Assessing and comparing the <scp>subseasonal</scp> variations of summer soil moisture of satellite products over eastern China

Zhang, Yang; Chen, Haishan; Sun, Shilei

doi:10.1002/joc.8073

Cited by 2 publications

(2 citation statements)

References 69 publications

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“…The data span from 1992 to 2013. Due to reliability and convenience, the SM product has been broadly utilized in nature eco-environment and agriculture investigations and assessments [61][62][63]. Figure 1 displays the geographic locations of these sites.…”

Section: Validation Datamentioning

confidence: 99%

Estimates of the Land Surface Hydrology from the Community Land Model Version 5 (CLM5) with Three Meteorological Forcing Datasets over China

Wang,

Mei

et al. 2024

Remote Sensing

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The land surface model (LSM) is extensively utilized to simulate terrestrial processes between land surface and atmosphere in the Earth system. Hydrology simulation is the key component of the model, which can directly reflect the capability of LSM. In this study, three offline LSM simulations were conducted over China using the Community Land Model version 5.0 (CLM5) driven by different meteorological forcing datasets, namely China Meteorological Forcing Dataset (CMFD), Global Soil Wetness Project Phase 3 (GSWP3), and bias-adjusted ERA5 reanalysis (WFDE5), respectively. Both gridded and in situ reference data, including evapotranspiration (ET), soil moisture (SM), and runoff, were employed to evaluate the performance levels of three CLM5-based simulations across China and its ten basins. In general, all simulations realistically replicate the magnitudes, spatial patterns, and seasonal cycles of ET over China when compared with remote-sensing-based ET observations. Among ten basins, Yellow River Basin (YRB) is the basin where simulations are the best, supported by the higher KGE value of 0.79. However, substantial biases occur in Northwest Rivers Basin (NWRB) with significant overestimation for CMFD and WFDE5 and underestimation for GSWP3. In addition, both grid-based or site-based evaluations of SM indicate that systematic wet biases exist in all three CLM5 simulations for shallower soil layer over nine basins of China. Comparatively, the performance levels in simulating SM for deeper soil layer are slightly better. Moreover, all three types of CLM5 simulate reasonable runoff spatial patterns, among which CMFD can capture more detailed information, but GSWP3 presents more comparable change trends of runoff when compared to the reference data. In summary, this study explored the capacity of CLM5 driven by different meteorological forcing data, and the assessment results may provide important insights for the future developments and applications of LSM.

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Section: Validation Datamentioning

confidence: 99%

Estimates of the Land Surface Hydrology from the Community Land Model Version 5 (CLM5) with Three Meteorological Forcing Datasets over China

Wang,

Mei

et al. 2024

Remote Sensing

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“…In addition, the SMAP L4 soil-moisture data was found to be significantly affected by seasonal variation in these land covers. Zhou et al [13], using the 43-station soil moisture over eastern China validated the satellite soil-moisture products from various sources, including Chinese FengYun 3C (FY3C), the level-2 neural network product of the European Soil Moisture and Ocean Salinity (SMOS), and the level-4 product of the U.S. Soil Moisture Active Passive (SMAP).…”

Section: Introductionmentioning

confidence: 99%

Investigating Correlations and the Validation of SMAP-Sentinel L2 and In Situ Soil Moisture in Thailand

Jotisankasa,

Torsri,

Supavetch

et al. 2023

Sensors

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Soil moisture plays a crucial role in various hydrological processes and energy partitioning of the global surface. The Soil Moisture Active Passive-Sentinel (SMAP-Sentinel) remote-sensing technology has demonstrated great potential for monitoring soil moisture with a maximum spatial resolution of 1 km. This capability can be applied to improve the weather forecast accuracy, enhance water management for agriculture, and managing climate-related disasters. Despite the techniques being increasingly used worldwide, their accuracy still requires field validation in specific regions like Thailand. In this paper, we report on the extensive in situ monitoring of soil moisture (from surface up to 1 m depth) at 10 stations across Thailand, spanning the years 2021 to 2023. The aim was to validate the SMAP surface-soil moisture (SSM) Level 2 product over a period of two years. Using a one-month averaging approach, the study revealed linear relationships between the two measurement types, with the coefficient of determination (R-squared) varying from 0.13 to 0.58. Notably, areas with more uniform land use and topography such as croplands tended to have a better coefficient of determination. We also conducted detailed soil core characterization, including soil–water retention curves, permeability, porosity, and other physical properties. The basic soil properties were used for estimating the correlation constants between SMAP and in situ soil moistures using multiple linear regression. The results produced R-squared values between 0.933 and 0.847. An upscaling approach to SMAP was proposed that showed promising results when a 3-month average of all measurements in cropland was used together. The finding also suggests that the SMAP-Sentinel remote-sensing technology exhibits significant potential for soil-moisture monitoring in certain applications. Further validation efforts and research, particularly in terms of root-zone depths and area-based assessments, especially in the agricultural sector, can greatly improve the technology’s effectiveness and usefulness in the region.

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Assessing and comparing the subseasonal variations of summer soil moisture of satellite products over eastern China

Cited by 2 publications

References 69 publications

Estimates of the Land Surface Hydrology from the Community Land Model Version 5 (CLM5) with Three Meteorological Forcing Datasets over China

Estimates of the Land Surface Hydrology from the Community Land Model Version 5 (CLM5) with Three Meteorological Forcing Datasets over China

Investigating Correlations and the Validation of SMAP-Sentinel L2 and In Situ Soil Moisture in Thailand

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