Search NASA⌕ Search

SEARCH · Search NASA

Results for “corn and soybean”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
NASA NTRS1986

Bidirectional reflectance modeling of non-homogeneous plant canopies

The objective of this research is to develop a 3-dimensional radiative transfer model for predicting the bidirectional reflectance distribution function (BRDF) for heterogeneous vegetation canopies. Leaf bidirectional reflectance and transmittance distribution functions were measured for corn and soybean leaves. The measurements clearly show that leaves are complex scatterers and considerable specular reflectance is possible. Because of the character of leaf reflectance, true leaf reflectance is larger than the nadir reflectances that are normally used to represent leaves. A 3-dimensional reflectance model, named BIGAR (Bidirectional General Array Model), was developed and compared with measurements from corn and soybean. The model is based on the concept that heterogeneous canopies can be described by a combination of many subcanopies, which contain all the foliage, and these subcanopy envelopes can be characterized by ellipsoids of various sizes and shapes. The model/measurement comparison results indicate that this relatively simple model captures the essential character of row crop BRDF's. Finally, two soil BDRF models were developed: one represents soil particles as rectangular blocks and the other represents soil particles as spheres. The sphere model was found to be superior.

Norman, John M.↗
NASA NTRS1977

Crop identification and area estimation by computer-aided analysis of Landsat data

This report describes the results of a study involving the use of computer-aided analysis techniques applied to Landsat MSS data for identification and area estimation of winter wheat in Kansas and corn and soybeans in Indiana. Key elements of the approach included use of aerial photography for classifier training, stratification of Landsat data and extension of training statistics to areas without training data, and classification of a systematic sample of pixels from each county. Major results and conclusions are: (1) Landsat data was adequate for accurate identification and area estimation of winter wheat in Kansas, but corn and soybean estimates for Indiana were less accurate; (2) computer-aided analysis techniques can be effectively used to extract crop identification information from Landsat MSS data, and (3) systematic sampling of entire counties made possible by computer classification methods resulted in very precise area estimates at county as well as district and state levels.

Bauer, M. E.↗
NASA NTRS1980

Research in the application of spectral data to crop identification and assessment, volume 2

The development of spectrometry crop development stage models is discussed with emphasis on models for corn and soybeans. One photothermal and four thermal meteorological models are evaluated. Spectral data were investigated as a source of information for crop yield models. Intercepted solar radiation and soil productivity are identified as factors related to yield which can be estimated from spectral data. Several techniques for machine classification of remotely sensed data for crop inventory were evaluated. Early season estimation, training procedures, the relationship of scene characteristics to classification performance, and full frame classification methods were studied. The optimal level for combining area and yield estimates of corn and soybeans is assessed utilizing current technology: digital analysis of LANDSAT MSS data on sample segments to provide area estimates and regression models to provide yield estimates.

Daughtry, C. S. T.↗
NASA NTRS1982

Crop identification with multifrequency, multipolarization, and multiangle radars

In a corn and soybeans test site in Webster County, Iowa, airborne radar scatterometers were used on August 19 and September 10, 1980, to investigate the backscattering properties of these crops at wavelengths of 2.3, 6.3, and 19 cm (Ku-, C-, and L-band, respectively). Both horizontal transmit-horizontal receiver (HH) polarization and horizontal transmit-vertical receive (HV) polarization combinations were used at L- and C-band. Only the VV polarization combination was available at Ku-band. Measurements were obtained at 10 angles of observation from 5 to 50 deg in steps of 5 deg (referenced to the nadir). Excellent separation between corn and soybeans was achieved when either C-band HV at 50 deg or when a defined depolarization factor was used at C-band. Good separation existed at L-band also using either L-band HH at 50 deg or an L-band polarization factor. Significant row direction effects were observed for all HH data near 10 deg. Significant effects of surface soil moisture were observed for all configurations at L-band and C-band.

Paris, J. F.↗
NASA NTRS1973

Application of ERTS-1 data to analysis of agricultural crops and forests in Michigan

The results reported are based on analysis of ERTS Frame 1033-15580 collected over southwestern Lower Michigan on August 25, 1972. Major agricultural crops such as corn and soybeans were approaching maturity at this data and forest canopies were dense. Extensive ground truth information was gathered by detailed field study of test strips. This detailed information was supplemented over larger areas by interpretation of RB-57 and C-47 photography and MSS imagery. Recognition processing of ERTS-1 MSS data was carried out on a digital computer. Fields and forest stands were selected as training sets and test areas. Aerial imagery was essential for locating the positions of these selected areas on ERTS digital tapes. The recognition process was successful for each type of vegetation which had a dense green canopy such as forests, corn, and soybeans. Bare soil was also recognizable as a category.

Safir, G. R.↗
DOE OSTI2026

Data for Clumping Index Estimation With 30°-tilted Cameras in Row Crops: Evaluation of Methods and Segment Size Effects

The clumping index (CI) quantifies the spatial distribution of foliage elements and is essential for accurately estimating the plant area index (PAI), canopy radiative transfer, and photosynthesis. Traditionally, the finite-length averaging method (LX), the gap size distribution method (CC), and a combined approach of CC and LX (CLX) have been applied to instruments like TRAC and digital hemispherical photography to estimate CI. However, a comprehensive evaluation of these methods in row crops remains limited, especially regarding the influence of segment size on CI. Meanwhile, digital cameras offer a cost-effective and user-friendly solution for canopy measurements in row crops, yet their application in this context remains underexplored. In this study, we employed a new approach using a 30°-tilted digital camera to estimate CI in corn and soybean fields, applying the LX, CC, and CLX methods. We systematically assessed the performance of these three methods by combining field measurements in real-world fields with simulations using the LESS 3D radiative transfer model. Our results showed that CLX applied to the whole image and 45° segment offered accurate estimation of CI (bias within ±0.1, RMSE < 0.2) and PAI (bias within ±0.4, RMSE < 1) in real-world fields and LESS simulations. The accuracy of the LX method was highly sensitive to segment size, with the best performance observed at the 15° segment (PAI bias within ±0.4). In contrast, the CC method remained stable across different segment sizes, and its performance was generally comparable to that of LX, except at the 15° segment. Across view zenith angles, CI derived from CC generally showed a continuous increase, while those from LX and CLX followed a rising trend at small zenith angles but began to decline at 68°, likely due to an increasing proportion of no-gap segments. Seasonally, LX tended to show decreasing CI during early growth stages but increased as the canopy matured, whereas CC and CLX showed gradually increasing CI before plateauing at peak PAI. The 30°-tilted camera effectively captured CI variations across different angles and growth stages, making it a practical and robust instrument for row crop canopy structure analysis. Applying these CI methods to digital cameras offers a low-cost and accessible CI estimation alternative, improving canopy structure monitoring accuracy in row crops.

Modeling↗
NASA NTRS1981

Determination of the optimal level for combining area and yield estimates

Several levels of obtaining both area and yield estimates of corn and soybeans in Iowa were considered: county, refined strata, refined/split strata, crop reporting district, and state. Using the CCEA model form and smoothed weather data, regression coefficients at each level were derived to compute yield and its variance. Variances were also computed with stratum level. The variance of the yield estimates was largest at the state and smallest at the county level for both crops. The refined strata had somewhat larger variances than those associated with the refined/split strata and CRD. For production estimates, the difference in standard deviations among levels was not large for corn, but for soybeans the standard deviation at the state level was more than 50% greater than for the other levels. The refined strata had the smallest standard deviations. The county level was not considered in evaluation of production estimates due to lack of county area variances.

Bauer, M. E.↗
NASA NTRS2014

Seasonal Parameterizations of the Tau-Omega Model Using the ComRAD Ground-Based SMAP Simulator

NASA's Soil Moisture Active Passive (SMAP) mission is scheduled for launch in November 2014. In the prelaunch time frame, the SMAP team has focused on improving retrieval algorithms for the various SMAP baseline data products. The SMAP passive-only soil moisture product depends on accurate parameterization of the tau-omega model to achieve the required accuracy in soil moisture retrieval. During a field experiment (APEX12) conducted in the summer of 2012 under dry conditions in Maryland, the Combined Radar/Radiometer (ComRAD) truck-based SMAP simulator collected active/passive microwave time series data at the SMAP incident angle of 40 degrees over corn and soybeans throughout the crop growth cycle. A similar experiment was conducted only over corn in 2002 under normal moist conditions. Data from these two experiments will be analyzed and compared to evaluate how changes in vegetation conditions throughout the growing season in both a drought and normal year can affect parameterizations in the tau-omega model for more accurate soil moisture retrieval.

SMAP↗
DOE OSTI2022

Cross-scale sensing of field-level crop residue cover: Integrating field photos, airborne hyperspectral imaging, and satellite data

Conservation tillage practices can bring benefits to agricultural sustainability. Accurate spatial and temporal resolved information of field-scale crop residue cover, which reflects tillage intensity, is highly valuable for evaluating the outcomes of government conservation programs and voluntary ecosystem service markets, as well as facilitating agroecosystem modeling to quantify cropland biogeochemical processes. Remote sensing has the potential to cost-effectively detect crop residue cover, however, existing regional-scale studies were limited by insufficient ground truth data, scale mismatch between coarse satellite pixels and ground data, and the lack of key spectral data for detecting crop residues. Therefore, this study developed an innovative cross-sensing framework to integrate proximal sensing, airborne hyperspectral imaging, and satellite Earth Observation through deep learning to quantify field-level crop residue cover fractions at the regional scale. Specifically, we have collected intensive ground orthographic photos and conducted airborne hyperspectral surveys at corn and soybean fields of Champaign and nearby counties in Illinois, the heartland of the U.S. Corn Belt. Through semi-automatic labeling aided by ResNet-50 and superpixel image segmentation, we obtained 6719 records of ground residue fractions. With these ground data, we developed the 1-dimensional convolution neural network (CNN) model using airborne hyperspectral reflectance, which has 0.5m spatial resolution and 3–5 nm spectral resolution from 400 to 2400 nm, to predict residue fractions. By applying the CNN model to airborne pixels, we augmented “ground truth” data of crop residues and further combined them with Harmonized Landsat and Sentinel-2 (HLS) satellite data to quantify regional residue fractions at 30 m resolution. Results show that airborne hyperspectral imagery with CNN can accurately detect residue fractions (R 2 = 0.82, relative RMSE = 11.73%) to effectively generate quasi “ground truth” data to support satellite upscaling to all fields. With independent ground data for testing, we found that the ground-airborne-satellite integrative framework achieved better predictions in estimating crop residue cover (R 2 = 0.67, relative RMSE = 17.53%) than the conventional ground-satellite upscaling (R 2 = 0.22, relative RMSE = 32.09%). Here we also found that the shortwave infrared wavelengths, particularly 2100–2300 nm, are vital for predicting crop residue cover. Sentinel-2 and Landsat-8 data have a comparable capability to track residue fractions due to similar shortwave infrared wavelengths. This study highlights the high accuracy of hyperspectral imaging to detect agroecosystem tillage management practices and the advantages of cross-scale sensing to cost-effectively integrate multi-source data to quantify field-level agroecosystem variables across scales.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1991

Polarization of light reflected by crop canopies

This paper presents an analysis of polarization measurements acquired over corn and soybean crop canopies. Simultaneous plant physiological data and canopy polarization and reflectance data were collected before and after the tasseling development stage of the corn canopy, and before and during a period when the soybean canopy was water stressed. A simple model based upon the hypothesis that leaves specularly reflect light according to the Fresnel equations relates the observed bidirectional effects to the architectural and phenological conditions of the canopy.

Rondeaux, Genevieve↗
NASA NTRSDate not supplied

Cropland and Land-Clearing Emission Factors From Firex-AQ

The Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) campaign is a NOAA/NASA interagency intensive study of North American fires that took place from July to September 2019. Agricultural fires were sampled on 7 flights from August 21st to September 3rd, 2019. We present emission factors for gas-phase and aerosol-phase species for the different fuel types identified during the campaign. We discuss the dependence of these emission factors on both fuel type and burning characteristics such as modified combustion efficiency (MCE). We analyze 73 fires encompassing four different agricultural fuels (corn, rice, soybean, wheat) and four different types of land-clearing fuels (grass, slash, pile, shrub). Approximately half of all sampled fires were burning corn agricultural residue. We report emission factors for 118 VOCs, where two thirds of emissions are made up of species with lifetimes against oxidation by OH of less than 12 hours (near-field impacts) and the remainder have longer-lifetimes (far-field impacts). We also include an additional 9 aerosol species, 12 halogenated species, 5 sulfur-containing species, and 22 nitrogen-containing species, including NH3 and NOx. There is a statistically significant difference in MCE between agricultural and land-clearing fires that accounts for some of the differences in emission factors across these fuels. VOCs generally have a strong negative correlation with MCE but with different slopes across fuel types. There is a significant positive relationship between MCE and three gas-phase (NO, NO2, SO2) and two aerosol-phase (pCl, pNH4) species. Many emission factors show additional dependence factors beyond MCE that could be related to fuel composition, fire temperature, soil moisture, or other possible differences in burning conditions. Therefore, important differences in emission factors may result when averaging together different types of agricultural and land-clearing fuels in compilations for use in modeling efforts.

biomass burning↗
DOE OSTI2023

Can upscaling ground nadir SIF to eddy covariance footprint improve the relationship between SIF and GPP in croplands?

Ground solar-induced chlorophyll fluorescence (SIF) is important for the mechanistic understanding of the dynamics of vegetation gross primary production (GPP) at fine spatiotemporal scales. However, eddy covariance (EC) observations generally cover larger footprint areas than ground SIF observations (a bare fiber with nadir), and this footprint mismatch between nadir SIF and GPP could complicate the canopy SIF-GPP relationships. Here, we upscaled nadir SIF observations to EC footprint and investigated the change in SIF-GPP relationships after the upscaling in cropland. We included 13 site-years data in our study, with seven site-years corn, four site-years soybeans, and two site-years miscanthus, all located in the US Corn Belt. All sites’ crop nadir SIF observations collected from the automated FluoSpec2 system (a hemispheric-nadir system) were upscaled to the GPP footprint-based SIF using vegetation indices (VIs) calculated from high spatiotemporal satellite reflectance data. We found that SIF-GPP relationships were not substantially changed after upscaling nadir SIF to GPP footprint at our crop sites planted with corn, soybean, and miscanthus, with R 2 change after the upscaling ranging from -0.007 to 0.051 and root mean square error (RMSE) difference from -0.658 to 0.095 umol m –2 s –1 relative to original nadir SIF-GPP relationships across all the site-years. The variation of the SIF-GPP relationship within each species across different site-years was similar between the original nadir SIF and upscaled SIF. Different VIs, EC footprint models, and satellite data led to marginal differences in the SIF-GPP relationships when upscaling nadir SIF to EC footprint. Furthermore, our study provided a methodological framework to correct this spatial mismatch between ground nadir SIF and GPP observations for croplands and potentially for other ecosystems. Our results also demonstrated that the spatial mismatch between ground nadir SIF and GPP might not significantly affect the SIF-GPP relationship in cropland that are largely homogeneous.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1985

Sources of scattering from vegetation canopies at 10 GHz

High-resolution backscatter measurements were made at X-band, with vertical polarization, and incidence angles of 30 and 50 deg, for winter wheat, soybeans, and corn during the summer of 1984. Plants were observed both under natural conditions and with partial-to-complete defoliation. The resolution volume of the measurement system is better than 20 x 20 x 11.25 cu cm within the entire range of measurements. Results are presented as a radar image, which also provides information on the spatial distribution of the (attenuated) scattering inside the canopy observed under natural conditions. Measured results indicate that: (1) for the full-grown wheat plants, heads are the dominant scatterers at the early heading stage and the ground is the dominant scatterer at the later growth stages; (2) for the soybeans, returns are mostly from the upper 30-cm portion of the plants, and leaves and stems dominate the radar signal at 30 and 50 deg incidence angles, respectively; (3) for the corn plants, leaves are the single dominant scatterers at both angles of incidence; and (4) ground returns are relatively unimportant as they are highly attenuated by the vegetation cover.

Wu, L.-K.↗
DOE OSTI2022

Economics of Crop Rotations With and Without Carinata for Sustainable Aviation Fuel Production in the SE United States

In 2019, the aviation sector in the United States emitted 255 million metric tons of carbon dioxide (CO 2 ) emissions, i.e., about five percent of the total domestic CO 2 emissions from the energy sector. The sustainable aviation fuel (SAF) derived from carinata (Brassica carinata) could reduce CO 2 emissions of the aviation sector in the United States. Therefore, it is important to estimate changes in farm economics with and without carinata for ascertaining its production feasibility. In this context, the current study first assesses a combination of 12 popular rotations of corn, cotton, peanut, and soybean with winter crops of winter wheat and carinata in South Georgia over 4 years. Then, the net present values (NPVs) of 292 feasible cropping systems over 4 years are calculated. Finally, this study develops a risk model for ascertaining the probability distributions of NPVs for selected cropping systems subject to uncertainties related to prices and yields of summer and winter crops. Carinata in the corn-corn-soybean rotation has the highest NPV ($\$$2,996/ha). The least risky rotation is cotton-cotton-peanut, with a 58.9% probability of a positive NPV. Carinata can decrease the risk level of crop rotations by 8.1%, only if a contract price of $\$$440.9/t is offered. Therefore, a risk averse, risk neutral, or risk acceptant farmer can potentially include carinata in the rotation. Overall, carinata would increase the profitability of farm operations and decrease risk in the SE United States, and therefore, a high likelihood exists, that farmers would adopt it for meeting the growing demand for SAF in the United States.

09 BIOMASS FUELS↗
DOE OSTI2023

Designing Iowa Agricultural Landscapes to Improve Environmental Co-Benefits of Bioenergy Production

Cellulosic bioenergy feedstocks are needed to improve carbon (C) management while provisioning biomass for bioproducts and biofuel. The transition to increased cellulosic biomass production can be guided by land management plans designed to improve economic, environmental, and ecological performance. We constructed a sustainability model to compare landscape designs for biofuel production from corn (Zea mays L.) stover and switchgrass (Panicum virgatum L.) in central Iowa, USA. We used the model to compare environmental and socioeconomic outcomes associated with four landscape management strategies, with and without cellulosic biomass markets. We evaluated (1) a fuelshed area containing over 1.2 million ha (3 million acres) of corn and soybean (Glycine max (L.) Merr.) within 80 km (50 miles) of a commercial-scale cellulosic biorefinery in Nevada, Iowa, and (2) the South Fork watershed containing over 72,000 ha (178,000 acres) of these row crops within eight north central Iowa HUC-12 (hydrologic unit code) watersheds. At both landscape scales, we found that it is possible to achieve multiple environmental and socioeconomic benefits concomitantly with cellulosic biomass production by strategically collecting corn stover and converting the 10% of the lowest-profitability row crop land to perennial switchgrass. Potential benefits from landscape design include increased biodiversity, soil and water quality improvements, increased soil carbon sequestration for climate change mitigation, and reduced fertilizer use and cost. Our model results showed that increasing benefits can accrue when complementary conservation practices (e.g., reduced tillage, use of a rye cover crop) are combined and integrated throughout a fuelshed or watershed area. We conclude that ecologically based landscape designs offer valuable insights about costs and benefits of land management alternatives, with relevance for achieving stakeholder goals.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Microscale spatiotemporal patterns of water, soil organic carbon, and enzymes in plant litter detritusphere

Understanding biophysical and biochemical processes in detritusphere is critical to quantifying and modeling plant residue decomposition dynamics and subsequent soil organic carbon (C) accrual. The objectives of the study were to explore (i) micro-environmental conditions within the detritusphere formed around decomposing corn and soybean leaves, and (ii) the relationships between moisture distribution, enzyme activity, and C dynamics during decomposition. We assessed spatial and temporal dynamics of moisture distribution using X-ray and neutron computed tomography, and activities of β-glucosidase and chitinase, two enzymes involved in soil C and N processing, using zymography. We used 13 C labeled residue to track residue contribution to atmospheric CO 2 and soil organic C. Moisture redistribution pattern varied depending on the residue type. While the water was immediately absorbed by the corn leaves and maintained afterward, switchgrass leaves absorbed water more slowly and created water-deficient zones within ~1mm from the residue. This initial moisture depletion led to lower chitinase activity and residue-derived CO 2 emissions in switchgrass. In contrast to chitinase, β-glucosidase activity was influenced by a combination of vegetation history and residue type, and it was higher when the origin of the residue matched the vegetation history of the soil. Pore size had an opposite impact on the studied enzymes, supporting the notion that contrasting soil pore architecture can stimulate activities of different enzymes through a selection of dominant enzyme producers. We concluded that the decomposition dynamics of plant residues is not only a simple function of residue chemistry, but rather a combined effect of the vegetation history, in part through its effect on microbial community composition, the plant residue chemical and likely physical characteristics, and the soil pore structure in the detritusphere. Together, they create temporally dynamic micro-environmental conditions influencing decomposition. Specifically, our study demonstrated that the initial micro-environment formulated in detritusphere can play an important role in enzyme activities and consequent C dynamics.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1985

Inclusion of specular reflectance in vegetative canopy models

A detailed comparison of the observed soybeans and corn canopy reflectance with that calculated from three vegetative canopy reflectance models have shown systematic angular deviations. A likely cause of these effects is noninclusion of leaf specular reflectance. In this paper a formulation to calculate the component of canopy specular reflectance as a function of incident solar and view zenith angles, leaf area index, leaf angle distribution, and leaf specular reflectance has been developed. The SAIL model has been modified to include this component and the results are compared with an extensive observational data set on soybeans. It is shown that the systematic differences between the SAIL model and observations dependent on scattering azimuth are removed. An analysis of variance shows model improvement of 30 percent over the uncorrected SAIL model.

Reyna, E.↗
DOE OSTI2021

Planting miscanthus instead of row crops may increase the productivity and economic performance of farmed potholes

Abstract Climate change projections indicate that precipitation events in the central United States are expected to become more intense, more frequent in the spring, and less frequent in the summer. Such a precipitation shift could adversely impact crop yields, especially in subfield areas known as farmed potholes, which are highly susceptible to flooding and ponding, and crop death is more likely to occur, particularly early in the growing season. This suggests that planting alternative crops, such as more flood tolerant perennials, in these areas may be a more profitable option. Using observations of crop growth and yield along with ponding depth of a specific field and farmed pothole in the central United States, we developed a spatially explicit version of the agroecosystem model Agro‐IBIS to estimate water depth and crop yield. After evaluating the model, we conducted a case study for a specific farmed pothole with a range of future precipitation scenarios with Agro‐IBIS to simulate the effects of contemporary (2002–2016) and future precipitation on a conventional corn/soybean ( Zea mays L. and Glycine max Merr.) rotation and an alternative perennial miscanthus ( Miscanthus × giganteus Greef et Deu.) cropping system. The depth and frequency of ponding increased under most future precipitation scenarios. The corn/soybean rotation had greater total loss (i.e., no yield) on average (>30%) for all scenarios in comparison to miscanthus (<10%). Under one future precipitation scenario with increased spring precipitation, both the corn/soybean rotation and miscanthus simulations showed an increase in yield. A simple budget analysis indicated that it is more profitable to plant miscanthus instead of corn or soybeans where yields in farmed potholes are consistently poor. Our findings show that potholes can be individually modeled, and their influence on yield can be quantified for use in future management decisions dictated by change in climate.

54 ENVIRONMENTAL SCIENCES↗