Validation of ECMWF Multi-Layer Reanalysis Soil Moisture Based on the OzNet Hydrology Network

Soil moisture reanalysis products can provide soil water information for the surface and root zone soil layers, which are significant for understanding the water cycle and climate change. However, the accuracy of multi-layer soil moisture datasets obtained from reanalysis products remains unclear in some areas. In this study, we evaluated the root zone soil moisture of the ERA-Interim soil moisture product, as well as the surface soil moisture based on in situ measurements from the OzNet hydrological measurement network over southeast Australia. In general, the ERA-Interim soil moisture product presents good agreement with in situ soil moisture values and can nicely reflect time variations, with correlation coefficient (R) values in the range of 0.73 to 0.84 and unbiased root mean square difference (ubRMSD) values from 0.035 m3·m−3 to 0.060 m3·m−3. Although the ERA-Interim soil moisture also can reflect temporal dynamics of soil moisture at root zone layer at depths of 28–100 cm, low correlations were found in winter. In addition, the ERA-Interim soil moisture product overestimates in situ measurements at depths of 0–7 cm and 7–28 cm, whereas the product shows underestimated values compared with in situ soil moisture at the root zone of 28–100 cm. Consequently, the ERA-Interim soil moisture product has both high absolute and temporal accuracy at depths of 7–28 cm, and the ERA-Interim soil moisture product can nicely capture temporal dynamics at all the evaluated soil level depths, except for the depth of 28–100 cm during the winter months. The contributions of terrain, vegetation cover, and soil texture to the model error were addressed by feature importance estimations using the random forest (RF) algorithm. Results indicate that terrain features may have an impact on the model errors. It is clear that the accuracy of the ERA-Interim soil moisture can be improved by adjusting the assimilation scheme, and the results of this study are expected to provide a comprehensive understanding of the model errors and references for optimizing the model.

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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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Assessment of Soil Moisture Anomaly Sensitivity to Detect Drought Spatio-Temporal Variability in Romania

Sensors ◽

10.3390/s21248371 ◽

2021 ◽

Vol 21 (24) ◽

pp. 8371

Author(s):

Irina Ontel ◽

Anisoara Irimescu ◽

George Boldeanu ◽

Denis Mihailescu ◽

Claudiu-Valeriu Angearu ◽

...

Keyword(s):

Soil Moisture ◽

Land Surface ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Root Zone ◽

Standardized Precipitation Index ◽

In Situ Measurements ◽

Near Surface ◽

Soil Moisture Anomaly

This paper will assess the sensitivity of soil moisture anomaly (SMA) obtained from the Soil water index (SWI) product Metop ASCAT, to identify drought in Romania. The SWI data were converted from relative values (%) to absolute values (m3 m−3) using the soil porosity method. The conversion results (SM) were validated using soil moisture in situ measurements from ISMN at 5 cm depths (2015–2020). The SMA was computed based on a 10 day SWI product, between 2007 and 2020. The analysis was performed for the depths of 5 cm (near surface), 40 cm (sub surface), and 100 cm (root zone). The standardized precipitation index (SPI), land surface temperature anomaly (LST anomaly), and normalized difference vegetation index anomaly (NDVI anomaly) were computed in order to compare the extent and intensity of drought events. The best correlations between SM and in situ measurements are for the stations located in the Getic Plateau (Bacles (r = 0.797) and Slatina (r = 0.672)), in the Western Plain (Oradea (r = 0.693)), and in the Moldavian Plateau (Iasi (r = 0.608)). The RMSE were between 0.05 and 0.184. Furthermore, the correlations between the SMA and SPI, the LST anomaly, and the NDVI anomaly were significantly registered in the second half of the warm season (July–September). Due to the predominantly agricultural use of the land, the results can be useful for the management of water resources and irrigation in regions frequently affected by drought.

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The benefit of brightness temperature assimilation for the SMAP Level-4 surface and root-zone soil moisture analysis

Hydrology and Earth System Sciences ◽

10.5194/hess-25-1569-2021 ◽

2021 ◽

Vol 25 (3) ◽

pp. 1569-1586

Author(s):

Jianxiu Qiu ◽

Jianzhi Dong ◽

Wade T. Crow ◽

Xiaohu Zhang ◽

Rolf H. Reichle ◽

...

Keyword(s):

Soil Moisture ◽

Brightness Temperature ◽

Coupling Strength ◽

Root Zone ◽

In Situ Measurements ◽

In Situ Measurement ◽

Surface Model ◽

Control Factors ◽

Skill Improvement

Abstract. The Soil Moisture Active Passive (SMAP) Level-4 (L4) product provides global estimates of surface soil moisture (SSM) and root-zone soil moisture (RZSM) via the assimilation of SMAP brightness temperature (Tb) observations into the NASA Catchment Land Surface Model (CLSM). Here, using in situ measurements from 2474 sites in China, we evaluate the performance of soil moisture estimates from the L4 data assimilation (DA) system and from a baseline “open-loop” (OL) simulation of CLSM without Tb assimilation. Using random forest regression, the efficiency of the L4 DA system (i.e., the performance improvement in DA relative to OL) is attributed to eight control factors related to the CLSM as well as τ–ω radiative transfer model (RTM) components of the L4 system. Results show that the Spearman rank correlation (R) for L4 SSM with in situ measurements increases for 77 % of the in situ measurement locations (relative to that of OL), with an average R increase of approximately 14 % (ΔR=0.056). RZSM skill is improved for about 74 % of the in situ measurement locations, but the average R increase for RZSM is only 7 % (ΔR=0.034). Results further show that the SSM DA skill improvement is most strongly related to the difference between the RTM-simulated Tb and the SMAP Tb observation, followed by the error in precipitation forcing data and estimated microwave soil roughness parameter h. For the RZSM DA skill improvement, these three dominant control factors remain the same, although the importance of soil roughness exceeds that of the Tb simulation error, as the soil roughness strongly affects the ingestion of DA increments and further propagation to the subsurface. For the skill of the L4 and OL estimates themselves, the top two control factors are the precipitation error and the SSM–RZSM coupling strength error, both of which are related to the CLSM component of the L4 system. Finally, we find that the L4 system can effectively filter out errors in precipitation. Therefore, future development of the L4 system should focus on improving the characterization of the SSM–RZSM coupling strength.

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Assessment of the SMAP Level-4 Surface and Root-Zone Soil Moisture Product Using In Situ Measurements

Journal of Hydrometeorology ◽

10.1175/jhm-d-17-0063.1 ◽

2017 ◽

Vol 18 (10) ◽

pp. 2621-2645 ◽

Cited By ~ 92

Author(s):

Rolf H. Reichle ◽

Gabrielle J. M. De Lannoy ◽

Qing Liu ◽

Joseph V. Ardizzone ◽

Andreas Colliander ◽

...

Keyword(s):

Soil Moisture ◽

Brightness Temperature ◽

Land Surface ◽

Root Zone ◽

Land Surface Model ◽

In Situ Measurements ◽

Surface Model ◽

Accuracy Requirement ◽

Temperature Observations

Abstract The Soil Moisture Active Passive (SMAP) mission Level-4 Surface and Root-Zone Soil Moisture (L4_SM) data product is generated by assimilating SMAP L-band brightness temperature observations into the NASA Catchment land surface model. The L4_SM product is available from 31 March 2015 to present (within 3 days from real time) and provides 3-hourly, global, 9-km resolution estimates of surface (0–5 cm) and root-zone (0–100 cm) soil moisture and land surface conditions. This study presents an overview of the L4_SM algorithm, validation approach, and product assessment versus in situ measurements. Core validation sites provide spatially averaged surface (root zone) soil moisture measurements for 43 (17) “reference pixels” at 9- and 36-km gridcell scales located in 17 (7) distinct watersheds. Sparse networks provide point-scale measurements of surface (root zone) soil moisture at 406 (311) locations. Core validation site results indicate that the L4_SM product meets its soil moisture accuracy requirement, specified as an unbiased RMSE (ubRMSE, or standard deviation of the error) of 0.04 m3 m−3 or better. The ubRMSE for L4_SM surface (root zone) soil moisture is 0.038 m3 m−3 (0.030 m3 m−3) at the 9-km scale and 0.035 m3 m−3 (0.026 m3 m−3) at the 36-km scale. The L4_SM estimates improve (significantly at the 5% level for surface soil moisture) over model-only estimates, which do not benefit from the assimilation of SMAP brightness temperature observations and have a 9-km surface (root zone) ubRMSE of 0.042 m3 m−3 (0.032 m3 m−3). Time series correlations exhibit similar relative performance. The sparse network results corroborate these findings over a greater variety of climate and land cover conditions.

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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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Assimilation of SMOS soil moisture into a distributed hydrological model and impacts on the water cycle variables over the Ouémé catchment in Benin

10.5194/hess-2015-548 ◽

2016 ◽

Author(s):

Delphine J. Leroux ◽

Thierry Pellarin ◽

Théo Vischel ◽

Jean-Martia Cohard ◽

Tania Gascon ◽

...

Keyword(s):

Soil Moisture ◽

Hydrological Model ◽

Water Cycle ◽

Positive Impact ◽

In Situ Measurements ◽

Efficiency Coefficient ◽

Satellite Precipitation ◽

The Impact ◽

Hydrological Variables

Abstract. The impact of the assimilation of surface soil moisture on the simulations of the physically based hydrological model DHSVM (Distributed Hydrology Soil Vegetation Model) is investigated in this paper for a 12 000 km catchment located in Benin, West Africa. Thanks to a large number of rain gauges spread all over the entire basin, reference simulations are performed from one year of calibration (in 2010) and two years of evaluation (2011 and 2012) based on in situ measurements of streamflow at the outlet and local observations of soil moisture at different soil depths and evapotranspiration. In a second step, several satellite products (PERSIANN, TRMM-3B42RT, and CMORPH) are used instead of in situ precipitation measurements. These products bring too much water (especially PERSIANN and CMORPH), sometimes not at the correct time of the year, which has a large impact on various hydrological variables. In order to correct for the wrong amount of input water brought by the satellite precipitation products, the SMOS satellite soil moisture observations are assimilated in the hydrological model. An optimal interpolation is implemented here using an influence radius in order to replicate the field of view of the SMOS instrument. The assimilation of SMOS data shows a positive impact on the soil moisture at different depths (5, 40, and 80 cm defined in the model), with a decrease of the bias compared to the in situ measurements. Streamflow is also positively impacted with a large improvement of the Nash efficiency coefficient after assimilation (from negative to positive for PERSIANN and CMORPH). Finally, the temporal evolution of the water table depth is also greatly improved (from 0.1–0.3 to 0.8–0.9 for PERSIANN and CMORPH). This work shows that the use of satellite precipitation products into a hydrological model can lead to large errors that can be reduced by assimilating satellite soil moisture, which has a positive impact on the estimation of hydrological variables at deeper layers and at other stages of the water cycle.

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Validation of SMOS-IC Soil Moisture over Brazilian Semiarid Using in situ Measurements

10.29007/19gn ◽

2018 ◽

Author(s):

Diego Cézar Dos Santos Araújo ◽

Suzana Maria Gico Lima Montenegro ◽

Ana Cláudia Villar E Luna Gusmão ◽

Diogo Francisco Borba Rodrigues

Keyword(s):

Soil Moisture ◽

Correlation Coefficient ◽

Pearson Correlation ◽

In Situ Measurements ◽

Remote Sensors ◽

Error Metrics ◽

Brazilian Semiarid ◽

Ocean Salinity ◽

Mean Square Difference

Soil Moisture and Ocean Salinity (SMOS) data validation has been widely performed worldwide since the product became available. However, there are few studies for Brazil. This study focused on the validation of a new version of SMOS product developed by the Institut National de la Recherche Agronomique (INRA) and Center d'Etudes Spatiales de la BIOsphère (CESBIO). One of the advantages of SMOS- INRA-CESBIO (SMOS-IC) is that the product is as independent as possible from auxiliary data. The validation was performed at 7 stations located in a semi-arid mesoregion of Pernambuco State, Northeast Brazil, for the year 2016. Bias, root mean square difference (RMSD), unbiased RMSD and the Pearson correlation coefficient (R) were computed between the SMOS-IC data and in situ measurements, considering only the ascending orbit (6 am local time). The results were consistent with those found in several studies, including error metrics. The correlation coefficient (r) ranged from 0.53 to 0.86 (mean 0.68) and the sensor was able to respond adequately to rainfall events. The results of this study are useful to demonstrate the need to continue validating soil moisture data obtained by remote sensors throughout the state, especially in more rainy locations.

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Soil Moisture Analyses at ECMWF: Evaluation Using Global Ground-Based In Situ Observations

Journal of Hydrometeorology ◽

10.1175/jhm-d-11-0107.1 ◽

2012 ◽

Vol 13 (5) ◽

pp. 1442-1460 ◽

Cited By ~ 82

Author(s):

C. Albergel ◽

P. de Rosnay ◽

G. Balsamo ◽

L. Isaksen ◽

J. Muñoz-Sabater

Keyword(s):

Soil Moisture ◽

Land Surface ◽

Root Zone ◽

Current Model ◽

Atmospheric Model ◽

Dry Land ◽

Climate Conditions ◽

Mean Square Difference ◽

Main Component

Abstract In situ soil moisture from 117 stations across the world and under different biome and climate conditions are used to evaluate two soil moisture products from the European Centre for Medium-Range Weather Forecasts (ECMWF)—namely, the operational analysis and the interim reanalysis [ECMWF Re-Analysis Interim (ERA-Interim)]. ECMWF’s operational Integrated Forecasting System (IFS) is based on a continuous effort to improve the analysis and modeling systems, resulting in frequent updates (a few times a year). The ERA-Interim reanalysis is produced by a fixed IFS version (for the main component of the atmospheric model and data assimilation). It has the advantage of being consistent over the whole period from 1979 onward and by design, reanalysis products are more suitable than their operational counterparts for use in climate studies. Although the two analyses show good skills in capturing surface soil moisture variability, they tend to overestimate soil moisture, particularly for dry land. Over the 2008–10 period, averaged statistical scores (correlation, bias, and root-mean-square difference) are 0.70, −0.081 m3 m−3, and 0.113 m3 m−3 for the operational product and 0.63, −0.079 m3 m−3, and 0.121 m3 m−3 for ERA-Interim. Compared to the scheme used in ERA-Interim, the current model used in the IFS has an improved match to soil moisture that is attributed to recent changes in the IFS. Indeed, major upgrades recently implemented in the operational land surface analysis and modeling system improve the surface and the root-zone soil moisture analyses.

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Mapping High Spatiotemporal-Resolution Soil Moisture by Upscaling Sparse Ground-Based Observations Using a Bayesian Linear Regression Method for Comparison with Microwave Remotely Sensed Soil Moisture Products

Remote Sensing ◽

10.3390/rs13020228 ◽

2021 ◽

Vol 13 (2) ◽

pp. 228

Author(s):

Jian Kang ◽

Rui Jin ◽

Xin Li ◽

Yang Zhang

Keyword(s):

Soil Moisture ◽

Large Scale ◽

Spatial Scales ◽

Microwave Remote Sensing ◽

In Situ Measurements ◽

Spatial Density ◽

Linear Regression Method ◽

Spatiotemporal Resolution ◽

Pixel Scale

In recent decades, microwave remote sensing (RS) has been used to measure soil moisture (SM). Long-term and large-scale RS SM datasets derived from various microwave sensors have been used in environmental fields. Understanding the accuracies of RS SM products is essential for their proper applications. However, due to the mismatched spatial scale between the ground-based and RS observations, the truth at the pixel scale may not be accurately represented by ground-based observations, especially when the spatial density of in situ measurements is low. Because ground-based observations are often sparsely distributed, temporal upscaling was adopted to transform a few in situ measurements into SM values at a pixel scale of 1 km by introducing the temperature vegetation dryness index (TVDI) related to SM. The upscaled SM showed high consistency with in situ SM observations and could accurately capture rainfall events. The upscaled SM was considered as the reference data to evaluate RS SM products at different spatial scales. In regard to the validation results, in addition to the correlation coefficient (R) of the Soil Moisture Active Passive (SMAP) SM being slightly lower than that of the Climate Change Initiative (CCI) SM, SMAP had the best performance in terms of the root-mean-square error (RMSE), unbiased RMSE and bias, followed by the CCI. The Soil Moisture and Ocean Salinity (SMOS) products were in worse agreement with the upscaled SM and were inferior to the R value of the X-band SM of the Advanced Microwave Scanning Radiometer 2 (AMSR2). In conclusion, in the study area, the SMAP and CCI SM are more reliable, although both products were underestimated by 0.060 cm3 cm−3 and 0.077 cm3 cm−3, respectively. If the biases are corrected, then the improved SMAP with an RMSE of 0.043 cm3 cm−3 and the CCI with an RMSE of 0.039 cm3 cm−3 will hopefully reach the application requirement for an accuracy with an RMSE less than 0.040 cm3 cm−3.

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Global soil moisture data derived through machine learning trained with in-situ measurements

Scientific Data ◽

10.1038/s41597-021-00964-1 ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Sungmin O. ◽

Rene Orth

Keyword(s):

Machine Learning ◽

Soil Moisture ◽

Large Scale ◽

Short Term Memory ◽

Temporal Dynamics ◽

Soil Moisture Data ◽

Wide Range ◽

Global Soil

AbstractWhile soil moisture information is essential for a wide range of hydrologic and climate applications, spatially-continuous soil moisture data is only available from satellite observations or model simulations. Here we present a global, long-term dataset of soil moisture derived through machine learning trained with in-situ measurements, SoMo.ml. We train a Long Short-Term Memory (LSTM) model to extrapolate daily soil moisture dynamics in space and in time, based on in-situ data collected from more than 1,000 stations across the globe. SoMo.ml provides multi-layer soil moisture data (0–10 cm, 10–30 cm, and 30–50 cm) at 0.25° spatial and daily temporal resolution over the period 2000–2019. The performance of the resulting dataset is evaluated through cross validation and inter-comparison with existing soil moisture datasets. SoMo.ml performs especially well in terms of temporal dynamics, making it particularly useful for applications requiring time-varying soil moisture, such as anomaly detection and memory analyses. SoMo.ml complements the existing suite of modelled and satellite-based datasets given its distinct derivation, to support large-scale hydrological, meteorological, and ecological analyses.

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