Shortwave Infrared Bands Research Articles

Relationship Between Biophysical Parameters and Synthetic Indices Derived from Hyperspectral Field Data in a Salt Marsh from Buenos Aires Province, Argentina

Remote sensing tools allow the environmental evaluation of coastal wetlands at a landscape scale, but a deeper understanding is needed of the interactions between biophysical parameters and the electromagnetic signal. The goal of this work was to analyze and quantify the influence of the aboveground biomass and the Leaf Area Index (LAI) on the spectral response of Spartina densiflora marshes in Mar Chiquita coastal lagoon, Argentina. Spectral reflectance at high resolution was measured in S. densiflora canopies under natural conditions, manipulating standing crop by means of successive harvest. Reflectance data were acquired using a spectroradiometer in visible, near infrared (IR) and shortwave IR bands. Spectral Vegetation Indices (VI) were calculated for each standing crop-LAI situation. Several VI significantly correlated with standing crop and LAI, including indices 1) based on the red-IR edge (IR Index (IRI), 695/760 ratio, Simple Ratio (SR), Red Edge Inflection Point (REIP), and different variations of the Normalized Difference VI (NDVI Rouse, NDVI amber, NDVI NOAA, NDVI Landsat, NDVI Modis), 2) indices based on the sharp change green-IR (green NDVI (GNDVI), 800/550 ratio) and 3) indices with a correction for soil noise (OSAVI: Optimized Soil Adjusted VI (OSAVI), and Modified SAVI (MSAVI). The indices with significant regressions with standing crop and LAI were IRI, NDVIAmber and REIP. The total and green standing crop showed better adjustments than LAI, showing R2 values of 0.5. These values were obtained with REIP index. Results indicate that LAI and standing crop of S. densiflora stands could be determined from spectral data but estimations should be taken carefully in high biomass scenarios, because of indexes saturation at higher LAI values.

Investigating the capability of WorldView-3 superspectral data for direct hydrocarbon detection

Abstract The recently launched WorldView-3 (WV-3) satellite is a high spatial resolution instrument with eight multispectral bands in the visible and near-infrared and an additional eight bands in the short-wave infrared (SWIR). Three of the SWIR bands, including bands 9, 12, and 16 (centered at 1210, 1730, and 2330 nm) respectively overlap with diagnostic absorption features of hydrocarbons (HCs) at 1200, 1700, and 2300 nm. This paper aims to investigate the capability of this superspectral instrument for direct HC detection. For this purpose, we have conducted several simulation experiments using multiple datasets comprising (i) spectral libraries of different HCs measured in the laboratory, (ii) close-range hyperspectral imagery of a well-known tar-sand sample acquired with a sisuCHEMA imaging system, and (iii) far-range ProSpecTIR hyperspectral imagery collected over twelve simulated HC-shows. These datasets were convolved to the spectral resolution of WV-3 and analyzed using a variety of spectral processing techniques. The absorption features of HCs manifest themselves in all cases albeit with varying intensity. The effect of a series of parameters on the detectability of the HCs was also scrutinized; these included background geology, spectral mixing, HC type, endmember set, spatial resolution, noise level, and topography. We demonstrate that the HC absorption feature in WV-3's band 12, accompanied by shoulders sustained at bands 11 and 13 (centered at 1660 and 2165 nm), is resilient enough and persist under various conditions. Potential applications of these finding include hydrocarbon exploration in frontier basins and environmental monitoring.

A Generic Algorithm to Estimate LAI, FAPAR and FCOVER Variables from SPOT4_HRVIR and Landsat Sensors: Evaluation of the Consistency and Comparison with Ground Measurements

The leaf area index (LAI) and the fraction of photosynthetically active radiation absorbed by green vegetation (FAPAR) are essential climatic variables in surface process models. FCOVER is also important to separate vegetation and soil for energy balance processes. Currently, several LAI, FAPAR and FCOVER satellite products are derived moderate to coarse spatial resolution. The launch of Sentinel-2 in 2015 will provide data at decametric resolution with a high revisit frequency to allow quantifying the canopy functioning at the local to regional scales. The aim of this study is thus to evaluate the performances of a neural network based algorithm to derive LAI, FAPAR and FCOVER products at decametric spatial resolution and high temporal sampling. The algorithm is generic, i.e., it is applied without any knowledge of the landcover. A time series of high spatial resolution SPOT4_HRVIR (16 scenes) and Landsat 8 (18 scenes) images acquired in 2013 over the France southwestern site were used to generate the LAI, FAPAR and FCOVER products. For each sensor and each biophysical variable, a neural network was first trained over PROSPECT+SAIL radiative transfer model simulations of top of canopy reflectance data for green, red, near-infra red and short wave infra-red bands. Our results show a good spatial and temporal consistency between the variables derived from both sensors: almost half the pixels show an absolute difference between SPOT and LANDSAT estimates of lower that 0.5 unit for LAI, and 0.05 unit for FAPAR and FCOVER. Finally, downward-looking digital hemispherical cameras were completed over the main land cover types to validate the accuracy of the products. Results show that the derived products are strongly correlated with the field measurements (R2 > 0.79), corresponding to a RMSE = 0.49 for LAI, RMSE = 0.10 (RMSE = 0.12) for black-sky (white sky) FAPAR and RMSE = 0.15 for FCOVER. It is concluded that the proposed generic algorithm provides a good basis to monitor the seasonal variation of the vegetation biophysical variables for important crops at decametric resolution.

Assessing fruit-tree crop classification from Landsat-8 time series for the Maipo Valley, Chile

Abstract Satellite image time series (SITS) provide spectral–temporal features that describe phenological changes in vegetation over the growing season, which is expected to facilitate the classification of crop types. While most SITS-based crop type classifications were focused on NDVI (normalized difference vegetation index) temporal profiles, less attention has been paid to using the complete image spectral resolution of the time series. In this work we assessed different approaches to SITS-based classification of four major fruit-tree crops in the Maipo Valley, central Chile, during the 2013–14 growing season. We compared four feature sets from a time series comprised of eight cloud-free Landsat-8 images: the full-band SITS, the NDVI and NDWI (normalized difference water index) temporal profiles, and an image stack with all the feature sets combined. State-of-the-art classifiers (linear discriminant analysis, LDA; random forest; and support vector machine) were applied on each feature set at different training sample sizes ( N = 100, 200, 400, 800 and 2291 fields), and classification results were assessed by cross-validation of the misclassification error rate (MER). For all the feature sets overall results were good (MERs ≤ 0.21) although substantially improved classification accuracies were achieved when the full-band SITS was employed (MER 0.14–0.05). Classifications applied on the NDVI temporal profile consistently had the worst performance. For a sample size of 200 fields, LDA using the full-band SITS of image dates 1, 3, 6 and 8 produced the best tradeoff between the number of images and classification accuracy (MER = 0.06), being the green, red, blue and SWIR (short-wave infrared) bands of image date 1 (acquired at the early greenup stage) the most relevant for crop type discrimination. Our results show the importance of considering the complete image spectral resolution for SITS-based crop type classifications as the commonly used NDVI temporal profile and their red and near infrared bands were not found the most significant to discriminate the crop types of interest. Furthermore, in light of the good results obtained, the methodology used here might be transferred to similar agricultural lands cultivated with the same crop types, thus providing a reliable and relatively efficient methodology for creating and updating crop inventories.

The Shortwave Infrared Bands’ Response to Stomatal Conductance in “Conference” Pear Trees (Pyrus communis L.)

In situ measurements consisting of stomatal conductance, air temperature, vapor pressure deficit and the spectral reflectance in the shortwave infrared (SWIR) regions of thirty “Conference” pear trees (Pyrus communis L.) were repeatedly measured for eighty-six days. The SWIR was segmented into eight regions between 1550 and 2365 nm, where distances ranged from 40–200 nm. Each of the regions was used to describe the change in canopy water status over a period of approximately three months. Stomatal conductance of the water stress treatment was first determined to be significantly different from the control group nine days after stress initiation. The most suitable SWIR region for this study had wavelengths between 1550 and 1750 nm, where the first significant difference was also measured nine days after stress was initiated. After the period of water stress ended, forty-seven days after stress was initiated, all of the trees received full irrigation, where the SWIR region between 1550 and 1750 nm determined that stomatal conductance of the stress treatment lagged behind the control group for thirty days. Using a temporal sequence of SWIR measurements, we were able to successfully measure the beginning and the recovery of water stress in pear trees.

ASTER-based mapping of ophiolitic rocks: examples from the Allaqi–Heiani suture, SE Egypt

ABSTRACTThe Allaqi–Heiani suture is an ~250 km-long ophiolite-associated fold-and-thrust belt in the South Eastern Desert of Egypt. It comprises imbricate thrust sheets of ophiolites (serpentinite, amphibolite, metagabbro, and metabasalt) and island-arc metavolcanic/metasedimentary rocks. Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER)-based mafic–ultramafic indices are combined with band ratioing (BR) and relative absorption band-depth techniques to comprehensively map the ophiolitic rocks along the eastern part of the Allaqi–Heiani suture. The applied techniques are found efficient in delineating the ophiolitic rocks in the study area, despite their highly tectonized and variably carbonate-altered character. Identification and mapping of the exposed mafic–ultramafic rocks were possible using shortwave infrared (SWIR) band ratio 4/8 and thermal infrared (TIR) band ratio 12/14. The refined mafic index (MI) provides an efficient discrimination of the ophiolitic mafic rocks, while the mafic-ultramafic indices (M1 and M2) show high sensitivity to ophiolitic ultramafic units. The mafic–ultramafic indices enabled an efficient discrimination of mafic ophiolites from mafic island-arc rocks. Combining mafic indices and SWIR band ratio 4/8 allowed an enhanced delineation of the ophiolitic mafic–ultramafic rocks from other rock units in the area. Detailed mapping of the ophiolitic units in the ophiolitic terranes was obtained using the relative absorption band depth (RBD8) together with the MI and band ratio 4/8. This approach offers a quick method to discriminate among ophiolitic rock units and to map their structural contacts, i.e. in such hitherto poorly studied and hardly accessible areas.

Validation of EO-1 Hyperion and Advanced Land Imager Using the Radiometric Calibration Test Site at Railroad Valley, Nevada

The Earth-Observing One (EO-1) satellite was launched in 2000. Radiometric calibration of Hyperion and the Advanced Land Imager (ALI) has been performed throughout the mission lifetime using various techniques that include ground-based vicarious calibration, pseudo-invariant calibration sites, and also the moon. The EO-1 mission is nearing its useful lifetime, and this work seeks to validate the radiometric calibration of Hyperion and ALI from 2013 until the satellite is decommissioned. Hyperion and ALI have been routinely collecting data at the automated Radiometric Calibration Test Site [RadCaTS/Railroad Valley (RRV)] since launch. In support of this study, the frequency of the acquisitions at RadCaTS has been significantly increased since 2013, which provides an opportunity to analyze the radiometric stability and accuracy during the final stages of the EO-1 mission. The analysis of Hyperion and ALI is performed using a suite of ground instrumentation that measures the atmosphere and surface throughout the day. The final product is an estimate of the top-of-atmosphere (TOA) spectral radiance, which is compared to Hyperion and ALI radiances. The results show that Hyperion agrees with the RadCaTS predictions to within 5% in the visible and near-infrared (VNIR) and to within 10% in the shortwave infrared (SWIR). The 2013–2014 ALI results show agreement to within 6% in the VNIR and 7.5% in the SWIR bands. A cross-comparison between ALI and the Operational Land Imager (OLI) using RadCaTS as a transfer source shows agreement of 3%–6% during the period of 2013–2014.

On-orbit calibration of Visible Infrared Imaging Radiometer Suite reflective solar bands and its challenges using a solar diffuser

The reflective solar bands (RSBs) of the Visible Infrared Imaging Radiometer Suite (VIIRS) on board the Suomi National Polar-Orbiting Partnership satellite are calibrated by a solar diffuser (SD) panel whose performance is itself monitored by an accompanying solar diffuser stability monitor (SDSM). In this comprehensive work we describe the SD-based calibration algorithm of the RSBs, analyze the calibration data, and derive the performance results-the RSB calibration coefficients or F-factors-for the current three and a half years of mission. The application of the newly derived product of the SD bidirectional reflectance factor and the vignetting function for the SD screen and the newly derived SD degradation, so-called H-factors, effectively minimizes the artificial seasonal patterns in the RSB calibration coefficients due to the errors of these ingredient inputs. The full illumination region, the "sweet spot," during calibration events for SD view is carefully examined and selected to ensure high data quality and to reduce noise owing to non-fully illuminated samples. A time-dependent relative spectral response (RSR), coming from the large out-of-band contribution and the VIIRS optical system wavelength-dependent degradation, is derived from an iterative approach and applied in the SD calibration for each RSB. The result shows that VIIRS RSBs degrade much faster at near-infrared (NIR) and shortwave-infrared (SWIR) wavelength ranges due to the faster degradation of the rotating telescope assembly against the remaining part of the system. The gains of the VIIRS RSBs have degraded 2.0% (410nm, Band M1), 0.2% (443nm, Band M2), -0.3% (486nm, Band M3), 0.2% (551nm, Band M4), 6.2% (640nm, Band I1), 11.0% (671nm, Band M5), 21.3% (745nm, Band M6), 35.8% (862nm, Band I2), and 35.8% (862nm, Band M7), respectively, since launch and 24.8% (1238nm, Band M8), 18.5% (1378nm, Band M9), 11.5% (1610nm, Band I3), 11.5% (1610, Band M10), and 4.0% (2250nm, Band M11), respectively, since 20 January 2012. It is established that the SD calibration accurately catches the on-orbit RSB degradation according to the instrument design and the calibration algorithm. However, due to the inherent nonuniform degradation of the SD affecting especially the short wavelength bands and the lack of capability of the SDSM calibration to catch degradation beyond 935nm, the direct and the unmitigated application of the SD calibration result will introduce nonnegligible error into the calibration coefficients resulting in long-term drifts in the sensor data records and consequently the high-level products. We explicitly unveil the effect of the nonuniformity in SD degradation in the RSB calibration coefficients but also briefly discuss a critical yet simple mitigation to restore the accuracy of the calibration coefficients based on lunar observations. The methodology presented here thus remains intact as the cornerstone of the RSB calibration, and our derived RSB calibration coefficients represent the optimal result. This work has the most impact on the quality of the ocean color products that sensitively depend on the moderate visible and NIR bands (M1-M7), as well as the SWIR bands (M8, M10, and M11).

A SWIR based algorithm to retrieve total suspended matter in extremely turbid waters

In ocean colour remote sensing, the use of Near Infra Red (NIR) spectral bands for the retrieval of Total Suspended Matter (TSM) concentration in turbid and highly turbid waters has proven to be successful. In extremely turbid waters (TSM>100mgL−1) however, these bands are less sensitive to increases in TSM. Here it is proposed to use Short Wave Infra Red (SWIR) spectral bands between 1000 and 1300nm for these extreme cases. This SWIR spectral region is subdivided into two regions, SWIR-I (1000nm to 1200nm) and SWIR-II (1200nm to 1300nm) which correspond to local minima in the pure water absorption spectrum. For both spectral regions the water reflectance signal was measured in situ with an ASD spectrometer in three different extremely turbid estuarine sites: Scheldt (Belgium), Gironde (France), and Río de la Plata (Argentina), along with the TSM concentration. A measurable water reflectance was observed for all sites in SWIR-I, while in the SWIR-II region the signal was not significant compared to the Signal-to-Noise Ratio (SNR) of current Ocean Colour (OC) sensors. For the spectral band at 1020nm (present in Ocean and Land Colour Instrument — OLCI, onboard Sentinel-3) and at 1071nm, an empirical single band TSM algorithm is defined which is valid for both the Gironde and Scheldt estuarine sites. This means that a single algorithm can be applied for both sites without expensive recalibration. The relationship between TSM and SWIR reflectance at 1020 and 1071nm is linear and did not show any saturation for the concentrations measured here (up to 1400mgL−1), while saturation was observed for the NIR wavelengths, as expected. Hence, for extremely turbid waters it is advised to switch from NIR to SWIR-I wavelengths to estimate TSM concentration. This was demonstrated for an airborne hyperspectral dataset (Airborne Prism Experiment, APEX) from the Gironde estuary having several spectral bands in the SWIR-I. The empirical single band SWIR TSM algorithm was applied to the atmospherically corrected scene providing a TSM concentration map of the Gironde from mouth to more upstream with concentrations expected in this region ranging from a few to several hundreds mgL−1. These results, i.e. the existence of a single relationship for the Scheldt and Gironde, not showing any decrease of sensitivity, highlights the importance of having SWIR bands in future ocean colour sensors for studying extremely turbid rivers, coastal areas and estuaries in the world. A further implication of these results is that there is a TSM limit for application of atmospheric correction algorithms which assume zero SWIR marine reflectance. That limit is defined here as function of wavelength and sensor noise level.

Assessing the influence of atmospheric and topographic correction and inclusion of SWIR bands in burned scars detection from high-resolution EO imagery: a case study using ASTER

In the present study, the effect of atmospheric and topographic correction to burned area delineation from Earth Observation (EO) imagery is explored. Furthermore, the potential added value of the inclusion of the shortwave infrared (SWIR) bands for improving retrievals of burned area cartography is investigated. In particular, the capability of ASTER imagery when combined with the maximum likelihood (ML) and the support vector machines (SVMs) classification techniques is examined herein. As a case study, a Mediterranean site on which a fire event occurred in Greece during 2007 and for which post-fire ASTER imagery was available is used. The combination of topographic correction (orthorectification) with the inclusion of the SWIR bands returned the most accurate results in burned area detection. SVMs showed the highest accuracy, showing the most promising potential in delineating the burned areas. The most accurate results for burned scar mapping were obtained from the combined use of SVMs with an orthorectified image and SWIR spectral bands, at least this was the case in our study site. Our results offer a very important contribution to the understanding of the capability of high-resolution imagery such as that from ASTER in burned area estimation. This study also corroborates the usefulness of topographic correction as an image processing step to be incorporated in modelling schemes for delineating burned areas from such data. Findings potentially provide very useful information towards the development of EO-based techniques that aim to operationally provide services related to the estimation of burned area. This is of considerable scientific and practical value to the wider scientific and users’ community given the continuation of free access today to observations from space from high-resolution sensors globally.

Long-wave infrared identification of smoldering peat fires in Indonesia with nighttime Landsat data

Smoldering peat fires in Indonesia are responsible for large quantities of trace gas and particulate emissions. However, to date no satellite remote sensing technique has been demonstrated for the identification of smoldering peat fires. Fires have two distinct combustion phases: a high temperature flaming and low temperature smoldering phases. The flaming phase temperature is approximately twice that of the smoldering phase. This temperature differential results in a spectral displacement of the primary radiant emissions of the two combustion phases. It it is possible to exploit this spectral displacement using widely separated wavelength ranges. This paper examines active fire features found in short-wave infrared (SWIR) and long-wave infrared (LWIR) nighttime Landsat data collected on peatlands in Sumatra and Kalimantan. Landsat 8’s SWIR bands are on the leading edge of flaming phase radiant emissions, with only minor contribution from the smoldering phase. Conversely, Landsat 8’s LWIR bands are on the trailing edge of smoldering phase radiant emissions. After examining the LWIR fire features, we conclude that they are the result of smoldering phase combustion. This has been confirmed with field validation. Detection limits for smoldering peat fires in Landsat 8 is in the 40–90 m2 range. These results could lead to improved management of peatland fires and emission modeling.

Validation of DigitalGlobe WorldView-3 Earth imaging satellite shortwave infrared bands for mineral mapping

WorldView-3 (WV-3) is a newly launched (August 2014) high-spatial resolution commercial multispectral satellite sensor with eight visible to near-infrared bands (0.42 to 1.04 μ m ) and eight shortwave infrared (SWIR) bands (1.2 to 2.33 μ m ). Previous analyses using hyperspectral imagery (HSI) data of Cuprite, Nevada, to simulate WV-3’s eight SWIR bands demonstrated identification and mapping of a wide variety of minerals, including kaolinite, alunite, buddingtonite, muscovite, calcite, and hydrothermal silica. These results, using partial unmixing, showed mineral occurrences similar to those mapped using full resolution HSI data and established WV-3’s potential as a valuable new mineral mapping tool. Confusion matrix analyses using the HSI data as ground truth did indicate, however, some difficulties with mapping spectrally similar minerals using the multispectral data. Follow-up mineral mapping, using on-orbit WV-3 data acquired September 19, 2014, for Cuprite, Nevada, and the same algorithms and methods used for WV-3 simulation, indicates that the WV-3 sensor is performing as expected. WV-3 SWIR data analyses closely match expectations for mineral mapping as predicted by the simulation. While not as capable as an HSI sensor, WV-3’s carefully selected eight SWIR bands provide new remote mineral mapping capabilities not available from any other spaceborne multispectral system.

Advantages of high quality SWIR bands for ocean colour processing: Examples from Landsat-8

Monitoring of water quality by satellite ocean colour data requires high quality atmospheric correction and especially the accurate quantification of the aerosol contribution to the top of atmosphere radiance. Several methods have been proposed for atmospheric correction over turbid waters, including modelling the marine contributions to the NIR signal or switching to longer short-wave infrared (SWIR) wavelengths where the signal even in turbid waters can be assumed zero. Here we present the use of the high quality SWIR bands of the Operational Land Imager (OLI) on Landsat-8, launched in 2013, to extend our existing turbid water atmospheric correction to extremely turbid waters. The atmospheric correction is image based, and no external measurements are required. The aerosol type is estimated using NIR and SWIR bands in clear water pixels, or in all water pixels using the two SWIR bands. The aerosol type is assumed to be constant over a single Landsat-8 tile (170 by 185km), or allowed to vary spatially when using both SWIR bands. Realistic spatial patterns of marine reflectances are retrieved, uncorrelated with the estimated aerosol reflectance. Taking spatial and temporal variability into account, products from Landsat-8 compare well with those of MODIS Aqua and Terra — also using the SWIR bands for atmospheric correction. The limitations of our previously published method (Vanhellemont & Ruddick, 2014a) are illustrated at higher turbidities, and removed by using the new method. The uncertainty caused by using a single aerosol type per scene is assessed. The advantages of the high spatial resolution L8/OLI data are clear for applications in coastal and estuarine waters. As an example of the advantage of high quality SWIR bands, and a SWIR-based atmospheric correction, an algorithm for detecting black suspended sediments from dredging and dumping operations is demonstrated here. In conclusion, L8/OLI is a powerful new tool for remote sensing of extremely turbid waters, and can be used as a precursor for future ocean colour missions with SWIR bands.

An evaluation of Suomi NPP-VIIRS data for surface water detection

Measuring surface water using remote sensing technology is an essential research topic in many research areas, including flood-related studies and water resource management. Recent advances in satellite remote sensing provide more efficient ways of monitoring surface water from space. As a newly available data source with a high frequency of global coverage and moderate resolution, Visible Infrared Imaging Radiometer Suite on board the Suomi National Polar-orbiting Partnership (Suomi NPP-VIIRS) has the advantage of monitoring the earth surface intensively and continuously. This study conducts an exploratory evaluation on the performance of Suomi NPP-VIIRS data for surface water detection. A Modified Histogram method was proposed and applied to its shortwave infrared (SWIR) band to estimate the water fraction within each single pixel. The estimated surface water fraction was then compared with that derived from a corresponding Moderate Resolution Imaging Spectroradiometer (MODIS) image and evaluated using a higher-resolution Landsat Operational Land Imager image. Evaluation results demonstrate that surface water was successfully delineated using the Suomi NPP-VIIRS SWIR band, even at sub-pixel level, bearing in mind that cloud and shadow may interfere surface water detection. A more precise detection result usually requires some additional approaches to help distinguish surface water from cloud and shadow. It was also revealed that the Suomi NPP-VIIRS can be an ideal surrogate of MODIS.

Face recognition in the SWIR band when using single sensor multi-wavelength imaging systems

In this paper, we study the problem of Face Recognition (FR) when using Single Sensor Multi-Wavelength (SSMW) imaging systems that operate in the Short-Wave Infrared (SWIR) band. The contributions of our work are four fold: First, a SWIR database is collected when using our developed SSMW system under the following scenarios, i.e. Multi-Wavelength (MW) multi-pose images were captured when the camera was focused at either 1150, 1350 or 1550nm. Second, an automated quality-based score level fusion scheme is proposed for the classification of input MW images. Third, a weighted quality-based score level fusion scheme is proposed for the automated classification of full frontal (FF) vs. nonfrontal (NFF) face images. Fourth, a set of experiments is performed indicating that our proposed algorithms, for the classification of (i) multiwavelength images and (ii) FF vs. NFF face images, are beneficial when designing different steps of multi-spectral face recognition (FR) systems, including face detection, eye detection and face recognition. We also determined that when our SWIR-based system is focused at 1350nm, the identification performance increases compared to focusing the camera at any of the other SWIR wavelengths available. This outcome is particularly important for unconstrained FR scenarios, where imaging at 1550nm, at long distances and when operating at night time environments, is preferable over different SWIR wavelengths.

Distinguishing surface cyanobacterial blooms and aquatic macrophytes using Landsat/TM and ETM + shortwave infrared bands

Satellite remote sensing can be considered a suitable approach to monitor the extent of cyanobacterial blooms compared with conventional ship surveys because of the patchiness and high spatial and temporal variability of the blooms. However, most of the existing algorithms are not capable of distinguishing cyanobacterial blooms and aquatic macrophytes due to their similar spectral characteristics in the red and near infrared (NIR) wavelengths. In this study, we conducted in situ spectral measurements and satellite data analyses for cyanobacterial blooms and aquatic macrophytes to find an effective method to distinguish them using medium-resolution Landsat satellite images. The reflectance spectra were measured for lake waters and cyanobacterial blooms with 13 different chlorophyll-a concentration levels (from 54 to 21,736μgL−1) and four types of aquatic macrophytes (two emerged and two floating-leaved macrophytes) in the wavelength range from 350 to 2500nm. In addition, seven Landsat images were collected for nine lakes in Japan or Indonesia. We calculated several selected indices, i.e., the normalized difference vegetation index (NDVI), five types of normalized difference water index (NDWI), and the floating algal index (FAI) to find an appropriate index for distinguishing cyanobacterial blooms and aquatic macrophytes.The results showed that the spectral characteristics of cyanobacterial blooms were significantly different from those of aquatic macrophytes in the short-wave infrared (SWIR) region, indicating that the SWIR bands are important for distinguishing cyanobacterial blooms and aquatic macrophytes. The results also showed that the combination of FAI and NDWI4,5 was an effective method for classifying lake areas. We first used the FAI for extracting lake waters, and we then used the NDWI4,5 to classify the remaining areas as cyanobacterial blooms or aquatic macrophytes. The results also showed that the threshold of NDWI4,5 was less sensitive to the effects of both the atmosphere and mixed pixels compared to the other indices. Our application of the FAI threshold of 0.05 and the NDWI4,5 threshold of 0.63 to six lakes in Japan and Indonesia showed that the proposed method could successfully distinguish lake water, cyanobacterial blooms, and aquatic macrophytes.

Developing MODIS-based retrieval models of suspended particulate matter concentration in Dongting Lake, China

To case-II waters, suspended particulate matter (SPM) is one of the dominant water constituents, SPM concentration (CSPM) is a key parameter describing water quality, and developing remote sensing-based CSPM retrieval models is foundation for obtaining its spatiotemporal distributions. This study aimed to develop moderate resolution imaging spectroradiometer (MODIS)-based CSPM empirical retrieval models in Dongting Lake, China. The 95 CSPM measurements on 31 August 2012 and 14 June 2013 and their corresponding MODIS Terra images were used to calibrate models, and the model calibration results showed that the 250m MODIS red band obtained better fitting accuracies than the near infrared band; the quadratic and exponential models of single red band explained 75% (estimated standard errors (SE)=6.19mg/l) and 71% (SE=6.54mg/l) of the variation of CSPM; and the quadratic and exponential models of red minus shortwave infrared (SWIR) band at 1240 and 1640nm explained 72–73% (SE=6.43–6.48mg/l) and 68–69% (SE=6.83–6.96mg/l) of the variations of CSPM, respectively. The quadratic and exponential models of red band and red minus SWIR band were applied to the MODIS Terra image on 16 September 2013 to estimate CSPM values. By comparing the estimated CSPM values on 16 September 2013 and the measured ones on 17 September 2013 at 40 sampling points for model validations, the results indicated that there exited significantly strong correlations between the measured and estimated CSPM values at a significance level of 0.05 for all models, and the exponential model of red minus SWIR band at 1240nm achieved the best estimation result within all models. Such result provided foundation for obtaining the spatiotemporal distribution information of CSPM from MODIS images in Dongting Lake, which will be helpful for understanding, managing and protecting this ecosystem.

NEW AUTOMATED CLOUD AND CLOUD-SHADOW DETECTION USING LANDSAT IMAGERY

Cloud cover has become a major problem in the use of optical satellite imageries, particularly in Indonesian region located along equator or tropical region with high cloud cover almost all year round. In this study, a new method for cloud and cloud shadow detection using Landsat imagery for specific Indonesian region was developed to provide a more efficient and effective way to detect clouds and cloud shadows. Landsat Top of Atmosphere (TOA) reflectance and Brightness Temperature (BT) were used as inputs into the model. The first step was to detect cloud based on cloud physical properties using albedo and thermal bands, the second step was to detect cloud shadows using the Near Infrared (NIR), and Short Wave Infrared (SWIR) bands, and finally, the geometric relationships were used to match the cloud and cloud shadow layer, before proceeding to the production of the final cloud and cloud shadow mask. The results were then compared with other method such as tree base cloud separation. It showed that method we proposed could provide better result than tree base method, the accuracy result of this method was 98.75%.

Land cover classification using Landsat 8 Operational Land Imager data in Beijing, China

The successful launch of Landsat 8 provides a new data source for monitoring land cover, which has the potential to significantly improve the characterization of the earth’s surface. To assess data performance, Landsat 8 Operational Land Imager (OLI) data were first compared with Landsat 7 ETM + data using texture features as the indicators. Furthermore, the OLI data were investigated for land cover classification using the maximum likelihood and support vector machine classifiers in Beijing. The results indicated that (1) the OLI data quality was slightly better than the ETM + data quality in the visible bands, especially the near-infrared band of OLI the data, which had a clear improvement; clear improvement was not founded in the shortwave-infrared bands. Moreover, (2) OLI data had a satisfactory performance in terms of land cover classification. In summary, OLI data were a reliable data source for monitoring land cover and provided the continuity in the Landsat earth observation.

Initial Stability Assessment of S-NPP VIIRS Reflective Solar Band Calibration Using Invariant Desert and Deep Convective Cloud Targets

The latest CERES FM-5 instrument launched onboard the S-NPP spacecraft will use the VIIRS visible radiances from the NASA Land Product Evaluation and Analysis Tool Elements (PEATE) product for retrieving the cloud properties associated with its TOA flux measurement. In order for CERES to provide climate quality TOA flux datasets, the retrieved cloud properties must be consistent throughout the record, which is dependent on the calibration stability of the VIIRS imager. This paper assesses the NASA calibration stability of the VIIRS reflective solar bands using the Libya-4 desert and deep convective clouds (DCC). The invariant targets are first evaluated for temporal natural variability. It is found for visible (VIS) bands that DCC targets have half of the variability of Libya-4. For the shortwave infrared (SWIR) bands, the desert has less variability. The brief VIIRS record and target variability inhibits high confidence in identifying any trends that are less than ±0.6%/yr for most VIS bands, and ±2.5%/yr for SWIR bands. None of the observed invariant target reflective solar band trends exceeded these trend thresholds. Initial assessment results show that the VIIRS data have been consistently calibrated and that the VIIRS instrument stability is similar to or better than the MODIS instrument.