Evaluating visible derivative spectroscopy by varimax-rotated, principal component analysis of aerial hyperspectral images from the western basin of Lake Erie

The Kent State University (KSU) spectral decomposition method provides information about the spectral signals present in multispectral and hyperspectral images. Pre-processing steps that enhance signal to noise ratio (SNR) by 7.37–19.04 times, enables extraction of the environmental signals captured by the National Aeronautics and Space Administration (NASA) Glenn Research Center\u27s, second generation, Hyperspectral imager (HSI2) into multiple, independent components. We have accomplished this by pre-processing of Level 1 HSI2 data to remove stripes from the scene, followed by a combination of spectral and spatial smoothing to further increase the SNR and remove non-Lambertian features, such as waves. On average, the residual stochastic noise removed from the HSI2 images by this method is 5.43 ± 1.42%. The method also enables removal of a spectrally coherent residual atmospheric bias of 4.28 ± 0.48%, ascribed to incomplete atmospheric correction. The total noise isolated from signal by the method is thu

Advanced InSAR atmospheric correction: MERIS/MODIS combination and stacked water vapour models

A major source of error for repeat-pass Interferometric Synthetic Aperture Radar (InSAR) is the phase delay in radio signal propagation through the atmosphere (especially the part due to tropospheric water vapour). Based on experience with the Global Positioning System (GPS)/Moderate Resolution Imaging Spectroradiometer (MODIS) integrated model and the Medium Resolution Imaging Spectrometer (MERIS) correction model, two new advanced InSAR water vapour correction models are demonstrated using both MERIS and MODIS data: (1) the MERIS/MODIS combination correction model (MMCC); and (2) the MERIS/MODIS stacked correction model (MMSC). The applications of both the MMCC and MMSC models to ENVISAT Advanced Synthetic Aperture Radar (ASAR) data over the Southern California Integrated GPS Network (SCIGN) region showed a significant reduction in water vapour effects on ASAR interferograms, with the root mean square (RMS) differences between GPS- and InSAR-derived range changes in the line-of-sight (LOS) direction decreasing from ,10mm before correction to ,5mm after correction, which is similar to the GPS/MODIS integrated and MERIS correction models. It is expected that these two advanced water vapour correction models can expand the application of MERIS and MODIS data for InSAR atmospheric correction. A simple but effective approach has been developed to destripe Terra MODIS images contaminated by radiometric calibration errors. Another two limiting factors on the MMCC and MMSC models have also been investigated in this paper: (1) the impact of the time difference between MODIS and SAR data; and (2) the frequency of cloud-free conditions at the global scale

Application of multi-window maximum cross-correlation to the mediterranean sea circulation by using MODIS data

In a previous study an improved Maximum Cross-Correlation technique, called Multi-Window Maximum Cross-Correlation (MW-MCC), was proposed, and applied to noise-free synthetic images in order to show its potential and limits in oceanographic applications. In this work, instead, the application of MW-MCC to high resolution MODIS images, and its capability to provide useful and realistic results for ocean currents, is studied. When applied to real satellite images, the MW-MCC is subject to cloud cover and image quality problems. As a consequence the number of useful MODIS images is greatly reduced. However, for every MODIS image, multiple spec-tral bands are available, and it is possible to apply the MW-MCC algorithm to the same scene as many times as the number of these bands, increasing the possibility of finding valid current vectors. Moreover, the comparison among the results from different spectral bands allows to verify both the consistency of the computed current vectors and the validity of using a spectral band as a good tracer for the ocean circulation. Due to the lack of systematic current measurements in the area considered, it has been not possible to perform an ex-tensive error analysis of the MW-MCC results, although a case study of a comparison between HF radar measurements and MW-MCC data is shown. Moreover, some comparison between numerical ocean model simulations and MW-MCC results are also shown. The coherence of the resulting circulation flow, the high number of current vectors found, the agreement among different spectral bands, and conformity with the currents measured by the HF radars or simulated by hydrodynamic models show the validity of the technique

Implementation and validation of the snow grain size retrieval SGSP from spectral reflectances of the satellite sensor MODIS

Snow is part of the cryosphere in the climate system of the Earth. It has a high albedo in the visible, decreasing towards the near-infrared. Snow on ground is a porous medium of ice, air, and possibly impurities like dust or soot. After deposition, it undergoes snow metamorphism changing the grain size, grain shape, and density. In the visible, the reflection characteristics of snow are mainly determined by the amount of impurities, and in the near-infrared by the size of the snow grains. Satellite sensors allow observing the snow in remote areas like the polar regions on a regular basis and on a global scale. A method to compute the snow grain size and impurity amount from optical satellite observations is the Snow Grain Size and Pollution amount (SGSP) retrieval. It uses data of three reflectance channels (here: at 0.47 µm, 0.86 µm, and 1.24 µm), has a reduced dependency on the snow grain shape, and is applicable at solar zenith angles up to 75°. In this work, the SGSP retrieval is implemented in a near-real time processing chain using data from the Moderate Resolution Imaging Spectrometer (MODIS) operating on the satellites Terra and Aqua. A sensitivity analysis reveals that currently only the snow grain size can be determined reliably by the SGSP retrieval, as the uncertainties of the MODIS instrument are too high for the amount of impurities typically occurring in polar regions. Sensitivity studies on the influence of vertically inhomogeneous snow, wet snow, and cirrus clouds show that the SGSP retrieval typically underestimates the grain size by 15% to 25% for those three cases. The SGSP-retrieved snow grain size is validated using six different ground truth data sets from the Arctic, the Antarctic, Greenland, and Japan from the years 2001 to 2009, and various subsurfaces (land, land ice, sea ice, lake ice). In general, the retrieved and ground-measured grain size are in good agreement. 17 cases have small differences (1 14%), 16 cases intermediate differences (18 53%), and four cases large differences (72 178%). The SGSP retrieval tends to underestimate the grain size for wet snow cases (by 18% 31%) and cirrus cloud cases (by 14% 31%), and overestimates it for surface hoar cases (by 30% 53%) and wind crust cases (by 23% 77%). A comparison of the SGSP retrieval with a previous retrieval using ground measurements from the Himalayan basin shows that the SGSP-retrieved grain size tends to be smaller (by 5 48 µm) and that vertically inhomogeneous snow influences the retrieval. A comparison of SGSP-retrieved snow grain size time series on the Ross ice shelf, Antarctica, at three Automatic Weather Stations (AWS) with snow depth change data from those three stations shows that a snow fall event of 6 cm is detected by the sudden decrease of the retrieved grain size from 200 µm to 50 µm. A comparison of the spectral snow albedo for the MODIS Channels 1 to 5 over 16 days on a large-scale area in Greenland between the SGSP-derived albedo and the spectral MODIS albedo product MOD43 shows a correlation of 0.82 for Channel 5,which is most sensitive to the snow grain size

Quantitative Mapping of Cyanobacterial Blooms Using Oceansat-1 OCM Satellite Data

Cyanobacteria represent a major harmful algal group in fresh to brackish water environments. Lac des Allemands, a freshwater lake of 49 km2 southwest of New Orleans, Louisiana, provides a natural laboratory for remote characterization of cyanobacteria blooms because of their seasonal occurrence. This dissertation makes a contribution to research methodology pertaining to atmospheric correction of satellite data and development of remote sensing algorithms to quantify cyanobacterial pigments. The Ocean Color Monitor (OCM) sensor provides radiance measurements similar to Sea-viewing Wide Field-of-View Sensor (SeaWiFS) but with higher spatial resolution. However, OCM does not have a standard atmospheric correction procedure and the comprehensive suite of atmospheric correction procedures for ocean (or lake) is not available in the literature in one place. Atmospheric correction of satellite data over inland lakes, estuaries and coastal waters is also challenging due to difficulties in the estimation of aerosol scattering accurately over these optically complex water bodies. Thus an atmospheric correction procedure was developed to obtain more accurate spectral remote sensing reflectance (Rrs) over Lac des Allemands from OCM data based on NASA’s extensive work for SeaWiFS. Since OCM was not well calibrated, a new vicarious calibration procedure was also developed to adjust OCM radiance values to SeaWiFS radiance as SeaWiFS is well calibrated over its entire life. Empirical inversion algorithms were developed to convert the OCM Rrs at bands centered at 510.6 and 556.4 nm to concentrations of phycocyanin (PC), the primary cyanobacterial pigment. For the algorithms to be uniformly valid over all areas (or all bio-optical regimes) of the lake, a holistic approach was developed to minimize the influence of other optically active constituents on the PC algorithms. Similarly, empirical algorithms to estimate chlorophyll a (Chl a) concentrations were developed using OCM bands centered at 556.4 and 669 nm. The best PC algorithm (R2=0.7450, p\u3c0.0001, n=72) yielded a root mean square error (RMSE) of 36.92 µg/L with a relative RMSE of 10.27%, and a mean absolute error (MAE) of 21.79 µg/L with a relative MAE of 6.06% (PC from 2.75 to 363.50 µg/L, n=48). The best algorithm for Chl a (R2=0.7510, p\u3c0.0001) produced an RMSE of 31.19 µg/L, with relative RMSE = 15.70% and a MAE of 16.56 µg/L, with relative MAE = 8.33% (Chl a from 9.46 to 212.76 µg/L, n=48). The results demonstrate the preliminary success of using the 360 x 236 m resolution OCM data to map cyanobacterial blooms in a small lake. While more field data are required to further validate the long-term performance of the algorithms, at present the algorithms may be implemented to process OCM data in an automated setup to provide timely information on the lake’s bloom conditions. Similarly, retrospective processing may provide a long-term time series of bloom characteristics to document potential trends. The applicability of the algorithms can be extended to other lakes after necessary testing

Machine Learning Approach to Retrieving Physical Variables from Remotely Sensed Data

Scientists from all over the world make use of remotely sensed data from hundreds of satellites to better understand the Earth. However, physical measurements from an instrument is sometimes missing either because the instrument hasn\u27t been launched yet or the design of the instrument omitted a particular spectral band. Measurements received from the instrument may also be corrupt due to malfunction in the detectors on the instrument. Fortunately, there are machine learning techniques to estimate the missing or corrupt data. Using these techniques we can make use of the available data to its full potential. We present work on four different problems where the use of machine learning techniques helps to extract more information from available data. We demonstrate how missing or corrupt spectral measurements from a sensor can be accurately interpolated from existing spectral observations. Sometimes this requires data fusion from multiple sensors at different spatial and spectral resolution. The reconstructed measurements can then be used to develop products useful to scientists, such as cloud-top pressure, or produce true color imagery for visualization. Additionally, segmentation and image processing techniques can help solve classification problems important for ocean studies, such as the detection of clear-sky over ocean for a sea surface temperature product. In each case, we provide detailed analysis of the problem and empirical evidence that these problems can be solved effectively using machine learning techniques

Estimation of leaf area index and its sunlit portion from DSCOVR EPIC data: theoretical basis

This paper presents the theoretical basis of the algorithm designed for the generation of leaf area index and diurnal course of its sunlit portion from NASA's Earth Polychromatic Imaging Camera (EPIC) onboard NOAA's Deep Space Climate Observatory (DSCOVR). The Look-up-Table (LUT) approach implemented in the MODIS operational LAI/FPAR algorithm is adopted. The LUT, which is the heart of the approach, has been significantly modified. First, its parameterization incorporates the canopy hot spot phenomenon and recent advances in the theory of canopy spectral invariants. This allows more accurate decoupling of the structural and radiometric components of the measured Bidirectional Reflectance Factor (BRF), improves scaling properties of the LUT and consequently simplifies adjustments of the algorithm for data spatial resolution and spectral band compositions. Second, the stochastic radiative transfer equations are used to generate the LUT for all biome types. The equations naturally account for radiative effects of the three-dimensional canopy structure on the BRF and allow for an accurate discrimination between sunlit and shaded leaf areas. Third, the LUT entries are measurable, i.e., they can be independently derived from both below canopy measurements of the transmitted and above canopy measurements of reflected radiation fields. This feature makes possible direct validation of the LUT, facilitates identification of its deficiencies and development of refinements. Analyses of field data on canopy structure and leaf optics collected at 18 sites in the Hyytiälä forest in southern boreal zone in Finland and hyperspectral images acquired by the EO-1 Hyperion sensor support the theoretical basis.Shared Services Center NAS

Atmospheric Correction of a Seasonal Time Series of Hyperion EO-1 Images and Red Edge Inflection Point Calculation

While spaceborne satellite data has been extensively used to extract biophysical forest characteristics through reflectance features and indices, there are still many questions regarding seasonal changes in reflectance. Boreal forests have already seen changes in growth patterns from climate change, and the large scale monitoring of these forests is becoming more important. Understanding seasonal changes in reflectance in the boreal region will allow for the monitoring of seasonal biophysical changes using satellite imagery. For this monitoring to be possible the satellite imagery needs to be preprocessed and atmospherically corrected to create a time series of hemispherical-directional reflectance factors. The red edge is the abrupt change in reflectance between 680 and 740 nm seen in vegetation spectra. The red edge inflection point is the wavelength, at which the slope is steepest in the red edge. The red edge inflection point is sensitive to plant chlorophyll content and has been extensively used for estimating vegetation biophysical parameters including: leaf-area index, biomass and plant health levels. Hyperion is a narrowband imaging spectrometer aboard the Earth Observer-1 satellite. Hyperion captures data across 242 spectral bands covering a spectral range of 356 to 2577 nm resulting in a nominal spectral range of 10 nm. While the high spectral resolution of Hyperion makes it possible to calculate the REIP, there is no consensus on how this should be done, with different methods producing conflicting results. This study explains the preprocessing and atmospheric correction of a seasonal time series of five Hyperion EO-1 images (Provided courtesy of the USGS) from Hyytiälä, Southern Finland (61° 51 N, 24° 17 E). The time series ranges from 31.5.2010 to 12.8.2010, covering much of the growing season and the seasonal changes in reflectance. The first derivative, four-point linear interpolation, Lagrangian interpolation, and fifth-order polynomial fitting methods for calculating the REIP are looked at to determine their applicability for Hyperion imagery using this time series. Hyperion data requires considerable preprocessing before atmospheric correction can be done. In this study the preprocessing covered: destriping, desmiling, atmospheric correction and finally geocorrection. Atmospheric correction was done using both FLAASH and ATCOR, both of which are MODTRAN based absolute atmospheric correction algorithms. The final atmospherically corrected HDRF images were evaluated using in situ handheld spectrometer reference measurements of a grass field in the area. An average RMSE value of around 3% was achieved with both algorithms. The corrected Hyperion images were also compared against two MODIS products, which also showed good agreement. The aerosol retrieval however did not work with either algorithm, on any scene. The use of a sun photometer for aerosol level estimation was also not effective. Due to the dynamics of the red edge and expected seasonal red edge inflection point trends, the fifth-order polynomial fitting method was seen as the best method for calculating the red edge inflection point. The red edge inflection point did not correlate strongly with leaf area index overall, however there was a strong correlation with individual plots. A strong correlation was observed between Hyperion red edge inflection point and understory red edge inflection point, both overall and for individual plots.Kaukokartoitusmenetelmiä on pitkään käytetty metsän biofyysisten ominaisuuksien arvioinnissa, käyttäen hyväksi niiden heijastusominaisuuksia ja kasvillisuusindeksejä. Metsän heijastuksen muutokset kasvukauden aikana eivät kuitenkaan ole täysin ymmärrettyjä. Boreaalisten metsien kasvussa on havaittu muutoksia ilmastonmuutoksen myötä, minkä vuoksi niiden monitorointi on erityisen tärkeää. Kaukokartoitusaineistoon pohjautuvaan monitorointiin vaaditaan ymmärrystä metsien heijastusominaisuuksien muutoksista kasvukauden aikana. Tätä myöten käytettävän aineiston täytyy olla esikäsitelty ja ilmakehäkorjattu. Niin kutsuttu punainen reuna (red edge) on tyypillinen ominaisuus kasvillisuuden heijastuksessa, joka näkyy äkillisenä muutoksena heijastuksessa 680 ja 740 nm välillä. Punaisen reunan käännepiste (red edge inflection point) on se aallonpituus, jossa heijastuksen muutos on jyrkimmillään. Punaisen reunan käännepiste on herkkä klorofyllin määrälle kasvillisuudessa, ja sitä on käytetty arvioimaan kasvillisuuden biofyysisiä parametreja, kuten lehtialaindeksiä, biomassaa ja kasvillisuuden terveyttä. Hyperion on kapeakanavainen kuvaava spektrometri EO-1 satelliitissa. Hyperion mittaa aallonpituusalueen 356–2577 nm heijastusta 242 kanavalla, ja sensorin nominaalinen spektrinen resoluutio on 10 nm. Korkea spektrinen resoluutio mahdollistaa punaisen reunan käännepisteen laskemisen. Laskemiseen on kuitenkin useita menetelmiä, jotka tuottavat erilaisia tuloksia. Tämä Pro Gradu - tutkielma kattaa viiden Hyperion EO-1 kuvan esikäsittelyn ja ilmakehäkorjauksen. Kuvat ovat Hyytiälästä, Etelä-Suomesta (61° 51 N, 24° 17 E). Aikasarja alkaa 5. toukokuuta 2010 ja päättyy 11. heinäkuuta 2010, kattaen suurimman osan kasvukaudesta ja kasvillisuuden heijastuksen vaihtelusta. Seuraavia punaisen reunan käännepisteen laskentamenetelmien soveltuvuutta testattiin Hyperion-aineistolla: neljän pisteen lineaarinen interpolaatio, Lagrangian interpolaatio, ja viidennen asteen yhtälön sovittaminen. Hyperion-data vaatii paljon esikäsittelyä ennen kuin ilmakehäkorjaus voidaan suorittaa. Tässä tutkielmassa esikäsittely kattoi seuraavat vaiheet: spektrisen hymyn poisto, viivojen poisto, ilmakehäkorjaus, ja lopuksi geometrinen korjaus. Ilmakehäkorjaus toteutettiin käyttäen FLAASH ja ATCOR -algoritmeja, jotka ovat absoluuttisia ilmakehäkorjauksia ja käyttävät MODTRAN -algoritmia ilmakehän mallinnuksessa. Lopullisten, ilmakehäkorjattujen kuvien heijastusta verrattiin maastossa mitattuun tukiaineistoon. Maastoaineisto mitattiin tutkimusalueella sijaitsevalla ruohokentällä. Molemmat algoritmit tuottivat hyvän tuloksen, mutta kummankaan algoritmin automaattinen aerosolin määrän arviointi ei toiminut. Myöskään arviointi aurinkofotometrin avulla ei toiminut. Korjatut Hyperion-kuvat sopivat kuitenkin hyvin yhteen verrattaessa niitä kahteen MODIS-tuotteeseen. Punaisen reunan dynamiikan takia viidennen asteen yhtälön sovittaminen punaiseen reunaan todettiin parhaaksi menetelmäksi laskea punaisen reunan käännepiste. Hyperion-aineistosta johdettu punaisen reunan käännepiste ei korreloitunut voimakkaasti lehtialaindeksin kanssa, vaikka yksittäiset koealat korreloivatkin vahvasti. Sen sijaan Hyperion-aineiston punaisen reunan käännepisteen ja aluskasvillisuuden punaisen reunan käännepisteen välillä oli hyvin vahva korrelaatio

Image Restoration for Remote Sensing: Overview and Toolbox

Remote sensing provides valuable information about objects or areas from a distance in either active (e.g., RADAR and LiDAR) or passive (e.g., multispectral and hyperspectral) modes. The quality of data acquired by remotely sensed imaging sensors (both active and passive) is often degraded by a variety of noise types and artifacts. Image restoration, which is a vibrant field of research in the remote sensing community, is the task of recovering the true unknown image from the degraded observed image. Each imaging sensor induces unique noise types and artifacts into the observed image. This fact has led to the expansion of restoration techniques in different paths according to each sensor type. This review paper brings together the advances of image restoration techniques with particular focuses on synthetic aperture radar and hyperspectral images as the most active sub-fields of image restoration in the remote sensing community. We, therefore, provide a comprehensive, discipline-specific starting point for researchers at different levels (i.e., students, researchers, and senior researchers) willing to investigate the vibrant topic of data restoration by supplying sufficient detail and references. Additionally, this review paper accompanies a toolbox to provide a platform to encourage interested students and researchers in the field to further explore the restoration techniques and fast-forward the community. The toolboxes are provided in https://github.com/ImageRestorationToolbox.Comment: This paper is under review in GRS

Reduction of Radiometric Miscalibration—Applications to Pushbroom Sensors

The analysis of hyperspectral images is an important task in Remote Sensing. Foregoing radiometric calibration results in the assignment of incident electromagnetic radiation to digital numbers and reduces the striping caused by slightly different responses of the pixel detectors. However, due to uncertainties in the calibration some striping remains. This publication presents a new reduction framework that efficiently reduces linear and nonlinear miscalibrations by an image-driven, radiometric recalibration and rescaling. The proposed framework—Reduction Of Miscalibration Effects (ROME)—considering spectral and spatial probability distributions, is constrained by specific minimisation and maximisation principles and incorporates image processing techniques such as Minkowski metrics and convolution. To objectively evaluate the performance of the new approach, the technique was applied to a variety of commonly used image examples and to one simulated and miscalibrated EnMAP (Environmental Mapping and Analysis Program) scene. Other examples consist of miscalibrated AISA/Eagle VNIR (Visible and Near Infrared) and Hawk SWIR (Short Wave Infrared) scenes of rural areas of the region Fichtwald in Germany and Hyperion scenes of the Jalal-Abad district in Southern Kyrgyzstan. Recovery rates of approximately 97% for linear and approximately 94% for nonlinear miscalibrated data were achieved, clearly demonstrating the benefits of the new approach and its potential for broad applicability to miscalibrated pushbroom sensor data