Evaluation of soil moisture from CCAM-CABLE simulation, satellite-based models estimates and satellite observations: a case study of Skukuza and Malopeni flux towers

Abstract. Reliable estimates of daily, monthly and seasonal soil moisture are useful in a variety of disciplines. The availability of continuous in situ soil moisture observations in southern Africa barely exists; hence, process-based simulation model outputs are a valuable source of climate information, needed for guiding farming practices and policy interventions at various spatio-temporal scales. The aim of this study is to evaluate soil moisture outputs from simulated and satellite-based soil moisture products, and to compare modelled soil moisture across different landscapes. The simulation model consists of a global circulation model known as the conformal-cubic atmospheric model (CCAM), coupled with the CSIRO Atmosphere Biosphere Land Exchange model (CABLE). The satellite-based soil moisture data products include satellite observations from the European Space Agency (ESA) and satellite-observation-based model estimates from the Global Land Evaporation Amsterdam Model (GLEAM). The evaluation is done for both the surface (0–10 cm) and root zone (10–100 cm) using in situ soil moisture measurements collected from two study sites. The results indicate that both the simulation- and satellite-derived models produce outputs that are higher in magnitude range compared to in situ soil moisture observations at the two study sites, especially at the surface. The correlation coefficient ranges from 0.7 to 0.8 (at the root zone) and 0.7 to 0.9 (at the surface), suggesting that models mostly are in an acceptable phase agreement at the surface than at the root zone, and this was further confirmed by the root mean squared error and the standard deviation values. The models mostly show a bias towards overestimation of the observed soil moisture at both the surface and root zone, with the CCAM-CABLE showing the least bias. An analysis evaluating phase agreement using the cross-wavelet analysis has shown that, despite the models' outputs being in phase with the in situ observations, there are time lags in some instances. An analysis of soil moisture mutual information (MI) between CCAM-CABLE and the GLEAM models has successfully revealed that both the simulation and model estimates have a high MI at the root zone as opposed to the surface. The MI mostly ranges between 0.5 and 1.5 at both the surface and root zone. The MI is predominantly high for low-lying relative to high-lying areas.

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Evaluation of soil moisture from CCAM-CABLE simulation, satellite based models estimates and satellite observations: Skukuza and Malopeni flux towers regional case study

10.5194/hess-2018-546 ◽

2018 ◽

Cited By ~ 1

Author(s):

Floyd Vukosi Khosa ◽

Mohau Jacob Mateyisi ◽

Martina Reynita van Der Merwe ◽

Gregor Timothy Feig ◽

Francois Alwyn Engelbrecht ◽

...

Keyword(s):

Time Series ◽

Soil Moisture ◽

Simulation Model ◽

Root Zone ◽

Time Lag ◽

European Space Agency ◽

Circulation Model ◽

Satellite Observation ◽

Satellite Observations

Abstract. Reliable estimates of daily, monthly and seasonal soil moisture are useful in a variety of disciplines. The availability of continuous in situ soil moisture observation records in Southern Africa barely exists. In this regard, process based simulation model outputs turns out to be a valuable source of climate information, which is needed for guiding farming practises and policy interventions at various spatio-temporal scales. Despite their ability to yield historic and future projections of climatic conditions, simulation model outputs often reflect a certain degree of systematic uncertainty hence it is very important to evaluate their representativeness of spatial and temporal patterns against observations. To this effect, this study presents an evaluation of soil moisture outputs from a simulation and satellite data based soil moisture products. The simulation model consists of a global circulation model known as the conformal-cubic atmospheric model (CCAM), coupled to the CSIRO Atmosphere Biosphere Land Exchange model (CABLE). The satellite based soil moisture products include; satellite observations from the European space agency (ESA) and satellite observation based model estimates from the Global Land Evaporation Amsterdam model (GLEAM). The evaluation is done for both the surface (0–10 cm) and root zone (10–100 cm) using in situ soil moisture measurements collected from two savanna sites, located in the Kruger National Park, South Africa. For the two chosen sites with different soil types and vegetation cover, the evaluation considers soil moisture time series aggregated to a monthly time scale from all the data sources. In order to reflect the inter-comparability of CCAM-CABLE simulation output, and GLEAM model estimates, a qualitative analysis of phase agreement, using wavelet analysis is presented. The onset and offset of the wet period, for the two specific sites, is calculated for each of the models and the soil moisture time series covariance between CCAM-CABLE and the GLEAM is discussed. Our results indicate that both the simulation and satellite observation based model outputs are generally consistent with the in situ soil moisture observations at the two study sites, especially at the surface. CCAM-CABLE and GLEAM inter-comparison also shows that the models are generally in phase, however with a time lag of about 12 and 20 days on average, for the surface and root zone respectively. In general the simulation compare well with the GLEAM model estimates, hence indicating that the key physical processes that drive soil moisture in CCAM-CABLE and GLEAM, at the surface and root zone, lead to an appreciable degree of mutual information. This is reinforced by a predominantly positive measure of covariance between the respective two soil moisture outputs.

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Error Propagation from Remotely Sensed Surface Soil Moisture Into Soil Water Index Using an Exponential Filter

10.29007/kvhb ◽

2018 ◽

Author(s):

Domenico De Santis ◽

Daniela Biondi

Keyword(s):

Soil Moisture ◽

Soil Water ◽

Error Propagation ◽

Surface Soil ◽

Root Zone ◽

In Situ Measurements ◽

Surface Soil Moisture ◽

Water Index ◽

Study Sites

In this study an error propagation (EP) scheme was introduced in parallel to exponential filter computation for soil water index (SWI) estimation. A preliminarily assessment of the computed uncertainties was carried out comparing satellite-derived SWI and reference root-zone in situ measurements. The EP scheme has shown skills in detecting potentially less reliable SWI values in the study sites, as well as a better understanding of the exponential filter shortcomings. The proposed approach shows a potential for SWI evaluation, providing simultaneous estimates of time-variant uncertainty.

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Multi-decadal analysis of root-zone soil moisture applying the exponential filter across CONUS

Hydrology and Earth System Sciences ◽

10.5194/hess-21-4403-2017 ◽

2017 ◽

Vol 21 (9) ◽

pp. 4403-4417 ◽

Cited By ~ 11

Author(s):

Kenneth J. Tobin ◽

Roberto Torres ◽

Wade T. Crow ◽

Marvin E. Bennett

Keyword(s):

Soil Moisture ◽

Vegetation Index ◽

Root Zone ◽

European Space Agency ◽

Department Of Energy ◽

Reference Network ◽

Optimization Approach ◽

Moisture Variation ◽

The Us

Abstract. This study applied the exponential filter to produce an estimate of root-zone soil moisture (RZSM). Four types of microwave-based, surface satellite soil moisture were used. The core remotely sensed data for this study came from NASA's long-lasting AMSR-E mission. Additionally, three other products were obtained from the European Space Agency Climate Change Initiative (CCI). These datasets were blended based on all available satellite observations (CCI-active, CCI-passive, and CCI-combined). All of these products were 0.25° and taken daily. We applied the filter to produce a soil moisture index (SWI) that others have successfully used to estimate RZSM. The only unknown in this approach was the characteristic time of soil moisture variation (T). We examined five different eras (1997–2002; 2002–2005; 2005–2008; 2008–2011; 2011–2014) that represented periods with different satellite data sensors. SWI values were compared with in situ soil moisture data from the International Soil Moisture Network at a depth ranging from 20 to 25 cm. Selected networks included the US Department of Energy Atmospheric Radiation Measurement (ARM) program (25 cm), Soil Climate Analysis Network (SCAN; 20.32 cm), SNOwpack TELemetry (SNOTEL; 20.32 cm), and the US Climate Reference Network (USCRN; 20 cm). We selected in situ stations that had reasonable completeness. These datasets were used to filter out periods with freezing temperatures and rainfall using data from the Parameter elevation Regression on Independent Slopes Model (PRISM). Additionally, we only examined sites where surface and root-zone soil moisture had a reasonably high lagged r value (r > 0. 5). The unknown T value was constrained based on two approaches: optimization of root mean square error (RMSE) and calculation based on the normalized difference vegetation index (NDVI) value. Both approaches yielded comparable results; although, as to be expected, the optimization approach generally outperformed NDVI-based estimates. The best results were noted at stations that had an absolute bias within 10 %. SWI estimates were more impacted by the in situ network than the surface satellite product used to drive the exponential filter. The average Nash–Sutcliffe coefficients (NSs) for ARM ranged from −0. 1 to 0.3 and were similar to the results obtained from the USCRN network (0.2–0.3). NS values from the SCAN and SNOTEL networks were slightly higher (0.1–0.5). These results indicated that this approach had some skill in providing an estimate of RZSM. In terms of RMSE (in volumetric soil moisture), ARM values actually outperformed those from other networks (0.02–0.04). SCAN and USCRN RMSE average values ranged from 0.04 to 0.06 and SNOTEL average RMSE values were higher (0.05–0.07). These values were close to 0.04, which is the baseline value for accuracy designated for many satellite soil moisture missions.

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Exploring trends for the H SAF ASCAT root-zone soil moisture data records

10.5194/egusphere-egu21-1379 ◽

2021 ◽

Author(s):

David Fairbairn ◽

Patricia de Rosnay ◽

Peter Weston

Keyword(s):

Soil Moisture ◽

Root Zone ◽

Weather Prediction ◽

Climate Change Impacts ◽

Soil Moisture Data ◽

Negative Root ◽

In Situ Observations ◽

Prediction Systems ◽

Water Scarce

Environmental (e.g. floods, droughts) and weather prediction systems rely on an accurate representation of soil moisture (SM). The EUMETSAT H SAF aims to provide high quality satellite-based hydrological products, including SM. ECMWF is producing ASCAT root zone SM for H SAF. The production relies on an Extended Kalman filter to retrieve root zone SM from surface SM satellite data. A 10 km sampling reanalysis product (1992-2020) forced by ERA5 atmospheric fields (H141/H142) is produced for H SAF, which assimilates ERS/SCAT (1992-2006) and ASCAT-A/B/C (2007-2020) derived surface SM. The root-zone SM performance is validated using sparse in situ observations globally and generally demonstrates a positive and consistent correlation over the period. A negative trend in root-zone SM is found during summer and autumn months over much of Europe during the period (1992-2020). This is consistent with expected climate change impacts and is particularly alarming over the water-scarce Mediterranean region. The recent hot and dry summer of 2019 and dry spring of 2020 are well captured by negative root-zone SM anomalies. Plans for the future H SAF data record products will be presented, including the assimilation of high-resolution EPS-SCA-derived soil moisture data.

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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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Towards an operationally capable satellite-based 0-100 cm soil moisture dataset from C3S

10.5194/egusphere-egu21-12268 ◽

2021 ◽

Author(s):

Adam Pasik ◽

Wolfgang Preimesberger ◽

Bernhard Bauer-Marschallinger ◽

Wouter Dorigo

Keyword(s):

Soil Moisture ◽

Soil Profile ◽

Land Surface ◽

Root Zone ◽

Soil Layer ◽

Land Surface Model ◽

In Situ Measurements ◽

Surface Model ◽

T Values

Multiple satellite-based global surface soil moisture (SSM) datasets are presently available, these however, address exclusively the top layer of the soil (0-5cm). Meanwhile, root-zone soil moisture cannot be directly quantified with remote sensing but can be estimated from SSM using a land surface model. Alternatively, soil water index (SWI; calculated from SSM as a function of time needed for infiltration) can be used as a simple approximation of root-zone conditions. SWI is a proxy for deeper layers of the soil profile which control evapotranspiration, and is hence especially important for studying hydrological processes over vegetation-covered areas and meteorological modelling.Here we introduce the advances in our work on the first operationally capable SWI-based root-zone soil moisture dataset from C3S Soil Moisture v201912 COMBINED product, spanning the period 2002-2020. The uniqueness of this dataset lies in the fact that T-values (temporal lengths ruling the infiltration) characteristic of SWI were translated into particular soil depths making it much more intuitive, user-friendly and easily applicable. Available are volumetric soil moisture values for the top 1 m of the soil profile at 10 cm intervals, where the optimal T-value (T-best) for each soil layer is selected based on a range of correlation metrics with in situ measurements from the International Soil Moisture Network (ISMN) and the relevant soil and climatic parameters. Additionally we present the results of an extensive global validation against in situ measurements (ISMN) as well as the results of investigations into the relationship between a range of soil and climate characteristics and the optimal T-values for particular soil depths.

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A regionally explicit, global SWI calibration based on ISMN observations

10.5194/egusphere-egu21-496 ◽

2021 ◽

Author(s):

Manolis G. Grillakis

Keyword(s):

Soil Moisture ◽

Soil Water ◽

Large Scale ◽

Root Zone ◽

Soil Depth ◽

European Space Agency ◽

Soil Layer ◽

Added Value ◽

Water Index ◽

Soil Moisture Data

Remote sensing has proven to be an irreplaceable tool for monitoring soil moisture. The European Space Agency (ESA), through the Climate Change Initiative (CCI), has provided one of the most substantial contributions in the soil water monitoring, with almost 4 decades of global satellite derived and homogenized soil moisture data for the uppermost soil layer. Yet, due to the inherent limitations of many of the remote sensors, only a limited soil depth can be monitored. To enable the assessment of the deeper soil layer moisture from surface remotely sensed products, the Soil Water Index (SWI) has been established as a convolutive transformation of the surface soil moisture estimation, under the assumption of uniform hydraulic conductivity and the absence of transpiration. The SWI uses a single calibration parameter, the T-value, to modify its response over time.Here the Soil Water Index (SWI) is calibrated using ESA CCI soil moisture against in situ observations from the International Soil Moisture Network and then use Artificial Neural Networks (ANNs) to find the best physical soil, climate, and vegetation descriptors at a global scale to regionalize the calibration of the T-value. The calibration is then used to assess a root zone related soil moisture for the period 2001 &#8211; 2018.The results are compared against the European Centre for Medium-Range Weather Forecasts, ERA5 Land reanalysis soil moisture dataset, showing a good agreement, mainly over mid-latitudes. The results indicate that there is added value to the results of the machine learning calibration, comparing to the uniform T-value. This work contributes to the exploitation of ESA CCI soil moisture data, while the produced data can support large scale soil moisture related studies.

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Assessment with Controlled In-Situ Data of the Dependence of L-Band Radiometry on Sea-Ice Thickness

Remote Sensing ◽

10.3390/rs12040650 ◽

2020 ◽

Vol 12 (4) ◽

pp. 650

Author(s):

Pablo Sánchez-Gámez ◽

Carolina Gabarro ◽

Antonio Turiel ◽

Marcos Portabella

Keyword(s):

Soil Moisture ◽

Sea Ice ◽

Brightness Temperature ◽

European Space Agency ◽

Ground Truth ◽

Ice Thickness ◽

Sea Ice Thickness ◽

In Situ Data ◽

L Band

The European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) and the National Aeronautics and Space Administration (NASA) Soil Moisture Active Passive (SMAP) missions are providing brightness temperature measurements at 1.4 GHz (L-band) for about 10 and 4 years respectively. One of the new areas of geophysical exploitation of L-band radiometry is on thin (i.e., less than 1 m) Sea Ice Thickness (SIT), for which theoretical and empirical retrieval methods have been proposed. However, a comprehensive validation of SIT products has been hindered by the lack of suitable ground truth. The in-situ SIT datasets most commonly used for validation are affected by one important limitation: They are available mainly during late winter and spring months, when sea ice is fully developed and the thickness probability density function is wider than for autumn ice and less representative at the satellite spatial resolution. Using Upward Looking Sonar (ULS) data from the Woods Hole Oceanographic Institution (WHOI), acquired all year round, permits overcoming the mentioned limitation, thus improving the characterization of the L-band brightness temperature response to changes in thin SIT. State-of-the-art satellite SIT products and the Cumulative Freezing Degree Days (CFDD) model are verified against the ULS ground truth. The results show that the L-band SIT can be meaningfully retrieved up to 0.6 m, although the signal starts to saturate at 0.3 m. In contrast, despite the simplicity of the CFDD model, its predicted SIT values correlate very well with the ULS in-situ data during the sea ice growth season. The comparison between the CFDD SIT and the current L-band SIT products shows that both the sea ice concentration and the season are fundamental factors influencing the quality of the thickness retrieval from L-band satellites.

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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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Evaluation of the Performance of SM2RAIN-Derived Rainfall Products over Brazil

Remote Sensing ◽

10.3390/rs11091113 ◽

2019 ◽

Vol 11 (9) ◽

pp. 1113 ◽

Cited By ~ 9

Author(s):

Franklin Paredes-Trejo ◽

Humberto Barbosa ◽

Carlos A. C. dos Santos

Keyword(s):

Soil Moisture ◽

Pearson Correlation ◽

European Space Agency ◽

Rainfall Events ◽

Error Components ◽

Space Agency ◽

Daily Scale ◽

Bioclimatic Conditions ◽

Heavy Rainfall Events

Microwave-based satellite soil moisture products enable an innovative way of estimating rainfall using soil moisture observations with a bottom-up approach based on the inversion of the soil water balance Equation (SM2RAIN). In this work, the SM2RAIN-CCI (SM2RAIN-ASCAT) rainfall data obtained from the inversion of the microwave-based satellite soil moisture (SM) observations derived from the European Space Agency (ESA) Climate Change Initiative (CCI) (from the Advanced SCATterometer (ASCAT) soil moisture data) were evaluated against in situ rainfall observations under different bioclimatic conditions in Brazil. The research V7 version of the Tropical Rainfall Measurement Mission Multi-satellite Precipitation Analysis (TRMM TMPA) was also used as a state-of-the-art rainfall product with an up-bottom approach. Comparisons were made at daily and 0.25° scales, during the time-span of 2007–2015. The SM2RAIN-CCI, SM2RAIN-ASCAT, and TRMM TMPA products showed relatively good Pearson correlation values (R) with the gauge-based observations, mainly in the Caatinga (CAAT) and Cerrado (CER) biomes (R median > 0.55). SM2RAIN-ASCAT largely underestimated rainfall across the country, particularly over the CAAT and CER biomes (bias median < −16.05%), while SM2RAIN-CCI is characterized by providing rainfall estimates with only a slight bias (bias median: −0.20%), and TRMM TMPA tended to overestimate the amount of rainfall (bias median: 7.82%). All products exhibited the highest values of unbiased root mean square error (ubRMSE) in winter (DJF) when heavy rainfall events tend to occur more frequently, whereas the lowest values are observed in summer (JJA) with light rainfall events. The SM2RAIN-based products showed larger contribution of systematic error components than random error components, while the opposite was observed for TRMM TMPA. In general, both SM2RAIN-based rainfall products can be effectively used for some operational purposes on a daily scale, such as water resources management and agriculture, whether the bias is previously adjusted.

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