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DOE OSTI2021

Grassland-to-cropland conversion increased soil, nutrient, and carbon losses in the US Midwest between 2008 and 2016

After decades of declining cropland area, the United States (US) experienced a reversal in land use/land cover change in recent years, with substantial grassland conversion to cropland in the US Midwest. Although previous studies estimated soil carbon (C) loss due to cropland expansion, other important environmental indicators, such as soil erosion and nutrient loss, remain largely unquantified. In this study, we simulated the environmental impacts from the conversion of grassland to corn and soybeans for 12 US Midwestern states using the EPIC (Environmental Policy Integrated Climate) model. Between 2008 and 2016, over 2 Mha of grassland were converted to crop production in these states, with much less cropland concomitantly abandoned or retired from production. The net grassland-cropland conversion increased annual soil erosion by 7.9%, nitrogen (N) loss by 3.7%, and soil organic carbon loss by 5.6% relative to that of existing cropland, despite an associated increase in cropland area of only 2.5%. Notably, the above estimates represent the scenario of converting unmanaged grassland to tilled corn and soybeans, and impacts varied depending upon crop type and tillage regime. Corn and soybeans are dominant biofuel feedstocks, yet the grassland conversion and subsequent environmental impacts simulated in this study are likely not attributable solely to biofuel-driven land use change since other factors also contribute to corn and soybean prices and land use decisions. Nevertheless, our results suggest grassland conversion in the Upper Midwest has resulted in substantial degradation of soil quality, with implications for air and water quality as well. Additional conservation measures are likely necessary to counterbalance the impacts, particularly in areas with high rates of grassland conversion (e.g. the Dakotas, southern Iowa).

54 ENVIRONMENTAL SCIENCES↗

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 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.↗

DOE OSTI2020

An Unmanned Aerial System (UAS) for concurrent measurements of solar-induced chlorophyll fluorescence and hyperspectral reflectance toward improving crop monitoring

Unmanned aerial system (UAS)-based remote sensing can provide high spatial- and temporal-resolution crop monitoring for precision management. Existing crop monitoring UAS primarily use conventional techniques such as multi-spectral broadband vegetation indices (VIs) to track canopy structure/biomass. Recently developed lightweight hyperspectral sensors can track crop physiology and performance via complementary signals such as solar-induced chlorophyll fluorescence (SIF) and photochemical reflectance index (PRI) that can be derived from hyperspectral reflectance. However, few existing UAS can acquire high-quality SIF and hyperspectral reflectance. Here, we designed a novel UAS that simultaneously captures far-red SIF and hyperspectral reflectance, using a single bifurcated fiber and motorized arm to measure downwelling and upwelling irradiance. The UAS was tested over (1) a heterogeneous corn field with spatially varying crop yield potential and (2) a set of soybean and corn plots under differing nutrient treatments. Our UAS can maintain stability under low-to-moderate winds (2–3 m/s) with <0.4 m geolocation accuracy, <0.1 m altitude drift, and <1.5° error of the fiber optic from nadir position during scanning. Weekly seasonal campaigns reveal that SIF outperforms conventional VIs such as the normalized difference vegetation index (NDVI) for distinguishing plots with different crop yield potential, especially after canopy closure when NDVI tends to saturate. The UAS can capture the diurnal dynamics of SIF and PRI for both corn and soybean. At the seasonal scale, SIF acquired from the UAS correlates well with that from a fixed SIF tower system ( R 2 = 0.81), which measured the same target albeit with different footprints, demonstrating the capability of UAS in characterizing the seasonal progression of crop activity. In conclusion, our newly developed UAS provides high-quality SIF and hyperspectral reflectance, facilitating mechanistic understanding of the physiological control on photosynthesis dynamics. Our findings also imply that this UAS could enable extension of fixed tower-based systems to monitor multiple targets from diurnal to seasonal scales and improve monitoring of heterogeneous fields.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS1980

AgRISTARS: Foreign commodity production forecasting. Corn/soybean decision logic development and testing

The development and testing of an analysis procedure which was developed to improve the consistency and objectively of crop identification using Landsat data is described. The procedure was developed to identify corn and soybean crops in the U.S. corn belt region. The procedure consists of a series of decision points arranged in a tree-like structure, the branches of which lead an analyst to crop labels. The specific decision logic is designed to maximize the objectively of the identification process and to promote the possibility of future automation. Significant results are summarized.

Dailey, C. L.↗

NASA NTRS1984

Laser-induced fluorescence of green plants. I - A technique for the remote detection of plant stress and species differentiation

The laser-induced fluorescence (LIF) of green plants was evaluated as a means of remotely detecting plant stress and determining plant type. Corn and soybeans were used as representatives of monocots and dicots, respectively, in these studies. The fluorescence spectra of several plant pigments was excited with a nitrogen laser emitting at 337 nm. Intact leaves from corn and soybeans also fluoresced using the nitrogen laser. The two plant species exhibited fluorescence spectra which had three maxima in common at 440, 690, and 740 nm. However, the relative intensities of these maxima were distinctly different for the two species. Soybeans had an additional slight maxima at 525 nm. Potassium deficiency in corn caused an increase in fluorescence at 690 and 740 nm. Simulated water stress in soybeans resulted in increased fluorescence at 440, 525, 690, and 740 nm. The inhibition of photosynthesis in soybeans by 3-(3-4-dichlorophenyl)-1-1-dimethyl urea (DCMU) gave incresed fluorescence primarily at 690 and 740 nm. Chlorosis as occurring in senescent soybean leaves caused a decrease in fluorescence at 690 and 740 nm. These studies indicate that LIF measurements of plants offer the potential for remotely detecting certain types of stress condition and also for differentiating plant species.

Chappelle, E. W.↗

NASA NTRS1987

Temporal observations of surface soil moisture using a passive microwave sensor

A series of 10 aircraft flights was conducted over agricultural fields to evaluate relationships between observed surface soil moisture and soil moisture predicted using passive microwave sensor observations. An a priori approach was used to predict values of surface soil moisture for three types of fields: tilled corn, no-till corn with soybean stubble, and idle fields with corn stubble. Acceptable predictions were obtained for the tilled corn fields, while poor results were obtained for the others. The source of error is suspected to be the density and orientation of the surface stubble layer; however, further research is needed to verify this explanation. Temporal comparisons between observed, microwave predicted, and soil water-simulated moisture values showed similar patterns for tilled well-drained fields. Divergences between the observed and simulated measurements were apparent on poorly drained fields. This result may be of value in locating and mapping hydrologic contributing areas.

Jackson, T. J.↗

NASA NTRS1983

Enhanced crop discrimination using the mid-IR (1.55-1.75 microns)

Improvements in crop discrimination can be realized by using mid-IR bands (1.55 -1.75 and 2.08 -2.35 microns) which are sensitive to canopy moisture content. Analyses of data from two growing seasons in Webster County, Iowa clearly indicate that corn and soybeans are highly separable in the mid-IR from early season through harvest. This contrasts sharply with visible and near-IR bands where corn and soybeans are confused throughout much of the growing season. The mid-IR temporal reflectance behavior appears to result from differences between C4 monocot and C3 dicot internal leaf structure. If this hypothesis holds, mid-IR observations should improve discrimination in other instances where similar differences in internal leaf structure are present.

Ungar, S. G.↗

DOE OSTI2022

Coupling localized Noah-MP-Crop model with the WRF model improved dynamic crop growth simulation across Northeast China

Croplands play a critical role in regulating the energy and moisture exchanges between the land surface and atmosphere. However, the interactions between cropland and climate are usually poorly represented due to a lack of detailed representation in crop types and field management. Here, we coupled the Noah-MP-Crop model with the state-of-the-art Weather Research and Forecasting (WRF) model to explore and evaluate the crop growth dynamics in response to climate variations across Northeast China. The default parameters of the crop model were not exactly suitable for the agricultural ecosystems in Northeast China. The detailed cropland distribution, and crop phenology parameters including growing degree days (GDD) and planting (harvesting) date were first created using multi-source remote sensing products and reanalysis data, and was then successfully used to simulate the growth and yield for corn and soybean and associated energy exchanges. We also optimized and calibrated other crop parameters using the time-series of the Moderate Resolution Imaging Spectroradiometer (MODIS) land surface products. The modified crop model substantially improved the simulation of crop growth, plant physiology, and biomass accumulation for both corn and soybean. Coupling the localized dynamic crop model into the WRF led to considerable decreases in the simulated mean-absolute-errors (MAEs) and biases of the leaf area index, evapotranspiration, and gross primary production compared with the MODIS observed values. Compared with the statistical yield from each province, the modified crop model underestimated the corn yield from 11.1% to 48.6%, whereas overestimated the soybean yield from 16.5% to 162.6%.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1975

Crop identification technology assessment for remote sensing (CITARS). Volume 10: Interpretation of results

The CITARS was an experiment designed to quantitatively evaluate crop identification performance for corn and soybeans in various environments using a well-defined set of automatic data processing (ADP) techniques. Each technique was applied to data acquired to recognize and estimate proportions of corn and soybeans. The CITARS documentation summarizes, interprets, and discusses the crop identification performances obtained using (1) different ADP procedures; (2) a linear versus a quadratic classifier; (3) prior probability information derived from historic data; (4) local versus nonlocal recognition training statistics and the associated use of preprocessing; (5) multitemporal data; (6) classification bias and mixed pixels in proportion estimation; and (7) data with differnt site characteristics, including crop, soil, atmospheric effects, and stages of crop maturity.

Bizzell, R. M.↗

DOE OSTI2021

Quantifying carbon budget, crop yields and their responses to environmental variability using the ecosys model for U.S. Midwestern agroecosystems

As one of the major agricultural production areas in the world, the United States (U.S.) Midwest plays a vital role in the global food supply and agricultural ecosystem services. Although significant efforts have been made in modeling the carbon cycle dynamics over this area, large uncertainty still exists in the previous simulations in terms of reproducing individual components of the carbon cycle and their responses to environmental variability. Here we evaluated the performance of an advanced agroecosystem model, ecosys, in simulating carbon budgets over the U.S. Midwest, considering both the magnitude of carbon flux/yield and its response to environmental (climate and soil) variability. We conducted model simulations and evaluations at 7 cropland eddy-covariance sites as well as over 293 counties of Illinois, Indiana, and Iowa in the U.S. Midwest. The site-level simulations showed that ecosys captured both the magnitude and seasonal patterns of carbon fluxes (i.e., net ecosystem carbon exchange (NEE), ecosystem gross primary production (GPP), and ecosystem respiration (Reco)), leaf area index (LAI), and dynamic plant carbon allocation processes, with R2 equal to 0.92, 0.87, 0.87, and 0.78 for GPP, NEE, Reco, and LAI, respectively across all the sites compared with the observations. For regional scale simulations, ecosys reproduced the spatial distribution and interannual variability of corn and soybean yields with the constraints of observed yields and a new remotely sensed GPP product, with R2 of multi-year averaged simulated and observed yield equal 0.83 and 0.80 for corn and soybean, respectively. The simulated responses of carbon cycle dynamics to environmental variability were consistent with that from the empirical observations at both site and regional scales. Furthermore, our results demonstrated the applicability of ecosys in simulating the carbon cycle and soil carbon dynamics of the U.S. Midwestern agroecosystems under different climate and soil conditions.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1973

Effective use of ERTS multisensor data in the Great Plains

The author has identified the following significant results. It appears that identification of corn and soybeans as separate classes may not be feasible for the part of the growing season around August 15. It is hoped that at other points in the growing season separation of corn from soybeans will be possible. Visual examination of September 5 imagery indicates that later in the growing season, it is possible. The resolution of the 1:300,000 scale enlargement remained sharp enough to distinguish features important for making soil association maps. The land use pattern (range vs cultivated) was sharp as were the stream bottoms, stream valley sides, and stock ponds. The resolution of the 1:1,000,000 scale enlargement was less sharp on detail but it appears that some features could be quantitatively analyzed. Among these are the hydrologic features present such as stock ponds and the streams. It would appear that the total area of stock pond water on a frame could be measured, and, after ground truth established their average depth, the volume of water stored could be calculated and be monitored.

Myers, V. I.↗

NASA NTRS1973

Crop identification using ERTS imagery

Digital analysis of August 15 ERTS-I imagery for southeastern South Dakota was performed to determine the feasibility of conducting crop surveys from satellites. Selected areas of bands 4, 5, 6, and 7 positive transparencies were converted to digital form utilizing Signal Analysis and Dissemination Equipment (SADE). The optical transmission values were printed out in a spatial format. Visual analysis of the printouts indicated that cultivated areas were readily distinguished from non-cultivated areas in all four bands. Bare soil was easily recognized in all four bands. Corn and soybeans, the two major crops in the area, were treated as separate classes rather than as a single class called row crops. Bands 6 and 7 provided good results in distinguishing between corn and soybeans.

Horton, M. L.↗

NASA NTRS1985

Shortwave infrared detection of vegetation

The potential of short wave infrared (SWIR) measurements in vegetation discrimination is further substantiated through a discussion of field studies and an examination of the physical bases which cause SWIR measurements to vary with the vegetation type observed. The research reported herein supported the AGRISTARS program objective to incorporate TM measurements in the analysis of agricultural activity. Field measurements on corn and soybeans in Iowa were conducted, and the mean and variance of canopy reflectance were computed for each observation date. The Suits canopy reflectance model was used to evaluate possible explanations of the observed corn/soybeans reflectance patterns /39/. The SWIR measurements were shown to effectively discriminate corn and soybeans on the basis of leaf absorption properties.

Goward, S. N.↗

NASA NTRS1984

Evaluation of corn/soybeans separability using Thematic Mapper and Thematic Mapper Simulator data

Multitemporal Thematic Mapper, Thematic Mapper Simulator, and detailed ground truth data were collected for a 9- by 11-km sample segment in Webster County, IA, in the summer of 1982. Three dates were acquired each with Thematic Mapper Simulator (June 7, June 23, and July 31) and Thematic Mapper (August 2, September 3, and October 21). The Thematic Mapper Simulator data were converted to equivalent TM count values using TM and TMS calibration data and model based estimates of atmospheric effects. The July 31, TMS image was compared to the August 2, TM image to verify the conversion process. A quantitative measure of proportion estimation variance (Fisher information) was used to evaluate the corn/soybeans separability for each TM band as a function of time during the growing season. The additional bands in the middle infrared allowed corn and soybeans to be separated much earlier than was possible with the visible and near-infrared bands alone. Using the TM and TMS data, temporal profiles of the TM principal components were developed. The greenness and brightness exhibited behavior similar to MSS greenness and brightness for corn and soybeans.

Pitts, D. E.↗

DOE OSTI2020

Chemical Imaging of Fine Mode Atmospheric Particles Collected from a Research Aircraft over Agricultural Fields

Vegetation and crops in agricultural areas are a significant source of both biogenic secondary organic aerosols and of directly emitted primary biological atmospheric particles (PBAPs) composed of plant fragments/debris, fungal spores, pollen, and bacteria among others. Seasonal crop harvesting significantly intensifies regional PBAP emissions and aerosolizes soil organic matter and dust particulates. In this paper, the atmospheric contributions and impact of PBAPs have been insufficiently investigated due to limited real-time observations and ambiguity in their detection by existing measurement techniques. We collected ambient particles on-board an instrumented Beechcraft BE76 Duchess research aircraft flying above actively harvested agriculture fields of corn and soybean crops. In this work, particle samples were analyzed using complementary chemical imaging techniques, including computer-controlled scanning electron microscopy coupled with elemental microanalysis to describe particle-type population and scanning transmission X-ray microscopy to provide quantitative metrics on particle internal heterogeneity. We show the particle-class contribution is consistent between corn and soybean harvesting across altitudes of 100–300 m AGL. The contribution of PBAPs in the size range of 0.15–1.25 μm is estimated to be 10–12% of ~39,000 analyzed particles. Determining viscosity with X-ray microanalysis has shown that the fine-mode organic particles collected are viscous/semisolid (10 2 –10 12 Pa s) while the majority of PBAP fragments are solid (>10 12 Pa s). The observation of submicrometer, solid carbonaceous fragments of biogenic origin has implications for the regional cloud condensation nuclei and ice nuclei budget. Therefore, seasonal harvesting of crops may play an important, yet unrecognized, role in regional cloud formation and climate.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Dynamic urban land extensification is projected to lead to imbalances in the global land-carbon equilibrium

Abstract Human-Earth System Models and Integrated Assessment Models used to explore the land-atmosphere implications of future land-use transitions generally lack dynamic representation of urban lands. Here, we conduct an experiment incorporating dynamic urbanization in a multisector model framework. We integrate projected dynamic non-urban lands from a multisector model with projected dynamic urban lands from 2015 to 2100 at 1-km resolution to examine 1 st -order implications to the land system, crop production, and net primary production that can arise from the competition over land resources. By 2100, future urban extensification could displace 0.1 to 1.4 million km 2 of agriculture lands, leading to 22 to 310 Mt of compromised corn, rice, soybean, and wheat production. When considering increased corn production required to meet demands by 2100, urban extensification could cut increases in yields by half. Losses in net primary production from displaced forest, grassland, and croplands ranged from 0.24 to 2.24 Gt C yr −1 , potentially increasing land emissions by 1.19 to 6.59 Gt CO 2 yr −1 . Although these estimates do not consider adaptive responses, 1 st -order experiments can elucidate the individual role of sub-sectors that would otherwise be masked by model complexity.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2017

Estimating the Relative Water Content of Leaves in a Cotton Canopy

Remotely sensing plant canopy water status remains a long-term goal of remote sensing research. Established approaches to estimating canopy water status the Crop Water Stress Index, the Water Deficit Index and the Equivalent Water Thickness involve measurements in the thermal or reflective infrared. Here we report plant water status estimates based upon analysis of polarized visible imagery of a cotton canopy measured by ground Multi-Spectral Polarization Imager (MSPI). Such estimators potentially provide access to the plant hydrological photochemistry that manifests scattering and absorption effects in the visible spectral region.Twice during one day, +- 3 hours from solar noon, we collected polarized imagery and relative water content data on a cotton test plot located at the Arid Land Agricultural Research Center, United States Department of Agriculture, Maricopa, AZ. The test plot, a small portion of a large cotton field, contained stressed plants ready for irrigation. The evening prior to data collection we irrigated several rows of plants within the test plot. Thus, ground MSPI imagery from both morning and afternoon included cotton plants with a range of water statuses. Data analysis includes classifying the polarized imagery into sunlit reflecting, sunlit transmitting, shaded foliage and bare soil. We estimate the leaf surface reflection and interior reflection based upon the per pixel polarization and sunview directions. We compare our cotton results with our prior polarization results for corn and soybean leaves measured in the lab and corn leaves measured in the field.

Cotton Canopy↗