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At least 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
NASA NTRS1982

Satellite remote sensing - An integral tool in acquiring global crop production information

Since NASA's program of research concerning remote sensing was initiated in the 1960s, one of its major objectives has been to advance the state-of-the-art in machine processing of satellite acquired multispectral data. Possibilities have been studied regarding a use of these data to identify type, to monitor condition, and to estimate the ontogenetic stage of cultural vegetation. The present investigation provides a review of the state-of-the-art of the technology used to make remote sensing crop production estimates in foreign regions. Attention is given to Landsat data acquisition, aspects of registration and preprocessing, questions of data transformation, data modeling, proportion estimation, labeling, development stage models, crop condition models, and an outlook regarding future developments.

Hall, F. G.↗
NASA NTRS1982

Update on a system for large area crop inventory from remotely sensed data

In connection with the conduction of the Large Area Crop Inventory Experiment (LACIE), a state-of-the-art technology for the inventory of crops using satellite data was developed and tested over a period of three years (1975-77). A method was obtained for determining the total wheat production in a given region by looking at only a subset of its area. The present investigation is concerned with the current state of the art as obtained in the aggregation technology of the Agriculture and Resources Inventory Surveys Through Aerospace Remote Sensing (AgRISTARS) program. Attention is given to details of AgRISTARS baseline aggregation technology, and to recent improvements and future research directions.

Baker, T. C.↗
NASA NTRS1982

Acquisition history simulation for evaluation of Landsat-based crop inventory systems

This paper describes the development and evaluation of a simulation procedure which produces patterns of Landsat data loss attributable to cloud patterns that are characteristic of a crop region. This simulation procedure is part of a simulation system under development which evaluates the performance of crop inventory system components over a number of years and under a variety of conditions.

Smith, J. H.↗
NASA NTRS1982

Profile modeling for crop discrimination

A complete automatic unsupervised classification approach has been developed to estimate the proportion of corn, soybeans, and other crops in a Landsat 5-by-6-nanometer segment. The technique involves extracting crop growth variables that are predictable from agromet models, it provides a signature extension not possible before. The technique has been applied in 3 years (1978-1980) for 56 segments and shows neither a year-to-year effect nor any geographic effect. The results to date are considered far better than those obtainable from any other currently available technique. Nontheless, the technique can be improved to reduce the bias.

Badhwar, G. D.↗
NASA NTRS1982

Crop identification using Landsat temporal-spectral profiles

The temporal-spectral profile is a detailed indicator of the physical state of a field through time. Characteristic profiles have been observed for a variety of crops and other cover classes from Landsat data in the United States Corn Belt. These profiles contain information to support crop identification at various levels.

Odenweller, J. B.↗
NASA NTRS1984

Effect of mixed (boundary) pixels on crop proportion estimation

In estimating acreage proportions of crop types in a segment using Landsat data, considerable problem is caused by the presence of mixed pixels. Due to lack of understanding of their spectral characteristics, mixed pixels have been treated in the past as pure while clustering and classifying the segment data. This paper examines this approach of treating mixed pixels as pure pixels and the effect of mixed pixels on the bias and variance of a crop type proportion estimate. First, the spectral response of a boundary pixel is modeled and an analytical expression for the bias and variance of a proportion estimate is obtained. This is followed by a numerical illustration of the effect of mixed pixels on bias and variance. It is shown that as the size of the mixed pixel class increases in a segment, the variance increases, however, such increase does not always affect the bias of the proportion estimate.

Chhikara, R. S.↗
NASA NTRS1984

Directional reflectance factor distributions of a cotton row crop

The directional reflectance factor distribution spanning the entire exitance hemisphere was measured for a cotton row crop (Gossypium barbadense L.) with 39 percent ground cover. Spectral directional radiances were taken in NOAA satellite 7 AVHRR bands 1 and 2 using a three-band radiometer with restricted 12 deg full angle field of view at half peak power points. Polar co-ordinate system plots of directional reflectance factor distributions and three-dimensional computer graphic plots of scattered flux were used to study the dynamics of the directional reflectance factor distribution as a function of spectral band, geometric structure of the scene, solar zenith and azimuth angles, and optical properties of the leaves and soil. The factor distribution of the incomplete row crops was highly polymodal relative to that for complete vegetation canopies. Besides the enhanced reflectance for the antisolar point, a reflectance minimum was observed towards the forwardscatter direction in the principle plane of the sun. Knowledge of the mechanics of the observed dynamics of the data may be used to provide rigorous validation for two- or three-dimensional radiative transfer models, and is important in interpreting aircraft and satellite data where the solar angle varies widely.

Kimes, D. S.↗
NASA NTRS1984

Active microwave responses - An aid in improved crop classification

A study determined the feasibility of using visible, infrared, and active microwave data to classify agricultural crops such as corn, sorghum, alfalfa, wheat stubble, millet, shortgrass pasture and bare soil. Visible through microwave data were collected by instruments on board the NASA C-130 aircraft over 40 agricultural fields near Guymon, OK in 1978 and Dalhart, TX in 1980. Results from stepwise and discriminant analysis techniques indicated 4.75 GHz, 1.6 GHz, and 0.4 GHz cross-polarized microwave frequencies were the microwave frequencies most sensitive to crop type differences. Inclusion of microwave data in visible and infrared classification models improved classification accuracy from 73 percent to 92 percent. Despite the results, further studies are needed during different growth stages to validate the visible, infrared, and active microwave responses to vegetation.

Rosenthal, W. D.↗
NASA NTRS1984

Research in satellite-aided crop forecasting

Evaluations of remote sensing procedures developed specifically to estimate non-U.S. spring small grains area show accuracies of less than 10 percent relative difference to reference statistics for North Dakota in 1978 and good comparison with 9000 square miles of observations over four states and Saskatchewan, Canada during the years 1976-79. Processing a 5 x 6-nautical-mile sample site requires a few minutes manual time and a few minutes central processing unit time on an AS-3000 computer. Evaluations of summer crop, corn, and soybeans area estimates show unbiased summer crops estimates in the U.S. central corn belt but significant bias in one of two years for area estimates of corn and soybeans. Based on results to date, a highly automated corn/sorghum/soybean area estimation procedure should be achieved that is applicable to Argentina.

Erickson, J. D.↗
NASA NTRS1986

On the design of classifiers for crop inventories

Crop proportion estimators that use classifications of satellite data to correct, in an additive way, a given estimate acquired from ground observations are discussed. A linear version of these estimators is optimal, in terms of minimum variance, when the regression of the ground observations onto the satellite observations in linear. When this regression is not linear, but the reverse regression (satellite observations onto ground observations) is linear, the estimator is suboptimal but still has certain appealing variance properties. In this paper expressions are derived for those regressions which relate the intercepts and slopes to conditional classification probabilities. These expressions are then used to discuss the question of classifier designs that can lead to low-variance crop proportion estimates. Variance expressions for these estimates in terms of classifier omission and commission errors are also derived.

Heydorn, R. P.↗
NASA NTRS1986

Crop acreage estimation using a Landsat-based estimator as an auxiliary variable

The problem of improving upon the ground survey estimates of crop acreages by utilizing Landsat data is addressed. Three estimators, called regression, ratio, and stratified ratio, are studied for bias and variance, and their relative efficiencies are compared. The approach is to formulate analytically the estimation problem that utilizes ground survey data, as collected by the U.S. Department of Agriculture, and Landsat data, which provide complete coverage for an area of interest, and then to conduct simulation studies. It is shown over a wide range of parametric conditions that the regression estimator is the most efficient unless there is a low correlation between the actual and estimated crop acreages in the sampled area segments, in which case the ratio and stratified ratio estimators are better. Furthermore, it is seen that the regression estimator is potentially biased due to estimating the regression coefficient from the training sample segments. Estimation of the variance of the regression estimator is also investigated. Two variance estimators are considered, the large sample variance estimator and an alternative estimator suggested by Cochran. The large sample estimate of variance is found to be biased and inferior to the Cochran estimate for small sample sizes.

Chhikara, R. S.↗
NASA NTRS1985

Evaluation of crop acreage estimation methods using Landsat data as auxiliary input

The regression and ratio estimators are studied in the context of improving upon the ground survey estimates of crop acreages by utilizing Landsat data. The approach is to formulate analytically the estimation problem that utilizes ground survey data, as collected by the U.S. Department of Agriculture, and Landsat data, which provide a complete coverage for an area of interest, and then to conduct simulation studies. It is shown over a wide range of conditions that the regression estimator is the most efficient unless there is a low correlation between the actual and estimated crop acreages in the sampled area segments, in which case a ratio type estimator is superior. Estimation of the variance of the regression estimator is also investigated.

Chhikara, R. S.↗
NASA NTRS1987

Procedures for the description of agricultural crops and soils in optical and microwave remote sensing studies

This paper describes procedures for characterizing agricultural crops and soils in remote sensing studies. The procedures are based on the accumulated experience of a number of researchers active in this field. Therefore, they represent a compromise between the theoretically desirable and the practically feasible, and should thus be an effective aid in further studies of this type. Although the guidelines were prepared specifically for microwave studies, adjustments were made to render the procedures applicable to optical studies as well. Given the increasing number of research teams involved in remote sensing applied to agriculture, there is an opportunity to acquire a broad data base on soils and crops in various geographic regions. To allow intercomparisons of such data, they must be obtained in a consistent manner. By following the proposed procedures and reporting results using the parameters described here, such intercomparisons should be possible on a continental or a global scale.

Cihlar, J.↗
NASA NTRS1990

A proposal to demonstrate production of salad crops in the space station mockup facility with particular attention to space, energy, and labor constraints

A desire for fresh vegetables for consumption during long term space missions has been foreseen. To meet this need in a microgravity environment within the limited space and energy available on Space Station requires highly productive vegetable cultivars of short stature to optimize vegetable production per volume available. Special water and nutrient delivery systems must also be utilized. As a first step towards fresh vegetable production in the microgravity of Space Station, several soil-less capillary action media were evaluated for the ability to support growth of two root crops (radish and carrot) which are under consideration for inclusion in a semi-automated system for production of salad vegetables in a microgravity environment (Salad Machine). In addition, productivity of different cultivars of radish was evaluated as well as the effect of planting density and cultivar on carrot production and size. Red Prince radish was more productive than Cherry Belle and grew best on Jiffy Mix Plus. During greenhouse studies, vermiculite and rock wool supported radish growth to a lesser degree than Jiffy Mix Plus but more than Cellular Rooting Sponge. Comparison of three carrot cultivars (Planet, Short n Sweet, and Goldinhart) and three planting densities revealed that Short n Sweet planted at 25.6 sq cm/plant had the greatest root fresh weight per pot, the shortest mean top length, and intermediate values of root length and top fresh weight per pot. Red Prince radish and Short n Sweet carrot showed potential as productive cultivars for use in a Salad Machine. Results of experiments with solid capillary action media were disappointing. Further research must be done to identify a solid style capillary action media which can productively support growth of root crops such as carrot and radish.

Brooks, Carolyn A.↗
NASA NTRS1989

Multi-temporal airborne synthetic aperture radar data for crop classification

This paper presents an approach to the classification of crop type using multitemporal airborne SAR data. Following radiometric correction of the data, the accuracy of a per-field crop classification reached 90 percent for three classes using data acquired on four dates. A comparable accuracy of 88 percent could be obtained for a classification of the same classes using data acquired on only two dates. Increasing the number of classes from three to seven reduced the classification accuracies to 55 percent and 69 percent when using data from two and four dates respectively.

Foody, G. M.↗
NASA NTRS1990

The theoretical relationship between foliage temperature and canopy resistance in sparse crops

One-dimensional, sparse-crop interaction theory is reformulated to allow calculation of the canopy resistance from measurements of foliage temperature. A submodel is introduced to describe eddy diffusion within the canopy which provides a simple, empirical simulation of the reported behavior obtained from a second-order closure model. The sensitivity of the calculated canopy resistance to the parameters and formulas assumed in the model is investigated. The calculation is shown to exhibit a significant but acceptable sensitivity to extreme changes in canopy aerodynamics, and to changes in the surface resistance of the substrate beneath the canopy at high and intermediate values of leaf area index. In very sparse crops changes in the surface resistance of the substrate are shown to contaminate the calculated canopy resistance, tending to amplify the apparent response to changes in water availability. The theory is developed to allow the use of a measurement of substrate temperature as an option to mitigate this contamination.

Shuttleworth, W. James↗
NASA NTRS1990

The CELSS Test Facility - A foundation for crop research in space

Under the NASA Space Biology Initiative, a CELSS Test Facility (CTF) is being planned for installation on Space Station Freedom. The CTF will be used to study the productivity of typical CELSS higher plant crops under the microgravity conditions of the Space Station Freedom (SSF). Such science studies will be supported under the CELSS Space Research Project. The CTF will be used to evaluate fundamental issues of crop productivity, such as the production rates of O2, food and transpired water, and CO2 uptake. A series of precursor tests that are essential to the development of the CTF will be flown on Space Shuttle flights. The tests will be used to validate and qualify technology concepts and to answer specific questions regarding seed germination, root/shoot orientation, water condensation and recycling, nutrient delivery, and liquid/gas phase interactions.

Straight, C. L.↗