USDA research and operational programs used remotely sensed data and related technologies to monitor, assess, and administer agricultural, rangeland, and forestry resources. The Agriculture Research Service (ARS) enhanced remote- sensing knowledge and developed productive applications at research facilities located throughout the United States. At the ARS Jornada Experimental Range, the ARS Hydrology and Remote Sensing Laboratory (HRSL) collected laser-scanning data and visible, thermal infrared, and video imagery to infer surface temperature, albedo, vegetation indices, roughness, and other land surface characteristics. These parameters were used as inputs for land-surface models, coupled with atmospheric models to determine heat and water balance for the area. With the launch of NASA’s Earth Observing System (EOS)-AM1 satellite, the HRSL has flown multispectral thermal infrared sensors over the Jornada Experimental Range to estimate surface emissivity and temperature for selected EOS-AM1 overpasses. ARS used these data to estimate the surface sensible heat flux. Thermal infrared radiation data from ASTER were used to map surface fluxes at the El Reno Grazinglands sites. The remote-sensing fluxes were in good agreement with ground measurements. Aircraft flights with a digital multispectral camera collected multiangle reflectance, an intrinsic surface characteristic needed for radiometric correction of optical remote-sensing data, for accurate estimates of shortwave albedo and for improved cover-type classification.

Research coordinated by a HRSL scientist demonstrated the feasibility of large-scale soil moisture detection using airborne and space microwave platforms. With these advances in the theory and the planned launches of new microwave remote-sensing satellites, it is feasible to implement a global observing soil moisture system. Research is focused on developing and implementing these tools through large-scale field experimentation in the United States and Asia. Although soil moisture is a critical variable for climate and agriculture, measuring soil moisture over continental scales has been hindered by a lack of appropriate instrumentation.

Scientists at the HRSL combined NOAA’s Advanced Very High Resolution Radiometer (AVHRR) satellite data with field-level measurements of ecosystem carbon dioxide exchange from other ARS locations to estimate carbon sequestration in rangelands of the Western United States at 1 kilometer resolution. Historic Landsat MSS and Landsat 7 Extended Thematic Mapper data were used to validate estimates of carbon sequestration at 30 to 100 meters resolution.

A simple operational approach has been developed at the HRSL for relating evapotranspiration (the amount of water evaporated from soil and transpired by plants) to satellite observations of surface temperature, vegetative cover and type, and measurements of near-surface wind speed and air temperature from the synoptic weather network. This scheme reduces both the errors associated with satellite observations and defining weather data at large scales, and thus, it has potential in providing regional scale assessment of evapotranspiration. This information will greatly enhance techniques for estimating crop yield and for assessing vegetation stress on a regional basis, ultimately improving agricultural management decisions.

The HRSL used airborne and satellite imagery for delineating consistent patterns of crop growth within fields for developing within-field management zones for precision farming. Scientists merged the use of crop growth models with remote-sensing data to quantify the amount of production in the growth patterns and used geostatistical techniques to improve airborne scanner image analysis to map within-field crop foliage density.

Scientists at the HRSL evaluated the application of MODIS to develop a crop yield map for the soybean and corn crop in McLean County, Illinois, in cooperation with the Illinois State Statistical Office and the Research and Development Division of National Agricultural Statistics Service (NASS) in Fairfax, Virginia. The Illinois State Water Survey cooperated in acquiring ground data.

Using remote-sensing and geospatial technologies, the HRSL evaluated the economic and environmental impact of three farming systems on surface and subsurface water quality. Subsurface flow patterns were mapped with ground- penetrating radar and linked to crop yields and remotely sensed images. A new spectral method was developed to assess chlorophyll content of plant canopies that indicates crop fertilizer needs. These assessments of spatial and temporal variability of crops will benefit farmers by providing a watershed-scale demonstration site where crop yields, profitability, and environmental impact can be compared under identical hydro-geological setting and climatic conditions.

The ARS Soil and Water Research Unit at Lincoln, Nebraska, used multispectral and hyperspectral data to evaluate crop vegetation indices in terms of chlorophyll meter readings and for prediction of yield for irrigated corn. Using combinations of individual reflectance bands, the most appropriate band combinations at each growth stage were determined for making relative crop yield maps.

At Weslaco, Texas, the ARS Integrated Farming and Natural Resources Unit used color-infrared aerial photography to successfully detect the invasive alien aquatic weed, giant salvinia, in Texas waterways. These data were used by Texas Parks and Wildlife Department personnel involved in controlling this aquatic weed. Airborne multispectral and hyperspectral images and yield monitor data were collected from several fields owned by Rio Farms, Inc., of Monte Alto, Texas, and used by Rio Farms to make farm management decisions. Multispectral digital imagery was used successfully to detect and assess foot rot infection in south Texas citrus orchards.

At the ARS National Soil Tilth Laboratory in Ames, Iowa, comparisons with plant tissue testing, leaf chlorophyll readings, broadband reflectance with ground-based instruments, and airborne sensors showed that detection of the nitrogen status early in the season is possible when canopy observations are combined with meteorological models for predicting the expected nitrogen use. Observations made during the grain-filling period related well to yield and showed where nitrogen was not a contributing factor to yield variation across the field. Remote sensing of the soil and crop provided a spatial representation of the agronomic variation across varying soils. Scientists used ground-based, narrow-band sensors to develop spectral libraries for corn, soybean, and wheat. Scientists used the information collected from this system to determine growth rates, light interception, biomass, and lead chlorophyll content across a range of soils and management practices.

At the ARS J. Phil Campbell, Sr., Natural Resource Conservation Center in Watkinsville, Georgia, researchers used Landsat satellite imagery to classify land use in the Upper Oconee Watershed of Georgia into 10 types. Land use within selected subwatersheds will be related to observations of water quality. These studies focused on portions of the Upper Oconee Watershed receiving Federal funding in the Environmental Quality Improvement Program (EQIP). These remote-sensing data are used to determine the efficacy of the EQIP program within the context of the predominant land use.

The ARS Plant Science and Water Conservation Research Laboratory, in Stillwater, Oklahoma, used commercially available high-spatial resolution multispectral imagery to determine reflectance characteristics of pest insect infested wheat. These data were used with spatial interpolation techniques to create maps of spatially varying pest density, and the spatial pattern metrics were used to develop a “spatial signature” for greenbug infestations in wheat fields from the processed imagery so that greenbug infested fields can be quickly, accurately, and inexpensively identified.

Scientists at the ARS Genetics and Precision Agriculture (GAPA) Research Unit at Mississippi State University, Mississippi, combined high-resolution, multispectral imagery and improved insect scouting methods to create a georeferenced pest density map on nearly 1,100 acres of cotton on Perthshire Farm in the Mississippi Delta. These maps were loaded into a ground sprayer to dispense pesticides and/or growth regulator chemicals (PIX) only where needed with the use of a variable rate controller. Several spatially variable prescriptions (Maps) were made throughout the growing season, pointing out the success of this cooperative, insect management effort between ARS, ITD Spectral Visions, GPS, Inc., Perthshire Farm, and farm consultants. GAPA scientists also collaborated with Mississippi State University scientists to identify spectrally narrow crop reflectance wavebands sensitive to nitrogen, potassium, and water deficit in upland cotton. Researchers developed algorithms for detecting crop nutrient and water stress conditions from hyperspectral or multispectral airborne platforms. In cooperation with Natural Resources Conservation Service (NRCS) scientists, GAPA scientists delineated soil management zones in a 400-acre, irrigated cotton field. By applying different fertilizer nitrogen prescriptions based on simulated yields from the ARS Cotton Model, they confirmed that natural soil boundaries are better than an arbitrary rectangular grid system when a decision support system is used to optimize soil nitrogen applications.

At the ARS Wind Erosion and Water Conservation Research Unit in Lubbock, Texas, irrigation timing, based on remotely measuring temperature of crop canopies was successfully demonstrated under field conditions. The correlation between canopy temperature and leaf water potential of corn and cotton was studied after irrigation rates were either increased or decreased. Significant changes were detected in canopy temperature and leaf water potential of cotton, but only in leaf water potential of corn. The absence of a measured canopy temperature response in corn suggests that a modification in the procedure for remotely monitoring corn temperature is needed to increase its sensitivity to the water status of corn to optimize irrigation management. The response of five vegetation indices was compared with the development of Leaf Area Index (LAI) and Fractional Vegetative Area (FVA) and compared with their impact on canopy Photosynthetically Active Radiation (PAR) absorption. The vegetation indices varied more linearly with FVA than LAI, and FVA is more influential in PAR absorption, thus linearity in FVA may be a more relevant criteria in choosing an index design to monitor crop productivity.

At the ARS Western Integrated Cropping Systems Research Unit in Shafter, California, aerial imagery was acquired of cotton and other crops using a multispectral digital camera to detect and characterize pests in cotton, including spider mites and aphids; for measurements of water stress using a thermal camera; midseason yield estimation; and development of remote sensing for targeted soil sampling for salinity management. Spectral signatures for mites and other stressors of cotton were developed using multispectral remote-sensing technologies, both on the ground and from aircraft. Researchers at the ARS Water Management Research Unit in Ft. Collins, Colorado, used ground-based, remote-sensing techniques (multispectral sensors mounted on a high-clearance tractor) to assess the plant Nitrogen status in irrigated corn for in-season nitrogen management. A previously developed Nitrogen Reflectance Index (NRI), calculated from canopy reflectance data acquired in the green and near-infrared portions of the electromagnetic spectrum from a nadir view (0°) and an oblique view (75°), was compared to measured plant nitrogen. The NRI was not representative of plant nitrogen at the sixth leaf growth stage (V6) for either view angle because of the soil background influence on canopy reflectance. However, the oblique view NRI was a good predictor of plant nitrogen at V9 and V12, as was the nadir view NRI at V12. The nadir view NRI was not as sensitive as the oblique view NRI at the V9 growth stage because soil was still visible through the canopy. Consequently, the nadir view NRI provides a conservative estimate of plant nitrogen prior to complete canopy cover.

The ARS Rangeland Resources Research Unit in Cheyenne, Wyoming, and BLM used an ultralight airplane to obtain Very-Large Scale Aerial (VLSA) 70-mm color photographs from 20 feet above ground to evaluate range condition. This effort demonstrated the practicality of using this type of aircraft to rapidly acquire a statistically adequate number of VLSA images (samples) over extensive rangeland areas. The imagery is being tested as a means for measuring ground cover and the leaf area of dominant functional plant types. The results are used to monitor rangeland health and for estimating CO₂ fluxes and carbon cycling.

The ARS National Sedimentation Laboratory in Oxford, Mississippi, has worked on projects that used the Next Generation Weather Radar, the Surface Radiation Network (SURFRAD), and the Soil Climate Analysis Network as part of the Global Energy and Water Cycle Experiment and its continental component based on the Mississippi River. Researchers used these data to model the variations of the global hydrological regime, its impact on atmospheric and surface dynamics, and variations in regional hydrological processes and water resources and their response to changes in the environment, such as the increase in greenhouse gases.

The ARS Southeast Watershed Research Laboratory in Tifton, Georgia, is working with scientists from the ARS Hydrology and Remote Sensing Laboratory, Georgia Institute of Technology, and University of South Carolina to perfect methods to estimate soil-water conditions at the land surface using remote sensing techniques. Past research has indicated the applicability of such a technique for sparsely vegetated landscapes. However, because of the difficulty associated with closed canopies in heavily vegetated landscapes, less work has been done in areas with dense vegetation. Initial studies conducted in July 2000 indicated that techniques can be developed for landscapes with dense canopies.

ARS scientists at the U.S. Water Conservation Laboratory (USWCL) in Phoenix, Arizona, refined methods to integrate remotely sensed information with computer models that predict crop growth, based on weather and soil conditions to help meet the information needs for precision farming. Remotely sensed data provide information on plant conditions at fine spatial resolution at select times during the season for improving the crop model predictions.

In cooperation with agricultural engineers at the University of Arizona, USWCL scientists developed a system (AgIIS) of visible, near infrared, and thermal sensors mounted on a cart that travels the length of a linear move irrigation system collecting measurements at 1-meter intervals. Researchers processed the data to generate an image that was used to detect crop and water stress. A Canopy Chlorophyll Content Index (CCCI) developed from multispectral reflectance data, obtained while using the AgIIS sensor in a cotton experiment, has now been modified for use in wheat. As chlorophyll content is a good indicator of fertilizer needs, the CCCI may find use in assessing midseason fertilizer requirements of wheat and in predicting grain quality. Scientists at the ARS South Central Agricultural Research Laboratory in Lane, Oklahoma, and at the Horticultural Research and Development Centre in Saint-Jean-sur-Richelieu, Quebec, Canada, provided quality assessment observations and evaluated the potential to integrate the remotely sensed data with models to predict crop quality.

Scientists at the ARS Northwest Irrigation and Soils Research Laboratory in Kimberly, Idaho, in cooperation with Utah State University made remote-sensing measurements with an aircraft to develop reflectance-based, evapotranspiration (ET) crop coefficients for irrigated bean, sugar beet, and potato. High resolution, airborne multispectral digital imagery were used to develop vegetation indices related to the spatial and temporal variation in crop growth and biophysical parameters obtained in field measurements. Ground-based measurements obtained for a field of beans verified the reflectance-based ET crop coefficients developed from the remotely sensed data. Airborne or multispectral satellite imagery can be used to develop spatially and temporally variable ET crop coefficients useable for precision irrigation scheduling with potential also for assessing aggregate irrigation water requirements and yield for mixed cropping patterns in large irrigated tracts.

The ARS Northwest Watershed Research Center (NWRC) in Boise, Idaho, used synthetic aperture radar to map the extent of frozen soil in rugged topography. Ground data on soil water content, snow depth, and soil freezing were collected in conjunction with overpasses of the RADARSAT platform. Summer scenes were acquired to obtain imagery during dry, unfrozen conditions to be used as a reference. Data are currently being analyzed to determine the optimal approach to differentiating freezing effects from those due to topography, vegetation, and surface roughness.

NWRC scientists conducted an analysis of Landsat and SPOT imagery to determine the relationship between satellite-derived vegetation indices and the soil water regime and found a good correlation between the vegetation index or Soil Adjusted Vegetation Index (SAVI) and the seasonal water stress, relative to evaporative demand in semiarid rangelands.

Invasion of cheatgrass, an exotic annual grass, into rangelands throughout the Intermountain West has dramatically altered the natural fire regime, thus impacting public safety, plant community integrity, and rangeland hydrology. NWRC scientists developed analysis tools to map fuel types, quantify fuel biomass and moisture, and assess fire severity in the Snake River Birds of Prey National Conservation Area near Boise, Idaho, using Landsat imagery. These tools will be useful to land management agencies for assessing fire hazards, planning fuels reduction treatments, predicting fire behavior, and evaluating postfire rehabilitation needs on rangelands.

NWRC scientists evaluated remote sensing to assess stream shading as a surrogate to direct stream temperature measurement. If good relationships exist between remotely sensed stream shading values and stream temperature, land managers could use these remotely sensed data to evaluate stream temperature variability for extensive and dynamic rangeland stream systems.

Scientists at the ARS Southwest Watershed Research Center (SWRC) in Tucson, Arizona, developed a spatially explicit hydro-ecological model calibrated with satellite images to produce daily estimates of regional plant growth, evaporation, and soil moisture. Over a 10-year period, this model simulated daily plant and root growth and rangeland health in the ARS Walnut Gulch Experimental Watershed with accuracies that were three times better than conventional products without satellite images. This breakthrough provided spatially distributed information about vegetation and soil conditions for day-to-day grassland management and for long-term evaluation of the effects of climate change and drought.

SWRC scientists cooperated with NASA and Michigan State University to develop a remote-sensing method to estimate biomass of senescent grasses. This information will be used with rangeland managers to make biomass information into usable data products, and finally, assess the potential for such information to be provided on an ongoing basis as a commercial product.

SWRC scientists worked with the Environmental Protection Agency to develop a PC-based GIS hydrologic tool to relate landscape patterns to watershed condition across multiple scales for applications across a wide range of conditions and geographies. This PC-based tool was applied to three watersheds of different sizes and climatic characteristics, ranging from the semiarid ecosystem in the ARS Walnut Gulch Experimental Watershed and San Pedro River Basin in Arizona to the humid Cannonsville watershed in New York.

Scientists at the ARS Grazinglands Research Laboratory (GRL) in El Reno, Oklahoma, combined remotely sensed near-surface estimates of soil water content with meteorological, vegetation, and soils data to produce estimates of total root zone soil water content at watershed scales. Scientists at GRL cooperated with scientists at the HRSL in the calibration and validation of satellite microwave sensors to provide a regional soil water content product. Scientists at the GRL also worked with scientists at the ARS Sub-Tropical Animal Research Station in Brooksville to detect forage quality using hand-held hyperspectral remote sensing.

The ARS Application and Production Technology Research Unit in Stoneville, Mississippi, used an aircraft system with a digital video camera and a GPS interface so images could be associated with ground position images to map weed populations from altitudes of 70 to 1,500 feet. Scientists analyzed images to distinguish weeds from the surrounding crops earlier in the season, when weed management plans need to be defined

Researchers at the ARS Southern Regional Research Center in New Orleans, Louisiana, designed submersed sensor arrays for monitoring harmful algal species. A prototype of this sensor was installed in the St. John’s River as part of collaborative research efforts with regional, State, and Federal research groups to monitor algal species in the river. Scientists at the ARS Catfish Genetics Research Unit in Stoneville, Mississippi, installed automated oxygen sensors for monitoring oxygen levels in catfish ponds. This sensor system controls aeration equipment to maintain desired oxygen levels for catfish growth.

The Foreign Agricultural Service’s (FAS) satellite remote-sensing program remained a critical element in USDA’s analysis of global agricultural production and crop conditions by providing timely, accurate, and unbiased estimates of global agriculture area, yield, and production. Satellite-derived early warnings of unusual crop conditions and production enabled more rapid, objective, and precise determinations of global supply conditions necessary for commodity price discovery. FAS used a full private-Government partnership program that contracts over 90 percent of its imagery from the commercial space industry and partners within other government agencies (NASA, NOAA, USGS) to ensure that FAS requirements are defined for mission planning and research. FAS continued to strengthen its abilities to extract the most from acquired data by sharing over 900 satellite scenes with partner USDA agencies. Over the past year, the FAS remote-sensing program provided global crop condition assessments in support of trade policy and food aid decisions made by USDA policymakers. These included crop assessments on China, the Korean Peninsula, Indonesia, Eastern Europe, North Africa, the countries of the former Soviet Union, India, the horn of Africa, and Mexico.

The Farm Service Agency (FSA) fielded reegineered business processes combining the use of digital orthophotography, GIS, GPS, and satellite imagery to replace the use of hardcopy aerial photography from the National Aerial Photography Program (NAPP) and aerial 35mm slides. Over 200 counties will be empowered with GIS technology by the end of 2000. FSA, through the Farm Service Agency-Foreign Agricultural Service Center for Remote Sensing, fielded compressed Landsat and AVHRR imagery to several State offices for disaster monitoring and compliance testing. FSA tested the use of the Space Imaging IKONOS imagery for use in digital compliance programs. FSA currently acquires aerial 35mm slides over much of the continental United States one to three times per year. The IKONOS imagery is being evaluated as one of many possible digital replacements. FSA continues to cost share with FAS analysis of imagery over the United States, receiving timely reports on U.S. crop conditions from FAS. These imagery-based reports, 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 continues to be a partner in NAPP but has been unable to partner in National Digital Orthophoto Program (NDOP) activities due to a lack of funds.

The National Agricultural Statistics Service (NASS) used remote-sensing data to construct area frames for statistical sampling, estimate crop area, create crop-specific land-cover data layers for GIS, and 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 a new remote-sensing-based area frame and sample for Pennsylvania and North Carolina. The remote-sensing acreage estimation project analyzed Landsat data from the 1999 crop season in Arkansas, Illinois, Mississippi, New Mexico, and North Dakota to produce crop acreage estimates for major crops at State and county levels, and a crop-specific categorization in the form of a digital mosaic of TM scenes distributed to users on a CD-ROM. For the 2000 crop season, NASS headquarters and several NASS field offices continued partnership agreements with State organizations to decentralize the Landsat processing and analysis tasks and expanded into Indiana and Iowa. Data for 2000 acreage estimation analysis were collected in Arkansas, Illinois, Indiana, Iowa, Mississippi, New Mexico, and North Dakota. Vegetation condition images, based on AVHRR data, were used with conventional survey data to assess crop conditions. The 2000 drought conditions in Nebraska and South Dakota, southern Texas, and the southeastern States were followed closely with these data.

The Natural Resources Conservation Service (NRCS) continued its cooperative partnership with Federal, State, and local agencies in developing 1-meter digital orthoimagery coverage of the Nation through NDOP and NAPP. By year’s end, approximately 2,500 counties were completed with digital orthoimagery coverage. NRCS delivered digital orthoimagery to its county field service centers for their use in a desktop GIS in place of using paper copies of aerial photographs. NRCS continued to advance the use of GPS at the county field offices, and at the end of the fiscal year, there were more than 1,000 GPS receivers in use.