy, G. V., King, R. L., Reddy, K. R., et al. Statistical estimation of daily maximum and minimum air temperatures from MODIS LST data over the state of Mississippi [J]. GIScience & Remote Sensing, 2006, 43(1): 78-110.
     [3] Xu, Y. M., Qin, Z. H., Shen, Y. Study on the estimation of near-surface air temperature from MODIS data by statistical methods [J]. International Journal of Remote Sensing, 2012, 33: 7629-7643.
     [4] Liu, J., Musialski, P., Wonka, P., et al. Tensor completion for estimating missing values in visual data [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(1): 208-220.
     [5] Ng, M. K. P., Yuan, Q. Q., Yan, L., et al. An adaptive weighted tensor completion method for the recovery of remote sensing images with missing data [J]. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(6): 3367-3381.
     [6] Ji, T. Y., Yokoya, N., Zhu, X. X., et al. Nonlocal tensor completion for multitemporal remotely sensed images’ inpainting [J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(6): 3047-3061.
     [7] Chen, Y., He, W., Yokoya, N., et al. Blind cloud and cloud shadow removal of multitemporal images based on total variation regularized low-rank sparsity decomposition [J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2019, 157: 93-107.
     [8] Chu, D., Shen, H. F., Guan, X. B., et al. Long time-series NDVI reconstruction in cloud-prone regions via spatio-temporal tensor completion [J]. Remote Sensing of Environment, 2021, 264: 112632.
     [9] Lin, J., Huang, T. Z., Zhao, X. L., et al. Robust thick cloud removal for multitemporal remote sensing images using coupled tensor factorization [J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 1-16.
     [10] Chen, Z. H., Zhang, P., Zhang, Y., et al. Thick cloud removal in multi-temporal remote sensing images via frequency spectrum-modulated tensor completion [J]. Remote Sensing, 2023, 15(5): 1230.
     [11] Zhang, Y. L., Li, B. Y., Liu, L. S., et al. Redetermine the region and boundaries of Tibetan Plateau [J]. Geographical Research, 2021, 40(6): 1543-1553.
     

Data Product:

ID Data Name Data Size Operation
1 MOD11A1_QTP_ORIGIN-1.zip 7275523.18KB
2 MOD11A1_QTP_ORIGIN-2.zip 6349158.06KB
3 MOD11A1_QTP_ORIGIN-3.zip 6362764.59KB
4 MOD11A1_QTP_ORIGIN-4.zip 6314531.37KB
5 MOD11A1_QTP_PART-1.zip 6394055.92KB
6 MOD11A1_QTP_PART-2.zip 5324148.56KB
7 MOD11A1_QTP_PART-3.zip 6989428.24KB
8 MOD11A1_QTP_TEMP-1.zip 8998683.20KB
9 MOD11A1_QTP_TEMP-2.zip 7503032.21KB
10 MOD11A1_QTP_TEMP-3.zip 7491827.06KB
11 MOD11A1_QTP_TEMP-4.zip 7495040.18KB
12 MYD11A1_QTP_ORIGIN-1.zip 5977959.02KB
13 MYD11A1_QTP_ORIGIN-2.zip 6497189.51KB
14 MYD11A1_QTP_ORIGIN-3.zip 7615230.22KB
15 MYD11A1_QTP_PART-1.zip 5572843.90KB
16 MYD11A1_QTP_PART-2.zip 6251151.51KB
17 MYD11A1_QTP_PART-3.zip 8263666.06KB
18 MYD11A1_QTP_TEMP-1.zip 6072739.88KB
19 MYD11A1_QTP_TEMP-2.zip 7632944.11KB
20 MYD11A1_QTP_TEMP-3.zip 9152098.35KB
21 MYD11A1_QTP_TEMP-4.zip 6097492.06KB
Co-Sponsors
Superintend