Error Estimates for Near-Real-Time Satellite Soil Moisture as Derived From the Land Parameter Retrieval Model

Parinussa, Robert; Meesters, A. G. C. A.; Liu, Y. Y.; Dorigo, Wouter; Wagner, Wolfgang; Jeu, R. A. M. de

doi:10.1109/lgrs.2011.2114872

Cited by 115 publications

(96 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This product describes soil moisture in degrees of saturation. For both products, errors commonly remain below 4 volumetric percent (See Figure S2), although the ASCAT product is known to have difficulties over desert areas and both products become less accurate over dense vegetation [Dorigo et al, 2010;Parinussa et al, 2011].…”

Section: Methods Summarymentioning

confidence: 99%

See 1 more Smart Citation

Afternoon rain more likely over drier soils

Taylor

Jeu

Guichard

et al. 2012

Nature

534

520

View full text Add to dashboard Cite

(2012), Afternoon rain more likely over drier soils, Nature, 489(7416), 423-426 (10.1038/nature11377) Land surface properties influence the partition of radiative energy between latent and sensible heat fluxes during daytime. A climatically important process during dry periods is the impact of soil water deficit on limiting evapotranspiration, leading to increased surface heating of the lower atmosphere 1-2 . Soil moisture can influence the development of convective storms through modification of low level atmospheric temperature and humidity 1,3 , which in turn feeds back on soil moisture. There is considerable uncertainty in how soil moisture affects convective storms across the world, due to both a lack of observational evidence and uncertainty in large-scale models 4 . Here we present the first global-scale observational analysis of the coupling between soil moisture and precipitation. This shows that across all six continents studied, afternoon rain falls preferentially over soils which are relatively dry compared to the surrounding area. The signal emerges most clearly in the observations over semi-arid regions, where surface fluxes are sensitive to soil moisture and convective events are frequent. We find no evidence in our analysis of a positive feedback i.e. preference for rain over wetter soils, at the 50-100 km scale studied. In contrast, our analysis of six state-of-the-art global weather and climate models demonstrates the dominance of a positive feedback of soil moisture on simulated precipitation. These results suggest a fundamental weakness in the sensitivity of convection to the land surface in large-scale models, which may contribute to excessive simulated droughts.Soil moisture influences precipitation on a range of time and space scales 5 . In drought-affected continental regions, weak evapo-transpiration leads to reduced atmospheric moisture content over a period of days, potentially suppressing subsequent precipitation 6 . When soil moisture anomalies are extensive, surface-induced perturbations to the atmospheric heat budget may modify synoptic scale circulations 2 , in turn affecting moisture advection from the oceans 7 . On smaller scales, the development of convective clouds and precipitation can be influenced by local surface fluxes over the course of the day 1,3 . Theoretical considerations [8][9] suggest that, in an undisturbed atmosphere, the likelihood and sign of a surface feedback will be determined by the atmospheric profiles of temperature and humidity. Thus one might expect regional variations in the strength and sign of convective sensitivity to soil moisture [10][11] . Mesoscale soil moisture variability can also influence the feedback via the development of daytime circulations 12 , which provide additional convergence to trigger convection [13][14] .There have been a number of studies examining the impact of the land surface on observed rainfall in different regions of the world. Analyses in Illinois 15 and West Africa 16 have indicated positive correlations between ...

show abstract

Section: Methods Summarymentioning

confidence: 99%

“…With ASCAT, we imposed a maximum threshold in the associated error flag of 15%, and for AMSR-E we used a similar threshold, and also incorporated a vegetation density product ] to screen the data. A maximum vegetation optical depth threshold of 0.8 was used to mask the soil moisture values with high uncertainties [Parinussa et al, 2011].…”

Section: Model Datasetsmentioning

confidence: 99%

Afternoon rain more likely over drier soils

Taylor

Jeu

Guichard

et al. 2012

Nature

534

520

View full text Add to dashboard Cite

show abstract

“…TC is a method to estimate the RMSE (and, if desired, correlation coefficients) of three spatially and temporally collocated measurements by assuming a linear error model between the measurements (McColl et al, 2014;Stoffelen, 1998). This methodology has been widely used in error estimation of land and ocean parameters, such as wind speed, sea surface temperature, soil moisture, evaporation, precipitation, f APAR, and in the rescaling of measurement systems to reference system for data assimilation purposes (Alemohammad et al, 2015;D'Odorico et al, 2014;Gruber et al, 2016;Hain et al, 2011;Lei et al, 2015;Miralles et al, 2010Miralles et al, , 2011bParinussa et al, 2011), as well as in validating categorical variables such as the soil freeze-thaw state (McColl et al, 2016). The relationship between each measurement and the true value is assumed to follow a linear model:…”

Section: Target Dataset: a Bayesian Prior Using Triple Collocationmentioning

confidence: 99%

Water, Energy, and Carbon with Artificial Neural Networks (WECANN): a statistically based estimate of global surface turbulent fluxes and gross primary productivity using solar-induced fluorescence

et al. 2017

View full text Add to dashboard Cite

Abstract.A new global estimate of surface turbulent fluxes, latent heat flux (LE) and sensible heat flux (H ), and gross primary production (GPP) is developed using a machine learning approach informed by novel remotely sensed solarinduced fluorescence (SIF) and other radiative and meteorological variables. This is the first study to jointly retrieve LE, H , and GPP using SIF observations. The approach uses an artificial neural network (ANN) with a target dataset generated from three independent data sources, weighted based on a triple collocation (TC) algorithm. The new retrieval, named Water, Energy, and Carbon with Artificial Neural Networks (WECANN), provides estimates of LE, H , and GPP from 2007 to 2015 at 1 • × 1 • spatial resolution and at monthly time resolution. The quality of ANN training is assessed using the target data, and the WECANN retrievals are evaluated using eddy covariance tower estimates from the FLUXNET network across various climates and conditions. When compared to eddy covariance estimates, WECANN typically outperforms other products, particularly for sensible and latent heat fluxes. Analyzing WECANN retrievals across three extreme drought and heat wave events demonstrates the capability of the retrievals to capture the extent of these events.Uncertainty estimates of the retrievals are analyzed and the interannual variability in average global and regional fluxes shows the impact of distinct climatic events -such as the 2015 El Niño -on surface turbulent fluxes and GPP.

show abstract

“…Snow covered areas or frozen ground are typically masked as well as dense or heterogeneously vegetated areas with high optical depth that are not expected to provide reliable soil moisture estimates (Loew, 2008;Parinussa et al, 2011). A pre-processing of the ECVSM data product is required to match it in space and time with the other datasets used in the present study.…”

Section: Multidecadal Satellite Soil Moisture Observations (Ecvsm)mentioning

confidence: 99%

Potential and limitations of multidecadal satellite soil moisture observations for selected climate model evaluation studies

Loew

Stacke

Dorigo

et al. 2013

Hydrol. Earth Syst. Sci.

Self Cite

120

View full text Add to dashboard Cite

Abstract. Soil moisture is an essential climate variable (ECV) of major importance for land-atmosphere interactions and global hydrology. An appropriate representation of soil moisture dynamics in global climate models is therefore important. Recently, a first multidecadal, observation-based soil moisture dataset has become available that provides information on soil moisture dynamics from satellite observations (ECVSM, essential climate variable soil moisture). The present study investigates the potential and limitations of this new dataset for several applications in climate model evaluation. We compare soil moisture data from satellite observations, reanalysis and simulations from a state-of-the-art land surface model and analyze relationships between soil moisture and precipitation anomalies in the different dataset. Other potential applications like model parameter optimization or model initialization are not investigated in the present study. In a detailed regional study, we show that ECVSM is capable to capture well the interannual and intraannual soil moisture and precipitation dynamics in the Sahelian region. Current deficits of the new dataset are critically discussed and summarized at the end of the paper to provide guidance for an appropriate usage of the ECVSM dataset for climate studies.

show abstract

Error Estimates for Near-Real-Time Satellite Soil Moisture as Derived From the Land Parameter Retrieval Model

Cited by 115 publications

References 21 publications

Afternoon rain more likely over drier soils

Afternoon rain more likely over drier soils

Water, Energy, and Carbon with Artificial Neural Networks (WECANN): a statistically based estimate of global surface turbulent fluxes and gross primary productivity using solar-induced fluorescence

Potential and limitations of multidecadal satellite soil moisture observations for selected climate model evaluation studies

Contact Info

Product

Resources

About