Multispectral Infrared And Visible Imaging Spectrometer Research Articles

Local, Daily, and Total Bio-Optical Models of Coastal Waters of Manfredonia Gulf Applied to Simulated Data of CHRIS, Landsat TM, MIVIS, MODIS, and PRISMA Sensors for Evaluating the Error

The spatial–temporal resolution of remote data covers coastal water variability, but this approach offers a lower accuracy than in situ observations. Two of the major error sources occur due to the parameterization of bio-optical models and spectral capability of the remote data. These errors were evaluated by exploiting data acquired in the coastal waters of Manfredonia Gulf. Chlorophyll-a concentrations, absorption of the colored dissolved organic material at 440 nm (aCDOM440nm), and tripton concentrations measured in situ varied between 0.09–1.76 mgm−3, 0.00–0.41 m−1, and 1.97–8.90 gm−3. In accordance with the position and time of in situ surveys, 36 local models, four daily models, and one total bio-optical model were parameterized and validated using in situ data before applying to Compact High-Resolution Imaging Spectrometer (CHRIS) mode 1, CHRIS mode 2, Landsat Thematic Mapper (TM), Multispectral Infrared and Visible Imaging Spectrometer (MIVIS), Moderate Resolution Imaging Spectroradiometer (MODIS), and Precursore Iperspettrale della Missione Applicativa (PRISMA) simulated data. Concentrations retrieved from PRISMA data using local models highlighted the smallest errors. Because tripton abundance is great and tripton absorptions were better resolved than those of chlorophyll-a and colored dissolved organic material (CDOM), tripton concentrations were adequately retrieved from all data using total models, while only local models adequately retrieved chlorophyll-a concentrations and aCDOM440nm from CHRIS mode 1, CHRIS mode 2, MIVIS, and MODIS data. Therefore, the application of local models shows smaller errors than those of daily and total models; however, the capability to resolve the absorption of water constituents and analyze their concentration range can dictate the model choice. Consequently, the integration of more models allows us to overcome the limitations of the data and sensors.

The use of suitable pseudo-invariant targets for MIVIS data calibration by the empirical line method

The Empirical Line Method (ELM) enables the calibration of multi- and hyper-airborne/satellite image converting DN or radiance to reflectance values performed by using at ground data. High quality outcome can be reached with the selection of appropriate Pseudo-Invariant Targets (PIT). In this paper, spectral variability of “usual” (asphalt and concrete) and “unusual” (calcareous gravel, basaltic paving, concrete bricks, tartan paving and artificial turf) PITs is retrieved for ELM application. Such PITs are used to calibrate the Multispectral Infrared and Visible Imaging Spectrometer (MIVIS) airborne sensor in 12 different Runs. Firstly, processing of field spectral data enables the evaluation of pseudo-invariance of targets by studying their spectral changes in space and in time. Finally, these surfaces are used as Ground Calibration (GCT) and Validation Targets (GVT) in ELM. High calibration accuracy values are observed in Visible (VIS) range (98.9%) while a general decrease of accuracy in Near-InfraRed (NIR) (96.6%) and Middle-InfraRed (SWIR) (88.1%) are reached. Outcomes show that “usual” surfaces as asphalt and concrete and “unusual” surfaces such as tartan can be successfully used for MIVIS image calibration.

Land Surface Temperature from Landsat 5 TM images: comparison of different methods using airborne thermal data

In this study, several methods to compute land surface temperatures (LST) from Landsat TM5 data are compared. Two different approaches are considered. An image based approach that takes into account atmospherically corrected data by using a dark object subtraction model (DOS-1) and computes the emissivity as NDVI function. The emissivity of a surface is controlled by such factors as water content, chemical composition, structure and roughness; it can be determined as the contribution of the different components that belong to the pixels according to their proportions. NDVI method takes into account that vegetation and soils are the main surface cover for the terrestrial component. This emissivity is used to compute the LST by the inversion of Planck function. The other approach applies atmospheric correction to thermal infrared band and considers a constant emissivity of 0.95. Furthermore, the land surface temperature is computed by hybrid methods that result from the merger of the two initially considered approaches. These results are compared with the surface temperature measured by airborne Multispectral Infrared and Visible Imaging Spectrometer (MIVIS). The LST measured by MIVIS sensor can be considered closer to the real surface temperature because the data are acquired at an altitude of 1500 m and are not affected by significant atmospheric effects such as for satellite data, acquired at 705 km from the Earth’s surface. The best results are obtained by considering variable emissivity.

Mapping of Asbestos Cement Roofs and Their Weathering Status Using Hyperspectral Aerial Images

The aims of this study were: (i) the mapping of asbestos cement roofs in an urban area; and (ii) the development of a spectral index related to the roof weathering status. Aerial images were collected through the Multispectral Infrared and Visible Imaging Spectrometer (MIVIS) sensor, which acquires data in 102 channels from the visible to the thermal infrared spectral range. An image based supervised classification was performed using the Spectral Angle Mapper (SAM) algorithm. The SAM was trained through a set of pixels selected on roofs of different materials. The map showed an average producer’s accuracy (PA) of 86% and a user’s accuracy (UA) of 89% for the asbestos cement class. A novel spectral index, the “Index of Surface Deterioration” (ISD), was defined based on measurements collected with a portable spectroradiometer on asbestos cement roofs that were characterized by different weathering statuses. The ISD was then calculated on the MIVIS images, allowing the distinction of two weathering classes (i.e., high and low). The asbestos cement map was handled in a Geographic Information System (GIS) in order to supply the municipalities with the cadastral references of each property having an asbestos cement roof. This tool can be purposed for municipalities as an aid to prioritize asbestos removal, based on roof weathering status.

Airborne hyperspectral data to assess suspended particulate matter and aquatic vegetation in a shallow and turbid lake

This paper presents an application of a physic-based method that relies on spectral inversion procedures to simultaneously estimate concentrations of water constituents, water column heights (cH) and benthic substrate types in Lake Trasimeno (Italy) from airborne imaging spectrometry. Complex waters of this lake are challenging due to the coexistence of optically-deep turbid waters and of optically-shallow waters, mostly characterised by dense submerged aquatic vegetation (SAV) beds. Airborne data acquired on 12 May 2009 by Multispectral Infrared and Visible Imaging Spectrometer (MIVIS) were converted into remote sensing reflectance Rrs(λ) with the atmospheric correction code ATCOR. A spectral inversion procedure implementing a bio-optical model (namely BOMBER), parameterised with in situ data, was firstly run to retrieve concentrations of suspended particulate matter (SPM), chlorophyll-a (chl-a) and coloured dissolved organic matter (i.e. aCDOM(440)) in the optically-deep waters. The areas where the retrieved optimisation error was higher than 10% were instead assumed as optically-shallow. In these areas two maps depicting the linear unmixing of three substrate types (i.e., silty-clay, Chara ssp. and other hydrophyte) and the water column heights were produced. The MIVIS-derived products were validated with field data providing a reliable estimation of SPM, chl-a, aCDOM(440) and cH (determination coefficients always R2>0.7). SPM concentrations were also similar to a 5.4-km long transect of flow-through turbidity data, and the SAV map was comparable to in situ observations. Generally, the colonisation patterns of SAV were reflecting the spatial distribution of SPM concentrations. In particular, the positive role of Chara on keeping SPM concentrations low was observed. Future research should extend this application to remote sensing data acquired in other seasons to trace the dynamics of SAV and its effect on spatial water clarity.

Integration of Field and Laboratory Spectral Data with Multi-Resolution Remote Sensed Imagery for Asphalt Surface Differentiation

The ability to classify asphalt surfaces is an important goal for the selection of suitable non-variant targets as pseudo-invariant targets during the calibration/validation of remotely-sensed images. In addition, the possibility to recognize different types of asphalt surfaces on the images can help optimize road network management. This paper presents a multi-resolution study to improve asphalt surface differentiation using field spectroradiometric data, laboratory analysis and remote sensing imagery. Multispectral Infrared and Visible Imaging Spectrometer (MIVIS) airborne data and multispectral images, such as Quickbird and Ikonos, were used. From scatter plots obtained by field data using λ = 460 and 740 nm, referring to MIVIS Bands 2 and 16 and Quickbird and Ikonos Bands 1 and 4, pixels corresponding to asphalt covering were identified, and the slope of their interpolation lines, assumed as asphalt lines, was calculated. These slopes, used as threshold values in the Spectral Angle Mapper (SAM) classifier, obtained an overall accuracy of 95% for Ikonos, 98% for Quickbird and 93% for MIVIS. Laboratory investigations confirm the existence of the asphalt line also for new asphalts, too.

Statistical Classification for Assessing PRISMA Hyperspectral Potential for Agricultural Land Use

The upcoming launch of the next generation of hyperspectral satellites (PRISMA, EnMap, HyspIRI, etc.) will meet the increasing demand for the availability/accessibility of hyperspectral information on agricultural land use from the agriculture community. To this purpose, algorithms for the classification of remotely sensed images are here considered for agricultural monitoring of cultivated area, exploiting remotely sensed high spectral resolution images. Classification is accomplished by procedures based on discriminant analysis tools that well suit hyperspectrality, circumventing what in statistics is called “the curse of dimensionality”. As a byproduct of classification, a full assessment of the spectral bands of the sensor is obtained, ranking them with the purpose of understanding their role in segmentation and classification. The methodology has been validated on two independent image datasets gathered by the MIVIS (Multispectral Infrared and Visible Imaging Spectrometer) sensor for which ground validations were available. A comparison with the popular multiclass SVM (Support Vector Machines) classifier is also presented. Results show that a good classification (minimum global success rate 95% through all experiments) is achieved by using the 10 spectral bands selected as the most discriminant by the proposed procedure; moreover, it also appears that nonparametric techniques generally outperform parametric ones. The present study confirms that the new generation of hyperspectral satellite data like PRISMA can ripen an end-user application for agricultural land-use of cultivated area.

Detection of Anomalies Produced by Buried Archaeological Structures Using Nonlinear Principal Component Analysis Applied to Airborne Hyperspectral Image

In this paper, airborne hyperspectral data have been exploited by means of Nonlinear Principal Component Analysis (NLPCA) to test their effectiveness as a tool for archaeological prospection, evaluating their potential for detecting anomalies related to buried archaeological structures. In the literature, the NLPCA was used to decorrelate the information related to a hyperspectral image. The resulting nonlinear principal components (NLPCs) contain information related to different land cover types and biophysical properties, such as vegetation coverage or soil wetness. From this point of view, NLPCA applied to airborne hyperspectral data was exploited to test their effectiveness and capability in highlighting the anomalies related to buried archaeological structures. Each component obtained from the NLPCA has been interpreted in order to assess any tonal anomalies. As a matter of a fact, since every analyzed component exhibited anomalies different in terms of size and intensity, the Separability Index (SI) was applied for measuring the tonal difference of the anomalies with respect to the surrounding area. SI has been evaluated for determining the potential of anomalies detection in each component. The airborne Multispectral Infrared and Visible Imaging Spectrometer (MIVIS) images, collected over the archaeological Park of Selinunte, were analyzed for this purpose. In this area, the presence of remains, not yet excavated, was reported by archaeologists. A previous analysis of this image, carried out to highlight the buried structures, appear to match the archaeological prospection. The results obtained by the present work demonstrate that the use of the NLPCA technique, compared to previous approaches emphasizes the ability of airborne hyperspectral images to identify buried structures. In particular, the adopted data processing flow chart (i.e., NLPCA and SI techniques, data resampling criteria and anomaly evaluations criteria) applied to MIVIS airborne hyperspectral data, collected over Selinunte Archaeological Park, highlighted the ability of the NLPCA technique in emphasizing the anomalies related to the presence of buried structure.

Retrospective assessment of macrophytic communities in southern Lake Garda (Italy) from in situ and MIVIS (Multispectral Infrared and Visible Imaging Spectrometer) data

In situ and hyperspectral MIVIS (Multispectral Infrared and Visible Imaging Spectrometer) images acquired over a period of 13 years are used to assess changes in macrophyte colonization patterns in the coastal zones of the Sirmione Peninsula in the southern part of Lake Garda (Italy). In situ data (abundance, cover density and diversity of macrophyte communities) and MIVIS-derived maps of colonized substrates are analyzed by considering the variability of the main hydrological and physicochemical variables in order to indicate the main factors that explain the spatiotemporal variability of macrophyte communities. The results show a considerable modification in terms of macrophyte structural complexity and colonized areas. Almost 98% of macrophyte meadows (in particular communities with a density of over 70%) are lost and subsequently replaced by moderate to extremely rare communities with density from 10% to 40%. Well-established submerged macrophytes are replaced by de-structured communities characterized by moderate to scarce density: on average lower than 30%. The study indicates that macrophyte distribution along the littoral zone of the Sirmione Peninsula is certainly linked to water transparency and water level fluctuation. The results also indicate that the worsening of eutrophication may be associated with the gradual disappearance of macrophyte meadows, but may also be accelerated by herbivorous aquatic birds grazing there. Lastly, the increasing frequency and number of catamaran tours could be considered a threat for the stability of these valuable communities.

Surveying the roofs of Rome

Abstract This study is aimed at investigating a portion of the city of Rome by means of remotely sensed Multispectral Infrared And Visible Imaging Spectrometer (MIVIS) data. A particular attention was devoted to building roofs described not only as the last defining touch given to a building, the aesthetic conclusion to a whole construction process, but also as expression and sign of a society's level of civilisation, culture and technical skill. Through the classification of objects and materials, we propose to combine history and science as different ways of interpreting a city, in this case Rome, and to implement an image processing technique as an effective tool in urban planning.

Chlorophyll concentration mapping with MIVIS data to assess crown discoloration in the Ticino Park oak forest

Airborne hyperspectral remote observations, characterized by high spatial and spectral resolution, allow the estimation of quantitative vegetation variables useful in forest condition assessment. In this research, total chlorophyll (a + b) concentration (C ab), a biochemical variable describing crown discoloration rate, was mapped to assess oak (Quercus robur L.) condition in the Ticino Regional Park. A simulation experiment was conducted to evaluate the error in C ab estimation due to ecological variables (i.e. canopy leaf area index and understorey characteristics) and to sun-sensor configurations when optical indices are used. Canopy reflectance was simulated by means of the PROSPECT leaf radiative transfer model (Jacquemoud and Baret 1990) coupled with the SAILH canopy radiative transfer model, a variation of the SAIL (Scattering by Arbitrarily Inclined Leaves) model modified to include the hot spot effect (Verhoef 1984, Kuusk 1991). The vegetation was modelled as a two layer medium with oak canopy as the top layer and the understorey as the bottom layer. Simulations were performed for varying leaf C ab and canopy Leaf Area Index (LAI) of the top layer, θl (mean leaf inclination angle) and LAI of the bottom layer (LAIu) and sun-sensor geometry. Optical indices were calculated and used in C ab retrieval. Simulations demonstrated that errors in C ab estimation were negligible when MTCI (MERIS Terrestrial Chlorophyll Index) was used, thus indicating that MTCI was the most reliable index in mapping C ab in this forest environment. Empirical models based on optical indices were developed to map C ab from Multispectral Infrared and Visible Imaging Spectrometer (MIVIS) images acquired by an airborne survey on the Park forested area. A regression analysis between C ab concentration measured in leaves sampled in field and optical indices computed from hyperspectral MIVIS data was conducted. The MTCI index showed the highest performances and was therefore used to map C ab concentration of the Ticino Park oak forest. The C ab map was then used to assess crown discoloration level.

A new methodology for in-flight radiometric calibration of the MIVIS imaging sensor

Sensor radiometric calibration is of great importance in computing physical values of radiance of the investigated targets, but often airborne scanners are not equipped with any in-flight radiometric calibration facility. Consequently, the radiometric calibration or airborne systems usually relies only on pre-flight and vicarious calibration or on indirect approaches. This paper introduces an experimental approach that makes use of on-board calibration techniques to perform the radiometric calibration of the CNR’s MIVIS (Multispectral Infrared and Visible Imaging Spectrometer) airborne scanner. This approach relies on the use of an experimental optical test bench originally designed at Politecnico di Milano University (Italy), called MIVIS Flying Test Bench (MFTB), to perform the first On-The-Fly (OTF) calibration of the MIVIS reflective spectral bands. The main task of this study is to estimate how large are the effects introduced by aircraft motion (e.g., e.m. noise or vibrations) and by environment conditions (e.g., environment temperature) on the radiance values measured by the MIVIS sensor during the fly. This paper describes the first attempt to perform an On-The-Fly (OTF) calibration of the MIVIS reflective spectral bands (ranging from 430 nm to 2.500 nm). Analysis of results seems to point out limitations of traditional radiometric calibration methodology based only on pre-flight approaches, with important implications for data quality assessment.

Aerial remote sensing hyperspectral techniques for rocky outcrops mapping

In this work the MIVIS (Multispectral Infrared and Visible Imaging Spectrometer) hyperspectral data, acquired during aerial campaigns made in 1998 over the Pollino National Park in the framework of the «Progetto Pollino», have been used to set up a supervised technique devoted to identify the presence of selected rocky outcrops. Tests have been performed over an extended area characterised by a complex orography. Within this area, serpentinite was chosen as a test-rock because it is present in isolated outcrops, distributed all over the test-area, besides subtending important problems of environmental nature as it contains asbestos. Geological information, coming from field observations or geological maps, was used to trigger the algorithms and as ground truth for its validation. Two spectral analysis techniques, SAM (Spectral Angle Mapper) and LSU (Linear Spectral Unmixing), have been applied and their results n combined to automatically identify serpentinite outcrops and, in some cases, to mark its boundaries. The approach used in this work is characterised by simplicity (no atmosphere and illumination corrections were performed on MIVIS data), robustness (material of interest is identified for certainty) and intrinsic exportability (the method proposed can be applied on different geographic areas and, in theory, to identify any kind of material because no datum about atmospheric and illumination conditions is required).

Hyperspectral remote sensing for light pollution monitoring

A possible application of hyperspectral remote sensing regards the assessment of light pollution due to cities and industries. In this paper we introduce the results from a remote sensing campaign performed in September 2001 at night time. For the first time nocturnal light pollution was measured at high spatial and spectral resolution us- ing two airborne hyperspectral sensors, namely the Multispectral Infrared and Visible Imaging Spectrometer (MIVIS) and the Visible InfraRed Scanner (VIRS-200). These imagers, generally employed for day-time Earth remote sensing, were flown over the Tuscany coast (Italy) on board of a Casa 212/200 airplane from an altitude of 1.5-2.0 km. We describe the experimental activities which preceded the remote sensing campaign, the opti- mization of sensor configuration, and the images as far acquired. The obtained results point out the novelty of the performed measurements and highlight the need to employ advanced remote sensing techniques as a spec- troscopic tool for light pollution monitoring.

Specific spectral bands for different land cover contexts to improve the efficiency of remote sensing archaeological prospection: The Arpi case study

Archaeological remains can be automatically extracted by remote sensing data when their spectral characteristics are discernible from their background; however, problems arise when extracting and classifying archaeological spectral features relative to subsurface structures, as archaeological remains do not have unique shape or spectral characteristics. Under the unidentifiable spectral condition, the main aim of this research is to verify the effectiveness of limited and specific spectral bands, retrieved by a hyperspectral remote sensing methodology described in [1], for the detection of surface anomalies related to archeological structures. The archaeological relevance of these extracted spectral anomalies is determined by an expert using traditional photo-interpretation methods. The results, compared with the archaeological knowledge of this area, stress the usefulness of the methodology to identify the specific spectral ranges to detect surface anomalies related to subsurface archaeological structures as a function of the surfacing land cover. The selected area for this study is located in the Arpi archaeological area (Italy) and the hyperspectral imagery used are Multispectral Infrared and Visible Imaging Spectrometer (MIVIS) airborne data. The paper highlights how airborne hyperspectral remote sensing can be an effective and cost-efficient tool to perform a preliminary analysis of subsurface remains in archaeological areas by prioritizing and localizing the sites where one can apply near surface geophysical and archaeological surveys. © 2009 Elsevier Masson SAS. All rights reserved.

A neural network method for analysis of hyperspectral imagery with application to the Cassas landslide (Susa Valley, NW-Italy)

Geomorphological analysis is an essential element of many landslide monitoring and hazard assessment studies. Remote sensing techniques have long been an effective tool for such analyses. This paper presents results regarding a geomorphological investigation performed exploiting information derived from high resolution hyper-spectral airborne sensor MIVIS ( Multi-spectral Infrared and Visible Imaging Spectrometer) images. The authors propose a workflow based on neural network algorithms used both for image geometric correction and classification. The case study area is the Middle Susa Valley (Italian Western-Alps). It includes the Cassas landslide, a well-known complex landslide affecting slopes on the southern side of the valley, near the town of Salbertrand. At this location several “lifelines” (railway, main roads, hydroelectric tunnels, etc.) are concentrated, and they suffer from a constant threat of landslide activity. Traditional geomorphological characterization of the landslide slope was improved by comparing field campaigns with further information extracted from remote-sensed datasets. Presented results include the identification and characterization of major elements of the present-day active unstable slope (debris-covered areas, fractured/disjointed rock walls, landslide accumulation borders), as well as of individual structural features and landforms (major faults and fractures, trenches, elongated depressions, counterslope scarps), related to long-term deep-seated gravitational slope deformation.

Spatial variations in lava flow field thermal structure and effusion rate derived from very high spatial resolution hyperspectral (MIVIS) data

High spatial resolution hyperspectral measurements of volcanic thermal anomalies allow for an unconstrained solution of a two‐component thermal model. This can be used for identification of lava flow emplacement style and the calculation of lava flow heat and volume flux. The multispectral infrared and visible imaging spectrometer (MIVIS) is an airborne sensor equipped with 72 bands in the short infrared range and 10 bands in the thermal infrared region of the spectrum. We used MIVIS acquired for Mount Etna (Italy) during the July–August 2001 eruption to solve the dual band equations in an unconstrained fashion using three bands of unsaturated data. Our results suggest a complex thermal structure for Etnean lava flows. This is characterized by a downflow transition from a lightly crusted active channel to a more heavily crusted distal section, both surrounded by zones of stagnant cooling flow where exposed molten material is absent and maximum temperatures are thus lower. The total flow field effusion rate obtained for 29 July 2001 (0700 local time) of 8–16 m3/s is in excellent agreement with that obtained from ground‐based measurements and Advanced Very High Resolution Radiometer data. Flow‐by‐flow effusion rates obtained from the MIVIS data vary depending on whether the vent is linked to the central conduit or the dyke that was injected from greater depth, as well as vent elevation, with lower elevation vents experiencing higher effusion rates.

Recent changes in macrophyte colonisation patterns: an imaging spectrometry-based evaluation of southern Lake Garda (northern Italy)

Temporal variation in the extent of submerged macrophytes along the littoral zone of Sirmione Peninsula in the southern part of Lake Garda (Northern Italy) was investigated using imaging spectrometry. Two images, with a spatial resolution of 5 m were acquired by the Multispectral Infrared and Visible Imaging Spectrometer (MIVIS) in the summers of 1997 and 2005. Image data were first geocoded and then corrected for both atmospheric and skylight reflection effects at the water surface using the 6S radiative transfer code. The two images were inverted using a bio-optical model, which was parameterised with the inherent optical properties of the lake. The inversion utilized the spectral range from 0.48-0.60 µm because it simultaneously provided the lowest environmental noise and the best atmospheric correction performances for the two scenes and produced images of bottom depth and of two substrate classes: bare sand and submerged vegetation, representing a mixture of valuable freshwater species. The MIVIS-derived bottom depth ranges and patterns were comparable to a bathymetry chart with a deviation less than 5%. In 2005, the image was consistent with contemporaneous in-situ derived knowledge on macrophyte distribution. In 1997, the substrate image map was deemed reasonable with respect to the macrophyte distribution documented in 2000. The comparison of the substrate products for the two dates showed a marked decrease in macrophyte beds, with a concomitant increase in sandy substrates. In the 8-year interval the extent of submerged macrophyte decreased from 72% to 52%. We expect that this study will contribute to increased knowledge of macrophyte colonisation patterns of the Sirmione Peninsula, where, despite their ecological significance, changes have been poorly documented.

Estimation of leaf and canopy water content in poplar plantations by means of hyperspectral indices and inverse modeling

This study investigates the applicability of empirical and radiative transfer models to estimate water content at leaf and landscape level. The main goal is to evaluate and compare the accuracy of these two approaches for estimating leaf water content by means of laboratory reflectance/transmittance measurements and for mapping leaf and canopy water content by using airborne Multispectral Infrared and Visible Imaging Spectrometer (MIVIS) data acquired over intensive poplar plantations (Ticino, Italy). At leaf level, we tested the performance of different spectral indices to estimate leaf equivalent water thickness (EWT) and leaf gravimetric water content (GWC) by using inverse ordinary least squares (OLS) regression, and reduced major axis (RMA) regression. The analysis showed that leaf reflectance is related to changes in EWT rather than GWC, with best results obtained by using RMA regression by exploiting the spectral index related to the continuum removed area of the 1200 nm water absorption feature with an explained variance of 61% and prediction error of 6.6%. Moreover, we inverted the PROSPECT leaf radiative transfer model to estimate leaf EWT and GWC and compared the results with those obtained by means of empirical models. The inversion of this model showed that leaf EWT can be successfully estimated with no prior information with mean relative errors of 14% and determination coefficient of 0.65. Inversion of the PROSPECT model showed some difficulties in the simultaneous estimation of leaf EWT and dry matter content, which led to large errors in GWC estimation. At landscape level with MIVIS data, we tested the performance of different spectral indices to estimate canopy water per unit ground area (EWT canopy). We found a relative error of 20% using a continuum removed spectral index around 1200 nm. Furthermore, we used a model simulation to evaluate the possibility of applying empirical models based on appositely developed MIVIS double ratios to estimate mean leaf EWT at landscape level ( EWT ― ). It is shown that combined indices (double ratios) yielded significant results in estimating leaf EWT at landscape level by using MIVIS data (with errors around 2.6%), indicating their potential in reducing the effects of LAI on the recorded signal. The accuracy of the empirical estimation of EWT canopy and EWT ― was finally compared with that obtained from inversion of the PROSPECT + SAILH canopy reflectance model to evaluate the potential of both methods for practical applications. A relative error of 27% was found for EWT canopy and an overestimation of leaf EWT ― with relative errors around 19%. Results arising from this remote sensing application support the robustness of hyperspectral regression indices for estimating water content at both leaf and landscape level, with lower relative errors compared to those obtained from inversion of leaf and 1D canopy radiative transfer models.

Statistical Analysis of Hyper-Spectral Data: A Non-Gaussian Approach

We investigate the statistical modeling of hyper-spectral data. The accurate modeling of experimental data is critical in target detection and classification applications. In fact, having a statistical model that is capable of properly describing data variability leads to the derivation of the best decision strategies together with a reliable assessment of algorithm performance. Most existing classification and target detection algorithms are based on the multivariate Gaussian model which, in many cases, deviates from the true statistical behavior of hyper-spectral data. This motivated us to investigate the capability of non-Gaussian models to represent data variability in each background class. In particular, we refer to models based on elliptically contoured (EC) distributions. We consider multivariate EC-t distribution and two distinct mixture models based on EC distributions. We describe the methodology adopted for the statistical analysis and we propose a technique to automatically estimate the unknown parameters of statistical models. Finally, we discuss the results obtained by analyzing data gathered by the multispectral infrared and visible imaging spectrometer (MIVIS) sensor.