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20-m/12-d Soil Moisture Dataset covers Panzhuang Irrigation District of China (2020)


WANG Junjie1SHI Huijuan2WEI Zheng*3LIN Rencai3WANG Jin4ZHANG Di3
1 Operation and Maintenance Center of Panzhuang Irrigation District,Dezhou 253000,China2 Water Conservancy Bureau of Dezhou,Dezhou 253014,China3 China Institute of Water Resources and Hydropower Research,Beijing 100038,China4 Aerospace Information Research Institute,Chinese Academy of Sciences,Beijing 100101,China

DOI:10.3974/geodb.2021.10.08.V1

Published:Oct. 2021

Visitors:7951       Data Files Downloaded:114      
Data Downloaded:30333.49 MB      Citations:

Key Words:

soil moisture,Sentinel-1,back scattering coefficient,Panzhaung Irrigation District,Shandong

Abstract:

Soil moisture is an important factor affecting energy cycle, water-carbon cycle, agricultural process, hydrometeorology and so on. The 20-m/12-d Soil Moisture Dataset covers Panzhuang Irrigation District of China (2020) was developed based on the series Sentinel-1 SAR images in 2020. A linear regression model was established between the backscattering coefficient and surface soil moisture. At the same time, the method of supporting vector machine in machine learning was used to identify and extract the farmland in Panzhuang of China. The dataset includes: (1) boundary data of Panzhuang Irrigation District; (2) soil moisture data in 31 periods of 2020. The temporal resolution is 12 d and the spatial resolution is 20 m. The dataset is archived in .shp and .tif data formats, and consists of 43 data files with data size of 5.16 GB (compressed to 4 files with 1.09 GB).Browse

Foundation Item:

Ministry of Science and Technology of P. R. China (2017YFC0403202)

Data Citation:

WANG Junjie, SHI Huijuan, WEI Zheng*, LIN Rencai, WANG Jin, ZHANG Di. 20-m/12-d Soil Moisture Dataset covers Panzhuang Irrigation District of China (2020)[J/DB/OL]. Digital Journal of Global Change Data Repository, 2021. https://doi.org/10.3974/geodb.2021.10.08.V1.

WANG Junjie ,SHI Huijuan, WEI Zheng, et al. 20-m/12-d surface soil moisture dataset for the Panzhuang Irrigation District of China (2020) [J]. Journal of Global Change Data & Discovery, 2022, 6(1): 133–141.

References:

[1] Bai, L. L., Long, D., Yan, L. Estimation of Surface Soil Moisture With Downscaled Land Surface Temperatures Using a Data Fusion Approach for Heterogeneous Agricultural Land[J]. Water Resources Research, 2019, 55(2): 1105-1128.
     [2] Zheng, C.L., Hu, G.C., Chen, Q.T., et al. Impact of remote sensing soil moisture on the evapotranspiration estimation[J]. National Remote Sensing Bulletin, 2021, 25(04): 990-999.
     [3] Xie, Q.X., Jia, L., Chen, Q.T., et al.Evaluation of microwave remote sensing soil moisture products farming-pastoral area of Shandian river basin [J]. National Remote Sensing Bulletin, 2021, 25(04): 974-989.
     [4] Merlin, O., Escorihuela, M.J., Mayoral, M.A., et al. Self-calibrated evaporation-based disaggregation of SMOS soil moisture: An evaluation study at 3 km and 100 m resolution in Catalunya, Spain [J]. Remote Sensing of Environment, 2013, 130: 25-38.
     [5] Chan, S. K., Bindlish, R., O'Neill, P., et al. Development and assessment of the SMAP enhanced passive soil moisture product [J]. Remote Sensing of Environment, 2018, 204: 2539-2542.
     [6] Das, N. N., Entekhabi, D., Dunbar, R.S., et al. The SMAP mission combined active-passive soil moisture product at 9 km and 3 km spatial resolutions [J]. Remote Sensing of Environment, 2018, 211: 204-217.
     [7] Zribi, M., Andre, C., Decharme, B. A method for soil moisture estimation in Western Africa based on ERS Scatterometer [J]. IEEE Transactions on Geoscience and Remote Sensing, 2008, 46(2): 438-448.
     [8] Zribi, M., Gorrab, A., Baghdadi, N. A newsoil roughness parameter for themodelling of radar backscattering over bare soil [J]. Remote Sensing of Environment, 2014, 152: 62-73.
     [9] Gorrab, A., Zribi, M., Baghdadi, N., et al. Potential of X-Band TerraSAR-X and COSMO-SkyMed SAR data for the assessment of physical soil parameters [J]. Remote Sensing, 2015, 7(1): 747-766.
     [10] Gao, Q., Zribi, M., Escorihuela, M.J., et al. Synergetic use of Sentinel-1 and Sentinel-2 data for soil moisture mapping at 100 m resolution [J]. Sensors , 2017, 17(9): 1966.
     [11] Li, J., Wang, S. Using SAR-derived vegetation descriptors in a Water Cloud Model to improve soil moisture retrieval [J]. Remote Sensing, 2018, 10(9): 1370.
     [12] Bousbih, S., Zribi, M., El Hajj, M., et al. Soil moisture and irrigation mapping in a semi-arid region, based on the synergetic use of Sentinel-1 and Sentinel-2 data [J]. Remote Sensing, 2018, 10(12): 1953.
     [13] Amazirh, A., Merlin, O., Er-Raki, S., et al. Retrieving surface soil moisture at high spatio-temporal resolution from a synergy between Sentinel-1 radar and Landsat thermal data: A study case over bare soil [J]. Remote Sensing of Environment, 2018, 211: 321-337.
     [14] Bao, Y., Lin, L., Wu, S., et al. Surface soil moisture retrievals over partially vegetated areas from the synergy of Sentinel-1 and Landsat 8 data using a modified water-cloud model [J]. International Journal of Applied Earth Observation and Geoinformation, 2018, 72: 76-85.
     [15] Gao, Q., Zribi, M., Escorihuela M. et al. Irrigation Mapping Using Sentinel-1 Time Series at Field Scale [J]. Remote Sensing, 2018, 10(9): 1495.
     [16] Li, J. M., Wang, J. J, Yan, Q. H. Analysis of water and soil resources balance in Panzhuang Irrigated Disrrict, Shandong Province [J]. Water Resources Development Research, 2020, 20(9): 47-50+58.
     [17] Feng, Y. Q., Li, Q. Y., Wang, H. J., et al. Analysis on the network scheme of ultrasonic water level system in Panzhuang Irrigation District, Shandong Province [J]. Ground Water, 2007, (4): 117-118.
     [18] Zhang, D. Y., Dai, Z., Xu, X.G., et al. Crop classification of modern agricultural park based on time series Sentinel-2 images [J]. Infrared and Laser Engineering, 2021, 50(5): 262-272.
     [19] Yu, F., Zhao, Y. A new semi-empirical model for soil moisture content retrieval by ASAR and TM data in vegetation-covered areas [J]. Science China Earth Sciences, 2011, 54(12): 1955-1964.
     

Data Product:

ID Data Name Data Size Operation
0Datapaper_SM_Panzhuang_2020.pdf4276.00kbDownLoad
1 PanzhuangIrrigationDistrict.rar 32.60KB
2 SM_Panzhuang_2020_1.rar 374267.21KB
3 SM_Panzhuang_2020_2.rar 368160.98KB
4 SM_Panzhuang_2020_3.rar 409018.46KB
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