Hyperspectral Infrared Research Articles

Deep convective cloud characterizations from both broadband imager and hyperspectral infrared sounder measurements

AbstractDeep convective storms have contributed to airplane accidents, making them a threat to aviation safety. The most common method to identify deep convective clouds (DCCs) is using the brightness temperature difference (BTD) between the atmospheric infrared (IR) window band and the water vapor (WV) absorption band. The effectiveness of the BTD method for DCC detection is highly related to the spectral resolution and signal‐to‐noise ratio (SNR) of the WV band. In order to understand the sensitivity of BTD to spectral resolution and SNR for DCC detection, a BTD to noise ratio method using the difference between the WV and IR window radiances is developed to assess the uncertainty of DCC identification for different instruments. We examined the case of AirAsia Flight QZ8501. The brightness temperatures (Tbs) over DCCs from this case are simulated for BTD sensitivity studies by a fast forward radiative transfer model with an opaque cloud assumption for both broadband imager (e.g., Multifunction Transport Satellite imager, MTSAT‐2 imager) and hyperspectral IR sounder (e.g., Atmospheric Infrared Sounder) instruments; we also examined the relationship between the simulated Tb and the cloud top height. Results show that despite the coarser spatial resolution, BTDs measured by a hyperspectral IR sounder are much more sensitive to high cloud tops than broadband BTDs. As demonstrated in this study, a hyperspectral IR sounder can identify DCCs with better accuracy.

Regionalization of Uncovered Agricultural Soils Based on Organic Carbon and Soil Texture Estimations

The determination of soil texture and organic carbon across agricultural areas provides important information to derive soil condition. Precise digital soil maps can help to till agricultural fields with more accuracy, greater cost-efficiency and better environmental protection. In the present study, the laboratory analysis of sand, silt, clay and soil organic carbon (SOC) content was combined with hyperspectral image data to estimate the distribution of soil texture and SOC across an agricultural area. The aim was to identify regions with similar soil properties and derive uniform soil regions based on this information. Soil parameter data and corresponding laboratory spectra were used to calibrate cross-validated (leave-one-out) partial least squares regression (PLSR) models, resulting in robust models for sand (R2 = 0.77, root-mean-square error (RMSE) = 5.37) and SOC (R2 = 0.89, RMSE = 0.27), as well as moderate models for silt (R2 = 0.62, RMSE = 5.46) and clay (R2 = 0.53, RMSE = 2.39). The regression models were applied to Airborne Imaging Spectrometer for Applications DUAL (aisaDUAL) hyperspectral image data to spatially estimate the concentration of these parameters. Afterwards, a decision tree, based on the Food and Agriculture Organization (FAO) soil texture classification scheme, was developed to determine the soil texture for each pixel of the hyperspectral airborne data. These soil texture regions were further refined with the spatial SOC estimations. The developed method is useful to identify spatial regions with similar soil properties, which can provide a vital information source for an adapted treatment of agricultural fields in terms of the necessary amount of fertilizers or water. The approach can also be adapted to wider regions with a larger sample size to create detailed digital soil maps (DSMs). Further, the presented method should be applied to future hyperspectral satellite missions like Environmental Mapping and Analysis Program (EnMap) and Hyperspectral Infrared Imager (HyspIRI) to cover larger areas in shorter time intervals. Updated DSMs on a regular basis could particularly support precision farming aspects.

Linking seasonal foliar traits to VSWIR-TIR spectroscopy across California ecosystems

Abstract Vegetation traits provide critical information on ecosystem function that can be used to assess the effects of disturbance, land use, and climate change. Recent studies have demonstrated the use of spectroscopy to predict vegetation traits accurately and efficiently. To date, most spectroscopic studies have utilized data from the Visible Short Wave Infrared spectrum (VSWIR) or, occasionally, the Thermal Infrared spectrum (TIR), but not in combination. This study focuses on VSWIR and TIR synergy to evaluate the ability to predict leaf level cellulose, lignin, leaf mass per area (LMA), nitrogen, and water content across seasons. We used fresh leaves from sixteen common California shrub and tree species collected in the 2013 spring, summer, and fall seasons. The 284 samples exhibited a wide range of leaf traits as determined by standard analytical procedures: 4.2–27.3% for cellulose, 2.6–22.5% for lignin, 34.7–388.9 g/m 2 for LMA, 0.45–3.81% for nitrogen, and 20.2–76.9% for water content. For each leaf trait, partial least squares regression (PLSR) models were fit using different portions of the spectrum: VSWIR (0.35–2.5 μm), TIR (2.5–15.4 μm), and Full spectrum (0.35–15.4 μm). We also fit PLSR models using spectra resampled to simulate three airborne sensors: the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS; 0.4–2.5 μm), the Hyperspectral Thermal Emission Spectrometer (HyTES; 7.5–12 μm), and the Hyperspectral InfraRed Imager (HyspIRI; 0.4–12 μm). The majority of best performing models used the Full spectrum, demonstrating the value of combining TIR and VSWIR spectra for leaf trait prediction. Sensor-simulated PLSR models created with the entire data set yielded validation R 2 and root mean square error of prediction (RMSEP) values as follows: R 2 = 0.70 and RMSEP = 13.1% for cellulose, R 2 = 0.50 and RMSEP = 17.7% for lignin, R 2 = 0.56 and RMSEP = 18.3% for LMA, R 2 = 0.56 and RMSEP = 18.1% for nitrogen, and R 2 = 0.89 and RMSEP = 5.7% for water content. General models successfully captured the variability among all seasons and leaf forms for cellulose and water content, while the other leaf traits were better modeled with season or leaf form-specific models. This study successfully captured the large seasonal and geographical variation in leaf traits across California's diverse ecosystems, supporting the possibility of using HyspIRI's imagery for global mapping efforts of these traits.

Discriminating Rangeland Management Practices Using Simulated HyspIRI, Landsat 8 OLI, Sentinel 2 MSI, and VENµS Spectral Data

This study sought to spectrally discriminate grasses grown under different management practices (i.e., mowing, grazing, fertilizer application, and burning), using field spectrometer data resampled to Hyperspectral Infrared Imager (HyspIRI), Landsat 8 Operational Land Imager (OLI), Sentinel 2 Multispectral Instrument (MSI), and Vegetation and Environment monitoring on a new MicroSatellite (Venμs). The study is inspired by the long standing challenge of lack of suitable satellite data with high temporal, spectral, and spatial resolutions for rangelands monitoring. Specifically, this study spectrally discriminated grasses grown under 1) different rangeland management practices as well as 2) different levels of application of each practice. Results of this study show that the spectral setup of HyspIRI, Sentinel 2 MSI, Venus, and Landsat 8 OLI yielded high accuracies of up to 92%, 82%, 83%, and 75% overall accuracy, respectively, in discriminating grass grown under different rangeland management practices. The high classification accuracies were exhibited by the use of vegetation indices and wavebands located in the red edge (HyspIRI: 700, 740, and 780 nm and Sentinel 2 MSI: bands 5, 6, and 7 8a) and NIR (HyspIRI: 700, 740, and 780 nm and Sentinel 2 MSI: band 8a) spectra, respectively. Results of this study illustrate that although simulated Sentinel 2 MSI data yields lower classification accuracies when compared to HyspIRI, it offers better classification accuracies with high agreements between training and testing datasets when compared to the HyspIRI data. Overall, the findings of this study underscore the potential of upcoming satellite missions in ensuring informed rangeland monitoring and management applications.

Towards an Improved LAI Collection Protocol via Simulated and Field-Based PAR Sensing.

In support of NASA’s next-generation spectrometer—the Hyperspectral Infrared Imager (HyspIRI)—we are working towards assessing sub-pixel vegetation structure from imaging spectroscopy data. Of particular interest is Leaf Area Index (LAI), which is an informative, yet notoriously challenging parameter to efficiently measure in situ. While photosynthetically-active radiation (PAR) sensors have been validated for measuring crop LAI, there is limited literature on the efficacy of PAR-based LAI measurement in the forest environment. This study (i) validates PAR-based LAI measurement in forest environments, and (ii) proposes a suitable collection protocol, which balances efficiency with measurement variation, e.g., due to sun flecks and various-sized canopy gaps. A synthetic PAR sensor model was developed in the Digital Imaging and Remote Sensing Image Generation (DIRSIG) model and used to validate LAI measurement based on first-principles and explicitly-known leaf geometry. Simulated collection parameters were adjusted to empirically identify optimal collection protocols. These collection protocols were then validated in the field by correlating PAR-based LAI measurement to the normalized difference vegetation index (NDVI) extracted from the “classic” Airborne Visible Infrared Imaging Spectrometer (AVIRIS-C) data ( was 0.61). The results indicate that our proposed collecting protocol is suitable for measuring the LAI of sparse forest (LAI < 3–5 ()).

Shortwave Infrared Hyperspectral Imaging: A Novel Method For Enhancing the Visibility of Sedimentary And Biogenic Features In Oil-Saturated Core

A common challenge of logging core from heavy-oil reservoirs is that sedimentary and biogenic features are difficult to see in fine-grained, well-sorted, and oil-saturated strata. In this study, hyperspectral imaging is shown to be an effective method for enhancing the visibility of sedimentary fabric and trace fossils in oil-saturated core. Shortwave infrared (SWIR) hyperspectral imagery of Lower Cretaceous McMurray Formation oil-sands core from northeastern Alberta, Canada, was investigated at various wavelengths and resolutions. Color composite imagery consisting of wavelength bands at 2162, 2199, and 2349 nm in the red, green, and blue channels, respectively, dramatically enhanced the clarity of sedimentary features in structureless-appearing oil sand. In many cases, spectral imagery revealed features that are completely invisible to the unaided eye. In coarser-grained sections (fine to coarse sand), the enhanced contrast of sedimentological features is attributed predominantly to variability in grain size and bitumen saturation. In finer-grained sections (very fine to fine sand), enhanced contrast is mainly ascribed to variability in relative abundance of clays. A spatial resolution of at least 0.25 mm/pixel is required for imaging trace fossils, while lower resolutions (1.2–1.5 mm/pixel) are sufficient for enhancing the visibility of most sedimentary structures.

Improvements in Land-Use Classification for Estimating Daytime Surface Temperatures and Sea-Breeze Flows in Southern California

Abstract Understanding the interactions between large-scale atmospheric and oceanic circulation patterns and changes in land cover and land use (LCLU) due to urbanization is a relevant subject in many coastal climates. Recent studies by Lebassi et al. found that the average maximum air temperatures during the summer in two populated California coastal areas decreased at low-elevation areas open to marine air penetration during the period of 1970–2005. This coastal cooling was attributed to an increase in sea-breeze activity. The aims of this work are to better understand the coastal flow patterns and sea–land thermal gradient by improving the land-cover classification scheme in the region using updated airborne remote sensing data and to assess the suitability of the updated regional atmospheric modeling system for representing maritime flows in this region. This study uses high-resolution airborne data from the NASA Hyperspectral Infrared Imager (HyspIRI) mission preparatory flight campaign over Southern California and surface ground stations to compare observations against model estimations. Five new urban land classes were created using broadband albedo derived from the Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) sensor and then assimilated into the Weather Research and Forecasting (WRF) Model. The updated model captures the diurnal spatial and temporal sea-breeze patterns in the region. Results show notable improvements of simulated daytime surface temperature and coastal winds using the HyspIRI-derived products in the model against the default land classification, reaffirming the importance of accounting for heterogeneity of urban surface properties.

Onboard Autonomy on the Intelligent Payload Experiment CubeSat Mission

This article is a part of the Special Issue on Intelligent Systems for Space Exploration.The Intelligent Payload Experiment (IPEX) is a CubeSat that flew from December 2013 through January 2015 and validated autonomous operations for onboard instrument processing and product generation for the Intelligent Payload Module of the Hyperspectral Infrared Imager (HyspIRI) mission concept. IPEX used several artificial intelligence technologies. First, IPEX used machine learning and computer vision in its onboard processing. IPEX used machine-learned random decision forests to classify images onboard (to downlink classification maps) and computer vision visual salience software to extract interesting regions for downlink in acquired imagery. Second, IPEX flew the Continuous Activity Scheduler Planner Execution and Re-planner AI planner/scheduler onboard to enable IPEX operations to replan to best use spacecraft resources such as file storage, CPU, power, and downlink bandwidth. First, the ground and flight operations concept for proposed HyspIRI IPM operations is described, followed by a description the ground and flight operations concept used for the IPEX mission to validate key elements of automation for the proposed HyspIRI IPM operations concept. The use of machine learning, computer vision, and automated planning onboard IPEX is also described. The results from the over-1-year flight of the IPEX mission are reported.

Utilizing HyspIRI Prototype Data for Geological Exploration Applications: A Southern California Case Study

The purpose of this study was to demonstrate the value of the proposed Hyperspectral Infrared Imager (HyspIRI) instrument for geological mapping applications. HyspIRI-like data were collected as part of the HyspIRI airborne campaign that covered large regions of California, USA, over multiple seasons. This work focused on a Southern California area, which encompasses Imperial Valley, the Salton Sea, the Orocopia Mountains, the Chocolate Mountains, and a variety of interesting geological phenomena including fumarole fields and sand dunes. We have mapped hydrothermal alteration, lithology and thermal anomalies, demonstrating the value of this type of data for future geologic exploration activities. We believe HyspIRI will be an important instrument for exploration geologists as data may be quickly manipulated and used for remote mapping of hydrothermal alteration minerals, lithology and temperature anomalies.

Large-Area MEMS-Based Distributed Bragg Reflectors for Short-Wave and Mid-Wave Infrared Hyperspectral Imaging Applications

We present the design, fabrication, and optical characterization of silicon-air-silicon-based distributed Bragg reflectors, or quarter wavelength mirrors, in sizes ranging from 200 $\mu \text{m}\times 200~\mu \text{m}$ to 5 mm $\times$ 5 mm. Such mirrors can be used in conjunction with either single-element photodetectors or large-area focal plane arrays to realize tunable multispectral sensors or adaptive focal plane arrays from the short-wave infrared wavelength ranges (1500–3000 nm) to mid-wave infrared wavelength (3000–6000 nm) ranges. Surface optical profile measurements indicate a flatness of the order of 20–30 nm in the fabricated structures across several millimetres. Single point spectral measurements on devices show excellent agreement with simulated optical models. The fabricated distributed Bragg reflectors show $\sim 94$ % reflectivity, which is in close agreement with theoretical reflectivity. The demonstrated high reflectivity across a wide wavelength range renders them suitable as broadband reflectors. Finally, we present optical transmittance modeling results for Fabry-Perot filters based on these distributed Bragg reflectors. [2015-0161]

Urban Classification by the Fusion of Thermal Infrared Hyperspectral and Visible Data

The 2014 Data Fusion Contest, organized by the Image Analysis and Data Fusion (IADF) Technical Committee of the IEEE Geoscience and Remote Sensing Society, involved two datasets acquired at different spectral ranges and spatial resolutions: a coarser-resolution long-wave infrared (LWIR, thermal infrared) hyperspectral data set and fine-resolution data acquired in the visible (VIS) wavelength range. In this article, a novel multi-level fusion approach is proposed to fully utilize the characteristics of these two different datasets to achieve improved urban land-use and land-cover classification. Specifically, road extraction by fusing the classification result of the TI-HSI dataset and the segmentation result of the VIS dataset is first proposed. Thereafter, a novel gap inpainting method for the VIS data with the guidance of the TI-HSI data is presented to deal with the swath width inconsistency, and to facilitate an accurate spatial feature extraction step. The experimental results with the 2014 Data Fusion Contest datasets suggest that the proposed method can alleviate the multi-spectral-spatial resolution and multi-swath width problem to a great extent, and achieve an improved urban classification accuracy.

Radiometric consistency assessment of hyperspectral infrared sounders

Abstract. The radiometric and spectral consistency among the Atmospheric Infrared Sounder (AIRS), the Infrared Atmospheric Sounding Interferometer (IASI), and the Cross-track Infrared Sounder (CrIS) is fundamental for the creation of long-term infrared (IR) hyperspectral radiance benchmark data sets for both intercalibration and climate-related studies. In this study, the CrIS radiance measurements on Suomi National Polar-orbiting Partnership (SNPP) satellite are directly compared with IASI on MetOp-A and MetOp-B at the finest spectral scale and with AIRS on Aqua in 25 selected spectral regions through simultaneous nadir overpass (SNO) observations in 2013, to evaluate radiometric consistency of these four hyperspectral IR sounders. The spectra from different sounders are paired together through strict spatial and temporal collocation. The uniform scenes are selected by examining the collocated Visible Infrared Imaging Radiometer Suite (VIIRS) pixels. Their brightness temperature (BT) differences are then calculated by converting the spectra onto common spectral grids. The results indicate that CrIS agrees well with IASI on MetOp-A and IASI on MetOp-B at the long-wave IR (LWIR) and middle-wave IR (MWIR) bands with 0.1–0.2 K differences. There are no apparent scene-dependent patterns for BT differences between CrIS and IASI for individual spectral channels. CrIS and AIRS are compared at the 25 spectral regions for both polar and tropical SNOs. The combined global SNO data sets indicate that the CrIS–AIRS BT differences are less than or around 0.1 K among 21 of 25 spectral regions and they range from 0.15 to 0.21 K in the remaining four spectral regions. CrIS–AIRS BT differences in some comparison spectral regions show weak scene-dependent features.

The impact of spatial resolution on the classification of plant species and functional types within imaging spectrometer data

Abstract Several upcoming hyperspectral satellite sensor missions (e.g., the Hyperspectral Infrared Imager and the Environmental Mapping and Analysis Program) will greatly expand the opportunities for researchers to use imaging spectroscopy data for discriminating and mapping plant species and plant functional types (PFTs; defined in this study as combinations of leaf-type, leaf/plant duration and life form). Accurate knowledge of the spatial distribution of dominant plant species and PFTs is highly valuable to many scientific and management goals, including improved parameterization of ecosystem process and climate models, better invasive species distribution monitoring and forecasting, quantification of human and natural disturbance and recovery processes, and evaluations of terrestrial vegetation response to climate change. Most often, species-level discrimination has been achieved using fine spatial resolution (≤ 20 m) airborne imagery, but currently proposed spaceborne imaging spectrometers will have coarser spatial resolution (~ 30 to 60 m). In order to address the impact of coarser spatial resolutions on our ability to spectrally separate species and PFTs, we classified dominant species and PFTs in five contrasting ecosystems over a range of spatial resolutions. Study sites included a temperate broadleaf deciduous forest, a brackish tidal marsh, a mixed conifer/broadleaf montane forest, a temperate rainforest and a Mediterranean climate region encompassing grasslands, oak savanna, oak woodland and shrublands. Data were acquired by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) over each site, and spatially aggregated to 20, 40 and 60 m resolutions. Canonical Discriminant Analysis (CDA) was used to classify species and PFTs at each site and across scales with overall accuracies ranging from 61 to 96% for species and 83–100% for PFTs. The results of this study show accuracy increases at coarser resolutions (≥ 20 m) across ecosystems, supporting the use of imaging spectroscopy data at spatial resolutions up to 60 m for the purpose of discriminating among plant species and PFTs. In four of the five study sites, the best accuracies were achieved at 40 m resolution. However, at coarser resolutions, some fine-scale species variation is lost and classes that occur only in small patches cannot be mapped. We also demonstrate that spectral libraries developed from fine spatial resolution imagery can be successfully applied as training data to accurately classify coarser resolution data over multiple ecosystems.

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

In 2007, the NASA Hyperspectral InfraRed Imager (HyspIRI) mission was recommended in Earth Science and Applications from Space: National Imperatives for the Next Decade and Beyond (Decadal Survey) to address critical science questions in multiple areas, in particular ecosystems and natural hazards. HyspIRI is comprised of two instruments, a visible to short-wavelength infrared (VSWIR) imaging spectrometer and a thermal infrared (TIR) multispectral imager, together with an Intelligent Payload Module (IPM) for onboard processing and rapid downlink of selected data. The VSWIR instrument will have 10nm contiguous bands and cover the 380–2500nm spectral range with 30m spatial resolution and a revisit of 16days. The TIR instrument will have 8 discrete bands in the 4–13μm range with 60m spatial resolution and a revisit of 5days. With these two instruments in low Earth orbit, HyspIRI will be able to address key science and applications questions in a wide array of fields, ranging from ecosystem function and diversity to human health and urbanization.

Special issue on the Hyperspectral Infrared Imager (HyspIRI): Emerging science in terrestrial and aquatic ecology, radiation balance and hazards

The Hyperspectral Infrared Imager (HyspIRI) mission is proposed to be the first satellite system with the capability to provide global, repeat coverage across the visible and shortwave infrared spectrum, as well as eight channels in the midwave and thermal infrared. HyspIRI has stated objectives to address a host of pressing earth science questions, from radiation budgets to ecosystem functions. A sizable science community has grown to support the mission, and their ongoing research demonstrates HyspIRI's potential to greatly expand our knowledge of the earth system. This special issue features a collection of papers, some reviews and others novel science, that cover the wide array of topics relevant to HyspIRI's mission and reaffirm the necessity for HyspIRI.

Improved surface temperature estimates with MASTER/AVIRIS sensor fusion

Land surface temperature (LST) is an important parameter in many ecological studies. The current Root Mean Square Error (RMSE) in standard MODIS and ASTER LST products is greater than 1K, and for ASTER can be as large as 4K for graybody pixels such as vegetation. Errors of 3 to 8K have been observed for ASTER in humid conditions, making knowledge of atmospheric water vapor content critical in retrieving accurate LST. For this reason improved accuracy in LST measurements through the synthesis of visible-to-shortwave-infrared (VSWIR) derived water vapor maps and Thermal-Infrared (TIR) data is one goal of the Hyperspectral Infrared Imager, or HyspIRI, mission. The 2011 ER-2 Delano/Lost Hills flights acquired data with both the MODIS/ASTER Simulator (MASTER) and Airborne Visible InfraRed Imaging Spectrometer (AVIRIS) instruments flown concurrently. This study compares LST retrieval accuracies from the standard JPL MASTER temperature products produced using the temperature–emissivity separation (TES) algorithm, and the water vapor scaling (WVS) atmospheric correction method proposed for HyspIRI. The two retrieval methods are run both with and without high spatial resolution AVIRIS-derived water vapor maps to assess the improvement from VSWIR synthesis. We find improvement using VSWIR derived water vapor maps, with the WVS method being most accurate overall. For closed canopy agricultural vegetation we observed temperature retrieval RMSEs of 0.49K and 0.70K using the WVS method on MASTER data with and without AVIRIS derived water vapor, respectively.

Measuring freshwater aquatic ecosystems: The need for a hyperspectral global mapping satellite mission

Freshwater ecosystems underpin global water and food security, yet are some of the most endangered ecosystems in the world because they are particularly vulnerable to land management change and climate variability. The US National Research Council's guidance to NASA regarding missions for the coming decade includes a polar orbiting, global mapping hyperspectral satellite remote sensing mission, the Hyperspectral Infrared Imager (HyspIRI), to make quantitative measurements of ecosystem change. Traditionally, freshwater ecosystems have been challenging to measure with satellite remote sensing because they are small and spatially complex, require high fidelity spectroradiometry, and are best described with biophysical variables derived from high spectral resolution data. In this study, we evaluate the contribution of a hyperspectral global mapping satellite mission to measuring freshwater ecosystems. We demonstrate the need for such a mission, and evaluate the suitability and gaps, through an examination of the measurement resolution issues impacting freshwater ecosystem measurements (spatial, temporal, spectral and radiometric). These are exemplified through three case studies that use remote sensing to characterize a component of freshwater ecosystems that drive primary productivity. The high radiometric quality proposed for the HyspIRI mission makes it uniquely well designed for measuring freshwater ecosystems accurately at moderate to high spatial resolutions. The spatial and spectral resolutions of the HyspIRI mission are well suited for the retrieval of multiple biophysical variables, such as phycocyanin and chlorophyll-a. The effective temporal resolution is suitable for characterizing growing season wetland phenology in temperate regions, but may not be appropriate for tracking algal bloom dynamics, or ecosystem responses to extreme events in monsoonal regions. Global mapping missions provide the systematic, repeated measurements necessary to measure the drivers of freshwater biodiversity change. Archival global mapping missions with open access and free data policies increase end user uptake globally. Overall, an archival, hyperspectral global mapping mission uniquely meets the measurement requirements of multiple end users for freshwater ecosystem science and management.

Differentiating plant species within and across diverse ecosystems with imaging spectroscopy

Imaging spectroscopy has been used successfully to map species across diverse ecosystems, and with several spaceborne imaging spectrometer missions underway (e.g., Hyperspectral Infrared Imager (HyspIRI), Environmental Mapping and Analysis Program (EnMAP)), these data may soon be available globally. Still, most studies have focused only on single ecosystems, and many different classification strategies have been used, making it difficult to assess the potential for mapping dominant species on a broader scale. Here we compare a number of classification approaches across five contrasting ecosystems containing an expansive diversity of species and plant functional types in an effort to find a robust strategy for discriminating among dominant plant species within and across ecosystems. We evaluated the performance of combinations of methods of training data selection (stratified random selection and iterative endmember selection (IES)), spectral dimension reduction methods (canonical discriminant analysis (CDA) and partial least squares regression (PLSR)) and classification algorithms (linear discriminant analysis (LDA) and Multiple Endmember Spectral Mixture Analysis (MESMA)). Accuracy was assessed using an independent validation data set. Mean kappa coefficients for all strategies ranged from 0.48 to 0.85 for each ecosystem. Maximum kappa values and overall accuracies within each ecosystem ranged from 0.56 to 0.90 and 61–92%, respectively. Our findings show that both LDA and MESMA are able to discriminate among species to a high degree of accuracy in most ecosystems, with LDA performing slightly better. Spectral dimension reduction generally improved these results, particularly in conjunction with MESMA. Within each ecosystem, both the number and identities of functional types present, as well as the spatial distribution of dominant species, played a strong role in classification accuracy. In a pooled ecosystem classification, using CDA and LDA, we discriminated among 65 classes with an overall accuracy of 70% for the validation library, using only a 6% training sample. Our results suggest that a spaceborne imaging spectrometer such as HyspIRI will be able to map dominant plant species on a broader scale.

Remote sensing of phytoplankton functional types in the coastal ocean from the HyspIRI Preparatory Flight Campaign

The 2013–2015 Hyperspectral Infrared Imager (HyspIRI) Preparatory Flight Campaign, using the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and MODIS/ASTER Airborne Simulator (MASTER), seeks to demonstrate appropriate sensor signal, spatial and spectral resolution, and orbital pass geometry for a global mission to reveal ecological and climatic gradients expressed in the selected California, USA study area. One of the awarded projects focused on the flight transects covering the coastal ocean to demonstrate that the AVIRIS data can be used to infer phytoplankton functional types at the land–sea interface. Specifically, this project directly assesses whether HyspIRI can provide adequate signal in the complex aquatic environment of the coastal zone to address questions of algal bloom dynamics, water quality, transient responses to human disturbance, river runoff, and red tides. Phytoplankton functional type (PFT), or biodiversity, can be determined from ocean color using the Phytoplankton Detection with Optics (PHYDOTax) algorithm and this information can be used to detect and monitor for harmful algal blooms. PHYDOTax is sensitive to spectral shape and accurate retrievals of ocean color across the visible spectral range is needed. The specific goal of this paper is to address the challenges of sensor capabilities and atmospheric correction in coastal environments by assessing two atmospheric correction methods using AVIRIS data for the retrieval of ocean color for use in derived products of chlorophyll-a and phytoplankton functional type. The atmospheric correction algorithms Atmospheric Removal (ATREM) and Tafkaa were applied to AVIRIS imagery of Monterey Bay, CA collected on 10 April 2013 and 31 October 2013. Data products from the imagery were compared with shipboard measurements including chlorophyll-a from whole-water samples and phytoplankton community structure estimated from diagnostic pigment markers using CHEMical TAXonomy (CHEMTAX). Using ATREM and Tafkaa and a selected set of input parameters for the scenes, we were unable to produce accurate retrievals of ocean color for the determination of chlorophyll-a and phytoplankton diversity. A modified ATREM correction produced science-quality data in which chlorophyll-a was accurately estimated using the Ocean Color 3 (OC3) chlorophyll-a algorithm, but biodiversity using PHYDOTax was not accurately estimated. Improvements in sensor calibration, sensitivity, and atmospheric correction of the HyspIRI imagery data set is needed in order to adequately estimate biogeochemically meaningful data products for the ocean such as chlorophyll-a, inherent optical properties, or PFTs. The HyspIRI Science Team is seeking improvements so the HyspIRI Airborne Campaign data set can be used for algorithm development to understand biodiversity and ecosystem function of coastal habitats that are facing increasing threats of human impact and climate change.

Remote monitoring of giant kelp biomass and physiological condition: An evaluation of the potential for the Hyperspectral Infrared Imager (HyspIRI) mission

This study examines the potential of the Hyperspectral Infrared Imager (HyspIRI) mission for monitoring the biomass and physiological condition of giant kelp forests on local to global scales. Giant kelp is a highly dynamic foundation species that supports an ecologically and economically important ecosystem found throughout the globe. Satellite, airborne, and field data are used to evaluate the suitability of HyspIRI's spatial, temporal, and spectral coverage for capturing variability in giant kelp biomass and physiological state. We analyze a 28-year time series of giant kelp biomass derived from Landsat satellite imagery in order to identify the dominant temporal modes of variability in giant kelp biomass using the California coast as a model region likely to be relevant to other regions of the globe. Temporal variability in California kelp canopy biomass is compared to the expected availability of cloud-free HyspIRI Visible Shortwave Infrared (VSWIR) views for regions of the world that contain giant kelp populations. Spectral variability is explored by assessing how changes in the physiological condition of giant kelp canopy are exhibited in the reflectance and transmittance of kelp fronds. We compare chlorophyll a to carbon ratios (Chl:C) of kelp fronds collected off the coast of California to laboratory and airborne measurements of hyperspectral reflectance in order to develop metrics of kelp physiological condition.The seasonal cycle dominates giant kelp canopy biomass temporal variability. However, the strength and timing of this cycle varies in both space and time. Our projections of cloud-free HyspIRI coverage indicate that the sensor will be able to capture at least 1 cloud free view each season for nearly all of the global giant kelp habitats, thereby illustrating that HyspIRI will resolve the dominant seasonal cycles in giant kelp biomass. A novel spectral index developed here from field observations explained 76% of the variance in Chl:C and was applied to hyperspectral aircraft observations. These results demonstrate that the spatial, temporal, and spectral coverage provided by HyspIRI has the potential to provide new insights into the ecology and biophysiology of giant kelp.