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

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 Using vegetation indices for soil-moisture retrievals from passive microwave radiometry

2001 ◽  
Vol 5 (4) ◽  
pp. 671-678 ◽  
Author(s):  
E.J. Burke ◽  
W.J. Shuttleworth ◽  
A.N. French
Keyword(s):  
Soil Moisture ◽  
Optical Depth ◽  
Vegetation Index ◽  
Empirical Relationship ◽  
Vegetation Indices ◽  
Passive Microwave ◽  
Microwave Emission ◽  
Retrieval Algorithm ◽  
Emission Model ◽  
Normalised Difference Vegetation Index

Abstract. Surface soil moisture and the nature of the overlying vegetation both influence microwave emission from land surfaces significantly. One widely discussed but underused method for allowing for the effect of vegetation on soil-moisture retrievals from microwave observations is to use remotely sensed vegetation indices. This paper explores the potential for using the Normalised Difference Vegetation Index (NDVI) in soil-moisture retrievals from L-band (1.4 GHz) aircraft data gathered during the Southern Great Plains '97 (SGP97) experiment. A simplified version of MICRO-SWEAT, a soil vegetation atmosphere transfer (SVAT) scheme coupled with a microwave emission model, was used as the retrieval algorithm. Estimates of the optical depth of the vegetation, the parameter that describes the effect of the vegetation on microwave emission, were obtained by calibrating this retrieval algorithm against measurements of soil moisture at 15 field sites. A significant relationship was found between the optical depth so obtained and the observed NDVI at these sites, although this relationship changed with the resolution of the microwave brightness temperature observations used. Soil-moisture estimates made with the retrieval algorithm using the empirical relationship between optical depth and NDVI applied at two additional sites not used in the calibration show good agreement with field measurements. Keywords: NDVI, soil moisture, passive microwave, SGP97

scholarly journals Influence of surface water variations on VOD and biomass estimates from passive microwave sensors

2021 ◽  
Author(s):  
Emma Bousquet ◽  
Arnaud Mialon ◽  
Nemesio Rodriguez-Fernandez ◽  
Catherine Prigent ◽  
Fabien Wagner ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Surface Water ◽  
Aboveground Biomass ◽  
Passive Microwave ◽  
Microwave Emission ◽  
Retrieval Algorithm ◽  
Water Fraction ◽  
Standing Water ◽  
Flooded Forests ◽  
L Band

<p>Vegetation optical depth (VOD) is a remotely sensed indicator characterizing the attenuation of the Earth's thermal emission at microwave wavelengths by the vegetation layer. At L-band, VOD can be used to estimate and monitor aboveground biomass (AGB), a key component of the Earth's surface and of the carbon cycle. We observed a strong anti-correlation between SMOS (Soil Moisture and Ocean Salinity) L-band VOD (L-VOD) and soil moisture (SM) anomalies over seasonally inundated areas, confirming previous observations of an unexpected decline in K-band VOD during flooding (Jones et al., 2011). These results could be, at least partially, due to artefacts affecting the retrieval and could lead to uncertainties on the derived L-VOD during flooding. To study the behaviour of SMOS satellite L-VOD retrieval algorithm over seasonally inundated areas, the passive microwave L-MEB (L-band Microwave Emission of the Biosphere) model was used to simulate the signal emitted by a mixed scene composed of soil and standing water. The retrieval over this inundated area shows an overestimation of SM and an underestimation of L-VOD. This underestimation increases non-linearly with the surface water fraction. The phenomenon is more pronounced over grasslands than over forests. The retrieved L-VOD is typically underestimated by ~10% over flooded forests and up to 100% over flooded grasslands. This is mainly due to the fact that i) low vegetation is mostly submerged under water and becomes invisible to the sensor; and ii) more standing water is seen by the sensor. Such effects can distort the analysis of aboveground biomass (AGB) and aboveground carbon (AGC) estimates and dynamics based on L-VOD. Using the L-VOD/AGB relationship from Rodriguez-Fernandez et al. (2018), we evaluated that AGB can be underestimated by 15/20<sup></sup>Mg ha<sup>-1</sup> in the largest wetlands, and up to higher values during exceptional meteorological years. Such values are more significant over herbaceous wetlands, where AGB is ~30 Mg ha<sup>-1</sup>, than over flooded forests, which have typical AGB values of 150-300 Mg ha<sup>-1</sup>. Consequently, to better estimate the global biomass, surface water seasonality has to be taken into account in passive microwave retrieval algorithms.</p>

Comparing ERA-40-Based L-Band Brightness Temperatures with Skylab Observations: A Calibration/Validation Study Using the Community Microwave Emission Model

2009 ◽  
Vol 10 (1) ◽  
pp. 213-226 ◽  
Author(s):  
Matthias Drusch ◽  
Thomas Holmes ◽  
Patricia de Rosnay ◽  
Gianpaolo Balsamo
Keyword(s):  
Soil Moisture ◽  
Dynamic Range ◽  
Microwave Radiometer ◽  
Space Station ◽  
Microwave Emission ◽  
Emission Model ◽  
Soil Database ◽  
Brightness Temperatures ◽  
L Band ◽  
Rms Errors

Abstract The Community Microwave Emission Model (CMEM) has been used to compute global L-band brightness temperatures at the top of the atmosphere. The input data comprise surface fields from the 40-yr ECMWF Re-Analysis (ERA-40), vegetation data from the ECOCLIMAP dataset, and the Food and Agriculture Organization’s (FAO) soil database. Modeled brightness temperatures have been compared against (historic) observations from the S-194 passive microwave radiometer onboard the Skylab space station. Different parameterizations for surface roughness and the vegetation optical depth have been used to calibrate the model. The best results have been obtained for rather simple approaches proposed by Wigneron et al. and Kirdyashev et al. The rms errors after calibration are 10.7 and 9.8 K for North and South America, respectively. Comparing the ERA-40 soil moisture product against the corresponding in situ observations suggests that the uncertainty in the modeled soil moisture is the predominant contributor to these rms errors. Although the bias between model and observed brightness temperatures are reduced after the calibration, systematic differences in the dynamic range remain. For NWP analysis applications, bias correction schemes should be applied prior to data assimilation. The calibrated model has been used to compute a 10-yr brightness temperature climatology based on ERA-40 data.

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.

Parametric exponentially correlated surface emission model for L-band passive microwave soil moisture retrieval

2015 ◽  
Vol 83-84 ◽  
pp. 65-74 ◽  
Author(s):  
Tianjie Zhao ◽  
Jiancheng Shi ◽  
Rajat Bindlish ◽  
Thomas Jackson ◽  
Michael Cosh ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Passive Microwave ◽  
Emission Model ◽  
Surface Emission ◽  
L Band

scholarly journals Analysis of a Least-Squares Soil Moisture Retrieval Algorithm from L-band Passive Observations

2010 ◽  
Vol 2 (1) ◽  
pp. 352-374 ◽  
Author(s):  
María Piles ◽  
Mercè Vall-llossera ◽  
Adriano Camps ◽  
Marco Talone ◽  
Alessandra Monerris
Keyword(s):  
Soil Moisture ◽  
Least Squares ◽  
Retrieval Algorithm ◽  
L Band

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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