Comparison of soil moisture products from microwave remote sensing, land model, and reanalysis using global ground observations

Deng, Yuanhong; Wang, Shijie; Bai, Xiaoyong; Wu, Luhua; Cao, Yue; Li, Huiwen; Wang, Mingming; Li, Chao‐Jun; Yang, Yujie; Hu, Zhang; Lu, Qian

doi:10.1002/hyp.13636

Cited by 32 publications

(27 citation statements)

References 72 publications

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“…During weak monsoon conditions, ERAI overestimates SM over India, and SM correlates well with observed rainfall (Shrivastava et al, 2017). Using 670 SM stations worldwide, Deng et al (2020) found that NCEP performed poorly in December-February and June-August and in arid or temperate and dry climates.…”

Section: Introductionmentioning

confidence: 77%

Comprehensive evaluation of satellite-based and reanalysis soil moisture products using in situ observations over China

Ling¹,

Huang²,

Guo³

et al. 2021

Preprint

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Abstract. Soil moisture (SM) plays a critical role in the water and energy cycles of the earth system; consequently, a long-term SM product with high quality is urgently needed. In this study, five SM products, including one microwave remote sensing product [European Space Agency's Climate Change Initiative (ESA CCI)] and four reanalysis datasets [European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis-Interim (ERAI), National Centers for Environmental Prediction (NCEP), the Twentieth Century Reanalysis Project from National Oceanic and Atmospheric Administration (NOAA) and the European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ERA5)], are systematically evaluated using in situ measurements during 1981–2013 in four climate regions at different timescales over mainland China. The results show that ESA CCI is closest to the observations in terms of both the spatial distributions and magnitude of the monthly SM. All reanalysis products tend to overestimate soil moisture in all regions but have higher correlations than the remote sensing product except in Northwest China. The largest inconsistency is found in southern Northeast China, with a relative RMSE value larger than 0.1. However, none of the products can well reproduce the trends of interannual anomalies. The largest relative bias of 44.6 % is found for the ERAI SM product under severe drought conditions, and the lowest relative biases of 4.7 % and 9.5 % are found for the ESA CCI SM product under severe drought conditions and the NCEP SM product under normal conditions, respectively. As decomposing mean square errors in all the products suggests that the bias terms are the dominant contribution, the ESA CCI SM product is a good option for long-term hydrometeorological applications in mainland China. ERA5 is also a promising product, which is attributed to the incorporation of more observations. This long-term intercomparison study provides clues for SM product enhancement and further hydrological applications.

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Section: Introductionmentioning

confidence: 77%

Comprehensive evaluation of satellite-based and reanalysis soil moisture products using in situ observations over China

Ling¹,

Huang²,

Guo³

et al. 2021

Preprint

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“…Compared with ERA-Interim, the ERA5 4D-Var data assimilation system in the Integrated Forecasting System (IFS Cycle 41r2) has been improved with several modifications, representing a decade of research and development in modeling and data assimilation. The ERA soil moisture dataset was validated against the available data from multiple sources, and the use of this dataset for testing model simulations on daily to seasonal time scales has been confirmed [40,41]. In this study, we used the ERA5 soil moisture data from 1982 to 2015, and the depths of the four soil layers ranged from 0 to 7, 7-28, 28-100, and 100-289 cm, respectively.…”

Section: Data Sourcesmentioning

confidence: 99%

Time-Lagged Correlation between Soil Moisture and Intra-Annual Dynamics of Vegetation on the Mongolian Plateau

Bao

et al. 2021

Remote Sensing

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Soil moisture is a reliable water resource for plant growth in arid and semi-arid regions. Characterizing the interaction between soil moisture and vegetation is important for assessing the sustainability of terrestrial ecosystems. This study explores the spatiotemporal characteristics of four soil moisture layers (layer 1: 0–7 cm, layer 2: 7–28 cm, layer 3: 28–100 cm, and layer 4: 100–289 cm) and the time-lagged correlation with the normalized difference vegetation index (NDVI) for different vegetation types on an intra-annual scale on the Mongolian Plateau (MP). The most significant results indicated that: (1) the four layers of soil moisture can be roughly divided into rapid change (layers 1 and 2), active (layer 3), and stable (layer 4) layers. The soil moisture content in the different vegetation regions was forest > grassland > desert vegetation. (2) The soil moisture in layer 1 showed the strongest positive correlation with NDVI in the whole area; meanwhile, the soil moisture of layers 2 and 3 showed the strongest negative correlation with the NDVI mainly in grassland and desert, and layer 4 showed the strongest negative correlation with the NDVI in the forest. (3) Mutual responses of NDVI and deep layer soil moisture required a longer time compared with the shallow layer. In the annual time scale, the NDVI was affected by the change in soil moisture in most of the study area, except for coniferous forest and desert vegetation regions. (4) Under the different stages of vegetation change, the soil moisture changes advance than NDVI about 3 months during the greening stage, while the NDVI changes advance than soil moisture by 0.5 months during the browning stage. Regardless of the stage, changes in soil moisture are initiated from the shallow layer and advance to the deep layer. The results of this study provide deep insight into the relationship between soil moisture and vegetation in arid and semi-arid regions.

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“…Based on in situ SM observations, a variety of SM products have been evaluated at global and continental scales (e.g., Europe, North America, Southeast Asia, Australia, and Africa; Agrawal & Chakraborty, 2020; Albergel et al., 2012; Al‐Yaari et al., 2019; Bazzi et al., 2019; Beck et al., 2021; Das et al., 2019; Deng et al., 2020; Ford & Quiring, 2019; Griesfeller et al., 2013; Ray et al., 2017). A large number of metrics have been used in these studies to describe the ability of products to capture spatial patterns and temporal variability, such as the correlation coefficient, standard deviation, bias, root mean square error, and unbiased root mean square error.…”

Section: Introductionmentioning

confidence: 99%

“…Meanwhile, SM is profoundly influenced by precipitation, which is an important climatic factor. Their interrelationships and feedbacks are an integral part of hydrometeorology and water resources management (Deng et al., 2020). The study about the consistency between SM characterized by various products and precipitation is of significance for product comparison and further selection.…”

Section: Introductionmentioning

confidence: 99%

InterComparison and Evaluation of MultiSource Soil Moisture Products in China

Zhang

et al. 2021

Earth and Space Science

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As an important variable of the climate system, soil moisture (SM) plays a key role in the surface runoff, evapotranspiration, energy balance, and carbon cycle. And its heterogeneity and variability in space and time bring in continuous influences on the simulation and prediction of atmospheric circulation, surface processes, and land-atmosphere interactions (Fan et al., 2020;Schwingshackl et al., 2017;S. I. Seneviratne et al., 2010). SM is also a key variable in surface water fluxes and is critical for drought monitoring and assessment (Velpuri et al., 2016).Compared with other climate variables (e.g., precipitation and temperature), the measure of SM is more difficult and complicated. Moreover, due to the sparse distribution of gauge observation, the spatiotemporal coverage and representation are not well obtained. Thus, it prevents us from conducting reliable quantitative analysis and comprehensive studies. With the rapid development of remote sensing technology and the continuous improvement of relevant models, various SM estimation products (such as reanalysis, Land Data Assimilation Systems [LDAS], and satellite retrieval) are more widely used in the existing research because of their good spatiotemporal continuity. But due to the differences in data source, algorithms, coverage, and spatiotemporal resolution, all these products are subject to great uncertainties Chen et al., , 2021. On the other hand, many different products (e.g., ERA-Interim and ERA5) are constantly being iterated and updated. Therefore, effective evaluation of SM products is a prerequisite for more appropriate and targeted use of SM products in different regions and time scales. And it also provides scientific support for the application of SM products in hydrological research and drought monitoring (Chen &

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Comparison of soil moisture products from microwave remote sensing, land model, and reanalysis using global ground observations

Cited by 32 publications

References 72 publications

Comprehensive evaluation of satellite-based and reanalysis soil moisture products using in situ observations over China

Comprehensive evaluation of satellite-based and reanalysis soil moisture products using in situ observations over China

Time-Lagged Correlation between Soil Moisture and Intra-Annual Dynamics of Vegetation on the Mongolian Plateau

InterComparison and Evaluation of MultiSource Soil Moisture Products in China

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