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16 results for “spectral sensing”
Raw data: High-throughput screening of soybean di-nitrogen fixation and seed nitrogen content using spectral sensing
<p>Symbiotic di-nitrogen fixation of grain legumes has a substantial impact on crop performance, harvest product quality, and nitrogen (N) balance of crop rotations, particularly under organic management regimes. In soybean breeding, selection for increased nitrogen fixation is desirable for improving seed protein content and N balance of cropping systems. However, the lack of high-throughput screening methods for direct measurement of N 2 fixation rates prohibits practical breeding efforts. Therefore, hyperspectral canopy reflectance measurement as a field-based phenotyping method was evaluated in three environments for indirect estimation of N fixation and uptake of soil nitrogen in a set of early maturity soybean genotypes exhibiting a wide range in seed protein content. Reflectance spectra were collected in repeated measurements during flowering and early seed filling stages. Subsequently, various spectral reflectance indices (SRIs) were calculated for characterizing nitrogen accumulation of individual genotypes. Moreover, prediction models for seed protein content as an end-of-season target trait were developed utilizing full spectral information in partial-least-square regression (PLSR) models. A number of N-related SRIs calculated from spectral reflectance data recorded at the beginning of the seed filling stage were significantly correlated to seed protein content. The best prediction of seed protein content, however, was achieved in PLSR models (validation R 2 =0.805 across all three environments). Environments lower in initial soil mineral N content appeared as more favorable selection sites in terms of prediction accuracy, because N fixation is not masked by soil N uptake in such environments. Hyperspectral reflectance data proved to be a valuable method for determining genetic variation in crop N accumulation, which might be implemented in high-throughput screening protocols for N fixation in plant breeding programs.</p>
Miniaturized spectral sensing with a tunable optoelectronic interface
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Parcel level temporal variance of remotely sensed spectral reflectance predicts plant diversity
<p>Over the last two decades, considerable research has built on remote sensing of spectral diversity to assess plant diversity. The spectral variation hypothesis (SVH) proposes that spatial variation in reflectance data of an area is positively associated with plant diversity. While the SVH has exhibited validity in dense forests, it performs poorly in highly fragmented and temporally dynamic agricultural landscapes covered mainly by grasslands. Such underperformance can be attributed to the mosaic-like spatial structure of human-dominated landscapes with fields in varying phenological and management stages. Therefore, we argued for re-evaluating SVH's flawed window-based spatial analysis and underutilized temporal component. In particular, In particular, we captured the spatial and temporal variation in reflectance and assessed the relationships between spatial and temporal components of spectral diversity and plant diversity at the parcel level as a unit that relates to management patterns. Our investigation spanned three grasslands on two continents covering a wide spectrum of agricultural usage intensities. To calculate different components of spectral diversity, we used multi-temporal spaceborne Sentinel-2 data. We showed that plant diversity was negatively associated with the temporal component of spectral diversity across all sites. In contrast, the spatial component of spectral diversity was related to plant diversity in sites with larger parcels. Our findings highlighted that in agricultural landscapes, the temporal component of spectral diversity drives the spectral diversityplant diversity associations. Consequently, our results offer a novel perspective for remote sensing of plant diversity globally.</p>
Parcel level temporal variance of remotely sensed spectral reflectance predicts plant diversity
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Modelling Avian Habitat Suitability in Boreal Forest using Structural and Spectral Remote Sensing Data
<p>Data used in research regarding avian habitat suitability models in Harry's River Watershed in Newfoundland, Canada</p>
SpectralGPT: The first remote sensing foundation model customized for spectral data
<p>SpectralGPT is the first purpose-built foundation model designed explicitly for spectral RS data. It considers unique characteristics of spectral data, i.e., spatial-spectral coupling and spectral sequentiality, in the MAE framework with a simple yet effective 3D GPT network.</p> <p>We will gradually release the trained models (SpectralGPT, SpectralGPT+), the new benchmark dataset (SegMunich) for the downstream task of semantic segmentation, original code, and implementation instructions.</p>
Supplementary figures for the article 'Variability of remote sensing spectral indices in boreal lake basins'
<p><strong>Explanation of contents and notations:</strong></p> <p>AdivB Index correlations: correlations and p-values of the water quality parameters with the index family A/B, whole data and classification by basins and parameter values</p> <p>AmB Index correlations: correlations and p-values of the water quality parameters with the index family A-B, whole data and classification by basins and parameter values</p> <p>Band correlations: correlations and p-values of the water quality parameters with the band reflectances, whole data and classification by basins and parameter values</p> <p>Misc: Hiidenvesi map (basins and boat route), band wavelengths</p> <p>Mutual correlation of bands: correlations of bands with other bands, whole data and classification by basins</p> <p>Mutual correlation of parameters: correlations of water quality parameters with other water quality parameters, whole data and classification by basins</p> <p>Parameter profiles: parameter profiles of whole data, basins and parameter value classes (here e.g. Hist_30… means histogram with 30 bins)</p> <p>Scatter figures: scatter figures of various parameters vs indices</p> <p>Spectral reflectance signatures: spectral reflectance signatures of whole data, basins and parameter value classes (here e.g. Hist_30… means histogram with 30 bins)</p>
DISCOVER-AQ Colorado Deployment B200 Aircraft Remotely Sensed High Spectral Resolution Lidar (HSRL-2) Data
DISCOVERAQ_Colorado_AircraftRemoteSensing_B200_HSRL-2_Data contains remotely sensed data collected by the High Spectral Resolution Lidar-2 (HSRL-2) onboard NASA's B-200 aircraft during the Colorado (Denver) deployment of NASA's DISCOVER-AQ field study. This data product contains data for only the Colorado deployment and data collection is complete.Understanding the factors that contribute to near surface pollution is difficult using only satellite-based observations. The incorporation of surface-level measurements from aircraft and ground-based platforms provides the crucial information necessary to validate and expand upon the use of satellites in understanding near surface pollution. Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) was a four-year campaign conducted in collaboration between NASA Langley Research Center, NASA Goddard Space Flight Center, NASA Ames Research Center, and multiple universities to improve the use of satellites to monitor air quality for public health and environmental benefit. Through targeted airborne and ground-based observations, DISCOVER-AQ enabled more effective use of current and future satellites to diagnose ground level conditions influencing air quality.DISCOVER-AQ employed two NASA aircraft, the P-3B and King Air, with the P-3B completing in-situ spiral profiling of the atmosphere (aerosol properties, meteorological variables, and trace gas species). The King Air conducted both passive and active remote sensing of the atmospheric column extending below the aircraft to the surface. Data from an existing network of surface air quality monitors, AERONET sun photometers, Pandora UV/vis spectrometers and model simulations were also collected. Further, DISCOVER-AQ employed many surface monitoring sites, with measurements being made on the ground, in conjunction with the aircraft. The B200 and P-3B conducted flights in Baltimore-Washington, D.C. in 2011, Houston, TX in 2013, San Joaquin Valley, CA in 2013, and Denver, CO in 2014. These regions were targeted due to being in violation of the National Ambient Air Quality Standards (NAAQS).The first objective of DISCOVER-AQ was to determine and investigate correlations between surface measurements and satellite column observations for the trace gases ozone (O3), nitrogen dioxide (NO2), and formaldehyde (CH2O) to understand how satellite column observations can diagnose surface conditions. DISCOVER-AQ also had the objective of using surface-level measurements to understand how satellites measure diurnal variability and to understand what factors control diurnal variability. Lastly, DISCOVER-AQ aimed to explore horizontal scales of variability, such as regions with steep gradients and urban plumes.
DEVOTE UC-12 Aircraft Remotely Sensed High Spectral Resolution Lidar (HSRL) Data
DEVOTE_ AircraftRemoteSensing_UC12_HSRL_Data are remotely sensed data collected by the High Spectral Resolution Lidar (HSRL) onboard the UC-12 aircraft as part of the Development and Evaluation of satellite Validation Tools by Experimenters (DEVOTE) sub-orbital project. Data collection is complete.The Development and Evaluation of satellite Validation Tools by Experimenters (DEVOTE) project investigated aerosols and clouds with the specific goals of satellite validation and the improvement of satellite data retrieval algorithms. Conducted in September and October 2011, DEVOTE scientists collected measurements of aerosols and cloud optical and microphysical properties using airborne sensors over ground sites and along satellite overpasses to demonstrate the use of airborne platforms in future scientific measurement campaigns. These measurements were used to validate and improve satellite data retrieval algorithms from missions including the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) mission and the Aerosol, Cloud, Ecosystems (ACE) Decadal Survey mission.DEVOTE scientists conducted eleven science flights based at the NASA Langley Research Center throughout the campaign. The flight plans were specifically designed to coordinate with CALIPSO satellite overpasses and to fly over the Aerosol Robotic Network (AERONET) ground network sites. The DEVOTE sampling strategy required two aircraft dedicated to remote sensing and in-situ observations, which flew in coordinated flight patterns. This was implemented through use of the NASA UC-12 and the NASA B-200 airborne platforms. The UC-12 had the following remote sensing payload: the Research Scanning Polarimeter (RSP) and High Spectral Resolution Lidar (HSRL) instruments. The B-200 had an in-situ payload including the Polarized Imaging Nephelometer (PI-Neph), the Diode Laser Hygrometer (DLH), and Langley Aerosol Research Group Experiment (LARGE) instruments for aerosol microphysical and optical properties.DEVOTE was partly funded through the Hands-On Project Experience (HOPE) initiative. HOPE was a NASA development program designed to offer early career scientist opportunities to design, implement, and analyze small missions offering hands-on experience. Opportunities are increasingly limited for principal investigators, program managers, and system engineers to obtain mission life cycle training, and HOPE provides opportunities to those early on in their career or who are transitioning to a different field. Thus, DEVOTE had a focus on providing hands-on training in the mission life cycle to early career scientists in addition to its primary objective of using cloud and aerosol data collected from airborne sensors to validate and improve satellite data retrieval algorithms. Additionally, the information obtained from DEVOTE research was used to prepare for the implementation of ACE.
ACEPOL High Spectral Resolution Lidar 2 (HSRL-2) Remotely Sensed Data
ACEPOL High Spectral Resolution Lidar 2 (HSRL-2) Remotely Sensed Data (ACEPOL_AircraftRemoteSensing_HSRL2_Data) are remotely sensed measurements collected by the High-Spectral Resolution Lidar (HSRL-2) onboard the ER-2 during the Aerosol Characterization from Polarimeter and Lidar (ACEPOL) campaign. In order to improve our understanding of the effect of aerosols on climate and air quality, measurements of aerosol chemical composition, size distribution, height profile, and optical properties are of crucial importance. In terms of remotely sensed instrumentation, the most extensive set of aerosol properties can be obtained by combining passive multi-angle, multi-spectral measurements of intensity and polarization with active measurements performed by a High Spectral Resolution Lidar. During Fall 2017, the Aerosol Characterization from Polarimeter and Lidar (ACEPOL) campaign, jointly sponsored by NASA and the Netherlands Institute for Space Research (SRON), performed aerosol and cloud measurements over the United States from the NASA high altitude ER-2 aircraft. Six instruments were deployed on the aircraft. Four of these instruments were multi-angle polarimeters: the Airborne Hyper Angular Rainbow Polarimeter (AirHARP), the Airborne Multiangle SpectroPolarimetric Imager (AirMSPI), the Airborne Spectrometer for Planetary Exploration (SPEX Airborne) and the Research Scanning Polarimeter (RSP). The other two instruments were lidars: the High Spectral Resolution Lidar 2 (HSRL-2) and the Cloud Physics Lidar (CPL). The ACEPOL operation was based at NASA’s Armstrong Flight Research Center in Palmdale California, which enabled observations of a wide variety of scene types, including urban, desert, forest, coastal ocean and agricultural areas, with clear, cloudy, polluted and pristine atmospheric conditions. The primary goal of ACEPOL was to assess the capabilities of the different polarimeters for retrieval of aerosol and cloud microphysical and optical parameters, as well as their capabilities to derive aerosol layer height (near-UV polarimetry, O2 A-band). ACEPOL also focused on the development and evaluation of aerosol retrieval algorithms that combine data from both active (lidar) and passive (polarimeter) instruments. ACEPOL data are appropriate for algorithm development and testing, instrument intercomparison, and investigations of active and passive instrument data fusion, which make them valuable resources for remote sensing communities as they prepare for the next generation of spaceborne MAP and lidar missions.
TRACER-AQ JSC G-V Aircraft Remotely Sensed High Spectral Resolution Lidar-2 (HSRL-2) Data
TRACERAQ_AircraftRemoteSensing_GV_HSRL2_Data is the remotely sensed High Spectral Resolution Lidar-2 (HSRL-2) data collected onboard the Johnson Space Center (JSC) Gulfstream V (G-V) aircraft during the TRacking Aerosol Convection ExpeRiment – Air Quality (TRACER-AQ) field study. Data collection is complete.The TRacking Aerosol Convection ExpeRiment – Air Quality (TRACER-AQ) campaign is a field study co-sponsored by NASA and TCEQ (Texas Commission on Environmental Quality), with partners from DOE (Department of Energy) TRacking Aerosol Convection ExpeRiment (TRACER), and several academic institutions. This synergistic effort aims to gain an updated understanding in photochemistry and meteorological impact on ozone formation in the Houston region, particularly around the Houston Ship Channel, Galveston Bay, and the Gulf of America; and provide observations for evaluating air quality models and satellite observations.The primary TRACER-AQ field observations period lasted from mid-August to late September 2021, coinciding with the peak ozone season in East Texas, with a second deployment in summer 2022 with a subset of ground-based assets. The observing system includes airborne remote sensing, mobile (boat/vehicle) laboratories, and stationary ground-based assets.The airborne component was based on the NASA Gulfstream V aircraft instrumented with GCAS (GEOCAPE Airborne Simulator) for making measurements of column NO2 and HCHO as well as a lidar system, HSRL-2 (High Spectral Resolution Lidar-2), to measure O3 and aerosol vertical profiles over the course of 12 flight days. Ground-based assets include ground-based ozone lidars from the Tropospheric Ozone Lidar Network (TOLNet), ceilometers, Pandora spectrometers, AErosol RObotic NETwork (AERONET) remote sensors, ozonesondes, and stationary and mobile laboratories of in situ air quality and meteorological observations. This coordinated observing system provides updated or unseen perspectives in spatial and temporal distribution of the key photochemical species and atmospheric structure information, particularly with a focus on the temporal evolution of observations throughout the daytime in preparation for upcoming geostationary satellite air quality observations.
FIREX-AQ DC-8 High-Spectral Resolution Lidar (HSRL) Remotely Sensed Data
FIREXAQ_HSRL_AircraftRemoteSensing_DC8_Data are remotely sensed data collected by the High-Spectral Resolution Lidar (HSRL) onboard the DC-8 aircraft during FIREX-AQ. Data collection for this product is complete.Completed during summer 2019, FIREX-AQ utilized a combination of instrumented airplanes, satellites, and ground-based instrumentation. Detailed fire plume sampling was carried out by the NASA DC-8 aircraft, which had a comprehensive instrument payload capable of measuring over 200 trace gas species, as well as aerosol microphysical, optical, and chemical properties. The DC-8 aircraft completed 23 science flights, including 15 flights from Boise, Idaho and 8 flights from Salina, Kansas. NASA’s ER-2 completed 11 flights, partially in support of the FIREX-AQ effort. The ER-2 payload was made up of 8 satellite analog instruments and provided critical fire information, including fire temperature, fire plume heights, and vegetation/soil albedo information. NOAA provided the NOAA-CHEM Twin Otter and the NOAA-MET Twin Otter aircraft to measure chemical processing in the lofted plumes of Western wildfires. The NOAA-CHEM Twin Otter focused on nighttime plume chemistry, from which data is archived at the NASA Atmospheric Science Data Center (ASDC). The NOAA-MET Twin Otter collected measurements of air movements at fire boundaries with the goal of understanding the local weather impacts of fires and the movement patterns of fires. NOAA-MET Twin Otter data will be archived at the ASDC in the future. Additionally, a ground-based station in McCall, Idaho and several mobile laboratories provided in-situ measurements of aerosol microphysical and optical properties, aerosol chemical compositions, and trace gas species. The Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) campaign was a NOAA/NASA interagency intensive study of North American fires to gain an understanding on the integrated impact of the fire emissions on the tropospheric chemistry and composition and to assess the satellite’s capability for detecting fires and estimating fire emissions. The overarching goal of FIREX-AQ was to provide measurements of trace gas and aerosol emissions for wildfires and prescribed fires in great detail, relate them to fuel and fire conditions at the point of emission, characterize the conditions relating to plume rise, and follow plumes downwind to understand chemical transformation and air quality impacts.
DISCOVER-AQ Maryland Deployment UC-12 Aircraft Remotely Sensed High Spectral Resolution Lidar (HSRL) Data
DISCOVERAQ_Maryland_AircraftRemoteSensing_UC12_HSRL_Data contains remotely sensed data collected by the High Spectral Resolution Lidar (HSRL) onboard NASA's UC-12 aircraft during the Maryland deployment of NASA's DISCOVER-AQ field study. This data product contains data for only the Maryland deployment and data collection is complete.Understanding the factors that contribute to near surface pollution is difficult using only satellite-based observations. The incorporation of surface-level measurements from aircraft and ground-based platforms provides the crucial information necessary to validate and expand upon the use of satellites in understanding near surface pollution. Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) was a four-year campaign conducted in collaboration between NASA Langley Research Center, NASA Goddard Space Flight Center, NASA Ames Research Center, and multiple universities to improve the use of satellites to monitor air quality for public health and environmental benefit. Through targeted airborne and ground-based observations, DISCOVER-AQ enabled more effective use of current and future satellites to diagnose ground level conditions influencing air quality.DISCOVER-AQ employed two NASA aircraft, the P-3B and King Air, with the P-3B completing in-situ spiral profiling of the atmosphere (aerosol properties, meteorological variables, and trace gas species). The King Air conducted both passive and active remote sensing of the atmospheric column extending below the aircraft to the surface. Data from an existing network of surface air quality monitors, AERONET sun photometers, Pandora UV/vis spectrometers and model simulations were also collected. Further, DISCOVER-AQ employed many surface monitoring sites, with measurements being made on the ground, in conjunction with the aircraft. The B200 and P-3B conducted flights in Baltimore-Washington, D.C. in 2011, Houston, TX in 2013, San Joaquin Valley, CA in 2013, and Denver, CO in 2014. These regions were targeted due to being in violation of the National Ambient Air Quality Standards (NAAQS).The first objective of DISCOVER-AQ was to determine and investigate correlations between surface measurements and satellite column observations for the trace gases ozone (O3), nitrogen dioxide (NO2), and formaldehyde (CH2O) to understand how satellite column observations can diagnose surface conditions. DISCOVER-AQ also had the objective of using surface-level measurements to understand how satellites measure diurnal variability and to understand what factors control diurnal variability. Lastly, DISCOVER-AQ aimed to explore horizontal scales of variability, such as regions with steep gradients and urban plumes.
DISCOVER-AQ Texas Deployment B200 Aircraft Remotely Sensed High Spectral Resolution Lidar (HSRL-2) Data
DISCOVERAQ_Texas_AircraftRemoteSensing_B200_HSRL2_Data contains remotely sensed data collected by the High Spectral Resolution Lidar (HSRL-2) onboard NASA's UC-12 aircraft during the Texas (Houston) deployment of NASA's DISCOVER-AQ field study. This data product contains data for only the Texas deployment and data collection is complete.Understanding the factors that contribute to near surface pollution is difficult using only satellite-based observations. The incorporation of surface-level measurements from aircraft and ground-based platforms provides the crucial information necessary to validate and expand upon the use of satellites in understanding near surface pollution. Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) was a four-year campaign conducted in collaboration between NASA Langley Research Center, NASA Goddard Space Flight Center, NASA Ames Research Center, and multiple universities to improve the use of satellites to monitor air quality for public health and environmental benefit. Through targeted airborne and ground-based observations, DISCOVER-AQ enabled more effective use of current and future satellites to diagnose ground level conditions influencing air quality.DISCOVER-AQ employed two NASA aircraft, the P-3B and King Air, with the P-3B completing in-situ spiral profiling of the atmosphere (aerosol properties, meteorological variables, and trace gas species). The King Air conducted both passive and active remote sensing of the atmospheric column extending below the aircraft to the surface. Data from an existing network of surface air quality monitors, AERONET sun photometers, Pandora UV/vis spectrometers and model simulations were also collected. Further, DISCOVER-AQ employed many surface monitoring sites, with measurements being made on the ground, in conjunction with the aircraft. The B200 and P-3B conducted flights in Baltimore-Washington, D.C. in 2011, Houston, TX in 2013, San Joaquin Valley, CA in 2013, and Denver, CO in 2014. These regions were targeted due to being in violation of the National Ambient Air Quality Standards (NAAQS).The first objective of DISCOVER-AQ was to determine and investigate correlations between surface measurements and satellite column observations for the trace gases ozone (O3), nitrogen dioxide (NO2), and formaldehyde (CH2O) to understand how satellite column observations can diagnose surface conditions. DISCOVER-AQ also had the objective of using surface-level measurements to understand how satellites measure diurnal variability and to understand what factors control diurnal variability. Lastly, DISCOVER-AQ aimed to explore horizontal scales of variability, such as regions with steep gradients and urban plumes.
DISCOVER-AQ California Deployment B-200 Aircraft Remotely Sensed High Spectral Resolution Lidar (HSRL-2) Data
DISCOVERAQ_California_AircraftRemoteSensing_B200_HSRL_Data contains remotely sensed data collected by the High Spectral Resolution Lidar (HSRL-2) onboard NASA's B-200 aircraft during the California (San Joaquin Valley) deployment of NASA's DISCOVER-AQ field study. This data product contains data for only the Maryland deployment and data collection is complete.Understanding the factors that contribute to near surface pollution is difficult using only satellite-based observations. The incorporation of surface-level measurements from aircraft and ground-based platforms provides the crucial information necessary to validate and expand upon the use of satellites in understanding near surface pollution. Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) was a four-year campaign conducted in collaboration between NASA Langley Research Center, NASA Goddard Space Flight Center, NASA Ames Research Center, and multiple universities to improve the use of satellites to monitor air quality for public health and environmental benefit. Through targeted airborne and ground-based observations, DISCOVER-AQ enabled more effective use of current and future satellites to diagnose ground level conditions influencing air quality.DISCOVER-AQ employed two NASA aircraft, the P-3B and King Air, with the P-3B completing in-situ spiral profiling of the atmosphere (aerosol properties, meteorological variables, and trace gas species). The King Air conducted both passive and active remote sensing of the atmospheric column extending below the aircraft to the surface. Data from an existing network of surface air quality monitors, AERONET sun photometers, Pandora UV/vis spectrometers and model simulations were also collected. Further, DISCOVER-AQ employed many surface monitoring sites, with measurements being made on the ground, in conjunction with the aircraft. The B200 and P-3B conducted flights in Baltimore-Washington, D.C. in 2011, Houston, TX in 2013, San Joaquin Valley, CA in 2013, and Denver, CO in 2014. These regions were targeted due to being in violation of the National Ambient Air Quality Standards (NAAQS).The first objective of DISCOVER-AQ was to determine and investigate correlations between surface measurements and satellite column observations for the trace gases ozone (O3), nitrogen dioxide (NO2), and formaldehyde (CH2O) to understand how satellite column observations can diagnose surface conditions. DISCOVER-AQ also had the objective of using surface-level measurements to understand how satellites measure diurnal variability and to understand what factors control diurnal variability. Lastly, DISCOVER-AQ aimed to explore horizontal scales of variability, such as regions with steep gradients and urban plumes.
Remote sensing spectral vegetation indices for 2011 NWCA wetland sites
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