The soil moisture data bank: The ground-based, model-based, and satellite-based soil moisture data

Tavakol, Ameneh; McDonough, Kelsey R.; Rahmani, Vahid; Hutchinson, Stacy L.; Hutchinson, J.M. Shawn

doi:10.1016/j.rsase.2021.100649

Cited by 14 publications

(5 citation statements)

References 279 publications

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“…The 27 floods with better rainfall-flow relationships were selected based on the above information for subsequent analysis and research. Soil moisture is one of the key variables characterizing the earth's energy flow and hydrological cycle, which not only reflects evaporation, infiltration and runoff in hydrological models, but also has an important impact on flood prediction accuracy [29]. Currently, soil moisture data can be obtained by three main means, including traditional monitoring, model simulation and remote sensing observation.…”

Section: Data 221 Precipitation and Discharge Datamentioning

confidence: 99%

Application of Soil Moisture Data Assimilation in Flood Forecasting of Xun River in Hanjiang River Basin

Bai

Yan

et al. 2022

Water

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Accurate projection of floods is of great significance to safeguard economic and social development as well as people’s life and property. The development of hydrological models can improve the level of flood projection, however, the numerous uncertainties in the models limit the projection accuracy. By adding observations to correct the operation of prediction models, the accuracy can be improved to some extent. In this paper, taking the Xun River, of the Hanjiang River Basin in China, as the research object, combined with the soil moisture satellite data obtained by the soil moisture active and passive satellite (SMAP), the ensemble Kalman filter (EnKF) algorithm was used to assimilate the upper soil water content (WU) of the Xinanjiang model. In addition, based on the simultaneous assimilation of state variables and parameters, two improved assimilation schemes were proposed here, namely, the augmented ensemble Kalman filter (AEnKF) scheme and the dual ensemble Kalman filter (DEnKF) scheme. The results showed that compared with the WU assimilation scheme, the simultaneous assimilation of parameters and WU improved the prediction ability of the Xinanjiang model to a greater extent. The two improved schemes had similar effects on flood prediction accuracy, and improved the overall Nash–Sutcliffe efficiency coefficient (NSE) from 0.725 for non-assimilated, and 0.758 for assimilated WU, to 0.781. Among them, AEnKF and DEnKF schemes, respectively, improved the NSE by 10.1% and 11% at maximum. This study demonstrated that the application of data assimilation for the Xun River effectively improved the flood forecast accuracy of the Xinanjiang model, which will provide a reference basis and technical support for future flood prevention and mitigation and flood projection in this basin.

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Section: Data 221 Precipitation and Discharge Datamentioning

confidence: 99%

Application of Soil Moisture Data Assimilation in Flood Forecasting of Xun River in Hanjiang River Basin

Bai

Yan

et al. 2022

Water

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“…With the advancement of land surface models and remote sensing (RS) technology, continuous SM data in spatial and temporal dimensions have been obtained from RS and model simulations [11]. In comparison with land surface models which have large differences in the accuracy of simulated SM data because of the model structures and parameterizations [12], RS is able to directly estimate the spatial and temporal variations of SM at the regional and global scales according to the relationship between the electromagnetic spectrum and the top centimeters of soil water content. At present, RS SM data have been mainly derived from optimal RS, microwave RS, and thermal infrared sensors [13].…”

Section: Introductionmentioning

confidence: 99%

Spatial Downscaling of ESA CCI Soil Moisture Data Based on Deep Learning with an Attention Mechanism

Zhang,

Lu,

et al. 2024

Remote Sensing

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Soil moisture (SM) is a critical variable affecting ecosystem carbon and water cycles and their feedback to climate change. In this study, we proposed a convolutional neural network (CNN) model embedded with a residual block and attention module, named SMNet, to spatially downscale the European Space Agency (ESA) Climate Change Initiative (CCI) SM product. In the SMNet model, a lightweight Convolutional Block Attention Module (CBAM) dual-attention mechanism was integrated to comprehensively extract the spatial and channel information from the high-resolution input remote sensing products, the reanalysis meteorological dataset, and the topographic data. The model was employed to downscale the ESA CCI SM from its original spatial resolution of 25 km to 1 km in California, USA, in the annual growing season (1 May to 30 September) from 2003 to 2021. The original ESA CCI SM data and in situ SM measurements (0–5 cm depth) from the International Soil Moisture Network were used to validate the model’s performance. The results show that compared with the original ESA CCI SM data, the downscaled SM data have comparable accuracy with a mean correlation (R) and root mean square error (RMSE) of 0.82 and 0.052 m3/m3, respectively. Moreover, the model generates reasonable spatiotemporal SM patterns with higher accuracy in the western region and relatively lower accuracy in the eastern Nevada mountainous area. In situ site validation results in the SCAN, the SNOTEL network, and the USCRN reveal that the R and RMSE are 0.62, 0.63, and 0.77, and 0.077 m3/m3, 0.093 m3/m3, and 0.078 m3/m3, respectively. The results are slightly lower than the validation results from the original ESA CCI SM data. Overall, the validation results suggest that the SMNet downscaling model proposed in this study has satisfactory performance in handling the task of soil moisture downscaling. The downscaled SM model not only preserves a high level of spatial consistency with the original ESA CCI SM model but also offers more intricate spatial variations in SM depending on the spatial resolution of model input data.

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“…Soil water data are essential for understanding many land surface processes, such as the water, energy and carbon cycles of the earth system (Bai et al, 2019; Fisher & Koven, 2020; Harmsen et al, 2021; Rasheed et al, 2022; Tavakol et al, 2021). It is an important ecohydrological parameter for many modelling applications related to energy balance, such as numerical weather forecasting, climate prediction, radiative transfer modelling, global change modelling and satellite data validation (Njoku et al, 2003; Owe et al, 2008; Zhang et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Uniplanar multirod TDR probe for soil water content measurement over a larger spatial scale

Miyamoto,

Tokumoto,

Mojid

2023

Irrigation and Drainage

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This study developed a uniplanar multirod time domain reflectometry (TDR) probe and evaluated it in laboratory experiments for soil water measurement over a larger spatial scale. The sampling volume of the probe was determined by simulating the electric potential distribution around the probe rods by using the HYDRUS‐2D software package and verified by comparison with the measured dielectric constant in saturated sand. The performance of the probe was evaluated by comparing its dielectric measurements in wet sand over a wide range of water content during evaporative drying with that measured by CS640 probes. Additionally, the dependency of visible light reflection on the water content of the sand was evaluated. The results revealed that the average effective thickness of the probe's sampling volume was 5–6 cm on both sides of the plane of the probe rods. The probe accurately measured the soil water content over a 0.88 m2 spatial area. The visible light reflectance of the variably saturated sand surface and the corresponding water content of the sand follow a soil water retention function‐type relationship. The usage of the multirod TDR measurement system at a larger spatial scale carries practical implications, especially in increasing the accuracy of remote sensing data.

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The soil moisture data bank: The ground-based, model-based, and satellite-based soil moisture data

Cited by 14 publications

References 279 publications

Application of Soil Moisture Data Assimilation in Flood Forecasting of Xun River in Hanjiang River Basin

Application of Soil Moisture Data Assimilation in Flood Forecasting of Xun River in Hanjiang River Basin

Spatial Downscaling of ESA CCI Soil Moisture Data Based on Deep Learning with an Attention Mechanism

Uniplanar multirod TDR probe for soil water content measurement over a larger spatial scale

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