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At least 289 records · Page 16

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NASA NTRS1973

Remote sensing for monitoring a water transportation project - The California Aqueduct

Land use changes were investigated on the west side of the San Joaquin Valley, California, an arid region presently importing water via the California Aqueduct, utilizing airphoto mosaics (1957), high-altitude photography (1971), and Earth Resources Technology Satellite (ERTS-1) data (1972). Attention was focused on land use characteristics for the three dates, changes which had occurred, and the sequence of land use category change. Research indicated that water importation was contributing to an overall pattern of regional change trending from an oil-producing and grazing area into a cropping region. Furthermore, the general pattern of specific category change was trending in the direction of higher economic intensity land use succession.

Estes, J. E.↗

NASA NTRS1975

Remotely sensing wheat maturation with radar

The scattering properties of wheat were studied in the 8-18 GHz band as a function of frequency, polarization, incidence angle, and crop maturity. Supporting ground truth was collected at the time of measurement. The data indicate that the radar backscattering coefficient is sensitive to both radar system parameters and crop characteristics particularly at incidence angles near nadir. Linear regression analyses of the radar backscattering coefficient on both time and plant moisture content result in rather good correlation. Furthermore, by calculating the average time rate of change of the radar backscattering coefficient it is found that it undergoes rapid variations shortly before and after the wheat is harvested. Both of these analyses suggest methods for estimating wheat maturity and for monitoring the progress of harvest.

Bush, T. F.↗

NASA NTRS2023

Modeling Global Indices for Estimating Non-Photosynthetic Vegetation Cover

Non-photosynthetic vegetation (NPV) includes plant litter, senesced leaves, and crop residues. NPV plays an essential role in terrestrial ecosystem processes, and is an important indicator of drought severity, ecosystem disturbance, agricultural resilience, and wildfire danger. Current moderate spatial resolution multispectral satellite systems (e.g., Landsat and Sentinel-2) have only a single band in the 2000–2500 nm shortwave infrared “SWIR2” range where non-pigment biochemical constituents of NPV, including cellulose and lignin, have important spectral absorption features. Thus, these current systems have suboptimal capabilities for characterizing NPV cover. This research used simulated spectral mixtures accounting for variability among NPV and soils to evaluate globally-appropriate hyperspectral and multispectral indices for estimation of fractional NPV cover. The Continuum Interpolated NPV Depth Index (CINDI), a weighted ratio index measuring lignocellulose absorption near 2100 nm, was found to produce the lowest error in estimating NPV cover. CINDI was less sensitive to variability in soil spectra and green vegetation cover than competing indices. While CINDI was sensitive to the relative water content of soil and NPV, this sensitivity allowed for correcting error in estimated NPV cover as water content increased. CINDI bands were less capable than Dual Absorption NPV Index (DANI) bands for maintaining continuity with the heritage Landsat SWIR2 band, but combining multiple CINDI bands demonstrated adequate continuity. Three SWIR2 bands with band centers at 2038, 2108, and 2211 nm can provide superior capabilities for future moderate resolution multispectral/superspectral systems targeting NPV monitoring, including the next generation Landsat mission (Landsat Next). These bands and the associated CINDI index provide potential for global NPV monitoring using a constellation of future superspectral sensors and imaging spectrometers, with applications including improving soil management, preventing land degradation, evaluating impacts of drought, mapping ecosystem disturbance, and assessing wildfire danger.

Index optimization↗

NASA NTRS1991

IGARSS '91; Proceedings of the 11th Annual International Geoscience and Remote Sensing Symposium, Helsinki University of Technology, Espoo, Finland, June 3-6, 1991. Vols. 1, 2, 3, & 4

The papers presented at the symposium focus on global monitoring of the earth with emphasis on the solution of environmental problems and utilization of natural resources. Topics discussed include radar forestry modeling, hydrological remote sensing, polarimetric SAR observations of sea ice, optical and IR oceanography, and satellite remote sensing of precipitation and related phenomena. Other subject areas covered are microwave sensing of trace gases; atmospheric pressure, temperature, and humidity; spaceborne sensor technology; geodynamic and geophysical remote sensing; space radar missions; remote sensing of vegetation and crops; and remote sensing of weather systems.

Source record↗

NASA NTRS2010

Using Leaf Chlorophyll to Parameterize Light-Use-Efficiency Within a Thermal-Based Carbon, Water and Energy Exchange Model

Chlorophylls absorb photosynthetically active radiation and thus function as vital pigments for photosynthesis, which makes leaf chlorophyll content (C(sub ab) useful for monitoring vegetation productivity and an important indicator of the overall plant physiological condition. This study investigates the utility of integrating remotely sensed estimates of C(sub ab) into a thermal-based Two-Source Energy Balance (TSEB) model that estimates land-surface CO2 and energy fluxes using an analytical, light-use-efficiency (LUE) based model of canopy resistance. The LUE model component computes canopy-scale carbon assimilation and transpiration fluxes and incorporates LUE modifications from a nominal (species-dependent) value (LUE(sub n)) in response to short term variations in environmental conditions, However LUE(sub n) may need adjustment on a daily timescale to accommodate changes in plant phenology, physiological condition and nutrient status. Day to day variations in LUE(sub n) were assessed for a heterogeneous corn crop field in Maryland, U,S.A. through model calibration with eddy covariance CO2 flux tower observations. The optimized daily LUE(sub n) values were then compared to estimates of C(sub ab) integrated from gridded maps of chlorophyll content weighted over the tower flux source area. The time continuous maps of daily C(sub ab) over the study field were generated by focusing in-situ measurements with retrievals generated with an integrated radiative transfer modeling tool (accurate to within +/-10%) using at-sensor radiances in green, red and near-infrared wavelengths acquired with an aircraft imaging system. The resultant daily changes in C(sub ab) within the tower flux source area generally correlated well with corresponding changes in daily calibrated LUE(sub n) derived from the tower flux data, and hourly water, energy and carbon flux estimation accuracies from TSEB were significantly improved when using C(sub ab) for delineating spatio-temporal variations in LUE(sub n). The results demonstrate the synergy between thermal infrared and shortwave reflective wavebands in producing valuable remote sensing data for operational monitoring of carbon and water fluxes.

Houlborg, Rasmus↗

NASA NTRS1975

Utilization of EREP data in geological evaluation, regional planning, forest management, and water management in North Carolina

The author has identified the following significant results. The S190A, S190B, and S192 photographs and imagery were studied, using standard air-photo interpretation techniques supplemented by color additive viewing and density slicing. The EREP data were found to have potential usefulness for natural resource inventory work, water quality monitoring, and land use mapping for specific problems at scales up to 1:30,000. Distinctions between forest types in North Carolina are limited to conifers, mixed conifer-hardwoods, and hardwoods. Geologic interpretation was limited to detection of lineaments; lithologic differentiation and soil group mapping have proved infeasible in North Carolina except for differentiation of wetland soils in the coastal plain. Imagery from the S192 multispectral scanner has proved to be capable of useful discriminations for vegetation and crop analysis.

Welby, C. W.↗

NASA NTRS1989

CELSS Breadboard Project at the Kennedy Space Center

The CELSS Breadboard Project is described, noting that it was initiated to study aspects of a CELSS for long-term space missions. Topics for extensive investigation included air and water regeneration, engineering control, and food production. The many options available for growing food crops in commercial plant growth chambers were investigated and the best of this information was translated to the Biomass Production Chamber (BPC). The chamber contains 20 sq m of crop growing area under 96 400 W HPS lamps; sixteen 0.25 sq m plant growth trays used on each of four growing shelves for a total of 64 trays; and one 256-L nutrient solution reservoir with the appropriate continuous-flow, thin-film plumbing for each shelf. A heating, ventilating, and air-conditioning system maintains atmospheric conditions and serves to distribute oxygen and carbon dioxide and maintain pressure at 12 mm of water. The control and monitoring subsystem, which uses a programmable logic controller, manages the BPC subsystems.

Prince, R. P.↗

NASA NTRS1996

The Spacelab-Mir-1 "Greenhouse-2" experiment

The Spacelab-Mir-1 (SLM-1) mission is the first docking of the Space Shuttle Atlantis (STS-71) with the Orbital Station Mir in June 1995. The SLM-1 "Greenhouse-2" experiment will utilize the Russian-Bulgarian-developed plant growth unit (Svet). "Greenhouse-2" will include two plantings (1) designed to test the capability of Svet to grow a crop of Superdwarf wheat from seed to seed, and (2) to provide green plant material for post-flight analysis. Protocols, procedures, and equipment for the experiment have been developed by the US-Russian science team. "Greenhouse-2" will also provide the first orbital test of a new Svet Instrumentation System (SIS) developed by Utah State University to provide near real time data on plant environmental parameters and gas-exchange rates. SIS supplements the Svet control and monitoring system with additional sensors for substrate moisture, air temperature, IR leaf temperature, light, oxygen, pressure, humidity, and carbon-dioxide. SIS provides the capability to monitor canopy transpiration and net assimilation of the plants growing in each vegetation unit (root zone) by enclosing the canopy in separate, retractable, ventilated leaf chambers. Six times during the seed-to-seed experiment, plant samples will be collected, leaf area measured, and plant parts fixed and/or dried for ground analysis. A second planting initiated 30 days before the arrival of a U.S. Shuttle [originally planned to be STS-71] is designed to provide green material at the vegetative development stage for ground analysis. [As this paper is being edited, the experiment has been delayed until after the arrival of STS-71.].

Non-NASA Center↗

NASA NTRS2020

Remote monitoring of agricultural systems using NDVI time series and machine learning methods: a tool for an adaptive agricultural policy

This study aims to provide accurate information about changes in agricultural systems (AS) using phenological metrics derived from the NDVI time series. Use of such information could help land managers optimize land use choices and monitor the status of agricultural lands, under a variety of environmental and socioeconomic conditions. For this purpose, the Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI data were used to derive phenological metrics over the Oum Er-Rbia basin (central Morocco). Random forest (RF), support vector machine (SVM), and K-nearest neighbor (KNN) classifiers were explored and compared on their ability to classify AS classes over the study area. Four main AS classes have been considered: (1) irrigated annual crop (IAC), (2) irrigated perennial crop (IPC), (3) rainfed area (RA), and (4) fallow (FA). By comparing the accuracy of the three classifiers, the RF method showed the best performance with an overall accuracy of 0.97 and kappa coefficient of 0.96.The RF method was then chosen to examine time variations in AS over a 16-year period (2000–2016). The AS main variations were detected and evaluated for the four AS classes. These variations have been found to be linked well with other indicators of local agricultural land management, as well as the historical agricultural drought changes over the study area. Overall, the results present a tool for decision makers to improve agricultural management and provide a different perspective in understanding the spatiotemporal dynamics of agricultural systems.

Youssef Lebrini↗

NASA NTRS1983

Remote sensing investigations of wetland biomass and productivity for global biosystems research

Monitoring biomass of wetlands ecosystems can provide information on net primary production and on the chemical and physical status of wetland soils relative to anaerobic microbial transformation of key elements. Multispectral remote sensing techniques successfully estimated macrophytic biomass in wetlands systems. Regression models developed from ground spectral data for predicting Spartina alterniflora biomass over an entire growing season include seasonal variations in biomass density and illumination intensity. An independent set of biomass and spectral data were collected and the standing crop biomass and net primary productivity were estimated. The improved spatial, radiometric and spectral resolution of th LANDSAT-4 Thematic Mapper over the LANDSAT MSS can greatly enhance multispectral techniques for estimating wetlands biomass over large areas. These techniques can provide the biomass data necessary for global ecology studies.

Harkisky, M.↗

NASA NTRS2003

Rapid Assessment of In Situ Wheat Straw Residue Via Remote Sensing Platforms

Crop residues influence near surface soil organic carbon content (SOC), impact our ability to remotely assess soil properties, and play a role in global carbon budgets. Methods that measure crop residues are laborious, and largely inappropriate for regional estimates. The objective of this study was to evaluate remote sensing (RS) data for rapid quantification of residue cover. In March 2000 and April 2001, residue plots (15 m x 15 m) were established in the Coastal Plain and Appalachian Plateau physiographic regions of Alabama. Treatments consisted of five wheat (Triticum aestivum L.) straw cover rates (0, 10, 20, 50, and 80%) replicated 3 times. Soil water content and residue decomposition were monitored. Spectral measurements were acquired via spectroradiometer (350 - 1050 nm), Airborne Terrestrial Applications Sensor (ATLAS) (400 - 12,500 nm), airborne color photography (400 - 600 nm), and IKONOS satellite (450 - 900 nm). Spectroradiometer data were acquired monthly, aircraft images yearly, and satellite per availability. Results showed all platforms successfully estimated residue cover variability using red, near infrared (NIR) and thermal infrared (TIR) regions of the spectrum. Airborne ATLAS imagery was best explaining as much as 98% of the variability in wheat straw cover. Spectroradiometer, color infrared photography, and IKONOS imagery accounted for 84, 56, and 24% of the variability, respectively.

Sullivan, D. G.↗

NASA NTRS2019

Leafy Greens Grown on the International Space Station May Provide a Nutritious Supplement to Astronauts' Diet

Supplemental safe food production has been an essential goal of NASA to meet the nutritional needs of astronauts on the International Space Station (ISS) as well as for future long duration missions to the moon and beyond. Food crops grown in space experience different environmental conditions than plants grown on Earth (i.e. microgravity and spaceflight physical sciences impacts). To test the growth methods and effects of the space environment, red romaine lettuce Lactuca sativa cv. 'Outredgeous', was grown in Veggie plant growth chambers on the ISS. Microbiological food safety of the plants grown on the ISS was determined by heterotrophic plate counts to assess total microbial load for bacteria and fungi as well as screening for specific pathogens and isolate identification. Molecular characterization was completed using Next Generation Sequencing (NGS) to provide valuable information on the taxonomic composition and community structure of the plant microbiome. Chemical analyses of plant tissue were conducted to understand spaceflight-induced changes in key elements in the space diet, phenolics, anthocyanin levels, and Oxygen radical absorbance capacity (ORAC), a measure of antioxidant capacity. Three growth tests of red romaine lettuce were completed on ISS, VEG-01A, VEG-01B, and VEG-03A. Plants were harvested using two harvest methods, either a single terminal harvest (after 33 days) or cut-and-come-again repetitive harvesting (64 days total growth). Ground controls were grown simultaneously with a delay to accommodate condition monitoring and replication. A comparison of the plant tissue returned to Earth showed leaves from the second grow-out had significantly higher bacterial counts than the preceding or subsequent growth test or any of the ground controls. Fungal counts were significantly higher on the final cut-and-come-again harvest of the third grow out. None of the potential foodborne pathogens that were screened for were detected. Bacterial and fungal isolate identification and community characterization indicated similar diversity between VEG-01A and VEG-01B growth tests, however, there appeared to be subtle differences in diversity and distribution among the three growth tests. Chemical analysis of plant tissue revealed significant variation in a few elemental data, but variation in levels of phenolics, anthocyanins, and ORAC was not significantly different. This study indicated that leafy vegetable crops could safely provide an edible supplement to astronauts' diet, and our analysis provided baseline data for continual operation of the Veggie plant growth units on ISS. This research was funded by NASA's space biology program.

Food Production↗

NASA NTRSDate not supplied

Enhancing Air Quality Applications in the Hindu Kush-Himalayan Region Using Satellite, Model, and Machine Learning Techniques

Air pollution in the Hindu Kush Himalayan (HKH) region of South Asia is a severe issue, as increases in emissions over the past two decades have degraded air quality (AQ) across the region, which poses major threats to human health, the ecosystem, climate, and agriculture. A diversity of anthropogenic and natural emission sources including transportation, power plants, industries, open biomass burning of crop residue, forest fires, cooking and heating fires, and dust storms contribute to unhealthy AQ and transboundary pollution issues in the region. Further complicating matters is the importance of meteorology and terrain on AQ, especially in the Kathmandu Valley where extreme haze episodes frequently develop from the atmospherically stable weather conditions during the winter monsoon. This study uses state-of-the-art satellite observations and modeling capabilities in conjunction with machine learning techniques to develop a comprehensive toolkit for enhancing AQ monitoring and forecasting in HKH. The toolkit incorporates new generation satellite observations from the TROPOspheric Monitoring Instrument (TROPOMI), Geostationary Environment Monitoring Spectrometer (GEMS), and Advanced Meteorological Imager (AMI), which provide unprecedented resolution on aerosols and trace gases, including nitrogen dioxide (NO 2 ), formaldehyde (CH2O), sulfur dioxide (SO 2 ), carbon monoxide (CO), and ozone (O 3 ), and aerosol optical depth (AOD). Value-added products [e.g., Particulate matter with diameters less than 2.5 micrometers (PM2.5)] are developed from the suite of satellite observations to further improve AQ monitoring capabilities in the region. The satellite products are also used to assimilate a high-resolution chemical transport model tailored for the HKH region, which is providing daily, 54-hour AQ forecasts with horizontal grid spacings of 12- and 4-km. This presentation will provide an overview of the suite of satellite- and model-based products in the AQ toolkit and application and performance of the toolkit for AQ monitoring and forecasting in HKH.

Air Quality↗

NASA NTRSDate not supplied

Enhancing Air Quality Applications in the Hindu Kush-Himalayan Region Using Satellite, Model, and Machine Learning Techniques

Air pollution in the Hindu Kush Himalayan (HKH) region of South Asia is a severe issue, as increases in emissions over the past two decades have degraded air quality (AQ) across the region, which poses major threats to human health, the ecosystem, climate, and agriculture. A diversity of anthropogenic and natural emission sources including transportation, power plants, industries, open biomass burning of crop residue, forest fires, cooking and heating fires, and dust storms contribute to unhealthy AQ and transboundary pollution issues in the region. Further complicating matters is the importance of meteorology and terrain on AQ, especially in the Kathmandu Valley where extreme haze episodes frequently develop from the atmospherically stable weather conditions during the winter monsoon. This study uses state-of-the-art satellite observations and modeling capabilities in conjunction with machine learning techniques to develop a comprehensive toolkit for enhancing AQ monitoring and forecasting in HKH. The toolkit incorporates new generation satellite observations from the TROPOspheric Monitoring Instrument (TROPOMI), Geostationary Environment Monitoring Spectrometer (GEMS), and Advanced Meteorological Imager (AMI), which provide unprecedented resolution on aerosols and trace gases, including nitrogen dioxide, formaldehyde, sulfur dioxide, carbon monoxide, and ozone, and aerosol optical depth. Value-added products, such as level 4 PM2.5 products, are developed from the suite of satellite observations to further improve AQ monitoring capabilities in the region. The satellite products are also used to assimilate a high-resolution chemical transport model tailored for the HKH region, which is providing daily, 54-hour AQ forecasts with horizontal grid spacings of 12- and 4-km. This presentation will provide an overview of the suite of satellite- and model-based products in the AQ toolkit and application and performance of the toolkit for AQ monitoring and forecasting in HKH.

Forecasting↗

NASA NTRS1984

A Study of the Relation Between Crop Growth and Spectra Reflectance Parameters

The relationship between crop growth and spectra reflectance parameters was studied. The dynamic behavior of canopy reflectance versus time and the relationship of trajectories to canopy dynamics, ontogeny and morphology was investigated. The determination of which transformations of spectral data best suited for observing key canopy characteristics, such as leaf area index and vegetation identification are understood. A physical basis to understand how various spectral transform trajectories depend on vegetation type and models that describe the time delayed interaction of reflectance in the chlorophyll and water absorption bands to monitor stress were formulated. The general process of physical understanding of canopy reflectance is evaluated and existing canopy models are improved.

Badhwar, G.↗

NASA NTRS2008

Feasibility of Estimating Relative Nutrient Contributions of Agriculture using MODIS Time Series

Around the Gulf of Mexico, high-input crops in several regions make a significant contribution to nutrient loading of small to medium estuaries and to the near-shore Gulf. Some crops cultivated near the coast include sorghum in Texas, rice in Texas and Louisiana, sugarcane in Florida and Louisiana, citrus orchards in Florida, pecan orchards in Mississippi and Alabama, and heavy sod and ornamental production around Mobile and Tampa Bay. In addition to crops, management of timberlands in proximity to the coasts also plays a role in nutrient loading. In the summer of 2008, a feasibility project is planned to explore the use of NASA data to enhance the spatial and temporal resolution of near-coast nutrient source information available to the coastal community. The purpose of this project is to demonstrate the viability of nutrient source information products applicable to small to medium watersheds surrounding the Gulf of Mexico. Conceptually, these products are intended to complement estuarine nutrient monitoring.

Ross, Kenton W.↗

NASA NTRS2010

Feasibility of Estimating Relative Nutrient Contributions of Agriculture and Forests Using MODIS Time Series

Ross, Kenton W.↗

NASA NTRS1977

Economic consequences of improved temperature forecasts: An experiment with the Florida citrus growers (control group results)

A demonstration experiment is being planned to show that frost and freeze prediction improvements are possible utilizing timely Synchronous Meteorological Satellite temperature measurements and that this information can affect Florida citrus grower operations and decisions. An economic experiment was carried out which will monitor citrus growers' decisions, actions, costs and losses, and meteorological forecasts and actual weather events and will establish the economic benefits of improved temperature forecasts. A summary is given of the economic experiment, the results obtained to date, and the work which still remains to be done. Specifically, the experiment design is described in detail as are the developed data collection methodology and procedures, sampling plan, data reduction techniques, cost and loss models, establishment of frost severity measures, data obtained from citrus growers, National Weather Service, and Federal Crop Insurance Corp., resulting protection costs and crop losses for the control group sample, extrapolation of results of control group to the Florida citrus industry and the method for normalization of these results to a normal or average frost season so that results may be compared with anticipated similar results from test group measurements.

Source record↗