Bidirectional scattering characteristics of healthy green soybean and corn leaves in vivo
In vivo green soybean and corn leaves bidirectional IR reflection and transmission distribution functions
Breece, H. T., III↗
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Seasonal canopy reflectance patterns of wheat, sorghum, and soybean
An investigation was conducted of canopy-reflectance patterns as a basis for the determination of surface conditions. Two fields each of wheat, sorghum, and soybeans were selected in a bottom land area. One field contained a dark-colored, silty clay loam and the other a light-colored, silt loam. The study suggests that the reflectance ratio of the 545- to 655-nm-wavelengths may be used as an indicator of crop growth.
Kanemasu, E. T.↗
Economic Benefits of Improved Information on Worldwide Crop Production: An Optimal Decision Model of Production and Distribution with Application to Wheat, Corn, and Soybeans
An optimal decision model of crop production, trade, and storage was developed for use in estimating the economic consequences of improved forecasts and estimates of worldwide crop production. The model extends earlier distribution benefits models to include production effects as well. Application to improved information systems meeting the goals set in the large area crop inventory experiment (LACIE) indicates annual benefits to the United States of $200 to $250 million for wheat, $50 to $100 million for corn, and $6 to $11 million for soybeans, using conservative assumptions on expected LANDSAT system performance.
Andrews, J.↗
Temporal spectral measurements of corn and soybean crops
A ground-based, hand-held radiometer, configured to measure red and photographic infrared spectral radiances, was successfully used to collect in situ temporal spectral measurements of corn and soybean crops. Significant relationships were found between the radiance data and the biomass, plant height, percentage crop cover, percentage crop chlorosis, and percentage leaf loss. The results of this experiment show conclusively that hand-held radiometers can be used to collect spectral data that are highly correlated to several agronomic variables. These findings suggest approaches for agronomic research, and confirm the value of remote sensing of agricultural targets.
Tucker, C. J.↗
The use of Landsat data to inventory cotton and soybean acreage in North Alabama
This study was performed to determine if Landsat data could be used to improve the accuracy of the estimation of cotton acreage. A linear classification algorithm and a maximum likelihood algorithm were used for computer classification of the area, and the classification was compared with ground truth. The classification accuracy for some fields was greater than 90 percent; however, the overall accuracy was 71 percent for cotton and 56 percent for soybeans. The results of this research indicate that computer analysis of Landsat data has potential for improving upon the methods presently being used to determine cotton acreage; however, additional experiments and refinements are needed before the method can be used operationally.
Downs, S. W., Jr.↗
An assessment of LANDSAT data acquisition history on identification and area estimation of corn and soybeans
Multitemporally registered LANDSAT MSS data from four acquisitions during the 1978 growing season were used in classification of eight sample segments in Iowa and Indiana. The results illustrate that use of LANDSAT acquisition when corn has tasseled is critical, as this is the optimum time for separation of corn and soybeans. An early season acquisition when the summer crops appear as bare soil can be beneficial in reducing the confusion between these two crops and other cover types. A subset of one visible and one infrared band from each date was found to produce results not significantly different from the use of all bands. Selection of a subset of these bands may also be feasible for multitemporal analysis.
Hixson, M. M.↗
A project to evaluate moisture stress and phenological factors in corn and soybean areas of southwestern and south-central Minnesota
The capability of aerial color infrared photography for identifying and locating soilscapes most susceptible to drought development or those having different soil drainages was evaluated. Although west central and southwestern Minnesota had a significant precipitation deficit during the 1980 crop season, indications of plant stress were not observed either from ground observation or through interpretation of imagery at the selected sampling sites. Well-drained soil was more depleted during the "maximium green peak" period than the poorly drained soil. The recharge was more evident on the poorly drained site in the fall. Measurements of leaf water potential and leaf temperature for corn and soybean leaves were made by multispectral band scanner and recorded for analysis.
Rust, R. H.↗
An assessment of Landsat data acquisition history on identification and area estimation of corn and soybeans
During the past decade, numerous studies have demonstrated the potential of satellite remote sensing for providing accurate and timely crop area information. This study assessed the impact of Landsat data acquisition history on classification and area estimation accuracy of corn and soybeans. Multitemporally registered Landsat MSS data from four acquisitions during the 1978 growing season were used in classification of eight sample segments in the U.S. Corn Belt. The results illustrate the importance of selecting Landsat acquisitions based on spectral differences in crops at certain growth stages.
Hixson, M. M.↗
Soybean canopy reflectance as influenced by cultural practices
Experiments were conducted at West Lafayette, Indiana in 1978 and 1979 to study the reflectance factor of soybean canopies as affected by differences in row width, population, planting date, cultivar and soil type. Reflectance factor data were acquired throughout the growing season with a LANDSAT-band radiometer. Agronomic data included plant height, leaf area index, development stage, total fresh and dry biomass, percent soil cover, and grain yield. The results indicate that row width, planting date, and cultivar influence the percent soil cover, leaf area index, and biomass present, which are in turn related to the multispectral reflectance. Additionally, the reflectance data were quite sensitive to the onset of senescence. Soil color and moisture were found to be important factors influencing the reflectance in single LANDSAT bands, but the near infrared/red reflectance ratio and the greeness transformation were less sensitive than the single bands to the soil background present.
Bauer, M. E.↗
Spectral-agronomic relationships of corn, soybean and wheat canopies
During the past six years several thousand reflectance spectra of corn, soybean, and wheat canopies were acquired and analyzed. The relationships of biophysical variables, including leaf area index, percent soil cover, chlorophyll and water content, to the visible and infrared reflectance of canopies are described. The effects on reflectance of cultural, environmental, and stress factors such as planting data, seeding rate, row spacing, cultivar, soil type and nitrogen fertilization are also examined. The conclusions are that several key agronomic variables including leaf area index, development stage and degree of stress are strongly related to spectral reflectance and that it should be possible to estimate these descriptions of crop condition from satellite acquired multispectral data.
Bauer, M. E.↗
AgRISTARS. Preliminary technical results review of FY81 experiments, volume 2: Fiscal year 1981/1982 "corn and soybeans pilot" experiment
The performance of the technology exhibited significant proportion estimation errors, specifically, high mean error in both corn and soybeans area estimation. The data systems, technical approaches, and data assessment of the pilot experiment were reviewed. Results of proportion estimations procedure performance evaluations, and sensitivity evaluations are presented. The role of the pilot experiment in foreign technology development is discussed.
Source record↗
Normal crop calendars. Volume 3: The corn and soybean states of Illinois, Indiana, and Iowa
The state and crop reporting district crop calendars for Iowa, Illinois, and Indiana are presented. Crop calendars for corn, soybeans, sorghum, oats, wheat, barley, clover, flax, sugar beets, and tobacco are included.
West, W. L., III↗
Cultural and environmental effects on the spectral development patterns of corn and soybeans: Field data analysis
An overall approach to crop spectral understanding is presented which serves to maintain a strong link between actual plant responses and characteristics and spectral observations from ground based and spaceborne sensors. A specific technique for evaluating field reflectance data, as a part of the overall approach, is also described. Results of the application of this technique to corn and soybeans reflectance data collected by and at Purdue/LARS indicate that a number of common cultural and environmental factors can significantly affect the temporal spectral development patterns of these crops in tasseled cap greenness (a transformed variable of LANDSAT MSS signals).
Crist, E. P.↗
Relating thematic mapper bands TM3, TM4, and TM5 to agronomic variables for corn, cotton, sugarbeet, soybean, sorghum, sunflower and tobacco
Red, photographic infrared, near infrared spectral data of corn, cotton, soybeans, sugar beets, sorghum, sunflowers and tobacco were collected throughout the entire growing season by using a three band handheld radiometer. Different radiance patterns were found among these crops based on their morphology, green biomass duration and leaf size. Results show near infrared radiance is a good indicator of water content in plant tissue under small scale experimental conditions.
Fan, C. J.↗
Use of Landsat-derived temporal profiles for corn-soybean feature extraction and classification
A physical model is presented, which has been derived from multitemporal-multispectral data acquired by Landsat satellites to describe the behavior and new features that are crop specific. A feasibility study over 40 sites was performed to classify the segment pixels into those of corn, soybeans, and others using the new features and a linear classifier. Results agree well with other existing methods, and it is shown the multitemporal-multispectral scanner data can be transformed into two parameters that are closely related to the target of interest and thus can be used in classification. The approach is less time intensive than other techniques and requires labeling of only pure pixels.
Badhwar, G. D.↗
Development of a corn and soybean labeling procedure for use with profile parameter classification
Some essential processes for the development of a green-number-based logic for identifying (labeling) crops in LANDSAT imagery are documented. The supporting data and subsequent conclusions that resulted from development of a specific labeling logic for corn and soybean crops in the United States are recorded.
Magness, E. R.↗
Crop weather models of corn and soybeans for Agrophysical Units (APU's) in Iowa using monthly meteorological predictors
Models based on multiple regression were developed to estimate corn and soybean yield from weather data for agrophysical units (APU) in Iowa. The predictor variables are derived from monthly average temperature and monthly total precipitation data at meteorological stations in the cooperative network. The models are similar in form to the previous models developed for crop reporting districts (CRD). The trends and derived variables were the same and the approach to select the significant predictors was similar to that used in developing the CRD models. The APU's were selected to be more homogeneous with respect crop to production than the CRDs. The APU models are quite similar to the CRD models, similar explained variation and number of predictor variables. The APU models are to be independently evaluated and compared to the previously evaluated CRD models. That comparison should indicate the preferred model area for this application, i.e., APU or CRD.
Leduc, S.↗
Evaluation of the CEAS trend and monthly weather data models for soybean yields in Iowa, Illinois, and Indiana
The CEAS models evaluated use historic trend and meteorological and agroclimatic variables to forecast soybean yields in Iowa, Illinois, and Indiana. Indicators of yield reliability and current measures of modeled yield reliability were obtained from bootstrap tests on the end of season models. Indicators of yield reliability show that the state models are consistently better than the crop reporting district (CRD) models. One CRD model is especially poor. At the state level, the bias of each model is less than one half quintal/hectare. The standard deviation is between one and two quintals/hectare. The models are adequate in terms of coverage and are to a certain extent consistent with scientific knowledge. Timely yield estimates can be made during the growing season using truncated models. The models are easy to understand and use and are not costly to operate. Other than the specification of values used to determine evapotranspiration, the models are objective. Because the method of variable selection used in the model development is adequately documented, no evaluation can be made of the objectivity and cost of redevelopment of the model.
French, V.↗