scholarly journals Evaluation of AMSR-E-Derived Soil Moisture Retrievals Using Ground-Based and PSR Airborne Data during SMEX02

2005 ◽  
Vol 6 (6) ◽  
pp. 864-877 ◽  
Author(s):  
M. F. McCabe ◽  
H. Gao ◽  
E. F. Wood
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Microwave Emission ◽  
Moisture Distribution ◽  
Emission Model ◽  
Near Surface ◽  
The North ◽  
Microwave Sounding ◽  
Land Data Assimilation System ◽  
Land Surface Parameters

Abstract A Land Surface Microwave Emission Model (LSMEM) is used to derive soil moisture estimates over Iowa during the Soil Moisture Experiment 2002 (SMEX02) field campaign, using brightness temperature data from the Advanced Microwave Sounding Radiometer (AMSR)-E satellite. Spatial distributions of the near-surface soil moisture are produced using the LSMEM, with data from the North American Land Data Assimilation System (NLDAS), vegetation and land surface parameters estimated through recent Moderate Imaging Spectroradiometer (MODIS) land surface products, and standard soil datasets. To assess the value of soil moisture estimates from the 10.7-GHz X-band sensor on the AMSR-E instrument, retrievals are evaluated against ground-based sampling and soil moisture estimates from the airborne Polarimetric Scanning Radiometer (PSR) operating at C band. The PSR offers high-resolution detail of the soil moisture distribution, which can be used to analyze heterogeneity within the scale of the AMSR-E pixel. Preliminary analysis indicates that retrievals from the AMSR-E instrument at 10.7 GHz using the LSMEM are surprisingly robust, with accuracies within 3% vol/vol compared with in situ samples. Results from these AMSR-E comparisons also indicate potential in determining soil moisture patterns over regional scales, even in the presence of vegetation. Assessment of soil moisture determined through local-scale sampling within the larger-scale AMSR-E footprint reveals a consistent level of agreement over a range of meteorological and surface conditions, offering promise for improved land surface hydrometeorological characterization.

scholarly journals Required sampling-density of ground-based soil moisture and brightness temperature observations for calibration/validation of L-band satellite observations based on a virtual reality

2019 ◽  
Author(s):  
Shaoning Lv ◽  
Bernd Schalge ◽  
Pablo Saavedra Garfias ◽  
Clemens Simmer
Keyword(s):  
Soil Moisture ◽  
Brightness Temperature ◽  
Confidence Level ◽  
Land Surface ◽  
Microwave Remote Sensing ◽  
Global Scale ◽  
Microwave Emission ◽  
Near Surface ◽  
Temperature Observations ◽  
Better Than

Abstract. Microwave remote sensing is the most promising tool for monitoring global-scale near-surface soil moisture distributions. With the Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) missions in orbit, considerable efforts are made to evaluate their soil moisture products via ground observations, forward microwave transfer simulation, and retrievals. Due to the large footprint of the satellite radiometers of about 40 km in diameter and the spatial heterogeneity of soil moisture, minimum sampling densities for soil moisture are required to challenge the targeted precision. Here we use 400 m resolution simulations with the regional terrestrial system model TerrSysMP and its coupling with the Community Microwave Emission Modelling platform (CMEM) to quantify sampling distance required for soil moisture and brightness temperature validation. Our analysis suggests that an overall sampling resolution of better than 6 km is required to validate the targeted accuracy of 0.04 cm3/cm3 (70 % confidence level) in SMOS and SMAP over typical midlatitude European regions. The minimum sampling resolution depends on the land-surface inhomogeneity and the meteorological situation, which influence the soil moisture patterns, and ranges from about 7 km to 17 km for a 70 % confidence level for a typical year. At the minimum sampling resolution for a 70 % confidence level also the accuracy of footprint-averaged brightness temperature estimates is equal or better than 15 K/10 K for H/V polarization. Estimates strongly deteriorate with sparser sampling densities, e.g., at 3/9 km with 3/5 sampling sites the confidence level of derived footprint estimates can reach about 0.5–0.6 for soil moisture which is much less than the standard 0.7 requirements for ground measurements. The representativeness of ground-based soil moisture and brightness temperature observations – and thus their required minimum sampling densities – are only weakly correlated in space and time. This study provides a basis for a better understanding of sometimes strong mismatches between derived satellite soil moisture products and ground-based measurements.

The implementation of Community Microwave Emission Model (CMEM) as the observation operator in TerrsysMP-PDAF

2020 ◽  
Author(s):  
Shaoning Lv ◽  
Stefan Poll ◽  
Bernd Schalge ◽  
Pablo Garfias ◽  
Clemens Simmer
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Land Surface ◽  
Passive Microwave ◽  
Microwave Emission ◽  
Emission Model ◽  
Ideal Case ◽  
Observation Operator ◽  
Community Land Model ◽  
L Band

<p>Studies with satellite-based passive microwave L-band observations have been fostered strongly by the launch of NASA's Soil Moisture Active Passive (SMAP) satellite on January 31, 2015 (Entekhabi et al. 2010), which complements and extends the observations at L-band by the ESA's Soil Moisture Ocean Salinity (SMOS) mission in orbit since 2009 (Kerr et al. 2001, Mecklenburg et al. 2012, Lievens et al. 2014). SMOS and SMAP data assimilation studies started during their pre- and post-launch period. Flores et al. (2012) used an Ensemble Kalman Filter to constrain the uncertainties of the simulated soil moisture fields from physical-based hydrological models. Our work intends to explore the use and value of passive L-band satellite observations for ensemble-based data assimilation with fully-coupled terrestrial system models for mesoscale catchments. An observation operator for satellite-based passive microwave (PMW) observations based on the community microwave emission model (CMEM) (de Rosnay et al. 2009, Drusch et al. 2009) has been modified, applied and tested in an ideal case developed within the FOR2131 (Schalge et al. 2016) with the coupled subsurface-land surface-atmosphere simulation platform TerrSysMP (Shrestha et al. 2014), which couples ParFlow (subsurface), Community Land Model (CLM, surface), and COSMO (atmosphere). We achieve the development of a satellite simulator for passive L-band observations of the satellite missions SMAP and SMOS and its adaptation to the ideal case, and the lower-resolution TerrSysMP model applied for data assimilation (TerrSysMP-PDAF).</p>

scholarly journals Benchmarking NLDAS-2 Soil Moisture and Evapotranspiration to Separate Uncertainty Contributions

2016 ◽  
Vol 17 (3) ◽  
pp. 745-759 ◽  
Author(s):  
Grey S. Nearing ◽  
David M. Mocko ◽  
Christa D. Peters-Lidard ◽  
Sujay V. Kumar ◽  
Youlong Xia
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Model Parameters ◽  
Land Surface Models ◽  
Surface Mass ◽  
The North ◽  
Surface Models ◽  
Moisture State ◽  
Using Data ◽  
Land Data Assimilation System

Abstract Model benchmarking allows us to separate uncertainty in model predictions caused by model inputs from uncertainty due to model structural error. This method is extended with a “large sample” approach (using data from multiple field sites) to measure prediction uncertainty caused by errors in 1) forcing data, 2) model parameters, and 3) model structure, and use it to compare the efficiency of soil moisture state and evapotranspiration flux predictions made by the four land surface models in phase 2 of the North American Land Data Assimilation System (NLDAS-2). Parameters dominated uncertainty in soil moisture estimates and forcing data dominated uncertainty in evapotranspiration estimates; however, the models themselves used only a fraction of the information available to them. This means that there is significant potential to improve all three components of NLDAS-2. In particular, continued work toward refining the parameter maps and lookup tables, the forcing data measurement and processing, and also the land surface models themselves, has potential to result in improved estimates of surface mass and energy balances.

scholarly journals Required sampling density of ground-based soil moisture and brightness temperature observations for calibration and validation of L-band satellite observations based on a virtual reality

2020 ◽  
Vol 24 (4) ◽  
pp. 1957-1973
Author(s):  
Shaoning Lv ◽  
Bernd Schalge ◽  
Pablo Saavedra Garfias ◽  
Clemens Simmer
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Brightness Temperature ◽  
Confidence Level ◽  
Land Surface ◽  
System Modeling ◽  
Microwave Remote Sensing ◽  
Microwave Emission ◽  
Near Surface ◽  
Sampling Distance

Abstract. Microwave remote sensing is the most promising tool for monitoring near-surface soil moisture distributions globally. With the Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) missions in orbit, considerable efforts are being made to evaluate derived soil moisture products via ground observations, microwave transfer simulation, and independent remote sensing retrievals. Due to the large footprint of the satellite radiometers of about 40 km in diameter and the spatial heterogeneity of soil moisture, minimum sampling densities for soil moisture are required to challenge the targeted precision. Here we use 400 m resolution simulations with the regional Terrestrial System Modeling Platform (TerrSysMP) and its coupling with the Community Microwave Emission Modelling platform (CMEM) to quantify the maximum sampling distance allowed for soil moisture and brightness temperature validation. Our analysis suggests that an overall sampling distance of finer than 6 km is required to validate the targeted accuracy of 0.04 cm3 cm−3 with a 70 % confidence level in SMOS and SMAP estimates over typical mid-latitude European regions. The maximum allowed sampling distance depends on the land-surface heterogeneity and the meteorological situation, which influences the soil moisture patterns, and ranges from about 6 to 17 km for a 70 % confidence level for a typical year. At the maximum allowed sampling distance on a 70 % confidence level, the accuracy of footprint-averaged soil moisture is equal to or better than brightness temperature estimates over the same area. Estimates strongly deteriorate with larger sampling distances. For the evaluation of the smaller footprints of the active and active–passive products of SMAP the required sampling densities increase; e.g., when a grid resolution of 3 km diameter is sampled by three sites of footprints of 9 km sampled by five sites required, only 50 %–60 % of the pixels have a sampling error below the nominal values. The required minimum sampling densities for ground-based radiometer networks to estimate footprint-averaged brightness temperature are higher than for soil moisture due to the non-linearities of radiative transfer, and only weakly correlated in space and time. This study provides a basis for a better understanding of the sometimes strong mismatches between derived satellite soil moisture products and ground-based measurements.

scholarly journals How important are atmospheric depressions and mobile cyclones for emitting mineral dust aerosol in North Africa?

2014 ◽  
Vol 14 (17) ◽  
pp. 8983-9000 ◽  
Author(s):  
S. Fiedler ◽  
K. Schepanski ◽  
P. Knippertz ◽  
B. Heinold ◽  
I. Tegen
Keyword(s):  
Soil Moisture ◽  
West Africa ◽  
North Africa ◽  
Mineral Dust ◽  
Dust Emission ◽  
Satellite Observation ◽  
Emission Model ◽  
Small Contribution ◽  
Near Surface ◽  
The North

Abstract. This study presents the first quantitative estimate of the mineral dust emission associated with atmospheric depressions and mobile cyclones in North Africa. Atmospheric depressions are automatically tracked at 925 hPa based on ERA-Interim data from the European Centre for Medium-Range Weather Forecasts for 1989–2008. A set of filter criteria is applied to identify mobile cyclones, i.e. migrating and long-lived cyclones. The shorter term cyclone is used as a synonym for mobile cyclones. Dust emission is calculated with a dust emission model driven by 10 m winds and soil moisture from ERA-Interim. Emission peaks during winter and spring with spatial averages of 250–380 g m−2 per month. Comparison of the dust source activation frequency from the model against SEVIRI satellite observation shows a good agreement in the Bodélé Depression but differences in the north and west of North Africa. Depressions are abundant, particularly in summer when the Saharan heat low is situated over West Africa and during spring in the lee of the Atlas Mountains. Up to 90% (55% annually and spatially averaged) of dust emission occurs within 10 degrees of these depressions, with embedded mechanisms such as nocturnal low-level jets playing a role. Cyclones are rarer and occur primarily north of 20° N in spring in agreement with previous studies and over summertime West Africa consistent with near-surface signatures of African Easterly Waves. Dust emission within 10 degrees of cyclones peaks over Libya with up to 25% in spring. Despite the overall small contribution of 4% annually and spatially averaged, cyclones coincide with particularly intense dust emission events exceeding the climatological mean by a factor of four to eight. Soil moisture weakens dust emission during cyclone passage by about 10%.

Retrieval of basic soil physical properties by assimilating radiometry observations in the community land surface model

2020 ◽  
Author(s):  
Hong Zhao ◽  
Yijian Zeng ◽  
Bob Su ◽  
Xujun Han
Keyword(s):  
Soil Moisture ◽  
Soil Properties ◽  
Land Surface ◽  
Land Surface Model ◽  
Microwave Remote Sensing ◽  
Microwave Emission ◽  
Surface Model ◽  
Emission Model ◽  
The Impact ◽  
Parameter Retrieval

<p>Accurate basic soil properties information is fundamental for obtaining reliable soil moisture using land surface models. In view of the passive microwave remote sensing, basic soil properties have an impact on soil dielectric constant, together with soil moisture and temperature. The common link enables to use coupled land surface model with microwave emission model for retrieving basic soil properties in space, especially in remote areas such as the third pole region. The Maqu site in the eastern Tibetan Plateau, including ELBARA-III radiometry observations, was taken as the case. This paper employed an improved observation operator— a discrete scattering-emission model of L-band radiometry with an air-to-soil transition model embedded in, which considers both geometric and dielectric roughness impacts from heterogeneous topsoil structure on surface emission. Community Land Model 4.5 together with Local Ensemble Transform Kalman Filter algorithm were used by mean of the Open Source Multivariate Land Data Assimilation Framework. The retrieved basic soil properties were compared to in situ measurements, as well as the update soil moisture and temperature and energy fluxes. The impacts from surface roughness consideration and polarization configuration on parameter retrieval were also evaluated. To gain an insight on the impact from time interval of observations on parameter retrieval, results using observations at SMAP descending and ascending time were discussed.</p>

scholarly journals A simple parameterisation for retrieving soil moisture from passive microwave data

2001 ◽  
Vol 5 (1) ◽  
pp. 39-48 ◽  
Author(s):  
E. J. Burke ◽  
L. P. Simmonds
Keyword(s):  
Soil Moisture ◽  
Soil Water ◽  
Passive Microwave ◽  
Microwave Emission ◽  
Balance Model ◽  
Retrieval Algorithm ◽  
Emission Model ◽  
Near Surface ◽  
Wide Range ◽  
L Band

Abstract. MICRO-SWEAT, a physically based soil water and energy balance model coupled with a microwave emission model, was used to investigate the relationship between near surface soil moisture (θ0-5) and L-band microwave brightness temperature (TB) under a wide range of conditions. The effects of soil texture, look angle and vegetation on this relationship were parameterised and combined into a simple summary model relating θ0-5 to TB. This model retains much of the physical basis of MICRO-SWEAT but can be used in more data limiting circumstances. It was tested using a variety of truck-based L-band data sets collected between 1980 and 1982. This paper emphasises the need to have an accurate estimate of the vegetation optical depth (a parameter that describes the degree of influence of the vegetation on the microwave emission from the soil surface) in order to retrieve correctly the soil water content. Keywords: passive microwave, soil moisture, remote sensing, vegetation, retrieval algorithm

scholarly journals Evaluation and Comparison of Noah and Pleim–Xiu Land Surface Models in MM5 Using GÖTE2001 Data: Spatial and Temporal Variations in Near-Surface Air Temperature

2007 ◽  
Vol 46 (10) ◽  
pp. 1587-1605 ◽  
Author(s):  
J-F. Miao ◽  
D. Chen ◽  
K. Borne
Keyword(s):  
Soil Moisture ◽  
Air Temperature ◽  
Land Surface ◽  
Model Performance ◽  
Surface Air Temperature ◽  
Near Surface ◽  
Surface Models ◽  
Urban Heat ◽  
Soil Moisture Initialization ◽  
Evident Difference

Abstract In this study, the performance of two advanced land surface models (LSMs; Noah LSM and Pleim–Xiu LSM) coupled with the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5), version 3.7.2, in simulating the near-surface air temperature in the greater Göteborg area in Sweden is evaluated and compared using the GÖTE2001 field campaign data. Further, the effects of different planetary boundary layer schemes [Eta and Medium-Range Forecast (MRF) PBLs] for Noah LSM and soil moisture initialization approaches for Pleim–Xiu LSM are investigated. The investigation focuses on the evaluation and comparison of diurnal cycle intensity and maximum and minimum temperatures, as well as the urban heat island during the daytime and nighttime under the clear-sky and cloudy/rainy weather conditions for different experimental schemes. The results indicate that 1) there is an evident difference between Noah LSM and Pleim–Xiu LSM in simulating the near-surface air temperature, especially in the modeled urban heat island; 2) there is no evident difference in the model performance between the Eta PBL and MRF PBL coupled with the Noah LSM; and 3) soil moisture initialization is of crucial importance for model performance in the Pleim–Xiu LSM. In addition, owing to the recent release of MM5, version 3.7.3, some experiments done with version 3.7.2 were repeated to reveal the effects of the modifications in the Noah LSM and Pleim–Xiu LSM. The modification to longwave radiation parameterizations in Noah LSM significantly improves model performance while the adjustment of emissivity, one of the vegetation properties, affects Pleim–Xiu LSM performance to a larger extent. The study suggests that improvements both in Noah LSM physics and in Pleim–Xiu LSM initialization of soil moisture and parameterization of vegetation properties are important.

Vegetation controls on soil moisture distribution in the Valles Caldera, New Mexico, during the North American monsoon

Ecohydrology ◽  
2008 ◽  
Vol 1 (3) ◽  
pp. 225-238 ◽  
Author(s):  
Enrique R. Vivoni ◽  
Alex J. Rinehart ◽  
Luis A. Méndez-Barroso ◽  
Carlos A. Aragón ◽  
Gautam Bisht ◽  
...  
Keyword(s):  
Soil Moisture ◽  
New Mexico ◽  
North American ◽  
Moisture Distribution ◽  
North American Monsoon ◽  
Valles Caldera ◽  
The North ◽  
Soil Moisture Distribution

scholarly journals Summer temperature response to extreme soil water conditions in the Mediterranean transitional climate regime

2021 ◽  
Author(s):  
Stefano Materia ◽  
Constantin Ardilouze ◽  
Chloé Prodhomme ◽  
Markus G. Donat ◽  
Marianna Benassi ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Soil Moisture ◽  
Soil Water ◽  
Land Surface ◽  
Heat Waves ◽  
Climate Regime ◽  
Seasonal Forecasts ◽  
Near Surface ◽  
Surface Temperatures ◽  
The Mediterranean

AbstractLand surface and atmosphere are interlocked by the hydrological and energy cycles and the effects of soil water-air coupling can modulate near-surface temperatures. In this work, three paired experiments were designed to evaluate impacts of different soil moisture initial and boundary conditions on summer temperatures in the Mediterranean transitional climate regime region. In this area, evapotranspiration is not limited by solar radiation, rather by soil moisture, which therefore controls the boundary layer variability. Extremely dry, extremely wet and averagely humid ground conditions are imposed to two global climate models at the beginning of the warm and dry season. Then, sensitivity experiments, where atmosphere is alternatively interactive with and forced by land surface, are launched. The initial soil state largely affects summer near-surface temperatures: dry soils contribute to warm the lower atmosphere and exacerbate heat extremes, while wet terrains suppress thermal peaks, and both effects last for several months. Land-atmosphere coupling proves to be a fundamental ingredient to modulate the boundary layer state, through the partition between latent and sensible heat fluxes. In the coupled runs, early season heat waves are sustained by interactive dry soils, which respond to hot weather conditions with increased evaporative demand, resulting in longer-lasting extreme temperatures. On the other hand, when wet conditions are prescribed across the season, the occurrence of hot days is suppressed. The land surface prescribed by climatological precipitation forcing causes a temperature drop throughout the months, due to sustained evaporation of surface soil water. Results have implications for seasonal forecasts on both rain-fed and irrigated continental regions in transitional climate zones.

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