A fine-resolution soil moisture dataset for China in 2002–2018

Xiangjin Meng,Zhonghua Guo,Xinyi Shen,Jiancheng Shi,Jiangyuan Zeng,Lingmei Jiang,Kebiao Mao,Fei Meng,Yaokui Cui

doi:10.5194/essd-13-3239-2021

Abstract

Abstract. Soil moisture is an important parameter required for agricultural drought monitoring and climate change models. Passive microwave remote sensing technology has become an important means to quickly obtain soil moisture across large areas, but the coarse spatial resolution of microwave data imposes great limitations on the application of these data. We provide a unique soil moisture dataset (0.05∘, monthly) for China from 2002 to 2018 based on reconstruction model-based downscaling techniques using soil moisture data from different passive microwave products – including AMSR-E and AMSR2 (Advanced Microwave Scanning Radiometer for Earth Observing System) JAXA (Japan Aerospace Exploration Agency) Level 3 products and SMOS-IC (Soil Moisture and Ocean Salinity designed by the Institut National de la Recherche Agronomique, INRA, and Centre d’Etudes Spatiales de la BIOsphère, CESBIO) products – calibrated with a consistent model in combination with ground observation data. This new fine-resolution soil moisture dataset with a high spatial resolution overcomes the multisource data time matching problem between optical and microwave data sources and eliminates the difference between the different sensor observation errors. The validation analysis indicates that the accuracy of the new dataset is satisfactory (bias: −0.057, −0.063 and −0.027 m3 m−3; unbiased root mean square error (ubRMSE): 0.056, 0.036 and 0.048; correlation coefficient (R): 0.84, 0.85 and 0.89 on monthly, seasonal and annual scales, respectively). The new dataset was used to analyze the spatiotemporal patterns of soil water content across China from 2002 to 2018. In the past 17 years, China's soil moisture has shown cyclical fluctuations and a slight downward trend and can be summarized as wet in the south and dry in the north, with increases in the west and decreases in the east. The reconstructed dataset can be widely used to significantly improve hydrologic and drought monitoring and can serve as an important input for ecological and other geophysical models. The data are published in Zenodo at https://doi.org/10.5281/zenodo.4738556 (Meng et al., 2021a).

Highlights

Soil moisture (SM), which is one of the key variables in water cycle and atmospheric energy budget (Taylor et al, 2011; Shi et al, 2012; Guillod et al, 2015), has been widely used for flood forecasts (Bindlish et al, 2009), drought detection (Mao et al, 2010, 2012), crop yield estimation (Chen et al, 2011), weather prediction and hydrological modeling (Liu et al, 2017)
We provide a unique soil moisture dataset (0.05◦, monthly) for China from 2002 to 2018 based on reconstruction model-based downscaling techniques using soil moisture data from different passive microwave products – including AMSR-E and AMSR2 (Advanced Microwave Scanning Radiometer for Earth Observing System) JAXA (Japan Aerospace Exploration Agency) Level 3 products and SMOS-IC (Soil Moisture and Ocean Salinity designed by the Institut National de la Recherche Agronomique, INRA, and Centre d’Etudes Spatiales de la BIOsphère, CESBIO) products – calibrated with a consistent model in combination with ground observation data
The comparisons at seasonal and annual temporal scales are carried out, which is slightly better than the monthly scale results with R, bias and unbiased root mean square error (ubRMSE) ranging from 0.85 to 0.89, from −0.063 to −0.027 m3 m−3 and from 0.036 to 0.048 m3 m−3, respectively

Summary

Introduction

Soil moisture (SM), which is one of the key variables in water cycle and atmospheric energy budget (Taylor et al, 2011; Shi et al, 2012; Guillod et al, 2015), has been widely used for flood forecasts (Bindlish et al, 2009), drought detection (Mao et al, 2010, 2012), crop yield estimation (Chen et al, 2011), weather prediction and hydrological modeling (Liu et al, 2017). SM networks based on ground stations have made great contributions to establishing long-term SM datasets (Srivastava, 2016). The European Space Agency’s Water Cycle Multi-Mission Observation Strategy (ESA WACMOS) Support to Science Element (STSE) program has developed the first long-term SM data record from passive and active microwave data (Su et al, 2010). In 2012, the ESA’s Climate Change Initiative (CCI) program SM datasets were first publicized on the ESA CCI web portal. This CCI product was generated by merging different microwave sensor observations and attempting to produce a complete and consistent long-term time series of SM datasets (Dorigo et al, 2017; Gruber et al, 2019). The long-term availability of SM products has been validated against extensive model simulations and in situ measurements (Albergel et al, 2012; Loew et al, 2013; Zeng et al, 2015; Dorigo et al, 2015, Preimesberger et al, 2021)

Methods

Results

Conclusion