High Resolution Imaging Spectrometer Research Articles

The dicarbon bonding puzzle viewed with photoelectron imaging

Bonding in the ground state of C{}_{2} is still a matter of controversy, as reasonable arguments may be made for a dicarbon bond order of 2, 3, or 4. Here we report on photoelectron spectra of the C{}_{2}^{-} anion, measured at a range of wavelengths using a high-resolution photoelectron imaging spectrometer, which reveal both the ground {X}^{1}{Sigma}_{mathrm{g}}^{+} and first-excited {a}^{3}{Pi}_{{mathrm{u}}} electronic states. These measurements yield electron angular anisotropies that identify the character of two orbitals: the diffuse detachment orbital of the anion and the highest occupied molecular orbital of the neutral. This work indicates that electron detachment occurs from predominantly s-like (3{sigma}_{mathrm{g}}) and p-like (1{pi }_{{mathrm{u}}}) orbitals, respectively, which is inconsistent with the predictions required for the high bond-order models of strongly sp-mixed orbitals. This result suggests that the dominant contribution to the dicarbon bonding involves a double-bonded configuration, with 2pi bonds and no accompanying sigma bond.

Dissociative Electron Attachment to Molecular Acetonitrile.

Low-energy dissociative electron attachment to molecular acetonitrile (CH3CN) is investigated by recording the efficiency curves of CH2CN-, CHCN-, and CN- products, but the present curves are distinctly different from those in the previous reports. Now it is recommended that the reaction thresholds of e- + CH3CN → H2 + CHCN- and CH3 + CN- are respectively about 1.51 and 1.52 eV, and four shape-resonant states, one 2Π and three 2∑, of CH3CN- are involved in the fragmentations at the low and high electron-attachment energies. By using a high-resolution anion velocity-map imaging spectrometer, we obtain the CN- momentum images at the electron attachment energies of 7.10, 7.60, and 8.10 eV and interpret them with four pathways concerning two- and three-body dissociations.

Plasma impurities observed by a pulse height analysis diagnostic during the divertor campaign of the Wendelstein 7-X stellarator.

The paper reports on the optimization process of the soft X-ray pulse height analyzer installed on the Wendelstein 7-X (W7-X) stellarator. It is a 3-channel system that records X-ray spectra in the range from 0.6 to 19.6 keV. X-ray spectra, with a temporal and spatial resolution of 100 ms and 2.5 cm (depending on selected slit sizes), respectively, are line integrated along a line-of-sight that crosses near to the plasma center. In the second W7-X operation phase with a carbon test divertor unit, light impurities, e.g., carbon and oxygen, were observed as well as mid- to high-Z elements, e.g., sulfur, chlorine, chromium, manganese, iron, and nickel. In addition, X-ray lines from several tracer elements have been observed after the laser blow-off injection of different impurities, e.g., silicon, titanium, and iron, and during discharges with prefill or a gas puff of neon or argon. These measurements were achieved by optimizing light absorber-foil selection, which defines the detected energy range, and remotely controlled pinhole size, which defines photon flux. The identification of X-ray lines was confirmed by other spectroscopic diagnostics, e.g., by the High-Efficiency XUV Overview Spectrometer, HEXOS, and high-resolution X-ray imaging spectrometer, HR-XIS.

The Chlorophyll Fluorescence Imaging Spectrometer (CFIS), mapping far red fluorescence from aircraft

The Chlorophyll Fluorescence Imaging Spectrometer (CFIS) is an airborne high resolution imaging spectrometer built at NASA's Jet Propulsion Laboratory (JPL) for evaluating solar-induced fluorescence (SIF) from the Orbiting Carbon Observatory-2 (OCO-2). OCO-2 is a NASA mission designed to measure atmospheric CO2 but one of the novel data products is SIF, retrieved using reductions in the optical depth of Fraunhofer lines in OCO-2’s O2 A-band, covering 757–775 nm at 0.042 nm spectral resolution. CFIS was specifically designed to retrieve SIF within the wavelength range of OCO-2, but extends further down to 737 nm, nearly maintaining the high spectral resolution of the OCO-2 instrument (0.07 vs. 0.042 nm). Here, we provide an overview of the instrument calibration and performance as well as the retrieval strategy based on non-linear weighted least-squares. To illustrate the retrieval performance using actual flight data, we focus on data acquired over agricultural fields in Mead, Nebraska from an unpressurized Twin Otter (DHC-6) aircraft at a flight altitude of 3000 m above ground level (AGL). Spectral residuals are consistent with expected detector noise, which enables us to compute realistic 1-σ precision errors of 0.5–0.7 W/m2/sr/μm for typical SIF retrievals, which can be reduced to <0.2 W/m2/sr/μm when individual data is gridded at 30 m spatial resolution. The 30 m resolution also enabled direct comparison with the Crop Data Layer from the National Agricultural Statistics Service as well as Landsat imagery (NDVI, EVI, Tskin), taken just a day prior to the CFIS overflights. Results show consistently higher vegetation indices and SIF values over soy fields compared to corn, likely due to the respective phenological stage, which might already have affected chlorophyll content and canopy structure (August 15, 2016). While this work is intended to highlight the technical capabilities and performance of CFIS, the comparisons against Landsat and crop types provide insights into how CFIS can be used to study mechanisms related to photosynthesis at fine spatial scales, with the fidelity needed to obtain un-biased SIF retrievals void of atmospheric correction.

Simulating arbitrary hyperspectral bandsets from multispectral observations via a generic Earth Observation-Land Data Assimilation System (EO-LDAS)

This paper presents results of using multi-sensor and multi-angular constraints in the generic Earth Observation-Land Data Assimilation System (EO-LDAS) for reproducing arbitrary bandsets of hyperspectral reflectance at the top-of-canopy (TOC) level by merging observations from multispectral sensors with different spectral characteristics. This is demonstrated by combining Multi-angle Imaging Spectroradiometer (MISR) and Landsat Enhanced Thematic Mapper Plus (ETM+) data to simulate the Compact High Resolution Imaging Spectrometer CHRIS/PROBA hyperspectral signal over an agricultural test site, in Barrax, Spain. However, the method can be more generally applied to any combination of spectral data, providing a tool for merging EO data to any arbitrary hyperspectral bandset.Comparisons are presented using both synthetic and observed MISR and Landsat data, and retrieving surface biophysical properties. We find that when using simulated MISR and Landsat data, the CHRIS/PROBA hyperspectral signal is reproduced with RMSE 0.0001–0.04. LAI is retrieved with r2 from 0.97 to 0.99 and RMSE of from 0.21 to 0.38. The results based on observed MISR and Landsat data have lower performances, with RMSE for the reproduced CHRIS/PROBA hyperspectral signal varying from 0.007 to 0.2. LAI is retrievedwith r2 from 0.7 to 0.9 and RMSE from 0.7 to 1.4. We found that for the data considered here the main spectral variations in the visible and near infrared regions can be described by a limited number of parameters (3–4) that can be estimated from multispectral information. Results show that the method can be used to simulate arbitrary bandsets, which will be of importance to any application which requires combining new and existing streams of new EO data in the optical domain, particularly intercalibration of EO satellites in order to get continuous time series of surface reflectance, across programmes and sensors of different designs.

Satellite observations of sub-mesoscale vortex trains in the western boundary of the South China Sea

Abstract This study uses high resolution MEdium Resolution Imaging Spectrometer (MERIS) satellite data to analyze sub-mesoscale ocean vortex trains (OVTs) in the western boundary of the South China Sea (SCS). Thirteen cases of vortex trains on the lee side of the Phu Quý Island were observed in the boreal summer from May to August of 2005−2010. The average diameter of the vortices is 28.1 ± 13.8 km. The average spacing distance estimated between two consecutive vortices is 66.3 ± 27.8 km. The dynamic parameters of a case observed on July 26, 2009 were extracted using a theoretical model. The aspect ratio is 2.8. The ratio of the propagation velocity of the vortex to the undisturbed flow velocity is 0.73. We use a nested high-resolution numerical model with the finest grid of 500 m, to analyze the dynamics of OVT induced by the local topography in the western SCS (WSCS). The results give a propagation velocity of vortex 0.49 of m s−1 and a mean current velocity of undisturbed flow 0.58 m s−1. Therefore, the ratio is 0.84. The shedding frequency is determined to be 1.14 × 10−5 s−1, corresponding to 1.02 day. The results also suggest that the sub-mesoscale cyclonic vortices along the OVT in the WSCS are characterized by low temperature and high Rossby number. The strong vertical vorticity and associated horizontal velocity shear of an individual vortex brings uplifted cold water on its leading side and warm water on its trailing side, which could drive an enhanced vertical exchange between the surface and oceanic interior.

Barest Pixel Composite for Agricultural Areas Using Landsat Time Series

Many soil remote sensing applications rely on narrow-band observations to exploit molecular absorption features. However, broadband sensors are invaluable for soil surveying, agriculture, land management and mineral exploration, amongst others. These sensors provide denser time series compared to high-resolution airborne imaging spectrometers and hold the potential of increasing the observable bare-soil area at the cost of spectral detail. The wealth of data coming along with these applications can be handled using cloud-based processing platforms such as Earth Engine. We present a method for identifying the least-vegetated observation, or so called barest pixel, in a dense time series between January 1985 and March 2017, based on Landsat 5, 7 and 8 observations. We derived a Barest Pixel Composite and Bare Soil Composite for the agricultural area of the Swiss Plateau. We analysed the available data over time and concluded that about five years of Landsat data were needed for a full-coverage composite (90% of the maximum bare soil area). Using the Swiss harmonised soil data, we derived soil properties (sand, silt, clay, and soil organic matter percentages) and discuss the contribution of these soil property maps to existing conventional and digital soil maps. Both products demonstrate the substantial potential of Landsat time series for digital soil mapping, as well as for land management applications and policy making.

Using Land Long-Term Data Records to Map Land Cover Changes in China Over 1981–2010

China has undergone significant land cover changes since the 1980s. However, there are limited consistent and continuous dataset of national scale. Using advanced very high resolution radiometer and moderate resolution imaging spectrometer data from the land long-term data record, we developed a large-scale classification approach to produce a decadal 5-km resolution land cover dataset for China (ChinaLC) from 1981 to 2010. A total of 19 classes of training and validation samples were obtained from visual interpretation of high-resolution Google Earth images and historical vegetation maps. Combined efforts of standard criteria, rigid check, and detailed recording were conducted to strengthen the robustness of the multitemporal samples. The different compositions of metrics and parameters were tested to obtain the optimal support vector machine (SVM) classification results. The ChinaLC dataset has an average overall accuracy of approximately 75%, which is much higher compared with other large-scale land cover datasets. Furthermore, a high consistency was found between the land cover changes of ChinaLC and other studies using higher spatial resolution data. The decadal spatial–temporal transition patterns were analyzed and the important reasons for accelerated landscape changes were also explained over the 30 years.

Cross-Comparison of Albedo Products for Glacier Surfaces Derived from Airborne and Satellite (Sentinel-2 and Landsat 8) Optical Data

Surface albedo partitions the amount of energy received by glacier surfaces from shortwave fluxes and modulates the energy available for melt processes. The ice-albedo feedback, influenced by the contamination of bare-ice surfaces with light-absorbing impurities, plays a major role in the melting of mountain glaciers in a warming climate. However, little is known about the spatial and temporal distribution and variability of bare-ice glacier surface albedo under changing conditions. In this study, we focus on two mountain glaciers located in the western Swiss Alps and perform a cross-comparison of different albedo products. We take advantage of high spectral and spatial resolution (284 bands, 2 m) imaging spectrometer data from the Airborne Prism Experiment (APEX) and investigate the applicability and potential of Sentinel-2 and Landsat 8 data to derive broadband albedo products. The performance of shortwave broadband albedo retrievals is tested and we assess the reliability of published narrow-to-broadband conversion algorithms. The resulting albedo products from the three sensors and different algorithms are further cross-compared. Moreover, the impact of the anisotropy correction is analysed depending on different surface types. While degradation of the spectral resolution impacted glacier-wide mean albedo by about 5%, reducing the spatial resolution resulted in changes of less than 1%. However, in any case, coarser spatial resolution was no longer able to represent small-scale variability of albedo on glacier surfaces. We discuss the implications when using Sentinel-2 and Landsat 8 to map dynamic glaciological processes and to monitor glacier surface albedo on larger spatial and more frequent temporal scales.

HDO and SO2thermal mapping on Venus

We present the analysis of a four-year observational campaign using the TEXES high-resolution imaging spectrometer at the NASA Infrared Telescope Facility to map sulfur dioxide and deuterated water over the disk of Venus. Data have been recorded in two spectral ranges around 1345 cm −1 (7.4 µm) and 530 cm −1 (19 µm) in order to probe the cloudtop at an altitude of about 64 km (SO 2 and HDO at 7.4 µm) and a few kilometers below (SO 2 at 19 µm). Observations took place during six runs between January 2012 and January 2016. The diameter of Venus ranged between 12 and 33 arcsec. Data were recorded with a spectral resolving power up to 80 000 and a spatial resolution of about 1 arcsec (at 7.4 µm) and 2.5 arcsec (at 19 µm). Mixing ratios were estimated from HDO/CO 2 and SO 2 /CO 2 line depth ratios, using weak neighboring transitions of comparable depths. The whole dataset demonstrates that the two molecules behave very differently to each other. The HDO maps are uniform over the disk. The disk-integrated H 2 O mixing ratio (estimated assuming a D/H of 200 VSMOW in the mesosphere of Venus) show moderate variations (by less than a factor of 2) over the four-year period. A value of 1.0−1.5 ppmv is obtained in most of the cases. The SO 2 maps, in contrast, show strong variations over the disk of Venus, by a factor as high as 5. Long-term variations of SO 2 show that the disk-integrated SO 2 mixing ratio also varies between 2012 and 2016 by a factor as high as ten, with a minimum value of 30 +/−5 ppbv on February 26, 2014 an a maximum value of 300 +/−50 ppbv on January 14, 2016. The SO 2 maps also show a strong short-term variability. It can be seen that the SO 2 maximum feature usually follows the four-day rotation of the clouds over a timescale of two hours, which corresponds to a rotation of 7.5 deg over the planetary disk, but its morphology also changes, which suggests that the lifetime of this structure is not more than a few hours.

The Potential of Forest Biomass Inversion Based on Vegetation Indices Using Multi-Angle CHRIS/PROBA Data

Multi-angle remote sensing can either be regarded as an added source of uncertainty for variable retrieval, or as a source of additional information, which enhances variable retrieval compared to traditional single-angle observation. However, the magnitude of these angular and band effects for forest structure parameters is difficult to quantify. We used the Discrete Anisotropic Radiative Transfer (DART) model and the Zelig model to simulate the forest canopy Bidirectional Reflectance Distribution Factor (BRDF) in order to build a look-up table, and eight vegetation indices were used to assess the relationship between BRDF and forest biomass in order to find the sensitive angles and bands. Further, the European Space Agency (ESA) mission, Compact High Resolution Imaging Spectrometer onboard the Project for On-board Autonomy (CHRIS-PROBA) and field sample measurements, were selected to test the angular and band effects on forest biomass retrieval. The results showed that the off-nadir vegetation indices could predict the forest biomass more accurately than the nadir. Additionally, we found that the viewing angle effect is more important, but the band effect could not be ignored, and the sensitive angles for extracting forest biomass are greater viewing angles, especially around the hot and dark spot directions. This work highlighted the combination of angles and bands, and found a new index based on the traditional vegetation index, Atmospherically Resistant Vegetation Index (ARVI), which is calculated by combining sensitive angles and sensitive bands, such as blue band 490 nm/−55°, green band 530 nm/55°, and the red band 697 nm/55°, and the new index was tested to improve the accuracy of forest biomass retrieval. This is a step forward in multi-angle remote sensing applications for mining the hidden relationship between BRDF and forest structure information, in order to increase the utilization efficiency of remote sensing data.

A data fusion and spatial data analysis approach for the estimation of wheat grain nitrogen uptake from satellite data

ABSTRACTThe selection of the optimal band combination for the estimation of specific crop variables is a key aspect in order to obtain reliable estimation of in-field variability from multi- and hyperspectral remote-sensing data. The selection of the bands is strongly influenced by the phenological stage of the crop at the acquisition time. In this work, the influence of the growing stage on the combination of spectral bands related to grain nitrogen (N) uptake in wheat was evaluated using multispectral (Satellite Pour l’Observation de la Terre – SPOT) and hyperspectral (Compact High Resolution Imaging Spectrometer – CHRIS-PROBA) satellite images at different growth stages over two wheat growth seasons in central Italy. In order to identify the more appropriate covariates (spectral bands) for each phenological stage, stepwise regression with backward selection was combined with stepwise variance inflation factors (VIFs) analysis and linear mixed effect model (LMEM). The results obtained in this study suggest that the spectral region most related to N uptake varies over the growing season of the wheat crop. For SPOT data, near-infrared (NIR) region was selected at all the phenological stages in both growing seasons, except for the latest stage, with low chlorophyll content due to the onset of senescence, in which the red band was selected. At stem elongation, the shortwave infrared (SWIR) band of SPOT data was also selected. At this stage, the best N estimation accuracy was obtained using an LMEM (root mean square error, RMSE = 0.012 t ha−1). The inclusion of a spatial component in the estimation model by means of LMEMs provided a more accurate estimation than ordinary least square (OLS) models at all growth stages. The test carried out with CHRIS-PROBA data at the fourth stage confirmed the importance of NIR and in particular of the red-edge region for N uptake prediction. A novel methodology is proposed, which involves two crucial aspects in the context of the use of remote-sensing data in precision agriculture: i) the standardization of the spatial resolution for in-field and satellite data by a geostatistical data technique (data fusion); and ii) the selection of the most appropriate spectral bands for each phenological stage, taking into account both correlation with the target variable and collinearity.

Estimating crop chlorophyll content with hyperspectral vegetation indices and the hybrid inversion method

ABSTRACTA hybrid inversion method was developed to estimate the leaf chlorophyll content (LCC) and canopy chlorophyll content (CCC) of crops. Fifty hyperspectral vegetation indices (VIs), such as the photochemical reflectance index (PRI) and canopy chlorophyll index (CCI), were compared to identify the appropriate VIs for crop LCC and CCC inversion. The hybrid inversion models were then generated from different modelling methods, including the curve-fitting and least squares support vector regression (LS-SVR) and random forest regression (RFR) algorithms, by using simulated Compact High Resolution Imaging Spectrometer (CHRIS) datasets that were generated by a radiative transfer model. Finally, the remote-sensing mapping of a CHRIS image was completed to test the inversion accuracy. The results showed that the remote-sensing mapping of the CHRIS image yielded an accuracy of R2 = 0.77 and normalized root mean squared error (NRMSE) = 17.34% for the CCC inversion, and an accuracy of only R2 = 0.33 and NRMSE = 26.03% for LCC inversion, which indicates that the remote-sensing technique was more appropriate for obtaining chlorophyll content at the canopy scale (CCC) than at the leaf scale (LCC). The estimated results of various VIs and algorithms suggested that the PRI and CCI were the optimal VIs for LCC and CCC inversion, respectively, and RFR was the optimal method for modelling.

Estimation of leaf area index using an angular vegetation index based on in situ measurements and CHRIS/PROBA data

Abstract. The Normalized Difference Vegetation Index (NDVI) is widely used for Leaf Area Index (LAI) estimation. It is well documented that the NDVI is extremely subject to the saturation problem when LAI reaches a high value. A new multi-angular vegetation index, the Hotspot-darkspot Difference Vegetation Index (HDVI) is proposed to estimate the high density LAI. The HDVI, defined as the difference between the hot and dark spot NDVI, relative to the dark spot NDVI, was proposed based on the Analytical two-layer Canopy Reflectance Model (ACRM) model outputs. This index is validated using both in situ experimental data in wheat and data from the multi-angular optical Compact High-Resolution Imaging Spectrometer (CHRIS) satellite. Both indices, the Hotspot-Darkspot Index (HDS) and the NDVI were also selected to analyze the relationship with LAI, and were compared with new index HDVI. The results show that HDVI is an appropriate proxy of LAI with higher determination coefficients (R2) for both the data from the in situ experiment (R2=0.7342, RMSE=0.0205) and the CHRIS data (R2=0.7749, RMSE=0.1013). Our results demonstrate that HDVI can make better the occurrence of saturation limits with the information of multi-angular observation, and is more appropriate for estimating LAI than either HDS or NDVI at high LAI values. Although the new index needs further evaluation, it also has the potential under the condition of dense canopies. It provides the effective improvement to the NDVI and other vegetation indices that are based on the red and NIR spectral bands.

Estimation of leaf area index using an angular vegetation index based on in situ measurements and CHRIS/PROBA data

The Normalized Difference Vegetation Index (NDVI) is widely used for Leaf Area Index (LAI) estimation. It is well documented that the NDVI is extremely subject to the saturation problem when LAI reaches a high value. A new multi-angular vegetation index, the Hotspot-darkspot Difference Vegetation Index (HDVI) is proposed to estimate the high density LAI. The HDVI, defined as the difference between the hot and dark spot NDVI, relative to the dark spot NDVI, was proposed based on the Analytical two-layer Canopy Reflectance Model (ACRM) model outputs. This index is validated using both in situ experimental data in wheat and data from the multi-angular optical Compact High-Resolution Imaging Spectrometer (CHRIS) satellite. Both indices, the Hotspot-Darkspot Index (HDS) and the NDVI were also selected to analyze the relationship with LAI, and were compared with new index HDVI. The results show that HDVI is an appropriate proxy of LAI with higher determination coefficients (R2) for both the data from the in situ experiment (R2=0.7342, RMSE=0.0205) and the CHRIS data (R2=0.7749, RMSE=0.1013). Our results demonstrate that HDVI can make better the occurrence of saturation limits with the information of multi-angular observation, and is more appropriate for estimating LAI than either HDS or NDVI at high LAI values. Although the new index needs further evaluation, it also has the potential under the condition of dense canopies. It provides the effective improvement to the NDVI and other vegetation indices that are based on the red and NIR spectral bands.

Bidirectional reflectance of coral growth-forms

ABSTRACTIn situ spectral reflectance measurements of substrates in a coral reef are often obtained by viewing a substrate at nadir. However, it is likely that off-nadir oblique viewing would show different spectral characteristics for most coral reef substrates and provide valuable information on structural properties. To understand the relationship between substrate structure and spectral response, this study examined the bidirectional reflectance distribution function (BRDF) of various growth-forms of hard corals and algae, as well as rock, rubble, and sand. BRDF measurements were collected on Heron Reef, the Great Barrier Reef, Australia, during the spring (October to November) of 2010, using a visible and near-infrared (VNIR) spectroradiometer attached to a goniometer. The measurements were made in the same five view zenith angles as the PROBA-1 Compact High Resolution Imaging Spectrometer (CHRIS) satellite (+55°, +36°, 0°, −36°, −55°) in the solar principal plane (SPP). A correction algorithm was used to remove both water column and water surface effects. Uncorrected measurements for sand covered with benthic microalgae appeared to have BRDF effects, but when corrected showed an essentially diffuse spectral response. Corrected measurements for branching corals showed BRDF effects dependent on branch spacing, branch length, and colour. The results indicate that spectral reflectance does vary with view angle for complex substrates such as caespitose corals, macroalgae, and irregular beach-rock and to a lesser extent for digitate corals and rippled sand and that the morphology of the coral and the shadowing between branches determines total spectral response. It is concluded that BRDF information can provide additional discriminating features for some coral reef substrates, particularly in the wavelength range of 550–650 nm.

TES-Based X-ray Microcalorimeter Performances Under AC Bias and FDM for Athena

Athena is a European X-ray observatory, scheduled for launch in $$\sim $$ 2028. Athena will employ a high-resolution imaging spectrometer called X-ray integral field unit (X-IFU), consisting of an array of $$\sim $$ 4000 transition edge sensor (TES) microcalorimeter pixels. For the readout of X-IFU, we are developing frequency domain multiplexing, which is the baseline readout system. In this paper, we report on the performance of a TES X-ray calorimeter array fabricated at Goddard Space Flight Center (GSFC) at MHz frequencies for the baseline of X-IFU detector. During single-pixel AC bias characterization, we measured X-ray energy resolutions (at 6 keV) of about 2.9 eV at both 2.3 and 3.7 MHz. Furthermore, in the multiplexing mode, we measured X-ray energy resolutions of about 2.9 eV at 1.3 and 1.7 MHz.

A Multiscale Mapping Assessment of Lake Champlain Cyanobacterial Harmful Algal Blooms.

Lake Champlain has bays undergoing chronic cyanobacterial harmful algal blooms that pose a public health threat. Monitoring and assessment tools need to be developed to support risk decision making and to gain a thorough understanding of bloom scales and intensities. In this research application, Landsat 8 Operational Land Imager (OLI), Rapid Eye, and Proba Compact High Resolution Imaging Spectrometer (CHRIS) images were obtained while a corresponding field campaign collected in situ measurements of water quality. Models including empirical band ratio regressions were applied to map chlorophyll-a and phycocyanin concentrations; all sensors performed well with R2 and root-mean-square error (RMSE) ranging from 0.76 to 0.88 and 0.42 to 1.51, respectively. The outcomes showed spatial patterns across the lake with problematic bays having phycocyanin concentrations >25 µg/L. An alert status metric tuned to the current monitoring protocol was generated using modeled water quality to illustrate how the remote sensing tools can inform a public health monitoring system. Among the sensors utilized in this study, Landsat 8 OLI holds the most promise for providing exposure information across a wide area given the resolutions, systematic observation strategy and free cost.

An improved histogram matching algorithm for the removal of striping noise in optical remote sensing imagery

Striping noise is a well-known phenomenon that arises in most multi-detector optical imaging instruments. Such noise affects both visual interpretation and quantitative analysis. Therefore, destriping is an essential step before absolute calibration and image interpretation. Histogram matching is one of the most popular algorithms used to reduce striping. The assumption underlying histogram matching is that each detector has the same gray level distribution. This assumption is easily satisfied when the image is sufficiently large, but it often cannot be satisfied for small images. An improved histogram matching algorithm based on sliding windows is proposed in this paper. The algorithm presupposes that the gray level distribution of each column (taking the vertical striping noise as an example) is similar to the gray level distribution of the column-centered local area. The size of the local area is determined by a histogram growing algorithm. Compact High Resolution Imaging Spectrometer (CHRIS), Moderate Resolution Imaging Spectroradiometer (MODIS) and Hyperspectral Imager (HSI) images were used to test the new and traditional algorithms. These destriping results were compared using improvement factors, inverse coefficients of variation and mean profiles. The results of the comparison indicate that the improved histogram matching algorithm has obvious advantages over traditional method.

Effect of the Aerosol Model Assumption on the Atmospheric Correction over Land: Case Studies with CHRIS/PROBA Hyperspectral Images over Benelux

Surface reflectance has a central role in the analysis of land surface for a broad variety of Earth System studies. An accurate atmospheric correction, obtained by an appropriate selection of aerosol model, is the first requirement for reliable surface reflectance estimation. In the aerosol model, the type is defined by the physical and chemical properties, while the loading is usually described by the optical thickness at 550 nm. The aim of this work is to evaluate the radiative impact of the aerosol model on the surface reflectance obtained from Compact High Resolution Imaging Spectrometer (CHRIS) hyperspectral data over land by using the specifically developed algorithm CHRIS Atmospherically Corrected Reflectance Imagery (CHRIS@CRI) based on the 6SV radiative transfer model. We employed five different aerosol models: one provided by the AERONET inversion products (used as reference), three standard aerosol models in 6SV, and one obtained from the output of the GEOS-Chem global chemistry-transport model (CTM). The results obtained for the two case studies selected over Benelux show that in the absence of AERONET data on the scene, the best performing aerosol model is the one derived from CTM output.