10-m Grid Dataset of Walnut and Jujube Plantations in Southern Xinjiang Autonomous Region (2025)
GUI Shiyuan1LIU Yunfei*2LIU Qi3JIN Qian2LI Haowen1CHEN Yue1LIU Zhixue1LIN Jingwu2XIA Nan*1
1 Xinjiang University，Urumqi 830046，China2 Xinjiang Institute of Ecology and Geography，Chinese Academy of Sciences，Urumqi 830011，China3 King Abdullah University of Science and Technology，Thuwal 23955-6900，Saudi Arabia
DOI:10.3974/geodb.2026.07.08.V1
Published:July 2026
Visitors：67 Data Files Downloaded：0
Data Downloaded： 无 Citations：
Key Words：
Southern Xinjiang,walnut,jujube,plantation spatial distribution,10 m resolution,Sentinel-1,Sentinel-2,random forest
Abstract：
Based on the Google Earth Engine platform, the authors integrated Sentinel-2 optical imagery, Sentinel-1 synthetic-aperture radar imagery, DEM and reference samples to construct optical phenology, radar-backscatter and topographic features; and applied a random forest classifier to obtain the 10-m grid data of walnut and jujube plantations in Southern Xinjiang Autonomous Region (2025). The reference dataset contains 21,576 samples, of which 15,101 were used for model training and 6,475 for independent validation. The seven-class validation achieved an overall accuracy of 90.76% and a Kappa coefficient of 0.8902; the merged four-class product achieved an overall accuracy of 92.97% and a Kappa coefficient of 0.8953. Based on valid pixels in the final classification raster, the walnut area is 417,889.25 ha, and the jujube area is 311,428.37 ha. The dataset includes: (1) spatial data and its description of walnut and jujube plantations; (2) statistics of walnut and jujube plantation area and classification accuracy. The dataset is archived in .tif, .txt and .xlsx data formats, and consists of 4 data files with data size of 625 MB (Compressed into 1 file with 121 MB).
Foundation Item：
National Natural Science Foundation of China (42361144792); Xinjiang Uygur Autonomous Region (2023TSYCLJ0049)
Data Citation：
GUI Shiyuan, LIU Yunfei*, LIU Qi, JIN Qian, LI Haowen, CHEN Yue, LIU Zhixue, LIN Jingwu, XIA Nan*. 10-m Grid Dataset of Walnut and Jujube Plantations in Southern Xinjiang Autonomous Region (2025)[J/DB/OL]. Digital Journal of Global Change Data Repository, 2026. https://doi.org/10.3974/geodb.2026.07.08.V1. .
References：
[1] Mulla, D. J. Twenty five years of remote sensing in precision agriculture: key advances and remaining knowledge gaps [J]. Biosystems Engineering, 2013, 114(4): 358-371. DOI: 10.1016/j.biosystemseng.2012.08.009.
[2] Drusch, M., Del Bello, U., Carlier, S., et al. Sentinel-2: ESA's optical high-resolution mission for GMES operational services [J]. Remote Sensing of Environment, 2012, 120: 25-36. DOI: 10.1016/j.rse.2011.11.026.
[3] Torres, R., Snoeij, P., Geudtner, D., et al. GMES Sentinel-1 mission [J]. Remote Sensing of Environment, 2012, 120: 9-24. DOI: 10.1016/j.rse.2011.05.028.
[4] Gorelick, N., Hancher, M., Dixon, M., et al. Google Earth Engine: planetary-scale geospatial analysis for everyone [J]. Remote Sensing of Environment, 2017, 202: 18-27. DOI: 10.1016/j.rse.2017.06.031.
[5] Huete, A., Didan, K., Miura, T., et al. Overview of the radiometric and biophysical performance of the MODIS vegetation indices [J]. Remote Sensing of Environment, 2002, 83(1-2): 195-213. DOI: 10.1016/S0034-4257(02)00096-2.
[6] Gao, B. C. NDWI-A normalized difference water index for remote sensing of vegetation liquid water from space [J]. Remote Sensing of Environment, 1996, 58(3): 257-266. DOI: 10.1016/S0034-4257(96)00067-3.
[7] Orynbaikyzy, A., Gessner, U., Mack, B., et al. Crop type classification using fusion of Sentinel-1 and Sentinel-2 data: Assessing the impact of feature selection, optical data availability, and parcel sizes on the accuracies [J]. Remote Sensing, 2020, 12(17): 2779. DOI: 10.3390/rs12172779.
[8] Belgiu, M., Dragut, L. Random forest in remote sensing: a review of applications and future directions [J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2016, 114: 24-31. DOI: 10.1016/j.isprsjprs.2016.01.011.
[9] Breiman, L. Random forests [J]. Machine Learning, 2001, 45: 5-32. DOI: 10.1023/A:1010933404324.
[10] Olofsson, P., Foody, G. M., Herold, M., et al. Good practices for estimating area and assessing accuracy of land change [J]. Remote Sensing of Environment, 2014, 148: 42-57. DOI: 10.1016/j.rse.2014.02.015.
Data Product:
| ID |
Data Name |
Data Size |
Operation |
| 1 |
Walnut&JujubePlant.SXinjiang2025.rar |
124565.67KB |
|