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- Author or Editor: A. Chehbouni x
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Abstract
Near-surface soil moisture retrieved from Soil Moisture and Ocean Salinity (SMOS)-type data is downscaled and assimilated into a distributed soil–vegetation–atmosphere transfer (SVAT) model with the ensemble Kalman filter. Because satellite-based meteorological data (notably rainfall) are not currently available at finescale, coarse-scale data are used as forcing in both the disaggregation and the assimilation. Synthetic coarse-scale observations are generated from the Monsoon ‘90 data by aggregating the Push Broom Microwave Radiometer (PBMR) pixels covering the eight meteorological and flux (METFLUX) stations and by averaging the meteorological measurements. The performance of the disaggregation/assimilation coupling scheme is then assessed in terms of surface soil moisture and latent heat flux predictions over the 19-day period of METFLUX measurements. It is found that the disaggregation improves the assimilation results, and vice versa, the assimilation of the disaggregated microwave soil moisture improves the spatial distribution of surface soil moisture at the observation time. These results are obtainable regardless of the spatial scale at which solar radiation, air temperature, wind speed, and air humidity are available within the microwave pixel and for an assimilation frequency varying from 1/1 day to 1/5 days.
Abstract
Near-surface soil moisture retrieved from Soil Moisture and Ocean Salinity (SMOS)-type data is downscaled and assimilated into a distributed soil–vegetation–atmosphere transfer (SVAT) model with the ensemble Kalman filter. Because satellite-based meteorological data (notably rainfall) are not currently available at finescale, coarse-scale data are used as forcing in both the disaggregation and the assimilation. Synthetic coarse-scale observations are generated from the Monsoon ‘90 data by aggregating the Push Broom Microwave Radiometer (PBMR) pixels covering the eight meteorological and flux (METFLUX) stations and by averaging the meteorological measurements. The performance of the disaggregation/assimilation coupling scheme is then assessed in terms of surface soil moisture and latent heat flux predictions over the 19-day period of METFLUX measurements. It is found that the disaggregation improves the assimilation results, and vice versa, the assimilation of the disaggregated microwave soil moisture improves the spatial distribution of surface soil moisture at the observation time. These results are obtainable regardless of the spatial scale at which solar radiation, air temperature, wind speed, and air humidity are available within the microwave pixel and for an assimilation frequency varying from 1/1 day to 1/5 days.
Abstract
Models simulating the seasonal growth of vegetation have been recently coupled to soil–vegetation–atmosphere transfer schemes (SVATS). Such coupled vegetation–SVATS models (V–S) account for changes of the vegetation leaf area index (LAI) over time. One problem faced by V–S models is the high number of parameters that are required to simulate different sites or large areas. Therefore, efficient calibration procedures are needed. This study describes an attempt to calibrate a V–S model with satellite [Advanced Very High Resolution Radiometer (AVHRR)] data in the shortwave and longwave domains. A V–S model is described using ground data collected over three semiarid grassland sites during the Hydrological Atmospheric Pilot Experiment (HAPEX)-Sahel experiment. The effect of calibrating model parameters with time series of normalized difference vegetation index (NDVI) and thermal infrared (TIR) data is assessed by examining the simulated latent heat flux (LE) and LAI for a suite of calibration experiments. A sensitivity analysis showed that the parameters related to plant growth vigor and to soil evaporative resistance were the best candidates for calibration. The NDVI and TIR time series were used to calibrate these parameters, both independently and simultaneously, to assess their synergy. Ground-based, airborne, and satellite sensor (AVHRR) data were successively investigated. Both airborne and AVHRR NDVI data could be used to constrain the vegetation growth vigor. These calibrations significantly improved the simulation of the LAI and LE (rmse decreased by 21% for LE), and the site-to-site variability was greatly enhanced. The soil resistance could also be calibrated with ground-based TIR data, but the effect on the simulated variables was small. Although both NDVI and ground-based TIR data were suitable to constrain the V–S model, the synergy between the two wavelengths was not clearly established. Last, satellite TIR data from the AVHRR proved unsuitable for model calibration. Indeed, the AVHRR surface temperature values were systematically lower than both ground-based data and model outputs. The authors conclude that the calibration of a vegetation–SVAT model with shortwave AVHRR time series can be used to scale the energy and water fluxes up to the regional scale.
Abstract
Models simulating the seasonal growth of vegetation have been recently coupled to soil–vegetation–atmosphere transfer schemes (SVATS). Such coupled vegetation–SVATS models (V–S) account for changes of the vegetation leaf area index (LAI) over time. One problem faced by V–S models is the high number of parameters that are required to simulate different sites or large areas. Therefore, efficient calibration procedures are needed. This study describes an attempt to calibrate a V–S model with satellite [Advanced Very High Resolution Radiometer (AVHRR)] data in the shortwave and longwave domains. A V–S model is described using ground data collected over three semiarid grassland sites during the Hydrological Atmospheric Pilot Experiment (HAPEX)-Sahel experiment. The effect of calibrating model parameters with time series of normalized difference vegetation index (NDVI) and thermal infrared (TIR) data is assessed by examining the simulated latent heat flux (LE) and LAI for a suite of calibration experiments. A sensitivity analysis showed that the parameters related to plant growth vigor and to soil evaporative resistance were the best candidates for calibration. The NDVI and TIR time series were used to calibrate these parameters, both independently and simultaneously, to assess their synergy. Ground-based, airborne, and satellite sensor (AVHRR) data were successively investigated. Both airborne and AVHRR NDVI data could be used to constrain the vegetation growth vigor. These calibrations significantly improved the simulation of the LAI and LE (rmse decreased by 21% for LE), and the site-to-site variability was greatly enhanced. The soil resistance could also be calibrated with ground-based TIR data, but the effect on the simulated variables was small. Although both NDVI and ground-based TIR data were suitable to constrain the V–S model, the synergy between the two wavelengths was not clearly established. Last, satellite TIR data from the AVHRR proved unsuitable for model calibration. Indeed, the AVHRR surface temperature values were systematically lower than both ground-based data and model outputs. The authors conclude that the calibration of a vegetation–SVAT model with shortwave AVHRR time series can be used to scale the energy and water fluxes up to the regional scale.
Abstract
Eddy covariance (EC) and large aperture scintillometer (LAS) measurements were collected over an irrigated olive orchard near Marrakech, Morocco. The tall, sparse vegetation in the experimental site was relatively homogeneous, but during irrigation events spatial variability in soil humidity was large. This heterogeneity caused large differences between the source area characteristics of the EC system and the LAS, resulting in a large scatter when comparing sensible heat fluxes obtained from LAS and EC. Radiative surface temperatures were retrieved from thermal infrared satellite images from the Landsat Enhanced Thematical Mapper+ and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellites. Using these images in combination with an analytical footprint model, footprint-weighted radiative surface temperatures for the footprints of the LAS and the EC system were calculated. Comparisons between the difference in measured sensible heat fluxes and the difference in footprint-weighted radiative surface temperature showed that for differences between the footprint-weighted radiative surface temperatures larger than ±0.5 K, correlations with the difference in measured sensible heat flux were good. It was found that radiative surface temperatures, obtained from thermal infrared satellite imagery, can provide a good indication of the spatial variability of soil humidity, and can be used to identify differences between LAS and EC measurements of sensible heat fluxes resulting from this variability.
Abstract
Eddy covariance (EC) and large aperture scintillometer (LAS) measurements were collected over an irrigated olive orchard near Marrakech, Morocco. The tall, sparse vegetation in the experimental site was relatively homogeneous, but during irrigation events spatial variability in soil humidity was large. This heterogeneity caused large differences between the source area characteristics of the EC system and the LAS, resulting in a large scatter when comparing sensible heat fluxes obtained from LAS and EC. Radiative surface temperatures were retrieved from thermal infrared satellite images from the Landsat Enhanced Thematical Mapper+ and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellites. Using these images in combination with an analytical footprint model, footprint-weighted radiative surface temperatures for the footprints of the LAS and the EC system were calculated. Comparisons between the difference in measured sensible heat fluxes and the difference in footprint-weighted radiative surface temperature showed that for differences between the footprint-weighted radiative surface temperatures larger than ±0.5 K, correlations with the difference in measured sensible heat flux were good. It was found that radiative surface temperatures, obtained from thermal infrared satellite imagery, can provide a good indication of the spatial variability of soil humidity, and can be used to identify differences between LAS and EC measurements of sensible heat fluxes resulting from this variability.
Abstract
Successful prediction of possible climate change depends on realistic parameterization of land surface processes in climate models. Such parameterizations must take appropriate account of the heterogeneities that are found in most earth surfaces. In this study, different average strategies for aggregating patch-scale heterogeneities to scales that are appropriate for mesoscale and climate model gods have been explored. A simple model for estimating area-average “effective” surface flux parameters is evaluated. The model satisfies the energy balance equation and leads to a set of relationships between local and effective parameters in the governing equations for the surface energy balance. One outcome is that the resulting effective surface temperature is not a simple area-weighted average of component temperatures, but is a function of a specific combination of different resistance of the individual surface elements. A set of heterogeneous surfaces has been simulated to study the effective fluxes obtained using the described model. A comparison with results obtained by other investigators using different averaging methods is also performed.
Abstract
Successful prediction of possible climate change depends on realistic parameterization of land surface processes in climate models. Such parameterizations must take appropriate account of the heterogeneities that are found in most earth surfaces. In this study, different average strategies for aggregating patch-scale heterogeneities to scales that are appropriate for mesoscale and climate model gods have been explored. A simple model for estimating area-average “effective” surface flux parameters is evaluated. The model satisfies the energy balance equation and leads to a set of relationships between local and effective parameters in the governing equations for the surface energy balance. One outcome is that the resulting effective surface temperature is not a simple area-weighted average of component temperatures, but is a function of a specific combination of different resistance of the individual surface elements. A set of heterogeneous surfaces has been simulated to study the effective fluxes obtained using the described model. A comparison with results obtained by other investigators using different averaging methods is also performed.
Arid and semiarid rangelands comprise a significant portion of the earth's land surface. Yet little is known about the effects of temporal and spatial changes in surface soil moisture on the hydrologic cycle, energy balance, and the feedbacks to the atmosphere via thermal forcing over such environments. Understanding this interrelationship is crucial for evaluating the role of the hydrologic cycle in surface–atmosphere interactions.
This study focuses on the utility of remote sensing to provide measurements of surface soil moisture, surface albedo, vegetation biomass, and temperature at different spatial and temporal scales. Remote-sensing measurements may provide the only practical means of estimating some of the more important factors controlling land surface processes over large areas. Consequently, the use of remotely sensed information in biophysical and geophysical models greatly enhances their ability to compute fluxes at catchment and regional scales on a routine basis. However, model calculations for different climates and ecosystems need verification. This requires that the remotely sensed data and model computations be evaluated with ground-truth data collected at the same areal scales.
The present study (MONSOON 90) attempts to address this issue for semiarid rangelands. The experimental plan included remotely sensed data in the visible, near-infrared, thermal, and microwave wavelengths from ground and aircraft platforms and, when available, from satellites. Collected concurrently were ground measurements of soil moisture and temperature, energy and water fluxes, and profile data in the atmospheric boundary layer in a hydrologically instrumented semiarid rangeland watershed. Field experiments were conducted in 1990 during the dry and wet or “monsoon season” for the southwestern United States. A detailed description of the field campaigns, including measurements and some preliminary results are given.
Arid and semiarid rangelands comprise a significant portion of the earth's land surface. Yet little is known about the effects of temporal and spatial changes in surface soil moisture on the hydrologic cycle, energy balance, and the feedbacks to the atmosphere via thermal forcing over such environments. Understanding this interrelationship is crucial for evaluating the role of the hydrologic cycle in surface–atmosphere interactions.
This study focuses on the utility of remote sensing to provide measurements of surface soil moisture, surface albedo, vegetation biomass, and temperature at different spatial and temporal scales. Remote-sensing measurements may provide the only practical means of estimating some of the more important factors controlling land surface processes over large areas. Consequently, the use of remotely sensed information in biophysical and geophysical models greatly enhances their ability to compute fluxes at catchment and regional scales on a routine basis. However, model calculations for different climates and ecosystems need verification. This requires that the remotely sensed data and model computations be evaluated with ground-truth data collected at the same areal scales.
The present study (MONSOON 90) attempts to address this issue for semiarid rangelands. The experimental plan included remotely sensed data in the visible, near-infrared, thermal, and microwave wavelengths from ground and aircraft platforms and, when available, from satellites. Collected concurrently were ground measurements of soil moisture and temperature, energy and water fluxes, and profile data in the atmospheric boundary layer in a hydrologically instrumented semiarid rangeland watershed. Field experiments were conducted in 1990 during the dry and wet or “monsoon season” for the southwestern United States. A detailed description of the field campaigns, including measurements and some preliminary results are given.
