Landsat Image-based Retrieval Dataset of Total Suspended Solids in the Jiaozhou Bay (1984-2020)
HUANG Jue1ZHANG Xiang1ZHAO Yongfang*2
1 College of Geodesy and Geomatics，Shandong University of Science and Technology，Qingdao 266590，China2 Jiao Zhou Bay Marine Ecosystem Research Station，Chinese Academy of Sciences，Qingdao 266071，China
DOI:10.3974/geodb.2022.02.09.V1
Published:Feb. 2022
Visitors：7995 Data Files Downloaded：113
Data Downloaded：26249.58 MB Citations：
Key Words：
Jiaozhou Bay,Landsat,total suspended solids,quantitative retrieval,long-term monitoring,
Abstract：
The total suspended solids (TSS) concentration (mg/L) is an important water environment parameter in coastal and inland lakes. The Landsat Image-based Retrieval Dataset of Total Suspended Solids in the Jiaozhou Bay (1984 – 2020) was developed based on integrating the Landsat TM/ETM /OLI images from 1984 to 2020 with a quantitative retrieval model, the Leave-One-Out Cross-Validation (LOO-CV). 22 groups of remote sensing reflectance and TSS concentration data are used for modeling and the remaining one for verified. The above processes are carried out in turn in which a total of 23 models were obtained. The R² and RMSE, as well as the model with the highest accuracy as the quantitative inversion model of TSS concentration were accepted. The dataset includes: (1) boundary data of study area, Jiaozhou Bay in 1984 and 2020; (2) 318 TSS data with spatial resolution of 30 m. The dataset is archived in .shp and .tif data formats, and consists of 341 data files with data size of 236 MB (Compressed into one single file with 232 MB). The analysis paper based on the dataset was published at Remote Sensing, Vol. 13, No. 23, 2021.Browse
Foundation Item：
National Natural Science Foundation of China (42076185, 41706194); Ministry of Scienceand Technology of P. R. China (CNERN)
Data Citation：
HUANG Jue, ZHANG Xiang, ZHAO Yongfang*. Landsat Image-based Retrieval Dataset of Total Suspended Solids in the Jiaozhou Bay (1984-2020)[J/DB/OL]. Digital Journal of Global Change Data Repository, 2022. https://doi.org/10.3974/geodb.2022.02.09.V1.
HUANG Jue, ZHANG Xiang, ZHAO Yongfang. Dataset of total suspended solids in the Jiaozhou Bay retrieved from Landsat images (1984–2020) [J]. Journal of Global Change Data & Discovery, 2022, 6(3): 433-439.
References：
[1] Yang, G., Jiang, T., Zhao, Y., et al. Study on variation in chlorophyll-a concentration and its influencing factors of Jiaozhou Bay based on long term remote sensing images [J]. Haiyang Xuebao, 2019, 41(01): 183–190.
[2] Yuan, Y., Jalón-Rojas, I., Wang, X. H., et al. Design, construction and application of a regional ocean database: a case study in Jiaozhou Bay, China [J]. Limnology and Oceanography: Methods, 2019, 17(3): 210-222.
[3] Zhang, Y., Wu, Z., Liu, M. Thermal structure and response to long-term climatic changes in Lake Qiandaohu, a deep subtropical reservoir in China [J]. Limnology and Oceanography, 2014, 59(4): 1193-1202.
[4] Zhang, M. Distribution and seasonal variation of suspended matter in sea water of Jiaozhou Bay [J]. Studia Marina Sinica. 2000, 00, 49–54.
[5] Zhang, X., Song, Y., Chen, J. J., et al. Landsat Image-Based Retrieval and Analysis of Spatiotemporal Variation of Total Suspended Solid Concentration in Jiaozhou Bay, China[J]. Remote Sensing, 2021, 13(23): 4796-4796.
[6] Han, X. X., Chen, X. L., Feng, L., et al. Four decades of winter wetland changes in Poyang Lake based on Landsat observations between 1973 and 2013 [J]. Remote Sensing of Environment, 2015, 156: 426-437.
[7] Huang, J., Wu, M., Cui, T. W., et al. Quantifying DOC and Its Controlling Factors in Major Arctic Rivers during Ice-Free Conditions using Sentinel-2 Data [J]. Remote Sensing, 2019, 11(24): 2904.
[8] Gao, G., Wang, X., Bao, X., et al. The impacts of land reclamation on suspended-sediment dynamics in Jiaozhou Bay, Qingdao, China [J]. Estuarine Coastal and Shelf Science. 2018, 206: 61-75.
Data Product:
| ID |
Data Name |
Data Size |
Operation |
| 0 | Datapaper_JZBay_TSS_1984-2020.pdf | 244.00kb | DownLoad |
| 1 |
JZBay_TSS_1984-2020.rar |
237872.30KB |
|