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At least 217 records · Page 12

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NASA NTRS1985

Spectral characterization of the Landsat Thematic Mapper sensors

Relative spectral response data for the Landsat-4 and Landsat-4 backup multispectral scanner subsystems (MSS), the protoflight and flight models, are presented and compared to similar data for the Landsat 1, 2 and 3 scanners. Channel (six channels per band) outputs for soil and soybean targets were simulated and compared within each band and between scanners.The principal differences between the spectral responses of the Landsat-4 scanners and previous scanners are discussed. The simulated Landsat-4 scanner outputs were 3 to 10 percent lower in the red band and 3 to 11 percent higher in the first near-IR band than previous scanners for the soybeans targets. The Landsat-4 scanners were generally more uniform from channel to channel within bands than previous scanners. In the upper-band edge of the red band of the protoflight scanner, one channel was markedly different (12 nm) from the rest. For a soybeans target, this nonuniformity resulted in a within-band difference of 6.2 percent in simulated outputs between channels.

Markham, B. L.↗

NASA NTRS1987

Landsat classification of Argentina summer crops

A Landsat MSS and TM classification approach based on three features derived from the greenness profile has proved very effective in separating and identifying corn, soybeans, and other ground cover classes in the U.S. The objective of this study is to investigate the separation of summer crops in Argentina, one of the most important commodity exporters, using the same greenness profile features that have proved effective in the U.S. Corn Belt. The area chosen for study is a more complex cropping practice area located in the north-west corner of Buenos Aires province in Pampa Humeda, where corn, soybean, sorghum, sunflower, and pastures are cultivated. It is shown that the profile features can provide very effective separation, except in the case of corn from sorghum. Separation between corn and soybeans was found to be greater than in the U.S. This study suggests that the automatic, unsupervised classification approach developed in the U.S., with relatively minor modification, can be used for summer crop area estimation in Argentina.

Badhwar, G. D.↗

NASA NTRS1987

Inferring spectral reflectances of plant elements by simple inversion of bidirectional reflectance measurements

Inverting previously developed explicit expressions for a vertical architecture, bidirectional reflectances measured over corn viewing from the solar quadrant at azimuths near the principal plane are used to determine the spectral reflectances of plant elements. The leaf reflectance values extracted in three visible bands at viewing zenith angles of 70 deg, 60 deg, and 45 deg agree closely with laboratory-measured reflectances of corn leaves. At viewing zenith angle of 30 deg, the inversion breaks down, inasmuch as the inferred plant element reflectances are too high. Satisfactory results are also achieved when the same approach is applied to bidirectional reflectances measured over potted balsam firs, but when applied to soybeans reflectances, the procedure yields unreasonably high leaf reflectances. The failure in this case is attributed to the nonvertical architecture of the soybean canopy; however, for this canopy, inversion based on horizontal architecture is possible. The bidirectional reflectances measured from the solar quadrant, at viewing angles appreciably far from 'hot spot' viewing, approximately equal in magnitude half of the leaf reflectance. The 0.5 ratio is predicted by a previous analysis of opaque horizontal Lambertian facets, as the asymptotic value for a dense canopy at any viewing angle. For soybeans, this ratio applies very closely at 15 deg viewing zenith angle. The results suggest that inversion based on simple architecture, applying explicit expressions, might be of value, either in itself or as a preliminary step before inversion applying complex models.

Otterman, J.↗

NASA NTRS1992

Branching model for vegetation

In the present branching model for remote sensing of vegetation, the frequency and angular responses of a two-scale cylinder cluster are calculated to illustrate the importance of vegetation architecture. Attention is given to the implementation of a two-scale branching model for soybeans, where the relative location of soybean plants is described by a pair of distribution functions. Theoretical backscattering coefficients evaluated by means of hole-correction pair distribution are in agreement with extensive data collected from soybean fields. The hole-correction approximation is found to be the more realistic.

Yueh, Simon H.↗

NASA NTRS1992

Characterization of the Water Soluble Component of Inedible Residue from Candidate CELSS Crops

Recycling of inorganic nutrients required for plant growth will be a necessary component of a fully closed, bioregenerative life support system. This research characterized the recovery of plant nutrients from the inedible fraction of three crop types (wheat, potato, and soybean) by soaking, or leaching, in water. A considerable portion of the dry weight of the inedible biomass was readily soluble (29 percent for soybean, 43 percent for wheat, and 52 percent for potato). Greater weight loss from potato was a result of higher tissue concentrations of potassium, nitrate, and phosphate. Approximately 25 percent of the organic content of the biomass was water soluble, while the majority of most inorganic nutrients, except for calcium and iron, were recovered in the leachate. Direct use of the leachates in hydroponic media could provide between 40-90 percent of plant nutrient demands for wheat, and 20-50 percent of demand for soybean and potato. Further evaluation of leaching as a component of resource recovery scheme in a bioregenerative system requires study of (1) utilization of plant leachates in hydroponic plant culture; and (2) conversion of organic material (both soluble and insoluble) into edible, or other useful, products.

Garland, Jay↗

NASA NTRS1996

Ethylene Production by Plants in a Closed Environment

Ethylene production by 20-sq m stands of wheat, soybean, lettuce and potato was monitored throughout growth and development in NASA's Controlled Ecological Life Support System (CELSS) Biomass Production Chamber. Chamber ethylene concentrations rose during periods of rapid growth for all four species, reaching 120 parts per billion (ppb) for wheat, 60 ppb for soybean, and 40 to 50 ppb for lettuce and potato. Following this, ethylene concentrations declined during seed fill and maturation (wheat and soybean), or remained relatively constant (potato). Lettuce plants were harvested during rapid growth and peak ethylene production. The highest ethylene production rates (unadjusted for chamber leakage) ranged from 0.04 to 0.06 ml/sq m/day during rapid growth of lettuce and wheat stands, or approximately 0.8 to 1.1 ml/g fresh weight/h. Results suggest that ethylene production by plants is a normal event coupled to periods of rapid metabolic activity, and that ethylene removal or control measures should be considered for growing crops in a tightly closed CELSS.

Wheeler, R. M.↗

NASA NTRS2001

Composition and physical properties of starch in microgravity-grown plants

The effect of spaceflight on starch development in soybean (Glycine max L., BRIC-03) and potato (Solanum tuberosum, Astroculture-05) was compared with ground controls by biophysical and biochemical measurements. Starch grains from plants from both flights were on average 20-50% smaller in diameter than ground controls. The ratio delta X/delta rho (delta X --difference of magnetic susceptibilities, delta rho--difference of densities between starch and water) of starch grains was ca. 15% and 4% higher for space-grown soybean cotyledons and potato tubers, respectively, than in corresponding ground controls. Since the densities of particles were similar for all samples (1.36 to 1.38 g/cm3), the observed difference in delta X/delta rho was due to different magnetic susceptibilities and indicates modified composition of starch grains. In starch preparations from soybean cotyledons (BRIC-03) subjected to controlled enzymatic degradation with alpha-amylase for 24 hours, 77 +/- 6% of the starch from the flight cotyledons was degraded compared to 58 +/- 12% in ground controls. The amylose content in starch was also higher in space-grown tissues. The good correlation between the amylose content and delta X/delta rho suggests, that the magnetic susceptibility of starch grains is related to their amylose content. Since the seedlings from the BRIC-03 experiment showed elevated post-flight ethylene levels, material from another flight experiment (GENEX) which had normal levels of ethylene was examined and showed no difference to ground controls in size distribution, density, delta X/delta rho and amylose content. Therefore the role of ethylene appears to be more important for changes in starch metabolism than microgravity. c2001 COSPAR. Published by Elsevier Science Ltd. All rights reserved.

NASA Experiment Number 9305014 2/2↗

NASA NTRS2016

Spatial and Temporal Uncertainty of Crop Yield Aggregations

The aggregation of simulated gridded crop yields to national or regional scale requires information on temporal and spatial patterns of crop-specific harvested areas. This analysis estimates the uncertainty of simulated gridded yield time series related to the aggregation with four different harvested area data sets. We compare aggregated yield time series from the Global Gridded Crop Model Inter-comparison project for four crop types from 14 models at global, national, and regional scale to determine aggregation-driven differences in mean yields and temporal patterns as measures of uncertainty. The quantity and spatial patterns of harvested areas differ for individual crops among the four datasets applied for the aggregation. Also simulated spatial yield patterns differ among the 14 models. These differences in harvested areas and simulated yield patterns lead to differences in aggregated productivity estimates, both in mean yield and in the temporal dynamics. Among the four investigated crops, wheat yield (17% relative difference) is most affected by the uncertainty introduced by the aggregation at the global scale. The correlation of temporal patterns of global aggregated yield time series can be as low as for soybean (r = 0.28).For the majority of countries, mean relative differences of nationally aggregated yields account for10% or less. The spatial and temporal difference can be substantial higher for individual countries. Of the top-10 crop producers, aggregated national multi-annual mean relative difference of yields can be up to 67% (maize, South Africa), 43% (wheat, Pakistan), 51% (rice, Japan), and 427% (soybean, Bolivia).Correlations of differently aggregated yield time series can be as low as r = 0.56 (maize, India), r = 0.05∗Corresponding (wheat, Russia), r = 0.13 (rice, Vietnam), and r = −0.01 (soybean, Uruguay). The aggregation to sub-national scale in comparison to country scale shows that spatial uncertainties can cancel out in countries with large harvested areas per crop type. We conclude that the aggregation uncertainty can be substantial for crop productivity and production estimations in the context of food security, impact assessment, and model evaluation exercises.

Aggregation uncertainty↗

NASA NTRS2019

Exposure of the EU-28 Food Imports to Extreme Weather Disasters in Exporting Countries

EU-28 relies on a diversified foreign market, even for crops for which it has a high self-sufficiency.This study contributes to the discussion on the vulnerability of agri-food supply to the impacts of extreme weather disasters (EWD). We focus on the largest import commodities of the EU-28 and we aim to (1) map external dependencies of EU-28 agri-food sector, (2) estimate the impact of EWD on crop production in countries from which the EU-28 receives their imports, and (3) assess the exposure of EU-28 agri-food imports to such impacts. Crop and trade data areacquired through EUROSTAT and FAOSTAT, EWD records from EM-DAT, all between 1961 and 2016. A superposed epoch analysis is used to estimate the impact of EWD on the average national production, yield and harvested area of selected crops in exporting countries. The EU-28 imports between 35-100% of its consumption of soybeans, banana, tropical fruits, coffee and cocoa. Our study reveals a substantial impact of EWD, especially due to droughts andheat waves, on the production of soybeans, tropical fruits, and cocoa, with import weighted impacts of 3, 8, and 7%, respectively. Floods cause weighted impacts of 7% (soybeans) and 8% (tropical fruits). Coffee production shows gains during cold waves, but the inter-annual variability offsets these effects.

Teresa Armada Bras↗

NASA NTRSDate not supplied

The Added Value of SMAP Soil Moisture in Crop Yield Forecasting Over Argentina

Argentina is one of the major producers and exporter of soybeans, corn, and wheat to the world market; therefore, the accurate and timely forecasting of those crops yield is crucial to national crop management and global food security. Previous studies have mainly focused on developing forecasting models for a specific crop type and location using a single source of data (e.g., vegetation indices), thus providing little insight into the forecasting models' performance on different crop types and regions. Besides, these models are based on traditional statistical regression algorithms, while more advanced machine learning approaches have not been explored. This study investigated the estimation of crop yields of three major crops (corn, soybean, and winter wheat) using Multiple Linear Regression (MLR) and Support Vector Machine (SVM), over major growing provinces in Argentina. Our models were trained and evaluated on data from 2015 to 2020, where three remote sensing products (Normalized difference vegetation index (NDVI), SMAP soil moisture, and MODIS evapotranspiration) were used as predictors. Our results indicated that accurate crop yield forecasts using the developed regression models could be made one to two months before harvest. The MLR and SVM model performance varied among different crop types, where soybean and corn exhibited better predictability compare to the wheat. In most cases, the SVM outperformed the multiple linear regression model due to its ability to capture the nonlinear and complex features of the crop-production process. The forecasted model that combines data from multiple sources outperformed single-source satellite data. The highest accuracy was obtained when the three data sources were all considered in the model development. Results also indicated that the inclusion of SMAP soil moisture improved crop yield forecasting in most provinces, and the most significant improvements occurred in the drier region.

Nazmus Shams Sazib↗

NASA NTRS2022

Satellite-Based Characterization of Convection and Impacts from the Catastrophic 10 August 2020 Midwest U.S. Derecho

The catastrophic derecho that occurred on 10 August 2020 across the Midwest United States caused billions of dollars of damage to both urban and rural infrastructure as well as agricultural crops, most notably across the state of Iowa. This paper documents the complex evolution of the derecho through the use of low-Earth orbit passive-microwave imager and GOES-16satellite-derived products complemented by products derived from NEXRAD weather radar observations. Additional satellite sensors including optical imagers and synthetic aperture radar (SAR) were used to observe impacts to the power grid and agriculture in Iowa. SAR improved the identification and quantification of damaged corn and soybeans, as compared to true-color composites and Normalized Difference Vegetation Index (NDVI). A statistical approach to identify damaged corn and soybean crops from SAR was created with estimates of 1.97 million acres of damaged corn and 1.40 million acres of damaged soybeans in the state of Iowa. The damage estimates generated by this study were comparable to estimates produced by others after the derecho, including two commercial agricultural companies.

Derecho↗

NASA NTRS2020

Adaptation in U.S. Corn Belt Increases Resistance to Soil Carbon Loss With Climate Change

Increasing the amount of soil organic carbon (SOC) has agronomic benefits and the potential to mitigate climate change. Previous regional predictions of SOC trends under climate change often ignore or do not explicitly consider the effect of crop adaptation (i.e., changing planting dates and varieties). We used the DayCent biogeochemical model to examine the effect of adaptation on SOC for corn and soybean production in the U.S. Corn Belt using climate data from three models. Without adaptation, yields of both corn and soybean tended to decrease and the decomposition of SOC tended to increase leading to a loss of SOC with climate change compared to a baseline scenario with no climate change. With adaptation, the model predicted a substantially higher crop yield. The increase in yields and associated carbon input to the SOC pool counteracted the increased decomposition in the adaptation scenarios, leading to similar SOC stocks under different climate change scenarios. Consequently, we found that crop management adaptation to changing climatic conditions strengthen agroecosystem resistance to SOC loss. However, there are differences spatially in SOC trends. The northern part of the region is likely to gain SOC while the southern part of the region is predicted to lose SOC.

Soil Organic Carbon (SOC)↗

DOE OSTI2024

Native bee Pollination Ecosystem Services in Agricultural Wetlands and Riparian Protected Lands

Abstract Many freshwater wetlands and riparian systems are protected within agricultural landscapes. Yet, pollinator ecosystem services are seldom considered key ecosystem services provided by these conservation easements. The purpose of this study is to explore the extent of protected aquatic lands to provide pollination ecosystem services by assessing pollinator abundances, crop yield changes, and value estimations of increased soybean yields from a subset of common native solitary bees. We created a novel geodatabase of United States Department of Agriculture (USDA) conservation easements and used this database in the InVEST crop pollination model to model wild solitary bee pollination. We then estimated the monetary value of yield increases provided by pollinators. We found that wetland uplands provided the greatest potential for pollination services for ground nesting bees, followed by herbaceous and forested riparian respectively. Stem nesters preferred forested riparian, then upland habitats. In soybeans fields, we found wild pollinators can provide up to 5.5% yield response from current private aquatic conservation lands. The current landscape is not optimized to use wetlands and riparian conservation lands as pollinator habitat, but these results suggest protected aquatic lands can sustainably increase wild pollination services to agricultural crops if landscapes are managed, protected, and optimized with pollinator services as co-benefit.

Hinson, Audra L. (ORCID:0000000242314820)↗

DOE OSTI2025

Quantifying soil organic matter stock distribution and origin following over a century of maize-based cropping in the former tallgrass prairie region of central USA

Tallgrass prairie conversion to maize-based agriculture in central North America has resulted in substantial loss of soil organic carbon (SOC) in less than two centuries. However, evaluations of how management practices may mitigate SOC losses are generally limited in soil depth and/or duration, missing long-term SOC stock outcomes that manifest over timescales of decades or longer. To address this, we sampled soils in year 145 of the Morrow Plots experiment to (i) evaluate effects of crop rotation and fertility management on SOC stocks and (ii) distinguish prairie- versus maize-derived SOC after continuous maize cropping since 1876 using stable carbon isotope ( 13 C) natural abundance. Soil organic carbon stock by equivalent soil mass (ESM) was + 30.7 Mg C ha −1 (+31.7 %) higher under maize-oat-alfalfa than continuous maize, but similar between maize-soybean and continuous maize. NPK fertilization and manuring did not influence SOC stocks by ESM. Response of SOC stocks at 15 cm depth intervals to NPK fertilization varied by depth and crop rotation, with lower SOC stocks at 30–45 cm under continuous maize and maize-soybean. Maize-derived C ranged 19.5–59.6 % of SOC stock across depths, indicating the majority of SOC was still derived from tallgrass prairie even after 145 years of continuous maize cropping. Our results confirm the potential of diversified crop rotation for minimizing SOC losses relative to tallgrass prairie at the supracentennial scale, and highlight the importance of relic prairie soil organic matter for future crop production in central North America.

crop rotation↗

DOE OSTI2024

Thermally stable and self-healable lignin-based polyester

The increased use of plastics and the associated environmental impact has catalyzed research on the development of bio-derived polymers. Bio-based polyesters have gained increased attention due to the abundance of their starting materials and ease of processing. Lignin is naturally occurring in biomass with rich carbon content, whose functionality and rigidity make it an ideal bio-derived candidate for bio-based polyesters. Herein, a lignin-based polyester with good thermal stability and self-repairability was synthesized from carboxylated lignin and epoxidized soybean oil. The synthesized lignin/epoxidized soybean oil (ESO) vitrimer was brittle such that its mechanical performance could not be recorded. However, when polyethylene glycol (PEG) was incorporated as a plasticizer, polymer samples exhibited acceptable ductility. From thermomechanical analysis of the synthesized polyesters, the plasticizer did not impair thermal stability of polymers, but greatly enhanced mechanical properties. Notably, all samples exhibited stability at high temperatures, and good glass transition temperatures (51.0 ± 0.9–78.0 ± 1.2 °C). The highest tensile strength (3.983 ± 0.1 MPa) and storage modulus (1463.67 ± 12.6 MPa) were recorded for the polyester containing 6 % w/w PEG. Moreover, the polymer samples exhibited self-healing capability at 180 °C. This work expands on valorization of lignin through the synthesis of bio-derived materials.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Albedo of crops as a nature-based climate solution to global warming

Abstract Surface albedo can affect the energy budget and subsequently cause localized warming or cooling of the climate. When we convert a substantial portion of lands to agriculture, land surface properties are consequently altered, including albedo. Through crop selection and management, one can increase crop albedo to obtain higher levels of localized cooling effects to mitigate global warming. Still, there is little understanding about how distinctive features of a cropping system may be responsible for elevated albedo and consequently for the cooling potential of cultivated lands. To address this pressing issue, we conducted seasonal measurements of surface reflectivity during five growing seasons on annual crops of corn-soybean–winter wheat ( Zea mays L.- Glycine max L. Merrill - Triticum aestivum L. ; CSW) rotations at three agronomic intensities, a monoculture of perennial switchgrass ( Panicum virgatum L. ), and perennial polycultures of early successional and restored prairie grasslands. We found that crop-species, agronomic intensity, seasonality, and plant phenology had significant effects on albedo. The mean±SD albedo was highest in perennial crops of switchgrass (0.179±0.04), intermediate in early successional crops (0.170±0.04), and lowest in a reduced input corn systems with cover crops (0.154±0.02). The strongest cooling potentials were found in soybean (-0.450 kg CO 2 e m -2 yr -1 ) and switchgrass (-0.367 kg CO 2 e m -2 yr -1 ), with up to -0.265 kg CO 2 e m -2 yr -1 of localized climate cooling annually provided by different agroecosystems. We also demonstrated how diverse ecosystems, leaf canopy, and agronomic practices can affect surface reflectivity and provide another potential nature-based solution for reducing global warming at localized scales.

Lei, Cheyenne (ORCID:0000000272746309)↗

DOE OSTI2026

Contrasting Carbon–Water–Energy Dynamics in Perennial and Annual Bioenergy Agroecosystems Using Eddy Covariance and Interpretable Machine Learning

Understanding how agroecosystems respond to environmental variability is fundamental to predicting productivity and sustainability under a changing climate. We analyzed 55 site-years of high-frequency eddy covariance observations from five agroecosystems—two perennial grasses (miscanthus and switchgrass), two annual rotation systems (maize–soybean and sorghum–soybean), and a restored native prairie—to examine ecosystem-scale carbon, water, and energy fluxes. Using an interpretable machine-learning framework with regression tree ensembles, Shapley Additive Explanations, and Accumulated Local Effects, we quantified how environmental and temporal factors regulate gross primary productivity (GPP), evapotranspiration (ET), water-use efficiency, and the Bowen ratio. Perennials exhibited stronger physiological buffering and maintained fluxes across a broader range of temperature and moisture conditions, reflecting deeper rooting and persistent canopy cover. Annuals, in contrast, showed greater short-term variability and stronger coupling to atmospheric demand, with GPP and ET declining rapidly under low humidity or soil moisture. Differences in temperature sensitivity of Bowen ratio further revealed that perennials sustained proportionally greater sensible heat flux under cool conditions, whereas annuals exhibited constrained energy exchange when evaporative demand was low. Together, these results demonstrate that crop life cycle and canopy structure are fundamental determinants of ecosystem-scale carbon–water–energy coupling. By integrating long-term flux observations with interpretable machine learning, this study identifies the environmental drivers that shape agroecosystem function and highlights how conversion from annual to perennial feedstocks can enhance climatic resilience and alter land–atmosphere energy feedbacks. These findings provide a data-driven basis for improving crop and Earth-system models and for guiding bioenergy landscape design under future climate scenarios.

Accumulated Local Effects↗

DOE OSTI2026

Data for Soil Oxygen Dynamics: A Key Mediator of Tile Drainage Impacts on Coupled Hydrological, Biogeochemical, and Crop Systems

Tile drainage removes excess water and is an essential, widely adopted management practice to enhance crop productivity in the US Midwest and throughout the world. Tile drainage has been shown to significantly change hydrological and biogeochemical cycles by lowering the water table and reducing the residence time of soil water, although examining the complex interactions and feedbacks in an integrated hydrology–biogeochemistry–crop system remains elusive. Oxygen dynamics are critical to unraveling these interactions and have been ignored or oversimplified in existing models. Understanding these impacts is essential, particularly so because tile drainage has been highlighted as an adaptation under projected wetter springs and drier summers in the changing climate in the US Midwest. We used the ecosys model that uniquely incorporates first-principle soil oxygen dynamics and crop oxygen uptake mechanisms to quantify the impacts of tile drainage on hydrological and biogeochemical cycles and crop growth in corn–soybean rotation fields. The model was validated with data from a multi-treatment, multi-year experiment in Washington, IA. The relative root mean square error (rRMSE) for the corn and soybean yield in validation is 5.66 % and 12.57 %, respectively. The Pearson coefficient (r) of the monthly tile flow during the growing season is 0.78. Plant oxygen stress turns out as an emergent property of the equilibrium between the soil oxygen supply and biological demand. The impact of tile drainage on the system is achieved through a series of coupled feedback mechanisms. The model results show that tile drainage reduces the soil water content and enhances soil oxygenation. It additionally increases the subsurface discharge and elevates inorganic nitrogen leaching, with seasonal variations influenced by climate and crop phenology. The improved aerobic condition alleviates crop oxygen stress during wet springs, thereby promoting crop root growth during the early growth stage. The development of greater root density, in turn, mitigates water stress during dry summers, leading to an overall increase in the crop yield by ∼6 %. These functions indicate the potential of tile drainage in bolstering crop resilience to climate change and the use of this modeling tool for large-scale assessments of tile drainage. The model reveals the underlying causal mechanisms that drive the agroecosystem response to drainage on the coupled hydrology, biogeochemistry, and crop system dynamics.

Modeling↗