V. Lakshmi

Soil moisture retrieval from AMSR-E

The Advanced Microwave Scanning Radiometer (AMSR-E) on the Earth Observing System (EOS) Aqua satellite was launched on May 4, 2002. The AMSR-E instrument provides a potentially improved soil moisture sensing capability over previous spaceborne radiometers such as the Scanning Multichannel Microwave Radiometer and Special Sensor Microwave/Imager due to its combination of low frequency and higher spatial resolution (approximately 60 km at 6.9 GHz). The AMSR-E soil moisture retrieval approach and its implementation are described in this paper. A postlaunch validation program is in progress that will provide evaluations of the retrieved soil moisture and enable improved hydrologic applications of the data. Key aspects of the validation program include assessments of the effects on retrieved soil moisture of variability in vegetation water content, surface temperature, and spatial heterogeneity. Examples of AMSR-E brightness temperature observations over land are shown from the first few months of instrument operation, indicating general features of global vegetation and soil moisture variability. The AMSR-E sensor calibration and extent of radio frequency interference are currently being assessed, to be followed by quantitative assessments of the soil moisture retrievals.

Observations of soil moisture using a passive and active low-frequency microwave airborne sensor during SGP99

Data were acquired by the Passive and Active L- and S-band airborne sensor (PALS) during the 1999 Southern Great Plains (SGP99) experiment in Oklahoma to study remote sensing of soil moisture in vegetated terrain using low-frequency microwave radiometer and radar measurements. The PALS instrument measures radiometric brightness temperature and radar backscatter at L- and S-band frequencies with multiple polarizations and approximately equal spatial resolutions. The data acquired during SGP99 provide information on the sensitivities of multichannel low-frequency passive and active measurements to soil moisture for vegetation conditions including bare, pasture, and crop surface cover with field-averaged vegetation water contents mainly in the 0-2.5 kg m/sup -2/ range. Precipitation occurring during the experiment provided an opportunity to observe wetting and drying surface conditions. Good correlations with soil moisture were observed in the radiometric channels. The 1.41-GHz horizontal-polarization channel showed the greatest sensitivity to soil moisture over the range of vegetation observed. For the fields sampled, a radiometric soil moisture retrieval accuracy of 2.3% volumetric was obtained. The radar channels showed significant correlation with soil moisture for some individual fields, with greatest sensitivity at 1.26-GHz vertical copolarized channel. However, variability in vegetation cover degraded the radar correlations for the combined field data. Images generated from data collected on a sequence of flight lines over the watershed region showed similar patterns of soil moisture change in the radiometer and radar responses. This indicates that under vegetated conditions for which soil moisture estimates may not be feasible using current radar algorithms, the radar measurements nevertheless show a response to soil moisture change, and they can provide useful information on the spatial and temporal variability of soil moisture. An illustration of the change detection approach is given.

High-resolution change estimation of soil moisture using L-band radiometer and Radar observations made during the SMEX02 experiments

The soil moisture experiments held during June-July 2002 (SMEX02) at Iowa demonstrated the potential of the L-band radiometer (PALS) in estimation of near surface soil moisture under dense vegetation canopy conditions. The L-band radar was also shown to be sensitive to near surface soil moisture. However, the spatial resolution of a typical satellite L-band radiometer is of the order of tens of kilometers, which is not sufficient to serve the full range of science needs for land surface hydrology and weather modeling applications. Disaggregation schemes for deriving subpixel estimates of soil moisture from radiometer data using higher resolution radar observations may provide the means for making available global soil moisture observations at a much finer scale. This paper presents a simple approach for estimation of change in soil moisture at a higher (radar) spatial resolution by combining L-band copolarized radar backscattering coefficients and L-band radiometric brightness temperatures. Sensitivity of AIRSAR L-band copolarized channels has been demonstrated by comparison with in situ soil moisture measurements as well as PALS brightness temperatures. The change estimation algorithm has been applied to coincident PALS and AIRSAR datasets acquired during the SMEX02 campaign. Using AIRSAR data aggregated to a 100-m resolution, PALS radiometer estimates of soil moisture change at a 400-m resolution have been disaggregated to 100-m resolution. The effect of surface roughness variability on the change estimation algorithm has been explained using integral equation model (IEM) simulations. A simulation experiment using synthetic data has been performed to analyze the performance of the algorithm over a region undergoing gradual wetting and dry down.

Variation of hydrometeorological conditions along a topographic transect in Northwestern Mexico during the North American monsoon

Abstract Relatively little is currently known about the spatiotemporal variability of land surface conditions during the North American monsoon, in particular for regions of complex topography. As a result, the role played by land–atmosphere interactions in generating convective rainfall over steep terrain and sustaining monsoon conditions is still poorly understood. In this study, the variation of hydrometeorological conditions along a large-scale topographic transect in northwestern Mexico is described. The transect field experiment consisted of daily sampling at 30 sites selected to represent variations in elevation and ecosystem distribution. Simultaneous soil and atmospheric variables were measured during a 2-week period in early August 2004. Transect observations were supplemented by a network of continuous sampling sites used to analyze the regional hydrometeorological conditions prior to and during the field experiment. Results reveal the strong control exerted by topography on the spatial and tem...

Evaluation of Special Sensor Microwave/Imager Satellite Data for Regional Soil Moisture Estimation over the Red River Basin

Regional-scale estimation of soil moisture using in situ field observations is not possible due to problems with the representativeness of the sampling and costs. Remotely sensed satellite data are helpful in this regard. Here, the simulations of 19- and 37-GHz vertical and horizontal polarization brightness temperatures and estimation of soil moistures using data from the Special Sensor Microwave/Imager (SSM/I) for 798 0.25 83 0.258 boxes in the southwestern plains region of the United States for the time period between 1 August 1987 and 31 July 1988 are presented. A coupled land-canopy‐atmosphere model is used for simulating the brightness temperatures. The land-surface hydrology is modeled using a thin-layer hydrologic model. The canopy scattering is modeled using a radiative transfer model, and the atmospheric attenuation is characterized using an empirical model. The simulated brightness temperatures are compared with those observed by the SSM/I sensor aboard the Defense Metereological Satellite Program satellite. The observed brightness temperatures are used to derive the soil moistures using the canopy radiative transfer and atmospheric attenuation model. The discrepancies between the SSM/I-based estimates and the simulated soil moisture are discussed. The mean monthly soil moistures estimated using the 19-GHz SSM/I brightness temperature data are interpreted along with the mean monthly leaf area index and accumulated rainfall. The soil moistures estimated using the 19-GHz SSM/I data are used in conjunction with the hydrologic model to estimate cumulative monthly evaporation. The results of the simulations hold promise for the utilization of microwave brightness temperatures in hydrologic modeling for soil moisture estimation.

Analysis of Process Controls in Land Surface Hydrological Cycle Over the Continental United States

The paper uses two years (1997–1999) of data from the North American Land Data Assimilation System at National Centers for Environmental Prediction to analyze the variability of physical variables contributing to the hydrological cycle over the conterminous United States. The five hydrological variables considered in this study are precipitation, top layer soil moisture (0–10 cm), total soil moisture (0–200 cm), runoff, and potential evaporation. There are two specific analyses carried out in this paper. In the first case the principal components of the hydrological cycle are examined with respect to the loadings of the individual variables. This helps to ascertain the contribution of physical variables to the hydrological process in decreasing order of process importance. The results from this part of the study had revealed that both in annual and seasonal timescales the first two principal components account for 70–80% of the variance and that precipitation dominated the first principal component, the most dominant mode of spatial variability. It was followed by the potential evaporation as the secondmost dominant process controlling the spatial variability of the hydrologic cycle over the continental United States. In the second case each hydrological variable was examined individually to determine the temporal evolution of its spatial variability. The results showed the presence of heterogeneity in the spatial variability of hydrologic variables and the way these patterns of variance change with time. It has also been found that the temporal evolution of the spatial patterns did not resemble white noise; the time series of the scores of the principal components showed proper cyclicity at seasonal to annual timescales. The northwestern and the southeastern parts of the United States had been found to have contributed significantly toward the overall variability of potential evaporation and soil moisture over the United States. This helps in determining the spatial patterns expected from hydrological variability. More importantly, in the case of modeling as well as designing observing systems, these studies will lead to the creation of efficient and accurate land surface measurement and parameterization schemes.

A simple surface temperature assimilation scheme for use in land surface models

This paper examines the utilization of surface temperature as a variable which can be assimilated in off-line land surface hydrological models. The connection between the surface temperature and evapotranspiration is utilized in making adjustments to the model-computed surface soil moisture. This adjustment is a function of the difference between the model-computed and the observed surface temperature. Comparisons between the model-computed and satellite-observed surface temperatures have been carried out. The assimilation of surface temperature is carried out twice a day (corresponding to the A.M. and P.M. overpass of the NOAA 10) over the Red-Arkansas basin in the southwestern United States (31°50′N-36°N, 94°30′W-104°30′W) for a period of 1 year (August 1987 to July 1988). The soil moisture estimates resulting from the assimilation of surface temperature have a closer agreement with the values derived from the special sensor microwave imager than those from simulations without surface temperature assimilation. Assimilation reduces the effect of errors in precipitation and/or shortwave radiation on simulated soil moistures.

Soil moisture retrieval using the passive/active L- and S-band radar/radiometer

In the present study, remote sensing of soil moisture is carried out using the Passive and Active L- and S-band airborne sensor (PALS). The data in this paper were taken from five days of overflights near Chickasha, OK during the 1999 Southern Great Plains (SGP99) experiment. Presently, we analyze the collected data to understand the relationships between the observed signals (radiometer brightness temperature and radar backscatter) and surface parameters (surface soil moisture, temperature, vegetation water content, and roughness). In addition, a radiative transfer model and two radar backscatter models are used to simulate the PALS observations. An integration of observations, regression retrievals, and forward modeling is used to derive the best estimates of soil moisture under varying surface conditions.

The Effects of Satellite-Derived Vegetation Cover Variability on Simulated Land–Atmosphere Interactions in the NAMS

Substantial evolution of Normalized Difference Vegetation Index (NVDI)-derived vegetation cover (Fg) exists in the southwestern United States and Mexico. The intraseasonal and wet-/dry-year fluctuations of Fg are linked to observed precipitation in the North American monsoon system (NAMS). The manner in which the spatial and temporal variability of Fg influences the land–atmosphere energy and moisture fluxes, and associated likelihood of moist convection in the NAMS regions, is examined. For this, the regional climate model (RCM) is employed, with three different Fg boundary conditions to examine the influence of intraseasonal and wet-/dry-year vegetation variability. Results show that a strong link exists between evaporative fraction (EF), surface temperature, and relative humidity in the boundary layer (BL), which is consistent with a positive soil moisture feedback. However, contrary to expectations, higher Fg does not consistently enhance EF across the NAMS region. This is because the low soil moisture values simulated by the land surface model (LSM) yield high canopy resistance values throughout the monsoon season. As a result, the experiment with the lowest Fg yields the greatest EF and precipitation in the NAMS region, and also modulates regional atmospheric circulation that steers the track of tropical cyclones. In conclusion, the simulated influence of vegetation on land–atmosphere exchanges depends strongly on the canopy stress index parameterized in the LSM. Therefore, a reliable dataset, at appropriate scales, is needed to calibrate transpiration schemes and to assess simulated and realistic vegetation–atmosphere interactions in the NAMS region.

Comparison of TOVS‐derived land surface variables with ground observations

The Tiros Operational Vertical Sounder (TOVS) Pathfinder Path A retrieved surface skin temperature, surface air temperatures, and surface specific humidity are compared with data obtained from three large-scale field campaigns: the First ISLSCP (International Satellite Land Surface Climatology Project) Field Experiment (FIFE), the Hydrologic Atmospheric Pilot Experiment (HAPEX) in the Sahel, and the Boreal Ecosystem-Atmosphere Study (BOREAS). The long-term estimates of surface skin temperatures, surface air temperatures, and surface vapor pressure were unbiased, and the standard deviations of the errors were about 4°C, 3.5°C, and 3.5 mbar, respectively. The monthly mean variables obtained from the TOVS data at four times of the day (corresponding to the AM and PM overpass for each of two satellites) exhibited realistic diurnal and seasonal cycles when compared with corresponding ground observations.