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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRS1981

Fiscal year 1981 US corn and soybeans pilot preliminary experiment plan, phase 1

A draft of the preliminary experiment plan for the foreign commodity production forecasting project fiscal year 1981 is presented. This draft plan includes: definition of the phase 1 and 2 U.S. pilot objectives; the proposed experiment design to evaluate crop calendar, area estimation, and area aggregation components for corn and soybean technologies using 1978/1979 crop-year data; a description of individual sensitivity evaluations of the baseline corn and soybean segment classification procedure; and technology and data assessment in support of the corn and soybean estimation technology for use in the U.S. central corn belt.

Livingston, G. P.↗
NASA NTRS1980

Evaluation of results of US corn and soybeans exploratory experiment: Classification procedures verification test

The classification procedure utilized in making crop proportion estimates for corn and soybeans using remotely sensed data was evaluated. The procedure was derived during the transition year of the Large Area Crop Inventory Experiment. Analysis of variance techniques were applied to classifications performed by 3 groups of analysts who processed 25 segments selected from 4 agrophysical units (APU's). Group and APU effects were assessed to determine factors which affected the quality of the classifications. The classification results were studied to determine the effectiveness of the procedure in producing corn and soybeans proportion estimates.

Carnes, J. G.↗
NASA NTRS1980

Crop identification studies using Landsat data Separation of barley from other spring small grains and corn and soybean decision logic

Two labeling procedures were developed which identify various agricultural crops through the use of Landsat data. One procedure separates barley from other spring small grains, and the other identifies corn and soybeans. For both procedures, a minimum data set (critical acquisition time) has been designated. Landsat data in both image format and various graphic displays were used along with ancillary data to obtain information which aided in labeling the spectral signatures. The corn and soybean procedure also employed a structured decision logic. Test results for the barley separation procedure emphasized the importance of obtaining a critical acquisition and showed some success especially in areas where spring crops followed the expected growth patterns. Two tests of the corn and soybean procedure produced good labeling accuracies. Problems with the procedure were easy to identify, and some solutions were implemented for the second test. Automation of various parts of the procedure and extension to other crops and regions were recommended.

Dailey, C. L.↗
NASA NTRS1978

Monitoring corn and soybean crop development by remote sensing techniques

A system for spectrally monitoring the stages of crop development for corn and soybeans based upon red and photographic infrared spectral radiances is proposed. The red and photographic infrared spectral radiance, highly correlated with the green leaf area index or green leaf biomass, enable nondestructive monitoring of the crop canopy throughout the growing season. Five distinct periods are apparent which are related to crop development for corn and soybeans.

Tucker, C. J.↗
NASA NTRS1982

Corn and soybean Landsat MSS classification performance as a function of scene characteristics

In order to fully utilize remote sensing to inventory crop production, it is important to identify the factors that affect the accuracy of Landsat classifications. The objective of this study was to investigate the effect of scene characteristics involving crop, soil, and weather variables on the accuracy of Landsat classifications of corn and soybeans. Segments sampling the U.S. Corn Belt were classified using a Gaussian maximum likelihood classifier on multitemporally registered data from two key acquisition periods. Field size had a strong effect on classification accuracy with small fields tending to have low accuracies even when the effect of mixed pixels was eliminated. Other scene characteristics accounting for variability in classification accuracy included proportions of corn and soybeans, crop diversity index, proportion of all field crops, soil drainage, slope, soil order, long-term average soybean yield, maximum yield, relative position of the segment in the Corn Belt, weather, and crop development stage.

Batista, G. T.↗
NASA NTRS1983

Radar backscattering properties of corn and soybeans at frequencies of 1.6, 4.75, and 13.3. GHz

The NASA Johnson Space Center made an observational study of the radar-backscattering properties of corn and soybeans in commercial fields in a test site in Webster County, IA. Aircraft-based radar scatterometers measured the backscattering coefficient of the crops at three frequencies, 1.6 GHz (L-band), 4.75 GHz (C-band), and 13.3 GHz (Ku-band), at 10 sensor look-angles (5 to 50 degrees from the nadir in steps of 5 degrees), and with several polarization combinations. Among other findings, it was determined that: (1) row direction differences among fields affected significantly the radar-backscattering coefficient of the fields when the radar system used like-polarization at look-angles from 5 to 25 degrees; (2) row-direction differences had no effect on radar backscattering when the system used either cross-polarization or look-angles greater than 25 degrees regardless of the polarization; (3) wet surface-soil moisture conditions resulted in significantly poorer spectral separability of the two crops as compared to dry-soil conditions; and (4) on the dry-soil date, the best channel for separating corn from soybeans was the C-band cross-polarized measurement at a look-angle of 50 degrees.

Paris, J. F.↗
NASA NTRS2017

Intercomparison of Soil Moisture, Evaporative Stress, and Vegetation Indices for Estimating Corn and Soybean Yields Over the U.S.

This paper presents an intercomparative study of 12 operationally produced large-scale datasets describing soil moisture, evapotranspiration (ET), and or vegetation characteristics within agricultural regions of the contiguous United States (CONUS). These datasets have been developed using a variety of techniques, including, hydrologic modeling, satellite-based retrievals, data assimilation, and survey in-field data collection. The objectives are to assess the relative utility of each dataset for monitoring crop yield variability, to quantitatively assess their capacity for predicting end-of-season corn and soybean yields, and to examine the evolution of the yield-index correlations during the growing season. This analysis is unique both with regards to the number and variety of examined yield predictor datasets and the detailed assessment of the water availability timing on the end-of-season crop production during the growing season. Correlation results indicate that over CONUS, at state-level soil moisture and ET indices can provide better information for forecasting corn and soybean yields than vegetation-based indices such as normalized difference vegetation index. The strength of correlation with corn and soybean yields strongly depends on the interannual variability in yield measured at a given location. In this case study, some of the remotely derived datasets examined provide skill comparable to that of in situ field survey-based data further demonstrating the utility of these remote sensing-based approaches for estimating crop yield.

evapotranspiration↗
NASA NTRS2021

Assessing within-Field Corn and Soybean Yield Variability from WorldView-3, Planet, Sentinel-2, and Landsat 8 Satellite Imagery

Crop yield monitoring is an important component in agricultural assessment. Multispectral remote sensing instruments onboard space-borne platforms such as Advanced Very High Resolution Radiometer (AVHRR), Moderate Resolution Imaging Spectroradiometer (MODIS), and Visible Infrared Imaging Radiometer Suite (VIIRS) have shown to be useful for efficiently generating timely and synoptic information on the yield status of crops across regional levels. However, the coarse spatial resolution data inherent to these sensors provides little utility at the management level. Recent satellite imagery collection advances toward finer spatial resolution (down to 1 m) alongside increased observational cadence (near daily) implies information on crops obtainable at field and within-field scales to support farming needs is now possible. To test this premise, we focus on assessing the efficiency of multiple satellite sensors, namely WorldView-3, Planet/Dove-Classic, Sentinel-2, and Landsat 8 (through Harmonized Landsat Sentinel-2 (HLS)), and investigate their spatial, spectral (surface reflectance (SR) and vegetation indices (VIs)), and temporal characteristics to estimate corn and soybean yields at sub-field scales within study sites in the US state of Iowa. Precision yield data as referenced to combine harvesters’ GPS systems were used for validation. We show that imagery spatial resolution of 3 m is critical to explaining 100% of the within-field yield variability for corn and soybean. Our simulation results show that moving to coarser resolution data of 10 m, 20 m, and 30 m reduced the explained variability to 86%, 72%, and 59%, respectively. We show that the most important spectral bands explaining yield variability were green (0.560 µm), red-edge (0.726 µm), and near-infrared (NIR - 0.865 µm). Furthermore, the high temporal frequency of Planet and a combination of Sentinel-2/Landsat 8 (HLS) data allowed for optimal date selection for yield map generation. Overall, we observed mixed performance of satellite-derived models with the coefficient of determination (R^2) varying from 0.21 to 0.88 (averaging 0.56) for the 30 m HLS and from 0.09 to 0.77 (averaging 0.30) for 3 m Planet. R^2 was lower for fields with higher yields, suggesting saturation of the satellite-collected reflectance features in those cases. Therefore, other biophysical variables, such as soil moisture and evapotranspiration, at similar fine spatial resolutions are likely needed alongside the optical imagery to fully explain the yields.

agriculture; yield; within-field; corn; soybean; r↗
NASA NTRS1983

Field measurements, simulation modeling and development of analysis for moisture stressed corn and soybeans, 1982 studies

The experimental design, data acquisition and analysis procedures for agronomic and reflectance data acquired over corn and soybeans at the Sandhills Agricultural Laboratory of the University of Nebraska are described. The following conclusions were reached: (1) predictive leaf area estimation models can be defined which appear valid over a wide range of soils; (2) relative grain yield estimates over moisture stressed corn were improved by combining reflectance and thermal data; (3) corn phenology estimates using the model of Badhwar and Henderson (1981) exhibited systematic bias but were reasonably accurate; (4) canopy reflectance can be modelled to within approximately 10% of measured values; and (5) soybean pubescence significantly affects canopy reflectance, energy balance and water use relationships.

Blad, B. L.↗
NASA NTRS1983

Comparison of CRD, APU, and state models for Iowa corn and soybeans and North Dakota barley and spring wheat

A comparison was made among the CEAS crop reporting district (CRD), agrophysical unit (APU), and state level multiple regression yield models for corn and soybeans in Iowa and barley and spring wheat in North Dakota. The best predictions were made by the state model for North Dakota spring wheat, by the APU models for barley, by the CRD models for Iowa soybeans, and by APU covariance models for Iowa corn. Because of this lack of consistency of model performance, CRD models would be recommended due to the availability of the data.

French, V.↗
NASA NTRS1981

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↗
NASA NTRS1982

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.↗
NASA NTRS1982

The evaluation of a semi-automated procedure for classifying corn and soybeans without ground data

Since the launch of Landsat 1 in 1973, research has been conducted with the objective to develop technology which would make it possible to achieve large area crop estimates on the basis of Landsat Multispectral Sensor (MSS) data without the benefit of ground observed training data. The present investigation is concerned with the evaluation of a technology which was developed to produce estimates of corn and soybean acreage in the central U.S. Corn Belt (Iowa, Illinois, and Indiana). A description of the employed technique is provided and details regarding the test of the developed technology are discussed. The obtained results show that considerable progress has been made toward creating an automatic, self-adapting procedure which has favorable bias and variance characteristics.

Metzler, M. D.↗
NASA NTRS1989

Leaf bidirectional reflectance and transmittance in corn and soybean

Bidirectional optical properties of leaves must be adequately characterized to develop comprehensive and reliably predictive canopy radiative-transfer models. Directional reflectance and transmittance factors of individual corn and soybean leaves were measured at source incidence angles (SIAs) 20, 45, and 70 deg and numerous view angles in the visible and NIR. Bidirectional reflectance distributions changed with increasing SIA, with forward scattering most pronounced at 70 deg. Directional-hemispherical reflectance generally increased and transmittance decreased with increased SIA. Directional-hemispherical reflectance factors were higher and transmittances were lower than the nadir-viewed reflectance component.

Walter-Shea, E. A.↗
NASA NTRS1979

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.↗
NASA NTRS1982

Cultural and environmental influences on temporal-spectral development patterns of corn and soybeans

A technique for evaluating crop temporal-spectral development patterns is described and applied to the analysis of cropping practices and environmental conditions as they affect reflectance characteristics of corn and soybean canopies. Typical variations in field conditions are shown to exert significant influences on the spectral development patterns, and thereby to affect the separability of the two crops.

Crist, E. P.↗
NASA NTRS1980

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.↗
NASA NTRS1980

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.↗