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Barnett, T. L.

Publications and source records attributed to Barnett, T. L..

12 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS1983

Large-area relation of Landsat MSS and NOAA-6 AVHRR spectral data to wheat yields

Landsat MSS data transformed into Kauth-Thomas greenness were averaged over 5 n.mi x 6 n.mi. sample segments from the U.S. Great Plains winter and spring wheat (Triticum aestivum) regions, and related by regression analysis to yields reported by county, crop reporting district (CRD) and state levels. Evidence of a linear relation between winter- and spring-wheat yields and Landsat spectral data at a broad scale is shown for 1978 and 1979. A common slope of about 1.6 (Bu/A)/unit greenness is discerned for the relation between yield and spectral greenness. Tests at both a smaller scale on sets of field-level spectal data and yield and at a large scale on 25 mi. x 25 mi. gridded spectral data from the NOAA-6 AVHRR sensor support the relation. The implications of these results to yield estimation from satellite spectral data are discussed.

Barnett, T. L.↗
NASA NTRS1982

The use of large-area spectral data in wheat yield estimation

Large-area relations between satellite spectral data and end-of-season crop yield were investigated. Green Index Number (GIN) values from Landsat MSS data of sample segments throughout the U.S. Great Plains winter wheat belt in 1978 were correlated to county USDA-SRS reported yields. A linear relation between GIN and yield appeared to exist up to GIN values of 40 or 50, covering cases of severe to moderate stress. In a test on 1978 Texas winter wheat at the county level, GIN values for sample segments in the counties were used in conjunction with an agronomic-meteorological yield model. The combined fit explained significantly more of the observed yield variation at the county level than the agromet model alone.

Barnett, T. L.↗
NASA NTRS1982

Comparison of CEAS and Williams-type models for spring wheat yields in North Dakota and Minnesota

The CEAS and Williams-type yield models are both based on multiple regression analysis of historical time series data at CRD level. The CEAS model develops a separate relation for each CRD; the Williams-type model pools CRD data to regional level (groups of similar CRDs). Basic variables considered in the analyses are USDA yield, monthly mean temperature, monthly precipitation, and variables derived from these. The Williams-type model also used soil texture and topographic information. Technological trend is represented in both by piecewise linear functions of year. Indicators of yield reliability obtained from a ten-year bootstrap test of each model (1970-1979) demonstrate that the models are very similar in performance in all respects. Both models are about equally objective, adequate, timely, simple, and inexpensive. Both consider scientific knowledge on a broad scale but not in detail. Neither provides a good current measure of modeled yield reliability. The CEAS model is considered very slightly preferable for AgRISTARS applications.

Barnett, T. L.↗
NASA NTRS1981

Evaluation of the Williams-type model for barley yields in North Dakota and Minnesota

The Williams-type yield model is based on multiple regression analysis of historial time series data at CRD level pooled to regional level (groups of similar CRDs). Basic variables considered in the analysis include USDA yield, monthly mean temperature, monthly precipitation, soil texture and topographic information, and variables derived from these. Technologic trend is represented by piecewise linear and/or quadratic functions of year. Indicators of yield reliability obtained from a ten-year bootstrap test (1970-1979) demonstrate that biases are small and performance based on root mean square appears to be acceptable for the intended AgRISTARS large area applications. The model is objective, adequate, timely, simple, and not costly. It consideres scientific knowledge on a broad scale but not in detail, and does not provide a good current measure of modeled yield reliability.

Barnett, T. L.↗
NASA NTRS1981

Evaluation of the CEAS model for barley yields in North Dakota and Minnesota

The CEAS yield model is based upon multiple regression analysis at the CRD and state levels. For the historical time series, yield is regressed on a set of variables derived from monthly mean temperature and monthly precipitation. Technological trend is represented by piecewise linear and/or quadriatic functions of year. Indicators of yield reliability obtained from a ten-year bootstrap test (1970-79) demonstrated that biases are small and performance as indicated by the root mean square errors are acceptable for intended application, however, model response for individual years particularly unusual years, is not very reliable and shows some large errors. The model is objective, adequate, timely, simple and not costly. It considers scientific knowledge on a broad scale but not in detail, and does not provide a good current measure of modeled yield reliability.

Barnett, T. L.↗
NASA NTRS1979

Status of yield estimation technology: A review of second-generation model development and evaluation

Multiple regression models were studied in order to determine their yield estimation capability for any arbitrary unit area and to obtain greater responsiveness and accuracy through the use of additional data sources applied at smaller spatial and temporal scales. It was concluded that data base inadequacy was the factor limiting performance in the models studied and that each of the models has more yield predicting capability than was reached during LACIE.

Stuff, R. G.↗
NASA NTRS1979

The use of spectral data in wheat yield estimation - An assessment of techniques explored in LACIE

The object of the paper is to assess the results of the Large Area Crop Inventory Experiment (LACIE) and closely related research on yield estimation techniques based on remote sensing variables. The exploratory research conducted during LACIE substantiated the hypothesis of yield related information contained in Landsat multispectral scanner data and indicated some of its empirical characteristics. It is noted that leaf area and possibly other foliage features can be derived from spectral data for yield estimation through agrometeorological models and that multiple vegetative and grain related features may be discernable by Landsat derived wheat spectra at different points in the crop development.

Stuff, R. G.↗
NASA NTRS1977

Skylab S191 visible-infrared spectrometer

The paper describes the S191 visible-infrared spectrometer of the Skylab Earth Resources Experiment Package - a manually pointed two-channel instrument operating in the reflective (0.4-2.5 micron) and thermal emissive (6-15 micron) regions. A sensor description is provided and attention is given to data quality in the short wavelength and thermal infrared regions.

Barnett, T. L.↗
NASA NTRS1974

Atmospheric transmission computer program CP

A computer program is described which allows for calculation of the effects of carbon dioxide, water vapor, methane, ozone, carbon monoxide, and nitrous oxide on earth resources remote sensing techniques. A flow chart of the program and operating instructions are provided. Comparisons are made between the atmospheric transmission obtained from laboratory and spacecraft spectrometer data and that obtained from a computer prediction using a model atmosphere and radiosonde data. Limitations of the model atmosphere are discussed. The computer program listings, input card formats, and sample runs for both radiosonde data and laboratory data are included.

Pitts, D. E.↗
NASA NTRS1974

Summary of flight performance of the Skylab Earth Resources Experiment Package /EREP/

A group of six remote sensor systems (sensing visible, infrared, and microwave radiation) known as the Earth Resources Experiment Package (EREP) was flown on the NASA Skylab spacecraft to furnish data to numerous investigators in the earth sciences and in technology assessment. Inflight sensor performance in three categories (functional, geometric, and radiometric) was evaluated using: (1) ground measurements of surface, atmospheric, and illumination parameters; (2) ground deployment and operation of microwave receivers and transponders to monitor and excite the active EREP sensors; (3) measurement of surface and atmospheric parameters by instrumented aircraft on underflights of Skylab passes; and (4) analysis of the actual flight data. This resulted in identification and correction of anomalous sensor operation, quantization of geometric distortions or aberrations, improvement or confirmation of calibrations, and determination of sensitivity, accuracy, and stability of the sensors.

Potter, A. E.↗