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

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

10.5194/hess-2017-118 ◽

2017 ◽

Author(s):

Kenneth J. Tobin ◽

Roberto Torres ◽

Wade T. Crow ◽

Marvin E. Bennett

Keyword(s):

Soil Moisture ◽

Root Mean Square Error ◽

Mean Square Error ◽

Root Mean Square ◽

Root Zone ◽

Reference Network ◽

Mean Square ◽

Moisture Variation ◽

The U.S

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; CCI-Combined). All of these products were quarter degree and 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 U.S. 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 U.S. 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 reasonable high lagged correlation coefficient (r > 0.5). The unknown T value was constrained based on two approaches: optimization of root mean square error (RSME) and calculation based on the NDVI value. Both approaches yielded comparable results; although, as to be expected, the optimization approach generally outperformed NDVI based estimates. 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. Average Nash-Sutcliffe coefficients (NS) for ARM ranged from −0.1 to 0.3 and were similar to the results obtained from the USCRN network (0.2 to 0.3). NS values from the SCAN and SNOTEL networks were slightly higher (0.1 to 0.5). These results indicated that this approach had some skill in providing an estimate of RZSM. In terms of root mean square error (RMSE; in volumetric soil moisture) ARM values actually outperformed those from other networks (0.02 to 0.04). SCAN and USCRN RMSE average values ranged from 0.04 to 0.06 and SNOTEL average RMSE values were higher ranging (0.05 to 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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Evaluation of SMAP Level 2, 3, and 4 Soil Moisture Datasets over the Great Lakes Region

Remote Sensing ◽

10.3390/rs12223785 ◽

2020 ◽

Vol 12 (22) ◽

pp. 3785

Author(s):

Xiaoyong Xu

Keyword(s):

Soil Moisture ◽

Great Lakes ◽

Root Zone ◽

Evaluation Studies ◽

Reference Network ◽

Great Lakes Region ◽

Sparse Network ◽

Level 2 ◽

The U.S

Satellite sensor systems for soil moisture measurements have been continuously evolving. The Soil Moisture Active Passive (SMAP) mission represents one of the latest advances in this regard. Thus far, much of our knowledge of the accuracy of SMAP soil moisture over the Great Lakes region of North America has originated from evaluation studies using in situ data from the U.S. Department of Agriculture (USDA) Natural Resources Conservation Service Soil Climate Analysis Network and/or the U.S. Climate Reference Network, which provide only several in situ sensor stations for this region. As such, these results typically underrepresent the accuracy of SMAP soil moisture in this region, which is characterized by a relatively large soil moisture variability and is one of the least studied regions. In this work, SMAP Level 2‒4 soil moisture products: SMAP/Sentinel-1 L2 Radiometer/Radar Soil Moisture (SPL2SMAP_S), SMAP Enhanced L3 Radiometer Soil Moisture (SPL3SMP_E), and SMAP L4 Surface and Root-Zone Soil Moisture Analysis Update (SPL4SMAU) are evaluated over the southern portion of the Great Lakes region using in situ measurements from Michigan State University’s Enviro-weather Automated Weather Station Network. The unbiased root-mean-square error (ubRMSE) values for both SPL4SMAU surface and root zone soil moisture estimates are below 0.04 m3 m−3 at the 36-km scale, with an average ubRMSE of 0.045 m3 m−3 (0.037 m3 m−3) for the surface (root-zone) soil moisture against the sparse network. The ubRMSE values for SPL3SMP_E a.m. (i.e., descending overpasses) soil moisture retrievals are close to or below 0.04 m3 m−3 at the 36-km scale, with an average ubRMSE of ~0.06 m3 m−3 against the sparse network. The average ubRMSE values are ~0.05‒0.06 m3 m−3 for high-resolution SPL2SMAP_S soil moisture retrievals against the sparse network, with the skill of the baseline algorithm-based soil moisture retrievals exceeding that of the optional algorithm-based counterparts. Clearly, the skill of SPL4SMAU surface soil moisture exceeds that of the SPL3SMP_E and SPL2SMAP_S soil moisture retrievals.

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Information – based uncertainty decomposition in dual channel microwave remote sensing of soil moisture

10.5194/hess-2020-534 ◽

2020 ◽

Author(s):

Bonan Li ◽

Stephen P. Good

Keyword(s):

Remote Sensing ◽

Soil Moisture ◽

Mutual Information ◽

Microwave Remote Sensing ◽

Reference Network ◽

Brightness Temperatures ◽

Dual Channel ◽

The Us ◽

In Situ Observations

Abstract. NASA's Soil Moisture Active-Passive (SMAP) mission characterizes global spatiotemporal patterns in surface soil moisture using dual L-band microwave retrievals of horizontal, TBh, and vertical, TBv, polarized microwave brightness temperatures through a modeled relationship between vegetation opacity and surface scattering albedo (i.e. tau-omega model). Although this model has been validated against in situ soil moisture measurements across sparse validations sites, there is lack of systematic characterization of where and why SMAP estimates deviate from the in situ observations. Here, soil moisture observations from the US Climate Reference Network are used within a mutual information framework to decompose the overall retrieval uncertainty from SMAPs Modified Dual Channel Algorithm (MDCA) into random uncertainty derived from raw data itself and model uncertainty derived from the model’s inherent structure. The results shown that, on average, 12 % of the uncertainty in SMAP soil moisture estimates is caused by the loss of information in the MDCA model itself while the remainder is induced by inadequacy of TBh and TBv observations. We find the fraction of algorithm induced uncertainty is negatively correlated (pearson r of −0.48) with correlations between in-situ observations and MDCA estimates. A decomposition of mutual information between TBh, TBv and MDCA soil moisture shows that on average 55 % of the mutual information is redundantly shared by TBh and TBv, while the information provided uniquely from both TBh and TBv is 15 %. The fraction of information redundantly provided by TBh and TBv was found to be tightly correlated (pearson r = −0.7) to how well the MDCA output correlated to in situ observations. Thus, MDCA overall quality improves as TBh and TBv provide more redundant information for the MDCA. This suggests the informational redundancy between these remotely sensed observations can be used as independent metric to assess the overall quality of algorithms using these data streams. This study provides a baseline approach that can also be applied to evaluate other remote sensing models and understand informational loss as satellite retrievals are translated to end user products.

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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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Estimating growing-season root zone soil moisture from vegetation index-based evapotranspiration fraction and soil properties in the Northwest Mountain region, USA

10.21079/11681/42128 ◽

2021 ◽

Author(s):

Nawa Raj Pradhan

Keyword(s):

Soil Moisture ◽

Vegetation Index ◽

Root Zone ◽

Growing Season ◽

Threshold Value ◽

Reference Network ◽

Moisture Condition ◽

Soil Moisture Condition ◽

Reynolds Creek ◽

Dryness Index

A soil moisture retrieval method is proposed, in the absence of ground-based auxiliary measurements, by deriving the soil moisture content relationship from the satellite vegetation index-based evapotranspiration fraction and soil moisture physical properties of a soil type. A temperature–vegetation dryness index threshold value is also proposed to identify water bodies and underlying saturated areas. Verification of the retrieved growing season soil moisture was performed by comparative analysis of soil moisture obtained by observed conventional in situ point measurements at the 239-km2 Reynolds Creek Experimental Watershed, Idaho, USA (2006–2009), and at the US Climate Reference Network (USCRN) soil moisture measurement sites in Sundance, Wyoming (2012–2015), and Lewistown, Montana (2014–2015). The proposed method best represented the effective root zone soil moisture condition, at a depth between 50 and 100 cm, with an overall average R2 value of 0.72 and average root mean square error (RMSE) of 0.042.

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

Hydrology and Earth System Sciences ◽

10.5194/hess-24-1587-2020 ◽

2020 ◽

Vol 24 (4) ◽

pp. 1587-1609 ◽

Cited By ~ 1

Author(s):

Floyd Vukosi Khosa ◽

Mohau Jacob Mateyisi ◽

Martina Reynita van der Merwe ◽

Gregor Timothy Feig ◽

Francois Alwyn Engelbrecht ◽

...

Keyword(s):

Soil Moisture ◽

Simulation Model ◽

Mean Squared Error ◽

Root Zone ◽

European Space Agency ◽

Circulation Model ◽

Satellite Observations ◽

Time Lags ◽

Study Sites

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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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

<p>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.<br>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.</p>

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Global downscaling of remotely sensed soil moisture using neural networks

Hydrology and Earth System Sciences ◽

10.5194/hess-22-5341-2018 ◽

2018 ◽

Vol 22 (10) ◽

pp. 5341-5356 ◽

Cited By ~ 10

Author(s):

Seyed Hamed Alemohammad ◽

Jana Kolassa ◽

Catherine Prigent ◽

Filipe Aires ◽

Pierre Gentine

Keyword(s):

Soil Moisture ◽

Spatial Resolution ◽

Land Surface ◽

Vegetation Index ◽

Spatial Scales ◽

Ancillary Data ◽

Repeat Time ◽

Spatiotemporal Scales ◽

Better Than

Abstract. Characterizing soil moisture at spatiotemporal scales relevant to land surface processes (i.e., of the order of 1 km) is necessary in order to quantify its role in regional feedbacks between the land surface and the atmospheric boundary layer. Moreover, several applications such as agricultural management can benefit from soil moisture information at fine spatial scales. Soil moisture estimates from current satellite missions have a reasonably good temporal revisit over the globe (2–3-day repeat time); however, their finest spatial resolution is 9 km. NASA's Soil Moisture Active Passive (SMAP) satellite has estimated soil moisture at two different spatial scales of 36 and 9 km since April 2015. In this study, we develop a neural-network-based downscaling algorithm using SMAP observations and disaggregate soil moisture to 2.25 km spatial resolution. Our approach uses the mean monthly Normalized Differenced Vegetation Index (NDVI) as ancillary data to quantify the subpixel heterogeneity of soil moisture. Evaluation of the downscaled soil moisture estimates against in situ observations shows that their accuracy is better than or equal to the SMAP 9 km soil moisture estimates.

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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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