Peer-Reviewed Articles
615 The Agricultural Research Service's Remote Sensing
Program: An Example of Interagency Collaboration
Paul J. Pinter, Jr., Jerry C. Ritchie, Jerry L. Hatfield, and Galen F.
Hart
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Interagency programs have made it possible for scientists from the USDA, Agricultural
Research Service (ARS) to conduct remote sensing research with researchers
from various federal, state, and private organizations since 1965. Cumulative
results from these cooperative studies have established the scientific basis
for using remotely acquired imagery to better understand and manage the Earth's
agricultural and natural resources. Examples of these partnerships include
the 1965 Statement of Agreement between USDA and NASA that formally began
ARS's remote sensing research, the Large Area Crop Inventory Experiment (LACIE,
1974), the ARS Wheat Yield Project (1976), Agriculture and Resources Inventory
Surveys Through Aerospace Remote Sensing (AgRISTARS, 1980), the China Wheat
Project (1983), and AG 20/20 (1999). This paper provides a brief overview
of these collaborations.
619 Remote- and Ground-Based Sensor Techniquesto
Map Soil Properties
Edward M. Barnes, Kenneth A. Sudduth, John W. Hummel, Scott M.
Lesch, Dennis L. Corwin, Chenghai Yang, Craig S.T. Daughtry, and
Walter C. Bausch
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Farm managers are becoming increasingly aware of the spatial variability in
crop production with the growing availability of yield monitors. Often this
variability can be related to differences in soil properties (e.g., texture,
organic matter, salinity levels, and nutrient status) within the field. To
develop management approaches to address this variability, high spatial resolution
soil property maps are often needed. Some soil properties have been related
directly to a soil spectral response, or inferred based on remotely sensed
measurements of crop canopies, including soil texture, nitrogen level, organic
matter content, and salinity status. While many studies have obtained promising
results, several interfering factors can limit approaches solely based on
spectral response, including tillage conditions and crop residue. A number
of different ground-based sensors have been used to rapidly assess soil properties "on
the go" (e.g., sensor mounted on a tractor and data mapped with coincident
position information) and the data from these sensors compliment image-based
data. On-the-go sensors have been developed to rapidly map soil organic matter
content, electrical conductivity, nitrate content, and compaction. Model
and statistical methods show promise to integrate these groundand image-based
data sources to maximize the information from each source for soil property
mapping.
631 Remote Sensing Research in Hydrometeorology
William P. Kustas, Andrew N. French, Jerry L. Hatfield, Tom J.
Jackson, M. Susan Moran, Al Rango, Jerry C. Ritchie, and Tom J. Schmugge
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An overview of remote sensing research in hydrometeorology, with an emphasis
on the major contributions that have been made by United States Department
of Agriculture- Agricultural Research Service (USDA-ARS) scientists, is provided.
The major contributions are separated into deriving from remote sensing (1)
hydrometeorological state variables and (2) energy fluxes, particularly evapotranspiration
which includes plant water stress. For the state variables, remote sensing
algorithms have been developed for estimating land surface temperatures from
brightness temperature observations correcting for atmospheric and emissivity
effects, estimating near-surface soil moisture from passive microwave remote
sensing, determining snow cover from visible and snow water equivalent from
microwave data, and estimating landscape roughness, topography, vegetation
height, and fractional cover from lidar distancing technology. For the hydrometeorological
fluxes, including plant water stress, models estimating evapotranspiration
have been developed using land surface temperature as a key boundary condition
with recent schemes designed to more reliably handle partial vegetation cover
conditions. These research efforts in estimating evapotranspiration with
remotely sensed surface temperatures have been utilized by ARS researchers
in the development of the Crop Water Stress Index and Water Deficit Index
for assessing plant water stress. In addition, the development of the Thermal
Kinetic Window and Crop Specific Temperatures have revealed the dynamic interactions
among foliage temperature, plant species, and the physical environment. ARS
researchers continue to develop new and improved remote sensing algorithms
for evaluating state variables and fluxes. Moreover, they are involved in
new research directions to address science questions impeding hydrometeorological
research. These include investigating the utility of combining multifrequency
remote sensing data for improved estimation of land surface properties, and
incorporating remote sensing for evaluating the effects of landscape heterogeneity
on atmospheric dynamics and mean air properties and resulting feedbacks on
the land surface fluxes
647 Remote Sensing for Crop Management
Paul J. Pinter, Jr., Jerry L. Hatfield, James S. Schepers, Edward
M. Barnes, M. Susan Moran, Craig S.T. Daughtry, and Dan R. Upchurch
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Scientists with the Agricultural Research Service (ARS) and various government
agencies and private institutions have provided a great deal of fundamental
information relating spectral reflectance and thermal emittance properties
of soils and crops to their agronomic and biophysical characteristics. This
knowledge has facilitated the development and use of various remote sensing
methods for non-destructive monitoring of plant growth and development and
for the detection of many environmental stresses which limit plant productivity.
Coupled with rapid advances in computing and positionlocating technologies,
remote sensing from ground, air, and space-based platforms is now capable
of providing detailed spatial and temporal information on plant response
to their local environment that is needed for site specific agricultural
management approaches. This manuscript, which emphasizes contributions by
ARS researchers, reviews the biophysical basis of remote sensing; examines
approaches that have been developed, refined, and tested for management of
water, nutrients, and pests in agricultural crops; and assesses the role
of remote sensing in yield prediction. It concludes with a discussion of
challenges facing remote sensing in the future.
665 Crop Yield Assessment from Remote Sensing
Paul C. Doraiswamy, Sophie Moulin, Paul W. Cook, and Alan Stern
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Monitoring crop condition and production estimates at the state and county
level is of great interest to the U.S. Department of Agriculture. The National
Agricultural Statistical Service (NASS) of the U.S. Department of Agriculture
conducts field interviews with sampled farm operators and obtains crop cuttings
to make crop yield estimates at regional and state levels. NASS needs supplemental
spatial data that provides timely information on crop condition and potential
yields. In this research, the crop model EPIC (Erosion Productivity Impact
Calculator) was adapted for simulations at regional scales. Satellite remotely
sensed data provide a real-time assessment of the magnitude and variation
of crop condition parameters, and this study investigates the use of these
parameters as an input to a crop growth model. This investigation was conducted
in the semi-arid region of North Dakota in the southeastern part of the state.
The primary objective was to evaluate a method of integrating parameters
retrieved from satellite imagery in a crop growth model to simulate spring
wheat yields at the sub-county and county levels. The input parameters derived
from remotely sensed data provided spatial integrity, as well as a real-time
calibration of model simulated parameters during the season, to ensure that
the modeled and observed conditions agree. A radiative transfer model, SAIL
(Scattered by Arbitrary Inclined Leaves), provided the link between the satellite
data and crop model. The model parameters were simulated in a geographic
information system grid, which was the platform for aggregating yields at
local and regional scales. A model calibration was performed to initialize
the model parameters. This calibration was performed using Landsat data over
three southeast counties in North Dakota. The model was then used to simulate
crop yields for the state of North Dakota with inputs derived from NOAA AVHRR
data. The calibration and the state level simulations are compared with spring
wheat yields reported by NASS objective yield surveys.
675 Applications and Research Using Remote Sensing
for Rangeland Management
E. Raymond Hunt, Jr., James H. Everitt, Jerry C. Ritchie, M.
Susan Moran, D. Terrance Booth, Gerald L. Anderson, Patrick E. Clark,
and Mark S. Seyfried
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Rangelands are grasslands, shrublands, and savannas used by wildlife for habitat
and livestock in order to produce food and fiber. Assessment and monitoring
of rangelands are currently based on comparing the plant species present
in relation to an expected successional end-state defined by the ecological
site. In the future, assessment and monitoring may be based on indicators
of ecosystem health, including sustainability of soil, sustainability of
plant production, and presence of invasive weed species. USDA Agricultural
Research Service (ARS) scientists are actively engaged in developing quantitative,
repeatable, and low-cost methods to measure indicators of ecosystem health
using remote sensing. Noxious weed infestations can be determined by careful
selection of the spatial resolution, spectral bands, and timing of image
acquisition. Rangeland productivity can be estimated with either Landsat
or Advanced Very High Resolution Radiometer data using models of gross primary
production based on radiation use efficiency. Lidar measurements are useful
for canopy structure and soil roughness, indicating susceptibility to erosion.
The value of remote sensing for rangeland management depends in part on combining
the imagery with other spatial data within geographic information systems.
Finally, ARS scientists are developing the knowledge on which future rangeland
assessment and monitoring tools will be developed.
695 Remote Sensing Techniques to Assess Water Quality
Jerry C. Ritchie, Paul V. Zimba, and James H. Everitt
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Remote sensing techniques can be used to monitor water quality parameters (i.e.,
suspended sediments (turbidity), chlorophyll, and temperature). Optical and
thermal sensors on boats, aircraft, and satellites provide both spatial and
temporal information needed to monitor changes in water quality parameters
for developing management practices to improve water quality. Recent and
planned launches of satellites with improved spectral and spatial resolution
sensors should lead to greater use of remote sensing techniques to assess
and monitor water quality parameters. Integration of remotely sensed data,
GPS, and GIS technologies provides a valuable tool for monitoring and assessing
waterways. Remotely sensed data can be used to create a permanent geographically
located database to provide a baseline for future comparisons. The integrated
use of remotely sensed data, GPS, and GIS will enable consultants and natural
resource managers to develop management plans for a variety of natural resource
management applications.
705 Sensor Development and Radiometric Correction
for Agricultural Applications
S. Moran, G. Fitzgerald, A. Rango, C. Walthall, E. Barnes, W. Bausch, T.
Clarke, C. Daughtry, J. Everitt, D. Escobar, J. Hatfield, K. Havstad, T. Jackson,
N. Kitchen, W. Kustas, M. McGuire, P. Pinter, Jr., K. Sudduth, J. Schepers,
T. Schmugge, P. Starks, and D. Upchurch
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This review addresses the challenges and progress in sensor development and
radiometric correction for agricultural applications with particular emphasis
on activities within the U.S. Department of Agriculture (USDA) Agricultural
Research Service (ARS). Examples of sensor development include on-site development
of sensors and platforms, participation in cooperative research and development
agreements (CRADA) with commercial companies, and membership on NASA science
teams. Examples of progress made in sensor radiometric correction suitable
for agriculture are presented for both laboratory and field environments.
The direction of future sensor development includes integrated sensors and
systems, sensor standardization, and new sensor technologies measuring fluorescence
and soil electrical conductivity, and utilizing LIght Detection and Ranging
(lidar), hyperspectral, and multiband thermal wavelengths. The upcoming challenges
include definition of the core spectral regions for agriculture and the sensor
specifications for a dedicated, orbiting agricultural sensor, determination
of an operational approach for reflectance and temperature retrieval, and
enhanced communication between image providers, research scientists, and
users. This review concludes with a number of avenues through which USDA
could promote sensor development and radiometric correction for agricultural
applications. These include developing a network of large permanent calibration
targets at USDA ARS locations; investing in new technologies; pooling resources
to support large-scale field experiments; determining ARS-wide standards
for sensor development, calibration, and deployment; and funding interagency
agreements to achieve common goals.
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