Visible And Near-infrared Bands Research Articles

Radiometric Inter-Calibration between Himawari-8 AHI and S-NPP VIIRS for the Solar Reflective Bands

The Advanced Himawari Imager (AHI) on-board Himawari-8, which was launched on 7 October 2014, is the first geostationary instrument housed with a solar diffuser to provide accurate onboard calibrated data for the visible and near-infrared (VNIR) bands. In this study, the Ray-matching and collocated Deep Convective Cloud (DCC) methods, both of which are based on incidently collocated homogeneous pairs between AHI and Suomi NPP (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS), are used to evaluate the calibration difference between these two instruments. While the Ray-matching method is used to examine the reflectance difference over the all-sky collocations with similar viewing and illumination geometries, the near lambertian collocated DCC pxiels are used to examine the difference for the median or high reflectance scenes. Strong linear relationships between AHI and VIIRS can be found at all the paired AHI and VIIRS bands. Results of both methods indicate that AHI radiometric calibration accuracy agrees well with VIIRS data within 5% for B1-4 and B6 at mid and high reflectance scenes, while AHI B5 is generally brighter than VIIRS by ~6%–8%. No apparent East-West viewing angle dependent calibration difference can be found at all the VNIR bands. Compared to the Ray-matching method, the collocated DCC method provides less uncertainty of inter-calibration results at near-infrared (NIR) bands. As AHI has similar optics and calibration designs to the GOES-R Advanced Baseline Imager (ABI), which is currently scheduled to launch in fall 2016, the on-orbit AHI data provides a unique opportunity to develop, test and examine the cal/val tools developed for ABI.

An efficient approach for pixel decomposition to increase the spatial resolution of land surface temperature images from MODIS thermal infrared band data.

Land surface temperature (LST) images retrieved from the thermal infrared (TIR) band data of Moderate Resolution Imaging Spectroradiometer (MODIS) have much lower spatial resolution than the MODIS visible and near-infrared (VNIR) band data. The coarse pixel scale of MODIS LST images (1000 m under nadir) have limited their capability in applying to many studies required high spatial resolution in comparison of the MODIS VNIR band data with pixel scale of 250–500 m. In this paper we intend to develop an efficient approach for pixel decomposition to increase the spatial resolution of MODIS LST image using the VNIR band data as assistance. The unique feature of this approach is to maintain the thermal radiance of parent pixels in the MODIS LST image unchanged after they are decomposed into the sub-pixels in the resulted image. There are two important steps in the decomposition: initial temperature estimation and final temperature determination. Therefore the approach can be termed double-step pixel decomposition (DSPD). Both steps involve a series of procedures to achieve the final result of decomposed LST image, including classification of the surface patterns, establishment of LST change with normalized difference of vegetation index (NDVI) and building index (NDBI), reversion of LST into thermal radiance through Planck equation, and computation of weights for the sub-pixels of the resulted image. Since the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) with much higher spatial resolution than MODIS data was on-board the same platform (Terra) as MODIS for Earth observation, an experiment had been done in the study to validate the accuracy and efficiency of our approach for pixel decomposition. The ASTER LST image was used as the reference to compare with the decomposed LST image. The result showed that the spatial distribution of the decomposed LST image was very similar to that of the ASTER LST image with a root mean square error (RMSE) of 2.7 K for entire image. Comparison with the evaluation DisTrad (E-DisTrad) and re-sampling methods for pixel decomposition also indicate that our DSPD has the lowest RMSE in all cases, including urban region, water bodies, and natural terrain. The obvious increase in spatial resolution remarkably uplifts the capability of the coarse MODIS LST images in highlighting the details of LST variation. Therefore it can be concluded that, in spite of complicated procedures, the proposed DSPD approach provides an alternative to improve the spatial resolution of MODIS LST image hence expand its applicability to the real world.

Detection of Thrips Defect on Green-Peel Citrus Using Hyperspectral Imaging Technology Combining PCA and B-Spline Lighting Correction Method

In order to find an effective method of detecting thrips defect on green-peel citrus, a defect segmentation method was developed using a single threshold value based on combination of characteristic wavelengths principal component analysis (PCA) and B-spline lighting correction method in this study. At first, four characteristic wavelengths (523, 587, 700 and 768 nm) were obtained using PCA of Vis-NIR (visible and near-infrared) bands and analysis of weighting coefficients; secondarily, PCA was performed using characteristic wavelengths and the second principal component (PC2) was selected to classify images; then, B-spline lighting correction method was proposed to overcome the influence of lighting non-uniform on citrus when thrips defect was segmented; finally, thrips defect on citrus was extracted by global threshold segmentation and morphological image processing. The experimental results show that thrips defect in citrus can be detected with an accuracy of 96.5% by characteristic wavelengths PCA and B-spline lighting correction method. This study shows that thrips defect on green-peel citrus can be effectively identified using hyperspectral imaging technology.

The PROBA-V mission: the space segment

PROBA-V (Project for On-Board Autonomy – Vegetation) is an ESA (European Space Agency) mission developed within the framework of the Agency’s General Support Technology Programme (GSTP) devoted to the observation of the Earth’s vegetation, providing data continuity with the SPOT (Satellite pour l’Observation de la Terre) 4 and 5 VEGETATION payloads as a gap-filler to the ESA Sentinel-3 mission. The PROBA-V space segment is based on a three-axis stabilized PROBA small-satellite platform of about 140 kg equipped with a state-of-the-art compact 4-band multi-spectral imager with a large field of view. The instrument’s optomechanics is based on three very compact TMA (three mirror anastigmat) telescopes placed on an optical bench. At an altitude of 820 km, the instrument is able to provide daily coverage of the Earth in three VNIR (visible and near-infrared) bands and one SWIR (short-wave infrared) spectral band, with a spatial resolution of up to 100 m × 100 m at nadir for the VNIR. The instrument raw data will be downlinked with an X-band transmitter to the ground reception station in Kiruna, Sweden. The mission control centre is located in Redu, Belgium. The image processing centre, the so-called ‘user segment’, automatically accesses the raw data and is responsible for the processing and the dissemination of the data products towards the user community. The PROBA-V spacecraft was launched on board the new European launcher Vega on 7 May 2013. It is designed for a nominal mission lifetime of 2.5 years with a possible extension to 5 years.

Integration of classification tree analyses and spatial metrics to assess changes in supraglacial lakes in the Karakoram Himalaya

Alpine glacier responses to climate change reveal increases in significant retreat with corresponding increases in the production of glacier meltwater and development of supraglacial lakes. The rate of occurrence and spatial extent of lakes in the Himalaya are difficult to determine because current spectral-based image analyses of glacier surfaces are limited through anisotropic reflectance and lack of high-quality digital elevation models (DEMs). Additionally, the limitations of multivariate classification algorithms to classify glacier features in satellite imagery have led to an increased interest in non-parametric methods, such as classification and regression trees. This article demonstrates the utility of a semi-automated approach that integrates classification tree-based image segmentation and spatial metrics in an object-oriented analysis to differentiate supraglacial lakes from glacier debris, ice, ice-cliffs, and lateral and medial moraines. We used 2000 and 2004 Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) visible and near-infrared (VNIR) and shortwave infrared (SWIR) imagery to characterize and assess supraglacial conditions on the Baltoro Glacier in the Karakoram Himalaya. Input variables for the image segmentation include ASTER VNIR and SWIR spectral bands, indices (e.g. normalized difference water index (NDWI), normalized difference vegetation index (NDVI), and normalized difference snow index (NDSI)), image band ratios (e.g. NIR/red, middle infrared (MIR)/green, and MIR/red), and DEM derivatives. Classification tree analysis was used to generate initial image segments and it was particularly effective in differentiating water from ice and other glacier surface features. The object-oriented analysis included the use of Boolean logic and squared pixel (SqP) spatial metric to refine the classification tree output. The results of classification tree-based image segmentation show that NDWI is the most important single variable for characterizing glacier surface features followed by NDVI, NIR/red ratio, and green and red spectral bands. Lake features extracted from both images show that there were 131 lakes in 2000 as compared to 157 lakes in 2004. In general, there was a significant increase in the planimetric area of these lakes from 2000 to 2004, and we documented the formation of 26 new lakes. It appears that lake-size increments occur mostly in the lower part of the ablation zone, whereas most of the new lakes are formed in the upper part of the ablation zone. The classification tree outputs are intuitive and the data-derived thresholds eliminate commonly subjective visual determination of such threshold values used in image segmentation. The results of this study show that glacier lakes in high-mountain environments can be characterized without topographic information, which is difficult to obtain from a DEM. Also, the semi-automated method described in this article can potentially eliminate the often laborious visual multitemporal analysis of glacier lake surface change, thereby producing consistent and replicable results needed to assess the trends of alpine glacier response to climate change in the Himalaya.

Multispectral decomposition for the removal of out-of-band effects of visible/infrared imaging radiometer suite visible and near-infrared bands

The visible/infrared imaging radiometer suite (VIIRS) is now onboard the first satellite platform managed by the Joint Polar Satellite System of the National Oceanic and Atmospheric Administration and NASA. It collects scientific data from an altitude of approximately 830 km in 22 narrow bands located in the 0.4-12.5 μm range. The seven visible and near-infrared (VisNIR) bands in the wavelength interval between 0.4-0.9 μm are known to suffer from the out-of-band (OOB) responses--a small amount of radiances far away from the center of a given band that can pass through the filter and reach detectors in the focal plane. A proper treatment of the OOB effects is necessary in order to obtain calibrated at-sensor radiance data [referred to as the Sensor Data Records (SDRs)] from measurements with these bands and subsequently to derive higher-level data products [referred to as the Environmental Data Records (EDRs)]. We have recently developed a new technique, called multispectral decomposition transform (MDT), which can be used to correct/remove the OOB effects of VIIRS VisNIR bands and to recover the true narrow band radiances from the measured radiances containing OOB effects. An MDT matrix is derived from the laboratory-measured filter transmittance functions. The recovery of the narrow band signals is performed through a matrix multiplication--the production between the MDT matrix and a multispectral vector. Hyperspectral imaging data measured from high altitude aircraft and satellite platforms, the complete VIIRS filter functions, and the truncated VIIRS filter functions to narrower spectral intervals, are used to simulate the VIIRS data with and without OOB effects. Our experimental results using the proposed MDT method have demonstrated that the average errors after decomposition are reduced by more than one order of magnitude.

Application of ASTER Data to Geological Studies

The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) is an imaging radiometer with 14 spectral bands and 15 to 90 m spatial resolution that is to be launched in 1998 on NASA`s EOS AM-1 spacecraft. The ASTER spectrally spans the visible and near-infrared (VNIR), short-wave-infrared (SWIR), and thermal infrared (TIR) regions with a 60 km-wide image swath. The SWIR bands target characteristic absorption features of phyllosilicate and carbonate minerals for the purpose of lithologic mapping. Emissivity patterns derived from the TIR bands will be useful to characterize silicate rocks. Data from the VNIR bands with high spatial resolution will be helpful not only to assess iron oxide minerals and vegetation, but also to perform photogeologic interpretation of geologic structures and lithologic mapping based upon topographic textures.

Surface temperature estimation in Singhbhum Shear Zone of India using Landsat-7 ETM+ thermal infrared data

Land surface temperature (LST) is an important factor in global change studies, heat balance and as control for climate change. A comparative study of LST over parts of the Singhbhum Shear Zone in India was undertaken using various emissivity and temperature retrieval algorithms applied on visible and near infrared (VNIR), and thermal infrared (TIR) bands of high resolution Landsat-7 ETM+ imagery. LST results obtained from satellite data of October 26, 2001 and November 2, 2001 through various algorithms were validated with ground measurements collected during satellite overpass. In addition, LST products of MODIS and ASTER were compared with Landsat-7 ETM+ and ground truth data to explore the possibility of using multi-sensor approach in LST monitoring. An image-based dark object subtraction (DOS3) algorithm, which is yet to be tested for LST retrieval, was applied on VNIR bands to obtain atmospheric corrected surface reflectance images. Normalized difference vegetation index (NDVI) was estimated from VNIR reflectance image. Various surface emissivity retrieval algorithms based on NDVI and vegetation proportion were applied to ascertain emissivities of the various land cover categories in the study area in the spectral range of 10.4–12.5 μm. A minimum emissivity value of about 0.95 was observed over the reflective rock body with a maximum of about 0.99 over dense forest. A strong correlation was established between Landsat ETM+ reflectance band 3 and emissivity. Single channel based algorithms were adopted for surface radiance and brightness temperature. Finally, emissivity correction was applied on ‘brightness temperature’ to obtain LST. Estimated LST values obtained from various algorithms were compared with field ground measurements for different land cover categories. LST values obtained after using Valor’s emissivity and single channel equations were best correlated with ground truth temperature. Minimum LST is observed over dense forest as about 26 °C and maximum LST is observed over rock body of about 38 °C. The estimated LST showed that rock bodies, bare soils and built-up areas exhibit higher surface temperatures, while water bodies, agricultural croplands and dense vegetations have lower surface temperatures during the daytime. The accuracy of the estimated LST was within ±2 °C. LST comparison of ASTER and MODIS with Landsat has a maximum difference of 2 °C. Strong correlation was found between LST and spectral radiance of band 6 of Landsat-7 ETM+. Result corroborates the fact that surface temperatures over land use/land cover types are greatly influenced by the amount of vegetation present.

Lithologic mapping using ASTER imagery and multivariate texture

Lithologic mapping using Advanced Spaceborne Thermal Emission and Reflectance Radiometer (ASTER) data and multivariate texture features from different wavelength regions was investigated in an area mainly composed of sedimentary rocks. Nine reflected spectral bands were used, including three visible and near-infrared (VNIR) bands and six shortwave infrared (SWIR) bands. The multivariate texture features are measured by multivariate variogram and derived from multiple bands. The obtained texture features, as additional bands, were combined with spectral data for lithologic classification, and their performances were quantitatively evaluated. The results showed that lithologic mapping using nine ASTER reflected spectral bands alone produced a relatively high overall accuracy (80.3%). However, there are still obvious confusions between different lithologic units in the classification result. Lithologic mapping by combining spectral features and textural features from VNIR or SWIR bands each produced higher accuracy than that using spectral images alone, with an approximately 7% improvement in overall accuracy and 8.5% improvement in kappa coefficient. Moreover, the combined use of these multivariate textural features from both VNIR and SWIR bands and the spectral data in lithologic classification improved the mapping results even further, with a 9.5% increase in overall accuracy and an 11.6% increase in kappa coefficient compared with those using the spectral data alone. The results demonstrated the additional discrimination capability of image texture features for lithologic identification, which significantly reduced the confusion between lithologic units with similar spectral properties but different textural features.

Monitoring mine tailings revegetation using multitemporal hyperspectral image data

This paper investigates the use of hyperspectral remote sensing imagery in the 400–2500 nm wavelength range for the extraction of information suitable for monitoring mine tailings revegetation. The objectives were twofold: (i) demonstrate the usefulness of fractional texture for monitoring mine tailings revegetation using visible and near-infrared (VNIR) hyperspectral data, and (ii) investigate the benefit of adding the short-wave infrared (SWIR) bands. Compact Airborne Spectrographic Imager (casi) data were acquired over the Copper Cliff mine tailings impoundment area in the VNIR bands during the summers of 1996 and 1998. In addition, Probe-1 data were collected in the VNIR-SWIR region during the summer of 1999. Surface reflectance was retrieved from the three datasets, and spectra of the 1996 and 1998 casi datasets were resampled to match the 1999 Probe-1 spectral sampling characteristics, which resulted in 30 bands covering the 450–890 nm range. The three datasets were concatenated into one file, and 30 endmember spectra were automatically selected. Constrained linear spectral unmixing was performed using the 30 endmembers, which were then grouped into the six endmember categories, namely water, lime, fresh and oxidized tailings, and low and high photosynthetic vegetation. Image fractions were then normalized and image texture was extracted from the total vegetation fraction. Total vegetation fraction (high/low photosynthetic), total tailings fraction (fresh/oxidized), and texture of the vegetation fraction were used in a K-mean unsupervised classification, which produced the best results using seven classes (78.13% overall accuracy, Kappa coefficient of 0.74). Classification results were validated using a set of 34 ground estimates of vegetation cover and tailings. The full set of 128 bands of the 1999 Probe-1 dataset was used to investigate the contribution of the SWIR bands to monitoring the reclamation of mine tailings. A new vegetation endmember was identified as plant litter, which in many cases replaces areas labelled as low photosynthetic vegetation when using the VNIR bands only. Oxidized tailings could be separated into jarosite and goethite endmembers and lime into agricultural lime (CaMgCO3) and calcium oxide (CaO), also known as quicklime.

Tracing along-strike structural continuity in the Neoproterozoic Allaqi-Heiani Suture, southern Egypt using principal component analysis (PCA), fast Fourier transform (FFT), and redundant wavelet transform (RWT) of ASTER data

We have used the visible and near infrared (VNIR) and short wave infrared (SWIR) bands of the advanced space-borne thermal emission and reflectance radiometer (ASTER) to trace along-strike structures in the ophiolite-decorated Neoproterozoic Allaqi-Heiani Suture in southern Egypt. The suture is E-trending in the west, changes to N-trending in its central part, and E-trending further east. The western part of the suture is defined by a fold and thrust belt dominated by an autochthon in the south followed by a southern, central and northern allochthons that were emplaced top-to-the-south. Tracing along-strike structures in the Allaqi-Heiani Suture has been a challenging task due to structural complexity and change of structural style. Principal component analysis (PCA), fast Fourier transform (FFT), and redundant wavelet transform (RWT) are used to identify ophiolite components such as talc carbonate schist, gabbro, and serpentinite as well as Neoproterozoic ductile structures to trace along-strike continuation in the Allaqi-Heiani Suture. (1) The PCA is performed on the 9 × 9 covariance matrix of the ASTER VNIR and SWIR bands. PC5 is selected because the ophiolite components and the Neoproterozoic ductile structures are best emphasized. (2) The FFT is performed on PC5 to reduce noises and mosaicking boundaries of the ASTER scenes. (3) The RWT is performed to enhance the contrast between ophiolite components and the surrounding rock types. This work helped in tracing structures along the Allaqi-Heiani Suture and revealed structural features that have not been identified before: (1) Some folds associated with the S-verging fold and thrust belts are periclinal folds that might be associated with nappe emplacement. (2) Truncation of structures associated with the S-verging fold and thrust belts occurs along NNW-trending sinistral strike-slip shear zones that swing at their extreme ends to become parallel to the truncated structures. (3) Presence of N-trending folds close to the NNW-trending sinistral strike-slip shear zones. (4) Presence of N- and NE-trending brittle faults younger than the Neoproterozoic ductile structures. Nappes identified in the west are traced into the central part of the suture allowing for subsequent modification by younger structures. The difference in structural styles along the suture might be due to development of frontal and lateral ramps along the traces of the orogen front. This was followed by superimposition of E–W shortening to produce N-trending folds that are localized close to the lateral ramps. Subsequently, N- and NE-trending brittle faults were developed and deformed early Neoproterozoic ductile structures.

Radiometric performance evaluation of ASTER VNIR, SWIR, and TIR

Radiometric performance of the Advanced Spectrometer for Thermal Emission and Reflection Radiometer (ASTER) is characterized by using acquired imagery data. Noise-equivalent reflectance and temperature, sensitivity (gain), bias (offset), and modulation transfer function (MTF) are determined for the visible and near-infrared (VNIR), the shortwave infrared (SWIR), and the thermal infrared (TIR) radiometers that constitute ASTER. The responsivity evaluated from onboard calibration (OBC) and from instrumented scenes show similar trends for the VNIR: the OBC data yield 2.7% to 5.5% a year for the VNIR. The SWIR response changed less than 2% in the 3.5 years following launch. The zero-radiance offsets of most VNIR and SWIR bands have increased about 1/2 digital number per year. The in-orbit noise levels, calculated by the standard deviation of dark (VNIR and SWIR) or ocean (TIR) scenes, show that all bands are within specification. The MTF at Nyquist and 1/2 Nyquist frequencies was determined for all bands using the Moon (VNIR and SWIR) or terrestrial scenes with lines of sharp thermal contrast. In-orbit performance along-track and cross-track is better than prelaunch for the VNIR and SWIR bands in nearly all cases; the TIR effectively meets specification in-orbit.