Suggested publications:

Moran, M. S., Y. Inoue and E. M. Barnes. Opportunities and limitations for image-based remote sensing in precision crop management, Rem. Sens. Env. 61:319-346. Published 1997.

Moran, M. S. Image-based remote sensing for agricultural management - perspectives of image providers, research scientists and users, 2nd Intl. Conf. Geospatial Information in Agric. and For. 10-12 Jan., Orlando, Fla. I-23-I-30. Published 2000.

Sensor calibration

Reflectance factor retrieval

Bidirectional reflectance distribution function

Sensor calibration

University of Arizona scientists making atmospheric measurements with a solar radiometer (upper) and ARS scientist making surface reflectance measurements (lower) in support of satellite sensor calibrations at White Sands, New Mexico

Arizona ARS scientists have conducted in-flight calibrations of airplane and satellite-based sensors in cooperation with scientists from the University Of Arizona Optical Sciences Center. The results of over ten years of work have shown that: over time, sensor sensitivity degrades up to 27% sensor degradation depends on type and filter in-flight sensor calibration uncertainty is less than 5%, and regular calibration result in high-quality images These results show that in-flight sensor calibration is both accurate and essential for proper interpretation of remote sensing images for monitoring temporal changes in crop and soil conditions.

Suggested publication: Slater, P. N., S. F. Biggar, R. G. Holm, R. D. Jackson, Y. Mao, M. S. Moran, J. M. Palmer and B. Yuan. Reflectance-and radiance-based methods for the in-flight absolute calibration of multispectral sensors, Rem. Sens. Environ. 22:11-37. Published 1987.

Reflectance factor retrieval

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An illustration of atmospheric attenuation of a solar ray passing through the atmosphere to the earth, reflecting off the earth's surface, and returning through the atmosphere to the satellite sensor

Remote sensing products becomes infinitely more valuable if the digital numbers (dn) can be converted to a value that is independent of atmospheric and insulation variations, and is thus comparable over time for monitoring seasonal crop and soil conditions. The reflectance factor (the ratio of reflected and incident radiation at the surface) is a such a value, and has become the basic measurement required for most remote sensing algorithms and models. Retrieval of surface reflectance from image radiance can be accomplished through complicated measurements of atmospheric conditions, and modeling of atmospheric radiative transfer. ARS scientists have also explored the use of canvas reference tarps that could be deployed during each overpass and on-farm targets such as landing strips or dirt roads that could be used to normalize the images to a common reference.

Suggested publications: Moran, M.S., R.D. Jackson, T.R. Clarke, J. Qi, F. Cabot, K.J. Thome and B.N. Markham, Reflectance factor retrieval from Landsat TM and SPOT HRV data for bright and dark targets, Rem. Sens. Env. 52:218-230. 1995. Moran, M.S., T.R. Clarke, J. Qi and P.J. Pinter Jr., MADMAC: A test of multispectral airborne imagery as a farm management tool, Proc. 26th Intl. Symp. on Rem. Sens. Environ. Vancouver, B.C. Canada, 25-29 March. p. 612-617. 1996.

Bidirectional reflectance distribution function

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An illustration of the shape of the bidirectional reflectance distribution function (BRDF) for a disturbed packed-earth site at Walnut Gulch Experimental Watershed with a solar zenith angle of 45 degrees.

Spectral images are affected by interactions of the illumination source of the image and the pixel location within the image due to the bidirectional nature of most natural targets. These are often referred to as "geometric effects" or "viewing geometry effects" because the effect is primarily related to geometric configuration of the sensing system. The need to correct for geometric effects has been recognized ever since remote sensing has been used for scientific, quantitative purposes, but only in the past decade have the computing tools been available to make the correction practical for large imagery. The bi-directional reflectance distribution function (BRDF) models that have been developed to correct for this effect tend to be complicated and often cannot be generalized over images acquired over different surface types. ARS scientists have been working to develop simplified methods for practical and operational corrections of geometric effect.

Suggested publications: Qi, J., M. S. Moran, F. Cabot and G. Dedieu, Normalization of sun/view angle effects on vegetation indices with bidirectional reflectance function models, Rem. Sens. Env. 52:207-217. Published 1995. Qi, J., F. Cabot, M. S. Moran, G. dedieu and K. J. Thome, Biophysical parameter retrievals using multidirectional measurements, Rem. Sens. Env. 54:71-83. Published 1995.

Evaporation

Soil moisture

Vegetation

Evaporation

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.

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

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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.

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.

Vegetation

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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.

Monsoon '90

Walnut Gulch '92

SALSA

Landsat-7

RANGES

EO-1

Monsoon '90

The Monsoon'90 multidisciplinary field campaign was conducted at the USDA ARS Walnut Gulch Experimental Watershed (WGEW) in SE Arizona during June-September 1990. The objective of this combined ground, aircraft and satellite campaign was to assess the feasibility of utilizing remotely-sensed data coupled with water and energy balance modeling for large area estimates of fluxes in semiarid rangelands.

Scientists measuring surface spectral reflectance of the WGEW grassland using yoke-mounted multi-band radiometers.

Suggested publications: 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. Moran, M.S., T.R. Clarke, W.P. Kustas, M. Weltz, S.A. Amer and A.R. Huete, Evaluation of hydrologic parameters in semiarid rangeland using remotely sensed spectral data, Water Resource Res. 30:1287-1297. 1994.

Walnut Gulch '92

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The Walnut Gulch '92 field campaign was conducted during the dry, early-monsoon, mid-monsoon, post-monsoon and "drying" seasons from April through November 1992 at WGEW. The overall research goal was to investigate the seasonal hydrologic dynamics of the region and to define the information potential of combined optical-microwave remote sensing.

Images of surface temperature and reflectance (color composite) measured by the Landsat5 TM sensor on 17 June 1997 (left) and images of vegetation biomes (upper right) and surface elevation (lower right; from 800 to 3000 km) for the Upper San Pedro Basin at 100 m spatial resolutions.

Suggested publication: 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.

Semi-Arid Land Surface Atmosphere (SALSA) Program

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Semi-arid lands are especially vulnerable to the impacts of natural and human induced environmental stresses. The Semi-Aridland-Surface-Atmosphere (SALSA) program is an ARS-led,multi-agency research program that seeks to understand, model, and predict the consequences of widespread environmental (global) change on the water balance and ecological complexity of semi-arid river basins over a range of time scales. Combining both ground-based and remote sensing technologies, SALSA scientists from several countries are intensively studying the Upper San Pedro Basin of southeastern Arizona and northeastern Sonora, Mexico. The results of this effort will not only help the local community better manage resources in their basin but will also provide the scientific and technological understanding needed to resolve similar problems in other semi-arid regions of the USA and the world. Website: http://www.tucson.ars.ag.gov/salsa

Suggested publications: Goodrich, D.C., A. Chehbouni, B. Goff, B. MacNish, T. Maddock, S. Moran, et al., An overview of the 1997 activities of the semi-arid land-surface-atmosphere (SALSA) program, Amer. Meteorol. Soc., Spec. Symp. On Hydrology, 11-16 January, Phoenix, AZ, p. 1-7. 1998. Maddock, T. III, R.D. MacNish, D.C. Goodrich, D.G. Williams, W.J. Shuttleworth, B.A. Goff, R.L. Scott, M.S. Moran, D.I. Cooper, L.E. Hipps and A.G. Chehbouni, The overview of atmospheric and surface water couplingto regional groundwater models in semi-arid basins, Amer. Meteorol. Soc., Spec. Symp. On Hydrology, 11-16 January, Phoenix, AZ, p. 38-42. 1998. Moran, S., D. Williams, D. Goodrich, A. Chehbouni, et al., Overview of remote sensing of semi-arid ecosystem function in the Upper San Pedro River basin, Arizona, Amer. Meteorol. Soc., Spec. Symp. On Hydrology, 11-16 January, Phoenix, AZ, p. 49-54. 1998.

Landsat-7 Science Team Program

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A NASA-funded project has been underway for three years to develop operational techniques for the use of Landsat TM and ETM+ data for agricultural and natural resource management. The approach will include (1) supplementing Landsat TM data with synthetic aperture radar (SAR) data from other currently operating and proposed sensors to compensate for the infrequent coverage provided by Landsat, and (2) using the TM-derived determinations of surface plant and soil conditions to update modeled plant growth and evaporation based on physical models and meteorological data. The project is in the third stage of a three-stage schedule, where the first year's work was based solely on existing data sets. The products were algorithms to allow "interchangeable" usage of SAR data with TM data for vegetation status and moisture condition determination, and a plant growth model designed to assimilate infrequent inputs of plant parameters from remotely sensed data. In years 2-3, Landsat TM and ERS-2 SAR images were acquired at two sites in Arizona to validate these techniques at the regional scale for heterogeneous landscapes. Current work is focused on using the combined remote sensing/modeling approach for investigations of the daily to interannual changes of rangeland vegetation in the semiarid Southwest, and for aiding such farm management decisions as scheduling applications of fertilizer and irrigation water. This project has been conducted as part of the NASA Landsat-7 Science Team which provides expertise and information on such activities as instrument pre-development, science planning, instrument calibration, field validation, and algorithm development.  Landsat7 ETM+ Image of Upper San Pedro Basin 14 May 1999   Landsat-7 Enhanced Thematic Mapper Plus (ETM+) color composite image   Landsat-7 Enhanced Thematic Mapper Plus (ETM+) thermal image

Suggested publications: Nouvellon, Y.P., M.S. Moran, R.B. Bryant, W. Ni, P. Heilman, B. Emmerich, D. LoSeen, A. Bégué, S. Rambal and J. Qi, Combining a 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. Moran, M.S., Y. Nouvellon, R.B. Bryant and W. Ni, Assimilating Landsat imagery in a grassland growth model: a case study in Arizona, 14th William T. Pecora Memorial Remote Sensing Symp. 6-10 Dec., Denver, CO. 1999.

RANGES

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One half of all U.S. land and 60% of the landmass of the world is classified as rangeland and many of these rangelands have suffered severe degradation over the years. Ecological degradation and an increase in bare soil negatively impact livestock production, erosion control, biodiversity, and CO2 sequestration. To effectively manage these rangelands, managers need spatially and temporally distributed information on vegetation cover, biomass production, and green-up time. The RANGES program proposes to use NASA's EOS (Earth Observation System) products to provide critical information to end-users on an operational basis for livestock management, fire-fuel estimation, wildlife habitat analysis and rangeland health assessment. RANGES offers a) an operational procedure to transform EOS data into information products and b) a protocol that involves range managers, remote sensing scientists, and commercial firms to apply, and then to assess the value-added EOS products for management applications. Scientists will work hand in hand with range managers and ranchers to define, develop, evaluate, and prototype EOS-derived products. At the end of the project, an assessment of the EOS-derived products for rangeland management will be made by our end-users to determine their willingness to pay for the ongoing provision of EOS-derived products. http://ranges.geo.msu.edu/index.html

Suggested publication: Qi, J., R. Marsett, P. Heilman, 2000, Rangeland vegetation cover estimation from remotely sensed data, 2nd International conference on Geospatial Information in Agriculture and Forestry, Lake Buena Vista, Florida, 10-12 January, 2000. II-243-252.

Earth Observation 1 (EO-1) Validation Team Program

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ARS scientists at SWRC secured a grant from the National Aeronautics and Space Administration (NASA) to participate in validation of the Earth Observation-1 (EO-1) mission. The EO-1 satellite is scheduled for launch in August 2000 with a primary objective of evaluating selected technologies for maintaining the 27-year continuity of the Landsat data stream. The primary objective of the SWRC study is to determine the suitability of the EO-1 image quality and data products to meet the needs of Landsat-class observations, termed Landsat data continuity. One study site will be the USDA ARS Walnut Gulch Experimental Watershed (WGEW) near Tucson, Arizona. Using on-site measurements of atmospheric conditions and measurements of surface reflectance of large, uniform targets, it will be possible to determine the accuracy of image reflectance factor retrieval. For the same images, on-site measurements of soil and plant characteristics will be used to determine the accuracy of image-derived data products suitable for agricultural and rangeland applications.

The EO-1 Satellite at NASA Goddard Space Flight Center in the Clean Tent

Suggested publication:

Moran, M.S., J. Qi, W. Ni and D.T. Shannon, Water conservation Laboratory image and ground data archive (WIGDA99) for Arizona agricultural and rangeland regions, 2nd Intl. Conf. Geospatial Information in Agric. and For. 10-12 Jan., Orlando, Fla. II-229-II-236. 2000.