Page Banner

United States Department of Agriculture

Agricultural Research Service

Remote Sensing
headline bar
1 - Introduction
2 - Calibration and Signal Processing
3 - Algorithm and Model Development
4 - Field Experiments and Inter-Disciplinary Programs
5 - Data Archive
Algorithm and Model Development


Soil moisture 



Monitoring evaporation (E) at watershed scales is important for assessing the effect of climate and management on natural ecosystems. Techniques have been developed to evaluate E with remote sensing, which is the only technology that can efficiently and economically provide distributed estimates of E on a regional scale. These techniques are of three classes: empirical approaches, physically-based analytical approaches, and numerical models. Empirical techniques generally use the spatially-distributed multi-spectral image data to extrapolate a single (or multiple) in situ measurement(s) of E to a larger, heterogeneous surrounding region, or develop an empirical relation between a time series of in situ E measurements and multi-spectral measurements that can be applied to multi-spectral images to produce maps of E. Physically-based, analytical techniques directly evaluate energy balance (and thus E) through a combination of remotely sensed measurements of surface reflectance and temperature with in situ meteorological measurements. Numerical models use remotely sensed measurements as a source of intermittent grid-based information for soil-vegetation-atmosphere (SVAT) models to evaluate regional E continuously on an hourly or daily basis.

RAMS images

Fields of a) air temperature, b) wind speed, c) water vapor mixing ratio, and d) precipitable water at 4-km resolution over a region of 100 by 100 km, derived from near real-time runs of the refined Regional Atmospheric Modeling System (RAMS') for the Upper San Pedro Basin on 9 June 1997. North is at the top, the scale is approximately one inch to 40 km.

Suggested publications:

Moran, M.S., Use of remote sensing for monitoring evapotranspiration over managed watersheds, ASCE Conf. on Watershed Mgmt. 2000: Science and Engineering Tech. for the New Millennium, 21-24 June, Ft. Collins, CO (in press). 2000.

Moran, M.S., A.F. Rahman, J.C. Washburne, D.C. Goodrich, M.A. Weltz and W.P. Kustas, Combining the Penman-Monteith equation with measurements of surface temperature and reflectance to map regional evaporation rates, Agric. For. Meteorol.80:87-109. 1996.

Moran, M.S., W.P. Kustas, A. Vidal, D.I. Stannard, J.H. Blanford and W.D. Nichols, Use of ground-based remotely sensed data for surface energy balance evaluation of a semiarid rangeland, Water Resource Res. 30:1339-1349. 1994.

Soil Moisture
back to top

Knowledge of distributed surface soil moisture content (~5cm depth) is important for many hydrologic applications including mapping rainfall events, monitoring differential drying patterns, and assessing water availability for plant growth. Surface soil moisture can also be used to parameterize soil water simulation models that estimate soil moisture content with depth in the plant rooting zone. Though the demand for distributed surface soil moisture information is high, the means for obtaining such information are few. There is some evidence that satellite-based Synthetic Aperture Radar (SAR) sensors could provide a regional assessment of surface volumetric soil moisture content. Theoretically, SAR backscatter detected by orbiting satellite-based sensors is directly related to the target dielectric constant (e'), where e' is the real part of a complex parameter that describes the electrical properties of a medium relative to the dielectric constant of "free space". For water, e'~80; for dry soil, e'~2. Consequently, an increase in soil moisture content changes e' markedly, and results in a strong sensitivity of the SAR signal to volumetric soil moisture content. In practice, SAR backscatter is also highly influenced by topographic features, vegetation density, and variations in small-scale surface roughness. ARS scientists have addressed the difficult task of converting single-channel SAR images directly into maps of regional soil moisture content for heterogeneous terrain.


Soil moisture maps

Regional maps of surface volumetric soil moisture based on ERS-2 C-band SAR images. The maps of these two dates show a good contrast between regional soil moisture conditions in winter and spring 1997.

Suggested publications:

Sano, E.E., A.R. Huete, D. Troufleau, M.S. Moran and A. Vidal, Sensitivity analysis of ERS-1 synthetic aperture radar data to the surface moisture content of rocky soils in a semiarid rangeland, Water Res. Research 34:1491-1498. 1998.

Moran, M.S., D.C. Hymer, J. Qi, R.C. Marsett and M.K. Helfert, Soilmoisture evaluation using Synthetic Aperture Radar (SAR) and optical remote sensing in semiarid rangeland, Amer.Meteorol. Soc., Spec. Symp. On Hydrology, 11-16 January, Phoenix, AZ, p. 199-203. 1998.

Hymer, D.C., M.S. Moran and T.O. Keefer, Monitoring temporal soilmoisture variability with depth using calibrated in-situsensors, Amer. Meteorol. Soc., Spec. Symp. On Hydrology, 11-16 January, Phoenix, AZ, p. 204-207. 1998.

back to top

Vegetation density and distribution is one of the most important physical parameters controlling hydrological processes across geosphere-biosphere-atmosphere boundaries. Estimation of this parameter using remote sensing techniques has been associated with computation of vegetation indices, inversion of bidirectional reflectance distribution function (BRDF) models, and calibration of plant growth models with remotely sensed images. With advances in space technology, more remote sensing platforms are to launched with sensors suitable for such numerical and analytical approaches.

Suggested publication:

Nouvellon, Y.P., M.S. Moran, R.B. Bryant, W. Ni, P. Heilman, B. Emmerich, D. LoSeen, A. Bégué, S. Rambal and J. Qi, Combininga SVAT model with Landsat imagery for a ten-year simulation of grassland carbon and water budget, 2nd Intl. Conf. Geospatial Information in Agric. and For. 10-12 Jan., Orlando, Fla. I-257-I-264. 2000.

<< Previous    1     2     [3]     4     5     Next >>

Last Modified: 11/1/2005
Footer Content Back to Top of Page