to determine the relationship between soil moisture
climate conditions and the variability of soil moisture over
space and time. At the Jornada Experimental Range, the ARS
Hydrology Laboratory collected laser profiler data and visible,
thermal infrared and video imagery to infer surface temperature,
albedo, vegetation indices, roughness, and other land-surface
characteristics. At ARS laboratories in Ames, Iowa, Lincoln,
Nebraska, and Lubbock, Texas, scientists quantified relationships
among plant populations, nitrogen stress, water stress, and
remotely sensed information. In Iowa, for example, ARS determined
when in the growing season weeds can be differentiated from
economic crops, such as corn and soybeans, and how variations
in soil patterns are related to observed patterns in crops.
In Phoenix, Arizona, the ARS U.S. Water Conservation Laboratory
cooperated with NASA's Stennis Space Center to develop products
and applications using data from multispectral airborne sensors
to manage crops and soils.
research and operational programs made use of remotely
sensed data and related technologies to benefit agriculture
and forestry. This section of the report highlights
USDA agencies that advanced remote-sensing knowledge,
developed improved applications, and used imagery
and technology to produce more accurate and timely
products and services.
Agriculture Research Service (ARS) enhanced remote-sensing
knowledge and developed productive applications of
the technology at research facilities located throughout
the United States. At Weslaco, Texas, the ARS Integrated
Farming and Natural Resources Unit collected data
over the Southern Great
remote-sensing technology, the ARS Remote Sensing and Modeling
Laboratory (RSML) and the Hydrology Laboratory initiated
a long-term experiment to evaluate the economic and environmental
impact of four alternative farming practices on surface
and subsurface water quality. Working with the National
Agricultural Statistics Service (NASS), RSML assessed crop
yields at regional scales, using models and remote-sensing
data. Researchers succeeded in assessing spring wheat yields
in North Dakota in just 1 month following crop maturity,
compared to the usual 4 months. This increased speed and
improved spatial accuracy are very important to program
managers because of the potential for assessing foreign
crops. RSML also developed a method for using multispectral
fluorescence imaging to conduct nondestructive evaluations
of crop physiological status. Likewise, RSML used fluorescent
sensing to detect ozone damage, ultraviolet radiation damage
to vegetation, and the effect of increased carbon dioxide.
RSML research demonstrated that remote sensing can accurately
assess tropospheric environmental problems.
satellite remote-sensing program of the Foreign Agricultural
Service (FAS) remained a critical element in USDA's analysis
of global agricultural production and crop conditions by
providing timely, accurate, and unbiased estimates of global
area, yield, and production. Satellite-derived early warning
of unusual crop conditions and production anomalies enabled
more rapid and precise determinations of global supply conditions.
FAS used NOAA, Advanced Very High Resolution Radiometer
(AVHRR), Landsat, and French SPOT imagery, crop models,
weather data, attaché reports, field travel, and
ancillary data to forecast foreign grain, oilseed, and cotton
production. FAS remote sensing supported Department of State
(DoS) assessments of food needs in Indonesia, North Korea,
and the former Soviet Union. Also, FAS prepared detailed
analyses of the El Niño event, the record Argentine
soybean crop, the bumper wheat crop in Australia, the bumper
soybean crop in Brazil, flooding in China and North Korea,
and drought in Mexico and Indonesia.
Farm Service Agency (FSA) continued to share with FAS the
cost of analyzing imagery of the United States. Timely analysis
of U.S. crop conditions, combined with weather data, crop
model results, and GIS products, made possible the development
of accurate and timely projections and comprehensive evaluations
of crop disaster situations. FSA continued to be a partner
in NAPP and the National Digital Orthoquad Program (NDOP).
FSA started to field reengineered business processes that
combine the use of digital orthophotography, GIS, GPS, and
satellite imagery to replace the use of hardcopy NAPP aerial
photography and 35-millimeter slides.
USDA Forest Service and Stephen F. Austin University hosted
the Seventh Forest Service Remote Sensing Applications Conference,
in Nassau Bay, Texas. The conference, focusing on "Natural
Resources Management Using Remote Sensing and GIS," attracted
a wide range of representatives from Federal and State government,
the private sector, and academia, and it resulted in more
than 35 published papers. Besides using remotely sensed
data to manage 191 million acres of land in the National
Forest System, the Forest Service supported many international
projects by providing remotely sensed data and expertise.
These projects included prefire planning in Brazil, fire
suppression support in Mexico, and fire recovery work in
Indonesia. All three countries received fire mapping support,
for which remotely sensed data were essential in providing
timely and accurate information.
used remote-sensing data to construct area frames for statistical
sampling, to estimate crop area, to create crop-specific
land-cover data layers for GIS, and to assess crop conditions.
For area frame construction, NASS combined digital Landsat
and SPOT data with USGS digital line-graph data, enabling
the user to assign each piece of land in a State to a category,
based on the percentage of cultivation or other variables.
NASS implemented new remote-sensing-based area frames and
samples for Texas and Puerto Rico for the survey and census
of agriculture coverage measures. The remote-sensing acreage
estimation project analyzed Landsat data of the 1997 crop
season in Arkansas, North Dakota, and South Dakota and then
collected 1998 crop season data for the same three States.
End-of-season TM analysis produced crop acreage estimates
for major crops at State and county levels, as well as a
crop-specific categorization in the form of a digital mosaic
of TM scenes distributed to users on a CD-ROM. Vegetation
condition images based on AVHRR data were used with conventional
survey data to assess crop conditions.
Natural Resources Conservation Service (NRCS) continued
its partnership with Federal, State, and local agencies
in sharing costs to develop 1-meter digital ortho-imagery
coverage through NDOP. By year's end, 76 percent of the
Nation's digital ortho-imagery was either complete or in
progress. Federal-State cooperative agreements for statewide
ortho-imagery were started in Indiana, Kentucky, Louisiana,
Maryland, Missouri, Ohio, Texas, and West Virginia. Indiana
was the first project of this magnitude to use airborne
GPS horizontal ground control acquired concurrently with
new aerial imagery. The NRCS increased the use of remote
sensing to collect natural resource data for its National
Resources Inventory Program by contracting for low-level
high-resolution imagery over 6,000 sampling sites across
the Nation during the growing season.