Dataset List

Vol.|Area

No.11 Vol.11,2024
No.10 Vol.11,2024
No.09 Vol.11,2024
No.08 Vol.11,2024
No.07 Vol.11,2024
No.06 Vol.11,2024
No.05 Vol.11,2024
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

Daily Soil Moisture Dataset in Southeastern China Using CYGNSS (201901-202010)


YANG Ting1,2
1 The CAS Engineering Laboratory for Yellow River Delta Modern Agriculture，Institute of Geographic Sciences and Natural Resources Research，Chinese Academy of Sciences，Beijing 100101，China2 The Shandong Dongying Institute of Geographic Sciences，Dongying 257000，China

DOI:10.3974/geodb.2024.08.01.V1

Published:Aug. 2024

Visitors：2200       Data Files Downloaded：20      
Data Downloaded：432.79 MB      Citations：

Key Words：

spaceborne GNSS-R,CYGNSS,soil moisture,vegetation,roughness

Abstract：

It is of great significance for management of agricultural production and research on climate change to monitor large-scale soil moisture using remote sensing technology. In the dataset, 18°N-38°N, 97°E-122°36'E in southeastern China was selected as the study area. The space borne GNSS-R belongs to the cross-disciplinary category of satellite navigation applications and remote sensing, and its working band L is sensitive to soil moisture changes, which provides a new technical means for large-scale soil moisture detection. The author used publicly released space borne GNSS-R data, i.e., CYGNSS data, to realize an effective calculation method for complex surface soil moisture, and to generate the daily soil moisture dataset in southeastern China from January 2019 to October 2020. The dataset has a temporal resolution of daily and a spatial resolution of 0.36°x0.36°. The dataset includes the following data in the study area: (1) daily soil moisture in 2019; and (2) daily soil moisture from January to October in 2020. The dataset is archived in .tiff and .mdd formats, and consists of 1338 data files with data size of 40.1 MB (compressed to one file with 21.6 MB).

Foundation Item：

National Natural Science Foundation of China (42101376)

Data Citation：

YANG Ting. Daily Soil Moisture Dataset in Southeastern China Using CYGNSS (201901-202010)[J/DB/OL]. Digital Journal of Global Change Data Repository, 2024. https://doi.org/10.3974/geodb.2024.08.01.V1.

References：


     [1] Shi, J. C., Du, Y., Du, J. Y., et al. Progresses on microwave remote sensing of land surface parameters [J]. Science China: Earth Sciences, 2012, 55: 1052-1078.
     [2] Zhang, S. C., Guo, Q. Y., Ma, Z. M., et al. Research advances and some thoughts on soil moisture retrieval by space-borne GNSS-R [J]. Geomatics and information science of Wuhan University, 2024, 49(1): 15-26.
     [3] Zeng, J. Y, Shi, P. F., Chen, K. S., et al. Assessment and error analysis of satellite soil moisture products over the third pole [J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 60: 1-18.
     [4] Yan, Q. Y., Jin, S. G., Huang, W. M., et al. Retrievals of soil moisture and vegetation optical depth using CYGNSS data [J]. Journal of Nanjing University of Information Science & Technology (Natural Science Edition), 2021, 13(2): 194-203.
     [5] Yin, C. Soil Moisture Remote Sensing with Global Navigation Satellite System Reflectometry [D]. Nanjing University of Information Science and Technology, 2019.
     [6] Zhang, Y., Zhang, D. D., Meng, W. T., et al. Soil moisture retrieval using CYGNSS/SMAP data fusion semi-empirical model [J]. Journal of Beijing University of Aeronautics and Astronautics, 2023:1-15.
     [7] Clarizia, M. P., Pierdicca, N., Costantini, F., et al. Analysis of CYGNSS data for soil moisture retrieval [J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2019, 12(7): 2227-2235.
     [8] Yang, T., Wan, W., Wang, J. D, et al. A physics-based algorithm to couple CYGNSS surface reflectivity and SMAP brightness temperature estimates for accurate soil moisture retrieval [J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 1-15.
     [9] Chew, C., Small, E. Description of the UCAR/CU soil moisture product [J]. Remote Sensing, 2020, 12(10): 1558.
     [10] O'Neill, P., Bindlish, R., Chan, S., et al. Algorithm Theoretical Basis Document. Level 2 & 3 Soil Moisture (Passive) Data Products [M]. Pasadena, CA: Jet Propulsion Laboratory, 2018.
     [11] Piepmeier, J. R., Focardi, P., Horgan, K. A., et al. SMAP L-band microwave radiometer: Instrument design and first year on orbit [J]. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(4): 1954-1966.
     

Data Product:

ID Data Name Data Size Operation
1 SM_SEChina201901-202010.rar 22158.68KB
Co-Sponsors
Superintend