An introduction to the NASA Hyperspectral InfraRed Imager (HyspIRI) mission and preparatory activities

Lee, Christine M.; Cable, Morgan L.; Hook, Simon J.; Green, Robert O.; Ustin, Susan L.; Mandl, Daniel; Middleton, Elizabeth M.

doi:10.1016/j.rse.2015.06.012

Cited by 288 publications

(185 citation statements)

References 117 publications

Supporting

Mentioning

182

Contrasting

Unclassified

Order By: Relevance

“…Hyperspectral imagery for the ~30,000 km 2 study area was from NASA's Airborne Visible Infrared Imaging Spectrometer (AVIRIS) "Classic" sensor, flown in spring, summer and fall of year 2015 [1]. The AVIRIS-C sensor images spectral radiance in 224 bands from 370 nm (visible) to 2500 nm (shortwave infrared, SWIR) with 10 nm sampling and SNR of >1000:1 at 600 nm and >400:1 at 2200 nm [28,29].…”

Section: Simulated Hyspiri Imagerymentioning

confidence: 99%

“…Complete spring and fall runs were used while different dates were used to make a complete, cloud-free summer dataset (Table 1, Figure 1). Hyperspectral images from AVIRIC-C were used to simulate HyspIRI as part of a preparatory science campaign [1,6]. The current configuration of HyspIRI is a satellite sensor with 30-m spatial resolution, 185 km swath width, and 16-day repeat global coverage [1].…”

Section: Simulated Hyspiri Imagerymentioning

confidence: 99%

“…Hyperspectral images from AVIRIC-C were used to simulate HyspIRI as part of a preparatory science campaign [1,6]. The current configuration of HyspIRI is a satellite sensor with 30-m spatial resolution, 185 km swath width, and 16-day repeat global coverage [1]. The measurements would cover 380 to 2510 nm in ≤10-nm contiguous bands (~214 bands).…”

Section: Simulated Hyspiri Imagerymentioning

confidence: 99%

“…Hyperspectral, or imaging spectroscopy, data consist of hundreds of spectral bands, and capture more spectral detail and variability relative to conventional multispectral sensors used for mapping land cover. Terrestrial hyperspectral applications have shown success in mapping composition, physiology, and biochemistry of vegetation, ecosystem disturbance, and built-up environments [1]. The analysis of hyperspectral data presents issues in classification, due to large data volumes, and increased spectral variability as recorded by hundreds of correlated bands [2].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

One-Dimensional Convolutional Neural Network Land-Cover Classification of Multi-Seasonal Hyperspectral Imagery in the San Francisco Bay Area, California

Guidici

Clark

2017

Remote Sensing

View full text Add to dashboard Cite

Abstract:In this study, a 1-D Convolutional Neural Network (CNN) architecture was developed, trained and utilized to classify single (summer) and three seasons (spring, summer, fall) of hyperspectral imagery over the San Francisco Bay Area, California for the year 2015. For comparison, the Random Forests (RF) and Support Vector Machine (SVM) classifiers were trained and tested with the same data. In order to support space-based hyperspectral applications, all analyses were performed with simulated Hyperspectral Infrared Imager (HyspIRI) imagery. Three-season data improved classifier overall accuracy by 2.0% (SVM), 1.9% (CNN) to 3.5% (RF) over single-season data. The three-season CNN provided an overall classification accuracy of 89.9%, which was comparable to overall accuracy of 89.5% for SVM. Both three-season CNN and SVM outperformed RF by over 7% overall accuracy. Analysis and visualization of the inner products for the CNN provided insight to distinctive features within the spectral-temporal domain. A method for CNN kernel tuning was presented to assess the importance of learned features. We concluded that CNN is a promising candidate for hyperspectral remote sensing applications because of the high classification accuracy and interpretability of its inner products.

show abstract

Section: Simulated Hyspiri Imagerymentioning

confidence: 99%

Section: Simulated Hyspiri Imagerymentioning

confidence: 99%

Section: Simulated Hyspiri Imagerymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

One-Dimensional Convolutional Neural Network Land-Cover Classification of Multi-Seasonal Hyperspectral Imagery in the San Francisco Bay Area, California

Guidici

Clark

2017

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…The proposed NASA Hyperspectral InfraRed Imager (HyspIRI) mission for example will provide global wall-to-wall coverage of thermal and hyperspectral imagery, meeting shortcomings of precursor spaceborne imagers such as Landsat and ASTER (Abrams and Hook, 2013). HyspIRI will provide an imaging spectrometer (380 nm-2500 nm) with a 16-day revisit and an eight band multispectral thermal imager with a 5-day revisit, both at 30-m spatial resolution (Lee et al, 2015). The HyspIRI capabilities provide information that is specifically useful for natural disaster studies and vegetation monitoring.…”

Section: Advancementsmentioning

confidence: 99%

Food, water, and fault lines: Remote sensing opportunities for earthquake-response management of agricultural water

Rodriguez

Ustin

Sandoval-Solís

et al. 2016

Science of The Total Environment

View full text Add to dashboard Cite

Earthquakes often cause destructive and unpredictable changes that can affect local hydrology (e.g. groundwater elevation or reduction) and thus disrupt land uses and human activities. Prolific agricultural regions overlie seismically active areas, emphasizing the importance to improve our understanding and monitoring of hydrologic and agricultural systems following a seismic event. A thorough data collection is necessary for adequate postearthquake crop management response; however, the large spatial extent of earthquake's impact makes challenging the collection of robust data sets for identifying locations and magnitude of these impacts. Observing hydrologic responses to earthquakes is not a novel concept, yet there is a lack of methods and tools for assessing earthquake's impacts upon the regional hydrology and agricultural systems. The objective of this paper is to describe how remote sensing imagery, methods and tools allow detecting crop responses and damage incurred after earthquakes because a change in the regional hydrology. Many remote sensing datasets are long archived with extensive coverage and with well-documented methods to assess plant-water relations. We thus connect remote sensing of plant water relations to its utility in agriculture using a post-earthquake agrohydrologic remote sensing (PEARS) framework; specifically in agro-hydrologic relationships associated with recent earthquake events that will lead to improved water management.© 2016 Elsevier B.V. All rights reserved. Keywords:Agro-hydrology Disaster management Monitoring Plant-water relations Post-earthquake agrohydrologic remote sensing (PEARS) Remote sensing Background, scope & needEarthquake events threaten food security, resource management and human life, and are observed to be steadily increasing (Ellsworth, 2013). As observed earthquake events continue to increase, it is important to explore and improve response and recovery strategies. Infrastructural damages, especially in urban environments, are at the forefront of research in remote sensing of earthquake-associated damage. Agricultural environments also face catastrophic impacts, often due to adverse hydrologic behavior following the event. The earthquake-water linkage poses particular threats to agricultural productivity, yet difficulty lies in predicting the location, destructiveness, and extent of damage. While effects of earthquake-induced hydrologic changes on crops remain largely unexplored, remote sensing of crop water relations provide a suite of tools to monitor responses and to mitigate crop loss. Remote sensing can address these challenges with archived imagery that has extensive spatial coverage. There are conceptual models that explicitly walk through remote sensing in disaster management (Joyce et al., 2009b) and remote sensing of post-earthquake urban damage (Eguchi et al., 2003) -leaving a gap at the interface of post-earthquake remote sensing of agricultural impacts. We therefore draw attention to current research in understanding earthquake impacts on agric...

show abstract

Measuring cloud thermodynamic phase with shortwave infrared imaging spectroscopy

et al. 2016

Self Cite

View full text Add to dashboard Cite

Shortwave Infrared imaging spectroscopy enables accurate remote mapping of cloud thermodynamic phase at high spatial resolution. We describe a measurement strategy to exploit signatures of liquid and ice absorption in cloud top apparent reflectance spectra from 1.4 to 1.8 μm. This signal is generally insensitive to confounding factors such as solar angles, view angles, and surface albedo. We first evaluate the approach in simulation and then apply it to airborne data acquired in the Calwater‐2/ACAPEX campaign of Winter 2015. Here NASA's “Classic” Airborne Visible Infrared Imaging Spectrometer (AVIRIS‐C) remotely observed diverse cloud formations while the U.S. Department of Energy ARM Aerial Facility G‐1 aircraft measured cloud integral and microphysical properties in situ. The coincident measurements demonstrate good separation of the thermodynamic phases for relatively homogeneous clouds.

show abstract

An introduction to the NASA Hyperspectral InfraRed Imager (HyspIRI) mission and preparatory activities

Cited by 288 publications

References 117 publications

One-Dimensional Convolutional Neural Network Land-Cover Classification of Multi-Seasonal Hyperspectral Imagery in the San Francisco Bay Area, California

One-Dimensional Convolutional Neural Network Land-Cover Classification of Multi-Seasonal Hyperspectral Imagery in the San Francisco Bay Area, California

Food, water, and fault lines: Remote sensing opportunities for earthquake-response management of agricultural water

Measuring cloud thermodynamic phase with shortwave infrared imaging spectroscopy

Contact Info

Product

Resources

About