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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 NTRS1977

Airborne monitoring of crop canopy temperatures for irrigation scheduling and yield prediction

Airborne and ground measurements were made on April 1 and 29, 1976, over a USDA test site consisting mostly of wheat in various stages of water stress, but also including alfalfa and bare soil. These measurements were made to evaluate the feasibility of measuring crop temperatures from aircraft so that a parameter termed stress degree day, SDD, could be computed. Ground studies have shown that SDD is a valuable indicator of a crop's water needs, and that it can be related to irrigation scheduling and yield. The aircraft measurement program required predawn and afternoon flights coincident with minimum and maximum crop temperatures. Airborne measurements were made with an infrared line scanner and with color IR photography. The scanner data were registered, subtracted, and color-coded to yield pseudo-colored temperature-difference images. Pseudo-colored images reading directly in daily SDD increments were also produced. These maps enable a user to assess plant water status and thus determine irrigation needs and crop yield potentials.

Millard, J. P.↗
NASA NTRS1977

Airborne monitoring of crop canopy temperatures for irrigation scheduling and yield prediction

The aim of the program discussed was to develop techniques for remotely measuring crop irrigation needs and predicting crop yields, with emphasis on wheat. Airborne measurements, using an IR line scanner and color IR photography, were made to evaluate the feasibility of measuring minimum and maximum (dawn and afternoon) crop temperatures to compute a parameter, termed 'stress degree day' (SDD) - a valuable indicator of crop water needs, which can be related to irrigation scheduling and yield. Crop canopy temperature measurements by airborne IR techniques revealed the superiority of thermal IR data over color IR photography. Water stress undetected in the latter technique was clearly detected in thermal imagery. Color IR photography, however, is valuable in discerning vegetation. The pseudo-colored temperature-difference images (and pseudo-colored images, reading directly in daily SDD increments) are shown to be well suited for assessing plant water status and, thus, for determining the irrigation needs and crop yield potentials.

Millard, J. P.↗
NASA NTRS2018

Spatially-Explicit Monitoring of Crop Photosynthetic Capacity Through the Use of Space-Based Chlorophyll Fluorescence Data

Plant functional traits such as photosynthetic capacity are critical parameters for terrestrial biosphere models. However, their spatial and temporal characteristics are still poorly represented. In this study, we used satellite observations of sun-induced fluorescence (SIF) to estimate top-of-canopy photosynthetic capacity (maximum carboxylation rate, V(sub cmax) at a reference temperature of 25 deg C) for crops, which was in turn utilized to simulate regional gross primary production (GPP). We first estimate the key parameter, V(sub cmax), in the widely-used FvCB photosynthesis model using field measurements of CO2 and water fluxes during 2007-2012 at seven crop eddy covariance flux sites over the US Corn Belt. The results showed that satellite far-red SIF retrievals have a stronger link to V(sub cmax) at the seasonal scale (R(squared)=0.70 for C4 and R(squared)=0.63 for C3 crop) as compared with widely-used vegetation indices. We calibrate an empirical model linking V(sub cmax) with SIF that was used to estimate spatially and temporally varying crop V(sub cmax) for the US Corn Belt region. The resulting V(sub cmax) maps are used together with meteorological data from MERRA reanalysis data and vegetation structural parameters derived from the satellite- based spectral reflectance data to constrain the Soil-Canopy Observation of Photosynthesis and Energy (SCOPE) balance model in order to estimate regional crop GPP. Our results show a substantial improvement in the seasonal and spatial patterns of cropland GPP when compared with crop yield inventory data. The evaluation with tall tower atmospheric CO2 measurements further supports our estimation of spatiotemporal V(sub cmax) from space-borne SIF. Considering that SIF has a direct link to photosynthetic activity, our findings highlight the potential to infer regional V(sub cmax) using remotely sensed SIF data and to use this information for a better quantification of regional cropland carbon cycles.

Drought↗
NASA NTRS2020

Culture-Based Environmental Microbiology Monitoring of Crop-Based Space Food Systems (veggie Monitoring)

Crewmembers live and work in a closed environment that is monitored to ensure their health and safety. Quarterly monitoring of the microorganisms in the International Space Station (ISS) environment supports crew safety and contributes to a large set of microbial concentration and diversity data from air, surfaces and water samples. This study leverages quarterly operational Environmental Health System (EHS) sampling by collecting additional microbial samples from the surface of the station’s Veggie plant production system. Longer exploration missions may require spaceflight-based systems for growth of plants, and this investigation is expected to provide additional data to help establish requirements to protect these systems, plants, and crew, mitigating adverse microbial exposure.

Hamilton, Tanner↗
NASA NTRS2022

2022 Annual Report: Culture-Based Environmental Microbiology Monitoring of Crop-based Space Food Systems (Veggie Monitoring)

Crewmembers live and work in a closed environment that is monitored to ensure their health and safety. Quarterly monitoring of the microorganisms in the International Space Station (ISS) environment supports crew safety and contributes to a large set of environmental microbial data from the air, surface, and water samples that are collected. This study leverages quarterly operational Environmental Health System (EHS) sampling by collecting additional microbial samples from the surface of the station’s Veggie plant production system. Longer exploration missions may require spaceflight-based systems for growth of plants, and this investigation is expected to provide additional data to help establish requirements to protect these systems, plants, and crew, mitigating adverse microbial exposure.

Tanner Hamilton↗
DOE OSTI2024

Precision Agriculture using Networks of Degradable Analytical Sensors (PANDAS) (Final Technical Report)

Precision agriculture, where sensing of soil, environment and crop conditions are used to precisely synchronize inputs (such as water and fertilizer) to crop needs enhances input use efficiency. This can improve yields and farm profitability while mitigating environmental losses, improving soil carbon content and substantially decreasing energy use for food, feed and fuel crops. Unfortunately, farmers are not yet able to harness the full potential of these management technologies as there is a lack of available management information, and there is therefore a need for sensors that are able to economically measure spatio-temporal variability in soil and crop properties of extremely heterogeneous farm fields precisely at high resolution and at low cost. Real-time, in-situ monitoring of agricultural soil conditions is today carried out using devices that limit the total number of nodes that can be used economically to typically one per acre or less. Higher spatio-temporal resolution sensing would enable more precise agricultural input optimization, with significant benefits to the farmer and the environment. In order to address this issue, this project focused on developing additively manufactured, biodegradable, soil sensors with predicted costs of < $\$$1 per unit to monitor crop inputs (such as water and fertilizer) that predictably, harmlessly degrade away into the soil when no longer needed. These sensor nodes should be easy to place, accurately and continuously monitor soil and crop conditions for an entire season, be read remotely using existing farm equipment, require no ongoing maintenance, not impede farm operations and produce no persistent waste. This approach could enable a >100× increase in information density over current solutions for precision farming of row and other crops, and lead to significant reductions in input energy use and provide increased yield for biofuel crops. Over the course of this project the team at the University of Colorado Boulder, University of California Berkeley, and Colorado State University/Kansas State University investigated a wide range of printable biodegradable electronic materials and sensor designs for determining soil moisture and soil nitrate concentration. These efforts expanded the available materials set for printed soil degradable electronic materials, particularly for conductors, enabling high conductivity and stability. Printed soil moisture and nitrate sensors with suitable sensitivity and selectivity were developed and characterized. Low power and passive wireless electronic systems were integrated with the soil sensors, and testing was carried out with completed sensors to understand their functionality under agricultural conditions. Additionally, other sensor types enabled by the biodegradable materials set created during this project, such as soil microbial activity sensors, were also developed and demonstrated. Project outputs include 10 peer reviewed publications, 4 patent applications, 21 technical presentations, 3 PhD thesis, 10 media reports, 8 additional grants worth over $\$$6M, and the formation of 3 start-up companies.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS1990

AVHRR monitoring of U.S. crops during the 1988 drought

Effects of the 1988 drought on crops in the U.S. Corn Belt were assessed and monitored by the Foreign Crop Condition Assessment Division (FCCAD), U.S. Department of Agriculture. The primary data were vegetation index numbers (VINs), each of which was calculated as an average vegetation index of a geographically referenced cell of AVHRR pixels. Using VINs, the FCCAD was able to detect the existence of drought early in the season, monitor changing conditions, and provide objective assessments of the drought's extent and severity. Field observations confirmed the image analyses, and underlined the importance of the timing of extreme weather events with respect to crop stages for interpreting VINs. The analyses were conducted in an operational environment, providing a unique test of the AVHRR data for large area, near real-time crop monitoring. Because large area, operational remote sensing of crops is quite different from traditional, controlled, small plot research studies, more work is needed to link the two; this would improve crop assessment capabilities.

Teng, William L.↗
NASA NTRS1975

Agricultural applications of remote sensing: A true life adventure

A study of agricultural applications of remote sensing with a major US agricultural firm was undertaken in mid-1973. The study continued for eighteen months, and covered the areas of crop monitoring and management as well as large scale crop inventories. Pilot programs in the application of aircraft remote sensing and LANDSAT data were conducted. An operational aircraft survey program for ranch management has subsequently been implemented by the agricultural firm. LANDSAT data was successfully used to produce a ninety-seven percent accurate inventory of cotton over 4.8 million acres of California's San Joaquin Valley.

Schaller, E. S.↗
DOE OSTI2024

Zero-power wireless system for crop water content monitoring

Zero power wireless sensors, devices, and systems are used for crop water content monitoring. The sensors consume no power while monitoring for the presence of dry crop conditions. Infrared reflectance from plants is measured and when selected spectral conditions are met, a circuit is closed, activating an alarm, an RFID tag, or a radio transmitter. The deployed sensors consume no power while monitoring, reducing or eliminating the need to change batteries.

Rinaldi, Matteo↗
DOE OSTI2021

A daily, 250 m and real-time gross primary productivity product (2000–present) covering the contiguous United States

Abstract. Gross primary productivity (GPP) quantifies the amount of carbon dioxide (CO2) fixed by plants through photosynthesis. Although as a key quantity of terrestrial ecosystems, there is a lack of high-spatial-and-temporal-resolution, real-time and observation-based GPP products. To address this critical gap, here we leverage a state-of-the-art vegetation index, near-infrared reflectance of vegetation (NIRV), along with accurate photosynthetically active radiation (PAR), to produce a SatelLite Only Photosynthesis Estimation (SLOPE) GPP product for the contiguous United States (CONUS). Compared to existing GPP products, the proposed SLOPE product is advanced in its spatial resolution (250 m versus >500 m), temporal resolution (daily versus 8 d), instantaneity (latency of 1 d versus >2 weeks) and quantitative uncertainty (on a per-pixel and daily basis versus no uncertainty information available). These characteristics are achieved because of several technical innovations employed in this study: (1) SLOPE couples machine learning models with MODIS atmosphere and land products to accurately estimate PAR. (2) SLOPE couples highly efficient and pragmatic gap-filling and filtering algorithms with surface reflectance acquired by both Terra and Aqua MODIS satellites to derive a soil-adjusted NIRV (SANIRV) dataset. (3) SLOPE couples a temporal pattern recognition approach with a long-term Cropland Data Layer (CDL) product to predict dynamic C4 crop fraction. Through developing a parsimonious model with only two slope parameters, the proposed SLOPE product explains 85 % of the spatial and temporal variations in GPP acquired from 49 AmeriFlux eddy-covariance sites (324 site years), with a root-mean-square error (RMSE) of 1.63 gC m−2 d−1. The median R2 over C3 and C4 crop sites reaches 0.87 and 0.94, respectively, indicating great potentials for monitoring crops, in particular bioenergy crops, at the field level. With such a satisfactory performance and its distinct characteristics in spatiotemporal resolution and instantaneity, the proposed SLOPE GPP product is promising for biological and environmental research, carbon cycle research, and a broad range of real-time applications at the regional scale. The archived dataset is available at https://doi.org/10.3334/ORNLDAAC/1786 (download page: https://daac.ornl.gov/daacdata/cms/SLOPE_GPP_CONUS/data/, last access: 20 January 2021) (Jiang and Guan, 2020), and the real-time dataset is available upon request.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1984

Growth and reflectance characteristics of winter wheat canopies

A valuable input to crop growth and yield models would be estimates of current crop condition. If multispectral reflectance indicates crop condition, then remote sensing may provide an additional tool for crop assessment. The effects of nitrogen fertilization on the spectral reflectance and agronomic characteristics of winter wheat (Triticum aestivum L.) were determined through field experiments. Spectral reflectance was measured during the 1979 and 1980 growing seasons with a spectroradiometer. Agronomic data included total leaf N concentration, leaf chlorophyll concentration, stage of development, leaf area index (LAI), plant moisture, and fresh and dry phytomass. Nitrogen deficiency caused increased visible, reduced near infrared, and increased middle infrared reflectance. These changes were related to lower levels of chlorophyll and reduced leaf area in the N-deficient plots. Green LAI, an important descriptor of wheat canopies, could be reliably estimated with multispectral data. The potential of remote sensing in distinguishing stressed from healthy crops is demonstrated. Evidence suggests multispectral imagery may be useful for monitoring crop condition.

Hinzman, L. D.↗
NASA NTRS2021

Hybrid phenology matching model for robust crop phenological retrieval

Crop phenology regulates seasonal agroecosystem carbon, water, and energy exchanges, and is a key component in empirical and process-based crop models for simulating biogeochemical cycles of farmlands, assessing gross and net primary production, and forecasting the crop yield. The advances in phenology matching models provide a feasible means to monitor crop phenological progress using remote sensing observations, with a priori information of reference shapes and reference phenological transition dates. Yet the underlying geometrical scaling assumption of models, together with the challenge in defining phenological references, hinders the applicability of phenology matching in crop phenological studies. The objective of this study is to develop a novel hybrid phenology matching model to robustly retrieve a diverse spectrum of crop phenological stages using satellite time series. The devised hybrid model leverages the complementary strengths of phenometric extraction methods and phenology matching models. It relaxes the geometrical scaling assumption and can characterize key phenological stages of crop cycles, ranging from farming practice-relevant stages (e.g., planted and harvested) to crop development stages (e.g., emerged and mature). To systematically evaluate the influence of phenological references on phenology matching, four representative phenological reference scenarios under varying levels of phenological calibrations in terms of time and space are further designed with publicly accessible phenological information. The results indicate that the hybrid phenology matching model can achieve high accuracies for estimating corn and soybean phenological growth stages in Illinois, particularly with the year- and region-adjusted phenological reference (R-squared higher than 0.9 and RMSE less than 5 days for most phenological stages). The inter-annual and regional phenological patterns characterized by the hybrid model correspond well with those in the crop progress reports (CPRs) from the USDA National Agricultural Statistics Service (NASS). Compared to the benchmark phenology matching model, the hybrid model is more robust to the decreasing levels of phenological reference calibrations, and is particularly advantageous in retrieving crop early phenological stages (e.g., planted and emerged stages) when the phenological reference information is limited. This innovative hybrid phenology matching model, together with CPR-enabled phenological reference calibrations, holds 3 considerable promise in revealing spatio-temporal patterns of crop phenology over extended geographical regions.

Phenology↗
NASA NTRS1980

A model of plant canopy polarization response

Sensors to remotely measure the linear polarization of ground scenes have been proposed for the Multispectral Resource Sampler (MRS), a satellite sensor system proposed to complement the Thematic Mapper. At present justification for a sensor on MRS to measure scene polarization is limited. This paper discusses a model for the amount of linearly polarized light reflected by the shiny leaves of such crops as wheat, corn, and sorghum. The theory demonstrates that, potentially, measurements of the linearly polarized light from a crop canopy may be used as an additional feature to discriminate between crops. Examination of the model suggests that, potentially, satellite polarization measurements may be used to monitor crop development stage, leaf water content, leaf area index, hail damage, and certain plant diseases. The model adds to the understanding of the potential information content of scene polarization measurements acquired by future satellite sensor systems such as MRS.

Vanderbilt, V. C.↗
NASA NTRS2008

Remote-Sensing Time Series Analysis, a Vegetation Monitoring Tool

The Time Series Product Tool (TSPT) is software, developed in MATLAB , which creates and displays high signal-to- noise Vegetation Indices imagery and other higher-level products derived from remotely sensed data. This tool enables automated, rapid, large-scale regional surveillance of crops, forests, and other vegetation. TSPT temporally processes high-revisit-rate satellite imagery produced by the Moderate Resolution Imaging Spectroradiometer (MODIS) and by other remote-sensing systems. Although MODIS imagery is acquired daily, cloudiness and other sources of noise can greatly reduce the effective temporal resolution. To improve cloud statistics, the TSPT combines MODIS data from multiple satellites (Aqua and Terra). The TSPT produces MODIS products as single time-frame and multitemporal change images, as time-series plots at a selected location, or as temporally processed image videos. Using the TSPT program, MODIS metadata is used to remove and/or correct bad and suspect data. Bad pixel removal, multiple satellite data fusion, and temporal processing techniques create high-quality plots and animated image video sequences that depict changes in vegetation greenness. This tool provides several temporal processing options not found in other comparable imaging software tools. Because the framework to generate and use other algorithms is established, small modifications to this tool will enable the use of a large range of remotely sensed data types. An effective remote-sensing crop monitoring system must be able to detect subtle changes in plant health in the earliest stages, before the effects of a disease outbreak or other adverse environmental conditions can become widespread and devastating. The integration of the time series analysis tool with ground-based information, soil types, crop types, meteorological data, and crop growth models in a Geographic Information System, could provide the foundation for a large-area crop-surveillance system that could identify a variety of plant phenomena and improve monitoring capabilities.

McKellip, Rodney↗
NASA NTRS1982

Research in satellite-aided crop inventory and monitoring

Automated information extraction procedures for analysis of multitemporal LANDSAT data in non-U.S. crop inventory and monitoring are reviewed. Experiments to develope and evaluate crop area estimation technologies for spring small grains, summer crops, corn, and soybeans are discussed.

Erickson, J. D.↗
NASA NTRS1982

Research in satellite-aided crop inventory and monitoring

Automated information extraction procedures for analysis of multitemporal Landsat data in non-U.S. crop inventory and monitoring are reviewed. Experiments to develop and evaluate crop area estimation technologies for spring small grains, summer crops, corn, and soybeans are discussed. Previously announced in STAR as N82-32793

Erickson, J. D.↗
NASA NTRS1980

A model of plant canopy polarization

A model for the amount of linearly polarized light reflected by the shiny leaves of grain crops is based on the morphological and phenological characteristics of the plant canopy and upon the Fresnel equations which describe the light reflection process at the smooth boundary separating two dielectrics. The theory used demonstrates that, potentially, measurements of the linearly polarized light from a crop canopy may be used as an additional feature to discriminate between crops such as wheat and barley, two crops which are so spectrally similar that they are misclassified with unacceptable frequency. Examination of the model suggests that, potentially, satellite polarization measurements may be used to monitor crop development stage, leaf water content, leaf area index, hail damage, and certain plant diseases. The information content of these measurements is needed to evaluate the proposed polarization sensor for the satellite-borne multispectral resource sampler.

Vanderbilt, V. C.↗
NASA NTRS1981

Crop classification using multidate/multifrequency radar data

Both C- and L-band radar data acquired over a test site near Colby, Kansas during the summer of 1978 were used to identify three types of vegetation cover and bare soil. The effects of frequency, polarization, and the look angle on the overall accuracy of recognizing the four types of ground cover were analyzed. In addition, multidate data were used to study the improvement in recognition accuracy possible with the addition of temporal information. The soil moisture conditions had changed considerably during the temporal sequence of the data; hence, the effects of soil moisture on the ability to discriminate between cover types were also analyzed. The results provide useful information needed for selecting the parameters of a radar system for monitoring crops.

Ulaby, F. T.↗