Chinese Soil Moisture Observation Network and Time Series Data Set for High Resolution Satellite Applications

Chunmei Wang; Xingfa Gu; Xiang Zhou; Jian Yang; Tao Yu; Zui Tao; Hailiang Gao; Qiyue Liu; Yulin Zhan; Xiangqin Wei; Juan Li; Lili Zhang; Lei Li; Bingze Li; Zhuangzhuang Feng; Xigang Wang; Ruoxi Fu; Xingming Zheng; Chunnuan Wang; Yuan Sun; Bin Li; Wen Dong

doi:10.1038/s41597-023-02234-8

Chinese Soil Moisture Observation Network and Time Series Data Set for High Resolution Satellite Applications

Sci Data. 2023 Jul 1;10(1):424. doi: 10.1038/s41597-023-02234-8.

Authors

Chunmei Wang^{1

2}, Xingfa Gu^{3

4}, Xiang Zhou^{5

6}, Jian Yang^{7

8}, Tao Yu^{1

2}, Zui Tao^{1

2}, Hailiang Gao^{1

2}, Qiyue Liu⁹, Yulin Zhan^{1

2}, Xiangqin Wei^{1

2}, Juan Li^{1

2}, Lili Zhang^{1

2}, Lei Li^{10

11}, Bingze Li^{10

12}, Zhuangzhuang Feng^{10

11}, Xigang Wang^{10

13}, Ruoxi Fu^{10

12}, Xingming Zheng^{14

15}, Chunnuan Wang¹, Yuan Sun^{1

2}, Bin Li^{1

2}, Wen Dong^{1

2}

Affiliations

¹ Aerospace Information Research Institute, Chinese Academy of Sciences, 100094, Beijing, China.
² National Engineering Research Center of Satellite Remote Sensing Applications, 100094, Beijing, China.
³ Aerospace Information Research Institute, Chinese Academy of Sciences, 100094, Beijing, China. guxf@aircas.ac.cn.
⁴ National Engineering Research Center of Satellite Remote Sensing Applications, 100094, Beijing, China. guxf@aircas.ac.cn.
⁵ Aerospace Information Research Institute, Chinese Academy of Sciences, 100094, Beijing, China. zhouxiang@aircas.ac.cn.
⁶ National Engineering Research Center of Satellite Remote Sensing Applications, 100094, Beijing, China. zhouxiang@aircas.ac.cn.
⁷ Aerospace Information Research Institute, Chinese Academy of Sciences, 100094, Beijing, China. yangjian@aircas.ac.cn.
⁸ National Engineering Research Center of Satellite Remote Sensing Applications, 100094, Beijing, China. yangjian@aircas.ac.cn.
⁹ North China Institute is Aerospace Engineering, 065000, Langfang, China.
¹⁰ Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, 130102, Changchun, China.
¹¹ College of Resources and Environment, University of Chinses Academy of Sciences, 100049, Beijing, China.
¹² School of Geomatics and Prospecting Engineering, Jilin Jianzhu University, 130118, Changchun, China.
¹³ College of Geo-exploration Science and Technology, Jilin University, 130026, Changchun, China.
¹⁴ Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, 130102, Changchun, China. zhengxingming@iga.ac.cn.
¹⁵ Changchun Jingyuetan Remote Sensing Test Site, Chinese Academy of Sciences, 130102, Changchun, China. zhengxingming@iga.ac.cn.

Abstract

High-quality ground observation networks are an important basis for scientific research. Here, an automatic soil observation network for high-resolution satellite applications in China (SONTE-China) was established to measure both pixel- and multilayer-based soil moisture and temperature. SONTE-China is distributed across 17 field observation stations with a variety of ecosystems, covering both dry and wet zones. In this paper, the average root mean squared error (RMSE) of station-based soil moisture for well-characterized SONTE-China sites is 0.027 m³/m³ (0.014~0.057 m³/m³) following calibration for specific soil properties. The temporal and spatial characteristics of the observed soil moisture and temperature in SONTE-China conform to the geographical location, seasonality and rainfall of each station. The time series Sentinel-1 C-band radar signal and soil moisture show strong correlations, and the RMSE of the estimated soil moisture from radar data was lower than 0.05 m³/m³ for the Guyuan and Minqin stations. SONTE-China is a soil moisture retrieval algorithm that can validate soil moisture products and provide basic data for weather forecasting, flood forecasting, agricultural drought monitoring and water resource management.

Publication types

Dataset