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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
NASA NTRS1981

AgRISTARS: Agriculture and resources inventory surveys through aerospace remote sensing

The major objectives and FY 1980 accomplishments are described of a long term program designed to determine the usefulness, cost, and extent to which aerospace remote sensing data can be integrated into existing or future USDA systems to improve the objectivity, reliability, timeliness, and adequacy of information. A general overview, the primary and participating agencies, and the technical highlights of each of the following projects are presented: early warning/crop condition assessment; foreign commodity production forecasting; yield model development; supporting research; soil moisture; domestic crops and land cover; renewable resources inventory; and conservation and pollution.

Source record↗
NASA NTRS1982

AgRISTARS: Agriculture and resources inventory surveys through aerospace remote sensing

The rationale, objectives, participants, and approach of the AgRISTARS program are described. Progress is reported in activities related to early warning and crop condition assessment; inventory technology development (formerly foreign commodity production forecasting); yield model development; supporting research; soil moisture; renewable resources inventory; domestic crops and land cover; and conservation and pollution. Emphasis is on technological highlights.

Source record↗
NASA NTRS1985

Overview of the AgRISTARS research program. I

An account is given of the activities and accomplishments to date of the U.S. Department of Agriculture's Agriculture and Resources Inventory Surveys Through Aerospace Remote Sensing (AgRISTARS) program, which is a cooperative venture with NASA and the Departments of the Interior and of Commerce. AgRISTARS research activities encompass early warning and crop condition assessment, inventory technology development for production forecasting, crop yield model development, soil moisture monitoring, domestic crops and land cover sensing, renewable resources inventory, and conservation and pollution assessment.

Caudill, C. E.↗
NASA NTRS1990

Ground and aircraft infrared observations over a partially-vegetated area

Consideration is given to the feasibility of estimated a reliable composite surface temperature over a partially-vegetated and furrowed cotton field with an intensive ground sampling routing using hand-held radiometers. The ground-based data are compared with surface temperatures taken from an aircraft at an altitude of about 150 m. A simple model is presented for computing the composite surface temperature using component temperatures of the soil and vegetation and row crop dimensions and orientations. The results of the modeling are compared with the ground-based and airborne radiometric measurements. It is found that estimates of the composite surface temperature comparable to low-flying aircraft observations over row crops with sparse cover may be obtained using ground-based estimates of average canopy temperature.

Kustas, W. P.↗
NASA NTRS1990

The polarization phase difference of orchard trees

An image obtained by the Jet Propulsion Laboratory's airborne L-band polarimeter (SAR) over an agricultural area near Fresno, California, was analyzed for the signatures of polarization phase difference (PPD). The PPD of orchard trees was found to be distinctly different from that of bare fields or fields covered with other crops. Thus the PPD signatures obtained from a polarimeter may be useful in the understanding of the radar remote sensing of the earth's surface.

Wang, James R.↗
NASA NTRS2020

Riley County Water Resources Project Summary - Comparing Runoff Curve Calculation Methods to Inform Local Resiliency Initiatives in Riley County, Kansas

Riley County, Kansas, has observed increased levels of flooding, potentially due to changes in land use/land cover (LULC) and seasonal vegetation variation. This study contrasts two methods of generating runoff curve numbers (CN) from 2006-2020. (1) The traditional Soil Conservation Service CN calculation method uses a look-up table and tracked LULC to determine runoff changes. These tables allow for land cover-specific CN and account for various farming techniques but lack flexibility in calculations for various seasons or plant health. (2) A dynamic method employs normalized difference vegetation index (NDVI) compiled over the rainy season each year to calculate CN using seasonal vegetation. This method allows for a more precise analysis of runoff variability within and between rainy seasons because it can be updated with greater temporal detail and captures higher spatial resolutions by using NDVI as a proxy for LULC. This study further uses inputs from the United States Geological Survey (USGS) National Land Cover Database (NLCD), the United States Department of Agriculture (USDA) Cropland Data Layer, and Landsat imagery to create more precise LULC raster datasets including both urban cover and crop-specific land use and curve number maps of the area. Results can guide decision makers in the City of Manhattan, Riley County Department of Planning and Development, Riley County Conservation District, the Kansas Forest Service, and the Kansas Department of Health and Environment toward informed decisions on resiliency strategies to address future flooding.

DEVELOP Project Summary↗
NASA NTRS1981

Structure of the knowledge base for an expert labeling system

One of the principal objectives of the NASA AgRISTARS program is the inventory of global crop resources using remotely sensed data gathered by Land Satellites (LANDSAT). A central problem in any such crop inventory procedure is the interpretation of LANDSAT images and identification of parts of each image which are covered by a particular crop of interest. This task of labeling is largely a manual one done by trained human analysts and consequently presents obstacles to the development of totally automated crop inventory systems. However, development in knowledge engineering as well as widespread availability of inexpensive hardware and software for artificial intelligence work offers possibilities for developing expert systems for labeling of crops. Such a knowledge based approach to labeling is presented.

Rajaram, N. S.↗
NASA NTRS1986

Landsat Thematic Mapper digital information content for agricultural environments

Landsat Thematic Mapper (TM) data collected for Imperial Valley, California in December, 1982 were digitally examined to assess their utility to distinguish among agricultural and other land-covers. Statistics for thirty-seven training sites representing a variety of crops plus urban, water and desert land-covers were obtained and analyzed using transformed divergence (TD) calculations. TD values were employed to assess intraclass variability and the best bands for classification. Four subscenes were selected for clustering or unsupervised signature extraction. These areas were agriculture, urban, desert and water land-covers. The number of clusters for these subscenes were examined and the best TM bands for interclass separability were identified. The results of the clustering and training site analyses for interclass separability were compared. The TM data were useful for the digital delimitation of most crops and other cover types in this analysis. Four bands of data are adequate for classification with the best results obtained by the selection of one band from each of the available portions of the electromagnetic spectrum. Different band combinations are best for various land-cover intraclass separability.

Haack, Barry↗
NASA NTRS1982

Analysis of thematic mapper simulator data collected over eastern North Dakota

The results of the analysis of aircraft-acquired thematic mapper simulator (TMS) data, collected to investigate the utility of thematic mapper data in crop area and land cover estimates, are discussed. Results of the analysis indicate that the seven-channel TMS data are capable of delineating the 13 crop types included in the study to an overall pixel classification accuracy of 80.97% correct, with relative efficiencies for four crop types examined between 1.62 and 26.61. Both supervised and unsupervised spectral signature development techniques were evaluated. The unsupervised methods proved to be inferior (based on analysis of variance) for the majority of crop types considered. Given the ground truth data set used for spectral signature development as well as evaluation of performance, it is possible to demonstrate which signature development technique would produce the highest percent correct classification for each crop type.

Anderson, J. E.↗
NASA NTRS1975

NASA crop calendars: Wheat, barley, oats, rye, sorghum, soybeans, corn

Crop calenders used to determine when Earth Resources Technology Satellite ERTS data would provide the most accurate wheat acreage information and to minimize the amount of ground verified information needed are presented. Since barley, oats, and rye are considered 'confusion crops, i.e., hard to differentiate from wheat in ERTS imagery, specific dates are estimated for these crops in the following stages of development: (1) seed-bed operation, (2) planting or seeding, (3) intermediate growth, (4) dormancy, (5) development of crop to full ground cover, (6) heading or tasseling, and flowering, (7) harvesting, and (8) posting-harvest operations. Dormancy dates are included for fall-snow crops. A synopsis is given of each states' growing conditions, special cropping practices, and other characteristics which are helpful in identifying crops from ERTS imagery.

Stuckey, M. R.↗
DOE OSTI2022

CO 2 fertilization of crops offsets yield losses due to future surface ozone damage and climate change

Tropospheric ozone (O 3 ) is harmful to plant productivity and negatively impacts crop yields. O3 concentrations are projected to decrease globally in the optimistic Representative Concentration Pathway of 2.6 W m –2 (RCP2.6) but increase globally following the high-emission scenario under the RCP8.5, with substantial implications for global food security. The damaging effect of O 3 on future crop yield is affected by CO 2 fertilization and climate change, and their interactions for RCP scenarios have yet to be quantified. In this study, we used the Joint UK Land Environment Simulator modified to include crops (JULES-crop) to quantify the impacts, and relative importance of present-day and future O3, CO 2 concentration and meteorology on crop production at the regional scale until 2100 following RCP2.6 and RCP8.5 scenarios. We focus on eight major crop-producing regions that cover the production of wheat, soybean, maize, and rice. Our results show that CO 2 alone has the largest effect on regional yields, followed by climate and O 3 . However, the CO 2 fertilization effect is offset by the negative impact of tropospheric O 3 in regions with high O 3 concentrations, such as South Asia and China. Simulated crop yields in 2050 were compared with Food and Agriculture Organisation (FAO) statistics to investigate the differences between a socioeconomic and a biophysical process-based approach. Results showed that FAO estimates are closer to our JULES-crop RCP8.5 scenario. This study demonstrates that air pollution could be the biggest threat to future food production and highlights an urgent policy need to mitigate the threat of climate change and O 3 pollution on food security.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1970

Ground truth versus no ground truth

The area of study was the southeastern Arizona test site and three areas within the site were studied in detail: Safford, Point of Pines, and Fort Apache-White River. These areas have terrain contrast ranging from flat arid regions to high alpine mountains. Data were obtained from the Apollo 9 photographic missions, high altitude aerial photography, and simulated ERTS-A data from high altitude aircraft. Various monoscopic and steroscopic devices were used to analyze the features, and film density variations were studied. No ground-based data were permitted. Thematic maps were prepared for geology, geomorphology, vegetation, hydrology, and soils. Interpreted boundaries were delineated, with no collaborative data used in the interpretation. Ground-based data were gathered during the overflight of high altitude aerial photography. A further study was made using the ground truth, and the data gathered on the ground were compared with original mapping. 80% to 85% of the interpretations in the areas checked were correct. It was proved that it is possible to monitor gross features of the vigor of crop lands and vegetative cover, to type soils and classify geologic features, and to determine hydrologic conditions.

Torbert, G. B.↗
NASA NTRS1972

A study of Quaternary landforms and materials in the Midwest and Great Plains

The author has identified the following significant results. Measurements made from prints of ERTS-1, MSS 5 images, show practical limits of detectability for this imagery in the Midwest. The smallest high contrast object detectable has an approximate measured diameter of 150 feet. The smallest clearly identifiable cultural feature is roughly 300 feet for high contrast, and 400 to 500 feet for low contrast objects. Rural roadways, with an average width of 75 feet, are clearly defined due to high reflectivity, linearity, and the instantaneous field of view of the scanner. On the infrared a farm pond slightly greater than one acre is detectable. Crop and natural foliage cover in the Midwest during summar months obscures geologic and soils information and hinders detailed mapping. In the western Great Plains large-scale mapping of this kind may be possible, even at this time of year. In southwestern Iowa, topographic and drainage system anomalies, revealed by the imagery, are related to the slope of and depth to the buried bedrock surface. In eastern Iowa land use classification can be done from ERTS-1 imagery.

Morrison, R. B.↗
NASA NTRS1975

Agricultural applications of remote sensing: A true life adventure

A study of agricultural applications of remote sensing with a major US agricultural firm was undertaken in mid-1973. The study continued for eighteen months, and covered the areas of crop monitoring and management as well as large scale crop inventories. Pilot programs in the application of aircraft remote sensing and LANDSAT data were conducted. An operational aircraft survey program for ranch management has subsequently been implemented by the agricultural firm. LANDSAT data was successfully used to produce a ninety-seven percent accurate inventory of cotton over 4.8 million acres of California's San Joaquin Valley.

Schaller, E. S.↗
NASA NTRS1977

Wheat yield forecasts using Landsat data

Leaf area index and percentage of vegetative cover, two indices of crop yield developed from Landsat multispectral scanning data, are discussed. Studies demonstrate that the Landsat indicators may be as highly correlated with winter wheat yield as estimates based on traditional field sampling methods; in addition, the Landsat indicators may account for variations in individual field yield which are not explainable by meteorological data. A simple technique employing early-season Landsat data to make wheat yield predictions is also considered.

Colwell, J. E.↗
NASA NTRS1979

Detecting transition in agricultural systems

Remote sensing of agricultural phenomena has been largely concentrated on analysis of agriculture at the field level. Concern has been to identify crop status, crop condition, and crop distribution, all of which are spatially analyzed on a field-by-field basis. A more general level of abstraction is the agricultural system, or the complex of crops and other land cover that differentiate various agricultural economies. The paper reports on a methodology to assist in the analysis of the landscape elements of agricultural systems with Landsat digital data. The methodology involves tracing periods of photosynthetic activity for a fixed area. Change from one agricultural system to another is detected through shifts in the intensity and periodicity of photosynthetic activity as recorded in the radiometric return to Landsat. The Landsat-derived radiometric indicator of photosynthetic activity appears to provide the ability to differentiate agricultural systems from each other as well as from conterminous natural vegetation.

Neary, P. J.↗
NASA NTRS1984

Area estimation using multiyear designs and partial crop identification

Statistical procedures were developed for large area assessments using both satellite and conventional data. Crop acreages, other ground cover indices, and measures of change were the principal characteristics of interest. These characteristics are capable of being estimated from samples collected possibly from several sources at varying times, with different levels of identification. Multiyear analysis techniques were extended to include partially identified samples; the best current year sampling design corresponding to a given sampling history was determined; weights reflecting the precision or confidence in each observation were identified and utilized, and the variation in estimates incorporating partially identified samples were quantified.

Sielken, R. L., Jr.↗
NASA NTRS1993

Classification of simple vegetation types using POLSAR image data

Mapping basic vegetation or land cover types is a fairly common problem in remote sensing. Knowledge of the land cover type is a key input to algorithms which estimate geophysical parameters, such as soil moisture, surface roughness, leaf area index or biomass from remotely sensed data. In an earlier paper, an algorithm for fitting a simple three-component scattering model to POLSAR data was presented. The algorithm yielded estimates for surface scatter, double-bounce scatter and volume scatter for each pixel in a POLSAR image data set. In this paper, we show how the relative levels of each of the three components can be used as inputs to simple classifier for vegetation type. Vegetation classes include no vegetation cover (e.g. bare soil or desert), low vegetation cover (e.g. grassland), moderate vegetation cover (e.g. fully developed crops), forest and urban areas. Implementation of the approach requires estimates for the three components from all three frequencies available using the NASA/JPL AIRSAR, i.e. C-, L- and P-bands. The research described in this paper was carried out by the Jet Propulsion Laboratory, California Institute of Technology under a contract with the National Aeronautics and Space Administration.

Freeman, A.↗