Interannual Variations and Trends in Remotely Sensed and Modeled Soil Moisture in China

Abstract In this study, a microwave-based multisatellite merged product released from the European Space Agency’s Climate Change Initiative (ESA CCI) and two model-based simulations from the Community Land Model 4.5 (CLM4.5) and Global Land Data Assimilation System (GLDAS) were used to investigate interannual variations and trends of soil moisture in China between 1979 and 2010. They were also evaluated using in situ observations from the nationwide agrometeorological network. These three datasets show consistent drying trends for surface soil moisture in northeastern and central China, as well the eastern portion of Inner Mongolia, and wetting trends in the Tibetan Plateau, which are also identified by in situ observations. Trends in the root-zone soil moisture are in line with those of surface soil moisture seen in the CLM4.5 and GLDAS simulations obtained from most areas in China (78%–88%), except for northwestern China and southwest of the Tibetan Plateau. Moreover, the drying trend intensifies with increasing soil depth. Taking the in situ measurements as reference, it is found that ESA CCI has better accuracy in identifying the significant drying trends while CLM4.5 and GLDAS capture wetting trends better. Compared to temperature, precipitation is the primary factor responsible for these trends, which controls the direction of soil moisture changes, while increasing temperatures can also enhance soil drying during periods of decreased precipitation.

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Evaluation of a multi-satellite soil moisture product and the Community Land Model 4.5 simulation in China

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-12-5151-2015 ◽

2015 ◽

Vol 12 (5) ◽

pp. 5151-5186 ◽

Cited By ~ 6

Author(s):

B. Jia ◽

J. Liu ◽

Z. Xie

Keyword(s):

Soil Moisture ◽

Land Surface ◽

Surface Soil ◽

Eastern China ◽

Surface Model ◽

Surface Soil Moisture ◽

Community Land Model ◽

Soil Moisture Data ◽

In Situ Observations

Abstract. Twenty years of in situ soil moisture data from more than 300 stations located in China are used to perform an evaluation of two surface soil moisture datasets: a microwave-based multi-satellite product (ECV-SM) and the land surface model simulation from the Community Land Model 4.5 (CLM4.5). Both soil moisture products generally show a good agreement with in situ observations. The ECV-SM product has a low bias, with a root mean square difference (RMSD) of 0.075 m3 m-3, but shows a weak correlation with in situ observations (R = 0.41). In contrast, the CLM4.5 simulation, forced by an observation-based atmospheric forcing data, produces better temporal variation of surface soil moisture (R = 0.52), but shows a clear overestimation (bias = 0.05 m3 m-3) and larger RMSD (0.09 m3 m-3), especially in eastern China, caused by inaccurate descriptions of soil characteristics. The ECV-SM product is more likely to be superior in semi-arid regions, mainly because of the accurate retrievals and high observation density, but inferior over areas covered by dense vegetation. Furthermore, it shows a stable to slightly increasing performance in China, except for a decrease during the 2007–2010 blending period. Results from this study can provide comprehensive insight into the performances of the two soil moisture datasets in China, which will be useful for their improvements in merging algorithms or model simulations and for applications in soil moisture data assimilation.

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From near-surface to root-zone soil moisture using an exponential filter: an assessment of the method based on in-situ observations and model simulations

Hydrology and Earth System Sciences ◽

10.5194/hess-12-1323-2008 ◽

2008 ◽

Vol 12 (6) ◽

pp. 1323-1337 ◽

Cited By ~ 261

Author(s):

C. Albergel ◽

C. Rüdiger ◽

T. Pellarin ◽

J.-C. Calvet ◽

N. Fritz ◽

...

Keyword(s):

Soil Moisture ◽

Soil Water ◽

Surface Soil ◽

Root Zone ◽

Synthetic Data ◽

Surface Soil Moisture ◽

Data Set ◽

Water Index ◽

In Situ Observations

Abstract. A long term data acquisition effort of profile soil moisture is under way in southwestern France at 13 automated weather stations. This ground network was developed in order to validate remote sensing and model soil moisture estimates. In this paper, both those in situ observations and a synthetic data set covering continental France are used to test a simple method to retrieve root zone soil moisture from a time series of surface soil moisture information. A recursive exponential filter equation using a time constant, T, is used to compute a soil water index. The Nash and Sutcliff coefficient is used as a criterion to optimise the T parameter for each ground station and for each model pixel of the synthetic data set. In general, the soil water indices derived from the surface soil moisture observations and simulations agree well with the reference root-zone soil moisture. Overall, the results show the potential of the exponential filter equation and of its recursive formulation to derive a soil water index from surface soil moisture estimates. This paper further investigates the correlation of the time scale parameter T with soil properties and climate conditions. While no significant relationship could be determined between T and the main soil properties (clay and sand fractions, bulk density and organic matter content), the modelled spatial variability and the observed inter-annual variability of T suggest that a weak climate effect may exist.

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An Evaluation of the EnKF vs. EnOI and the Assimilation of SMAP, SMOS and ESA CCI Soil Moisture Data over the Contiguous US

Remote Sensing ◽

10.3390/rs11050478 ◽

2019 ◽

Vol 11 (5) ◽

pp. 478 ◽

Cited By ~ 6

Author(s):

Jostein Blyverket ◽

Paul Hamer ◽

Laurent Bertino ◽

Clément Albergel ◽

David Fairbairn ◽

...

Keyword(s):

Soil Moisture ◽

Surface Soil ◽

Open Loop ◽

Surface Zone ◽

Surface Soil Moisture ◽

Atmospheric Forcing ◽

Significance Level ◽

Soil Moisture Data ◽

In Situ Observations

A number of studies have shown that assimilation of satellite derived soil moisture using the ensemble Kalman Filter (EnKF) can improve soil moisture estimates, particularly for the surface zone. However, the EnKF is computationally expensive since an ensemble of model integrations have to be propagated forward in time. Here, assimilating satellite soil moisture data from the Soil Moisture Active Passive (SMAP) mission, we compare the EnKF with the computationally cheaper ensemble Optimal Interpolation (EnOI) method over the contiguous United States (CONUS). The background error–covariance in the EnOI is sampled in two ways: (i) by using the stochastic spread from an ensemble open-loop run, and (ii) sampling from the model spinup climatology. Our results indicate that the EnKF is only marginally superior to one version of the EnOI. Furthermore, the assimilation of SMAP data using the EnKF and EnOI is found to improve the surface zone correlation with in situ observations at a 95 % significance level. The EnKF assimilation of SMAP data is also found to improve root-zone correlation with independent in situ data at the same significance level; however this improvement is dependent on which in situ network we are validating against. We evaluate how the quality of the atmospheric forcing affects the analysis results by prescribing the land surface data assimilation system with either observation corrected or model derived precipitation. Surface zone correlation skill increases for the analysis using both the corrected and model derived precipitation, but only the latter shows an improvement at the 95 % significance level. The study also suggests that assimilation of satellite derived surface soil moisture using the EnOI can correct random errors in the atmospheric forcing and give an analysed surface soil moisture close to that of an open-loop run using observation derived precipitation. Importantly, this shows that estimates of soil moisture could be improved using a combination of assimilating SMAP using the computationally cheap EnOI while using model derived precipitation as forcing. Finally, we assimilate three different Level-2 satellite derived soil moisture products from the European Space Agency Climate Change Initiative (ESA CCI), SMAP and SMOS (Soil Moisture and Ocean Salinity) using the EnOI, and then compare the relative performance of the three resulting analyses against in situ soil moisture observations. In this comparison, we find that all three analyses offer improvements over an open-loop run when comparing to in situ observations. The assimilation of SMAP data is found to perform marginally better than the assimilation of SMOS data, while assimilation of the ESA CCI data shows the smallest improvement of the three analysis products.

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The impact of near-surface soil moisture assimilation at subseasonal, seasonal, and inter-annual timescales

Hydrology and Earth System Sciences ◽

10.5194/hess-19-4831-2015 ◽

2015 ◽

Vol 19 (12) ◽

pp. 4831-4844 ◽

Cited By ~ 19

Author(s):

C. Draper ◽

R. Reichle

Keyword(s):

Soil Moisture ◽

Data Assimilation ◽

Surface Soil ◽

Root Zone ◽

Surface Model ◽

Surface Soil Moisture ◽

Near Surface ◽

In Situ Observations ◽

The Impact

Abstract. A 9 year record of Advanced Microwave Scanning Radiometer – Earth Observing System (AMSR-E) soil moisture retrievals are assimilated into the Catchment land surface model at four locations in the US. The assimilation is evaluated using the unbiased mean square error (ubMSE) relative to watershed-scale in situ observations, with the ubMSE separated into contributions from the subseasonal (SMshort), mean seasonal (SMseas), and inter-annual (SMlong) soil moisture dynamics. For near-surface soil moisture, the average ubMSE for Catchment without assimilation was (1.8 × 10−3 m3 m−3)2, of which 19 % was in SMlong, 26 % in SMseas, and 55 % in SMshort. The AMSR-E assimilation significantly reduced the total ubMSE at every site, with an average reduction of 33 %. Of this ubMSE reduction, 37 % occurred in SMlong, 24 % in SMseas, and 38 % in SMshort. For root-zone soil moisture, in situ observations were available at one site only, and the near-surface and root-zone results were very similar at this site. These results suggest that, in addition to the well-reported improvements in SMshort, assimilating a sufficiently long soil moisture data record can also improve the model representation of important long-term events, such as droughts. The improved agreement between the modeled and in situ SMseas is harder to interpret, given that mean seasonal cycle errors are systematic, and systematic errors are not typically targeted by (bias-blind) data assimilation. Finally, the use of 1-year subsets of the AMSR-E and Catchment soil moisture for estimating the observation-bias correction (rescaling) parameters is investigated. It is concluded that when only 1 year of data are available, the associated uncertainty in the rescaling parameters should not greatly reduce the average benefit gained from data assimilation, although locally and in extreme years there is a risk of increased errors.

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First Assessment of Sentinel-1A Data for Surface Soil Moisture Estimations Using a Coupled Water Cloud Model and Advanced Integral Equation Model over the Tibetan Plateau

Remote Sensing ◽

10.3390/rs9070714 ◽

2017 ◽

Vol 9 (7) ◽

pp. 714 ◽

Cited By ~ 34

Author(s):

Xiaojing Bai ◽

Binbin He ◽

Xing Li ◽

Jiangyuan Zeng ◽

Xin Wang ◽

...

Keyword(s):

Integral Equation ◽

Soil Moisture ◽

Tibetan Plateau ◽

Surface Soil ◽

Cloud Model ◽

Equation Model ◽

The Tibetan Plateau ◽

Surface Soil Moisture ◽

Water Cloud

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An evaluation of ASCAT surface soil moisture products with in-situ observations in Southwestern France

Hydrology and Earth System Sciences ◽

10.5194/hess-13-115-2009 ◽

2009 ◽

Vol 13 (2) ◽

pp. 115-124 ◽

Cited By ~ 158

Author(s):

C. Albergel ◽

C. Rüdiger ◽

D. Carrer ◽

J.-C. Calvet ◽

N. Fritz ◽

...

Keyword(s):

Soil Moisture ◽

Surface Soil ◽

Mountainous Area ◽

Surface Soil Moisture ◽

Water Index ◽

In Situ Observations ◽

Weather Stations ◽

Term Data

Abstract. A long term data acquisition effort of profile soil moisture is currently underway at 13 automatic weather stations located in Southwestern France. In this study, the soil moisture measured in-situ at 5 cm is used to evaluate the normalised surface soil moisture (SSM) estimates derived from coarse-resolution (25 km) active microwave data of the ASCAT scatterometer instrument (onboard METOP), issued by EUMETSAT for a period of 6 months (April–September) in 2007. The seasonal trend is removed from the satellite and in-situ time series by considering scaled anomalies. One station (Mouthoumet) of the ground network, located in a mountainous area, is removed from the analysis as very few ASCAT SSM estimates are available. No correlation is found for the station of Narbonne, which is close to the Mediterranean sea. On the other hand, nine stations present significant correlation levels. For two stations, a significant correlation is obtained when considering only part of the ASCAT data. The soil moisture measured in-situ at those stations, at 30 cm, is used to estimate the characteristic time length (T) of an exponential filter applied to the ASCAT product. The best correlation between a soil water index derived from ASCAT and the in-situ soil moisture observations at 30 cm is obtained with a T-value of 14 days.

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