Technical note: Simple and robust removal of sun glint for mapping shallow‐water benthos

Specular reflection of solar radiation on non‐flat water surfaces is a serious confounding factor for benthic remote sensing in shallow‐water environments. This problem was recently overcome by Hochberg et al., who provided an effective method for the removal of ‘sun glint’ from remotely sensed images by utilization of the brightness in a near‐infrared (NIR) band. Application of the technique was shown to give an increase in the accuracy of benthic habitat classification. However, as presented, the method is sensitive to outlier pixels, requires a time‐consuming masking of land and cloud, and is not formulated in a manner leading to ease of implementation. We present a revised version of the method, which is more robust, does not require masking and can be implemented very simply. The practical approach described here will hopefully expedite the routine adoption of this effective and simple technique throughout the aquatic remote sensing community.     

Correction of hyperspectral reflectance measurements for surface objects and direct sun reflection on surface waters

Satellite, airborne, or platform-based remote sensing reflectance measurements of aquatic targets are frequently compromised by water-surface effects such as specular sun reflection (glint) or transient objects like buoys or boats. For temporal or spatial data series where sub-surface reflectance is of interest, the elimination of affected data may require time-consuming manual selection of spectra and substantial data loss. Here, we present a method for the automated elimination of data points containing surface objects or strong sun reflection, which is based on the spectral slope in the ultra-violet to blue (350 nm to 450 nm). To minimize data loss, an automated sun glint correction combining two previously published methods is also presented. The method operates by subtracting a glint spectrum by means of a regression curve characterized from low to medium glint data points and is further automated by selecting these low glint data on the basis of the oxygen absorption depth in the near infrared (NIR). The elimination and correction algorithms facilitate rapid automated processing of large bio-optical data sets for both spatial and temporally resolved remote-sensing reflectance data sets. Here we demonstrate their efficacy on a three-month data set of hourly light field measurements from a fixed platform in the northwest Mediterranean.     

An assessment of the black ocean pixel assumption for MODIS SWIR bands

Recent studies show that an atmospheric correction algorithm using shortwave infrared (SWIR) bands improves satellite-derived ocean color products in turbid coastal waters. In this paper, the black pixel assumption (i.e., zero water-leaving radiance contribution) over the ocean for the Moderate Resolution Imaging Spectroradiometer (MODIS) SWIR bands at 1240, 1640, and 2130 nm is assessed for various coastal ocean regions. The black pixel assumption is found to be generally valid with the MODIS SWIR bands at 1640 and 2130 nm even for extremely turbid waters. For the MODIS 1240 nm band, however, ocean radiance contribution is generally negligible in mildly turbid waters such as regions along the U.S. east coast, while some slight radiance contributions are observed in extremely turbid waters, e.g., some regions along the China east coast, the estuary of the La Plata River. Particularly, in the Hangzhou Bay, the ocean radiance contribution at the SWIR band 1240 nm results in an overcorrection of atmospheric and surface effects, leading to errors of MODIS-derived normalized water-leaving radiance at the blue reaching ~ 0.5 mW cm^− 2 μm^− 1 sr^− 1. In addition, we found that, for non-extremely turbid waters, i.e., the ocean contribution at the near-infrared (NIR) band < ~ 1.0 mW cm^− 2 μm^− 1 sr^− 1, there exists a good relationship in the regional normalized water-leaving radiances between the red and the NIR bands. Thus, for non-extremely turbid waters, such a red-NIR radiance relationship derived regionally can possibly be used for making corrections for the regional NIR ocean contributions without using the SWIR bands, e.g., for atmospheric correction of ocean color products derived from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS).     

A novel ocean color index to detect floating algae in the global oceans

Various types of floating algae have been reported in open oceans and coastal waters, yet accurate and timely detection of these relatively small surface features using traditional satellite data and algorithms has been difficult or even impossible due to lack of spatial resolution, coverage, revisit frequency, or due to inherent algorithm limitations. Here, a simple ocean color index, namely the Floating Algae Index (FAI), is developed and used to detect floating algae in open ocean environments using the medium-resolution (250- and 500-m) data from operational MODIS (Moderate Resolution Imaging Spectroradiometer) instruments. FAI is defined as the difference between reflectance at 859 nm (vegetation “red edge”) and a linear baseline between the red band (645 nm) and short-wave infrared band (1240 or 1640 nm). Through data comparison and model simulations, FAI has shown advantages over the traditional NDVI (Normalized Difference Vegetation Index) or EVI (Enhanced Vegetation Index) because FAI is less sensitive to changes in environmental and observing conditions (aerosol type and thickness, solar/viewing geometry, and sun glint) and can “see” through thin clouds. The baseline subtraction method provides a simple yet effective means for atmospheric correction, through which floating algae can be easily recognized and delineated in various ocean waters, including the North Atlantic Ocean, Gulf of Mexico, Yellow Sea, and East China Sea. Because similar spectral bands are available on many existing and planned satellite sensors such as Landsat TM/ETM+ and VIIRS (Visible Infrared Imager/Radiometer Suite), the FAI concept is extendable to establish a long-term record of these ecologically important ocean plants.     

Haze reduction from the visible bands of LANDSAT TM and ETM+ images over a shallow water reef environment

A method for haze reduction in the visible bands of Landsat TM and ETM+ images over a shallow water marine environment is presented in this paper. This method uses the near infrared (NIR) band to estimate the spatial distribution of haze intensity in each visible band through a linear regression model established over deep water areas. As a first order approximation, the signal received at the sensor is assumed to be the arithmetic sum of radiance contributed by haze and the radiance leaving the water surface. Reduction of haze is then carried out by a simple subtraction procedure. Images acquired over the Southern Tip of Palawan, Philippines are used for the experiments. Results show that the method works well for compensating signals contaminated by optically thin haze. Overcorrection occurs when haze is optically thick and geometrically complex. When images are acquired under hazy conditions the method can be applied to drastically improve image interpretability and may also be considered as a necessary pre-processing step for subsequent analyses and information extraction.     

Atmospheric correction of ENVISAT/MERIS data over case II waters: the use of black pixel assumption in oxygen and water vapour absorption bands

The Medium Resolution Imaging Spectrometer (MERIS) sensor, with its good physical design, can provide excellent data for water colour monitoring. However, owing to the shortage of shortwave-infrared (SWIR) bands, the traditional near-infrared (NIR)–SWIR algorithm for atmospheric correction in inland turbid case II waters cannot be extended to the MERIS data directly, which limits its applications. In this study, we developed a modified NIR black pixel method for atmospheric correction of MERIS data in inland turbid case II waters. In the new method, two special NIR bands provided by MERIS data, an oxygen absorption band (O₂ A-band, 761 nm) and a water vapour absorption band (vapour A-band, 900 nm), were introduced to keep the assumption of zero water-leaving reflectance valid according to the fact that both atmospheric transmittance and water-leaving reflectance are very small at these two bands. After addressing the aerosol wavelength dependence for the cases of single- and multiple-scattering conditions, we further validated the new method in two case lakes (Lake Dianchi in China and Lake Kasumigaura in Japan) by comparing the results with in situ measurements and other atmospheric correction algorithms, including Self-Contained Atmospheric Parameters Estimation for MERIS data (SCAPE-M) and the Basic ERS (European Remote Sensing Satellite) & ENVISAT (Environmental Satellite) (A)ATSR ((Advanced) Along-Track Scanning Radiometer) and MERIS (BEAM) processor. We found that the proposed method had acceptable accuracy in the bands within 560–754 nm (MERIS bands 5–10) (average absolute deviation (AAD) = 0.0081, average deviation (AD) = 0.0074), which are commonly used in the estimation models of chlorophyll-a (chl-a) concentrations. In addition, the performance of the new method was superior to that of the BEAM processor and only slightly worse than that of SCAPE-M in these bands. Considering its acceptable accuracy and simplicity both in principle and at implementation compared with the SCAPE-M method, the new method provides an option for atmospheric correction of MERIS data in inland turbid case II waters with applications aiming for chl-a estimation.     

Assessing the sun glint effect on the data bias of CrIS shortwave surface channels near 3.7 μm

The Suomi National Polar-orbiting Partnership (NPP) satellite was successfully launched on 28 October 2011. The on-board Cross-track Infrared Sounder (CrIS) provides the hyperspectral infrared radiance covering a spectral range of 3.92–15.4 μm, inheriting the task to improve numerical weather prediction (NWP) from previous hyperspectral sounders. The so-called sun glint effect results in large biases in CrIS shortwave surface channels near 3.7 μm and therefore impedes the usage of those channels in the operational data assimilation, because the data biases are required to be evaluated appropriately by any data assimilation system. This work assesses the sun glint effect on bias characteristics of those shortwave surface channels near 3.7 μm, with the help of a sun glint model developed in the community radiative transfer model (CRTM). It is demonstrated that the daytime biases of those shortwave surface channels are decreased markedly after applying sun glint correction with values close to 0 K. The dependence of daytime biases on sensor zenith angles is also eliminated by using the sun glint model. It is seen that the differences between daytime and night-time biases can reach 0.6 K near mid-latitudes in the southern hemisphere after including the sun glint effect, which implies that the sun glint model needs further enhancement. Overall, the direct assimilation of CrIS shortwave surface channels near 3.7 μm is possibly accomplished by utilizing the sun glint model implemented in CRTM during both daytime and night-time.     

Detection of turbid waters and absorbing aerosols for the MODIS ocean color data processing

Atmospheric correction for the ocean color products derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) uses two near-infrared (NIR) bands centered at 748 and 869 nm for identifying aerosol type and correcting aerosol contributions at the MODIS visible wavelengths. The ocean is usually assumed to be black for open oceans at these two NIR bands with modifications for the productive waters and aerosols are assumed to be non- or weakly absorbing. For cases with strongly absorbing aerosols and cases with the significant NIR ocean contributions, the derived ocean color products will have significant errors, e.g., the derived MODIS normalized water-leaving radiances are biased low considerably. Both cases lead to a significant drop of the sensor-measured radiance at the short visible wavelengths, and they both have similar and indistinguishable radiance characteristics at the short visible wavelengths. To properly handle such cases, the strongly absorbing aerosols and turbid waters need to be identified. Therefore, an appropriate approach (different from the standard procedure) may be carried out. In this paper, we demonstrate methods to identify the turbid waters and strongly absorbing aerosols using combinations of MODIS-measured radiances at the short visible, NIR, and short wave infrared (SWIR) bands. The algorithms are based on the fact that for the turbid waters the ocean has significantly large contributions at the NIR bands, whereas at the SWIR bands the ocean is still black due to much stronger water absorption. With detection of the turbid waters, the strongly absorbing aerosols can then be identified using the MODIS measurements at the short visible and NIR bands. We provide results and discussions for test and evaluation of the algorithm performance with various examples in the coastal regions for the turbid waters and for various absorbing aerosols (e.g., volcano ash plumes, dust, smoke). The proposed algorithms are efficient in the data processing, and can be carried out prior to the atmospheric correction procedure.     

Detection and correction of adjacency effects in hyperspectral airborne data of coastal and inland waters: the use of the near infrared similarity spectrum

A method for the detection and correction of water pixels affected by adjacency effects is presented. The approach is based on the comparison of spectra with the near infrared (NIR) similarity spectrum. Pixels affected by adjacency effects have a water-leaving reflectance spectrum with a different shape to the reference spectrum. This deviation from the similarity spectrum is used as a measure for the adjacency effect. Secondly, the correspondence with the NIR similarity spectrum is used to quantify and to correct for the contribution of the background radiance during atmospheric correction. The advantage of the approach is that it requires no a priori assumptions on the sediment load or related reflectance values in the NIR and can therefore be applied to turbid waters. The approach is tested on hyperspectral airborne data (Compact Airborne Spectrographic Imager (CASI), Airborne Hyperspectral Scanner (AHS)) acquired above coastal and inland waters at different flight altitudes and under varying atmospheric conditions. As the NIR similarity spectrum forms the basis of the approach, the method will fail for water bodies for which this similarity spectrum is no longer valid.     

山区可见光—近红外遥感影像浓密植被暗像元自动识别方法研究

暗目标法是目前气溶胶光学厚度遥感反演中应用最为广泛的方法,浓密植被暗像元的识别是暗目标法的基础。针对可见光—近红外影像缺少中红外波段难以有效识别浓密植被暗像元的问题,引入红波段直方图阈值法识别山区可见光—近红外影像的浓密植被暗像元。该方法利用浓密森林像元在可见光波段反射率低的特点,通过搜索红波段直方图的最小峰值自动识别浓密植被暗像元。试验中选取Landsat TM影像前4个波段利用红波段直方图阈值法识别可见光—近红外影像的浓密植被暗像元,并与在中红外波段影像和可见光—近红外影像中广泛应用的两种暗像元识别方法进行对比分析,探讨红波段直方图阈值法的有效性,最后将该方法应用于环境减灾卫星(HJ-1)CCD影像的暗像元识别和气溶胶反演。实验结果表明:红波段直方图阈值法明显优于常用的可见光—近红外影像暗像元识别方法,识别精度接近传统的中红外波段影像识别方法,相似度指数小于2和小于3的暗像元分别为83.12%和93.48%。该方法为山区可见光—近红外影像浓密植被暗像元自动识别提供了一种新的适用方法,识别结果能够满足暗目标法反演气溶胶光学厚度的要求。     

Spectral variations in the near-infrared ocean reflectance

The optical properties of natural waters beyond the visible range, in the near-infrared (NIR, 700-900 nm), have received little attention because they are often assumed to be mostly determined by the large absorption coefficient of pure water, and because of methodological difficulties. It is now growingly admitted that the NIR represents a potential optical source of unambiguous information about suspended sediments in turbid waters, thence the need for better understanding the NIR optical behaviour of such waters. It has recently been proposed (Ruddick et al., Limnology and Oceanography. 51, 1167-1179, 2006) that the variability in the shape of the surface ocean reflectance spectrum in the NIR is negligible in turbid waters. In the present study, we show, based on both in situ and remote sensing data, that the shape of the ocean reflectance spectrum in the NIR does vary in turbid to extremely turbid waters. Space-borne ocean reflectance data were collected using 3 different sensors (SeaWiFS, MODIS/Aqua and MERIS) over the Amazon, Mackenzie and Rio de la Plata turbid river plumes during extremely clear atmospheric conditions so that reliable removal of gas and aerosol effects on reflectance could be achieved. In situ NIR reflectance data were collected in different European estuaries where extremely turbid waters were found. In both data sets, a flattening of the NIR reflectance spectrum with increasing turbidity was observed. The ratio of reflectances at 765 nm and 865 nm, for instance, varied from ca. 2 down to 1 in our in situ data set, while a constant value of 1.61 had been proposed based on theory in a previous study. Radiative transfer calculations were performed using a range of realistic values for the seawater inherent optical properties, to determine the possible causes of variations in the shape of the NIR reflectance spectrum. Based on these simulations, we found that the most significant one was the gradual increase in the contribution of suspended sediments to the color of surface waters, which often leads to the flattening of the reflectance spectrum. Changes in the scattering and absorption properties of particles also contributed to variations in the shape of the NIR surface ocean reflectance spectrum. The impact of such variations on the interpretation of ocean color data is discussed.     

A classification-based assessment of the optimal spectral and spatial resolutions for Great Lakes coastal wetland imagery

We analyzed hyperspectral airborne imagery (CASI 2 with 46 contiguous VIS/NIR bands) that was acquired over a Lake Huron coastal wetland. To support detailed Great Lakes coastal wetland mapping, the optimal spatial resolution of imagery was determined to be less than 2 m. There was a 23% change in classification resiliency using the SAM classifier upon resampling the original 1-meter, 18-band imagery to 2-meter pixels, and further classifications with larger pixels (4 and 8 m) increased overall classification change to 35% and 50%, respectively.We performed a series of image classification experiments incorporating three independent band selection methodologies (derivative magnitude, fixed interval and derivative histogram), in order to explore the effects of spectral resampling on classification resiliency. This research verified that a minimum of seven, strategically located bands in the VIS-NIR wavelength region (425.4 nm, 514.9 nm, 560.1 nm, 685.5 nm, 731.5 nm, 812.3 nm and 916.7 nm) are necessary to maintain a classification resiliency above the 85% threshold. Significantly, these seven bands produced the highest classification resiliency using the fewest number of bands of any of the 63 band-reduction strategies that were tested.Analyzing only derivative magnitudes proved to be an unreliable tool to identify optimal bands. The fixed interval method was adversely influenced by the starting band location, making its implementation problematic. The combined use of derivative magnitude and frequency of occurrence appears to be the best method to determine the “optimal” bands for a wetland mapping hyperspectral application.     

Evaluation of the NIR-SWIR atmospheric correction algorithm for MODIS-Aqua over the Eastern China Seas

The performance of the near-infrared (NIR) and short-wave infrared (SWIR) combined atmospheric correction algorithm (NIR-SWIR) for Moderate Resolution Imaging Spectroradiometer (MODIS)-Aqua over the Eastern China Seas (ECS) was evaluated. The in situ data set for evaluation in this study was collected during 2005 and 2012 through eight cruises in the ECS, wherein 49 in situ observation points were successfully matched with MODIS-Aqua data. The remote-sensing reflectance derived from MODIS-Aqua data using the NIR-SWIR algorithm and the NIR algorithm were compared to the in situ measurements in the matched-up data set, which included ocean bands (412, 443, 488, 531, 547, 667, and 678 nm) and land bands (469, 555, and 645 nm). The results show that the performance of the NIR-SWIR algorithm has been improved in turbid waters, and the effect at the short-wave bands (blue and green bands) is more significant than that at the long-wave bands (red bands). In addition, MODIS-Aqua data at the land bands (469, 555, and 645 nm) show a similar performance to those of nearby ocean bands. However, the lower estimation accuracy is still a remarkable question at bands 412, 645, 667, 678 nm. The results from both the NIR-SWIR and NIR algorithms were applied to the images of MODIS-Aqua in the ECS and they indicate that the extent to which the quality of the derived remote-sensing reflectance using the NIR-SWIR algorithm can be improved shows major differences for different seasons. The minimum area is in summer, and the maximum area in winter. The NIR-SWIR algorithm should be used for the whole of the Bohai Sea in winter.     

Evaluation of MODIS SWIR and NIR-SWIR atmospheric correction algorithms using SeaBASS data

Using the NASA maintained ocean optical and biological in situ data that were collected during 2002-2005, we have evaluated the performance of atmospheric correction algorithms for the ocean color products from the Moderate Resolution Imaging Spectroradiometer (MODIS) on Aqua. Specifically, algorithms using the MODIS shortwave infrared (SWIR) bands and an approach using the near-infrared (NIR) and SWIR combined method are evaluated, compared to the match-up results from the NASA standard algorithm (using the NIR bands). The in situ data for the match-up analyses were collected mostly from non-turbid ocean waters. It is critical to assess and understand the algorithm performance for deriving MODIS ocean color products, providing science and user communities with the important data quality information. Results show that, although the SWIR method for data processing has generally reduced the bias errors, the noise errors are increased due mainly to significantly lower sensor signal-noise ratio (SNR) values for the MODIS SWIR bands, as well as the increased uncertainties using the SWIR method for the atmospheric correction. This has further demonstrated that future ocean color satellite sensors will require significantly improved sensor SNR performance for the SWIR bands. The NIR-SWIR combined method, for which the non-turbid and turbid ocean waters are processed using the NIR and SWIR method, respectively, has been shown to produce improved ocean color products.     

Remote estimation of chlorophyll a in optically complex waters based on optical classification

Accurate assessment of phytoplankton chlorophyll a (Chla) concentration in turbid waters by means of remote sensing is challenging due to optically complexity and significant variability of case 2 waters, especially in inland waters with multiple optical types. In this study, a water optical classification algorithm is developed, and two semi-analytical algorithms (three- and four-band algorithm) for estimating Chla are calibrated and validated using four independent datasets collected from Taihu Lake, Chaohu Lake, and Three Gorges Reservoir. The optical classification algorithm is developed using the dataset collected in Taihu Lake from 2006 to 2009. This dataset is also used to calibrate the three- and four-band Chla estimation algorithms. The optical classification technique uses remote sensing reflectance at three bands: Rrs(G), Rrs(650), and Rrs(NIR), where G indicates the location of reflectance peak in the green region (around 560 nm), and NIR is the location of reflectance peak in the near-infrared region (around 700 nm). Optimal reference wavelengths of the three- and four-band algorithm are located through model tuning and accuracy optimization. The three- and four-band algorithm accuracy is further evaluated using other three independent datasets. The improvement of optical classification in Chla estimation is revealed by comparing the performance of the two algorithms for non-classified and classified waters.Using the slopes of the three reflectance bands, the 138 reflectance spectra samples in the calibration dataset are classified into three classes, each with a specific spectral shape character. The three- and four-band algorithm performs well for both non-classified and classified waters in estimating Chla. For non-classified waters, strong relationships are yielded between measured and predicted Chla, but the performance of the two algorithms is not satisfactory in low Chla conditions, especially for samples with Chla below 30 mg m^− 3. For classified waters, the class-specific algorithms perform better than for non-classified waters. Class-specific algorithms reduce considerable mean relative error from algorithms for non-classified waters in Chla predicting. Optical classification makes that there is no need to adjust the optimal position to estimate Chla for other waters using the class-specific algorithms. The findings in this study demonstrate that optical classification can greatly improve the accuracy of Chla estimation in optically complex waters.     

The assessment of leaf water content using leaf reflectance ratios in the visible,near‐, and short‐wave‐infrared

The common features of spectral reflectance from vegetation foliage upon leaf dehydration are decreasing water absorption troughs in the near‐infrared (NIR) and short‐wave‐infrared (SWIR). We studied which leaf water index in the NIR and SWIR is most suitable for the assessment of leaf water content and the detection of leaf dehydration from the laboratory standpoint. We also examined the influence of the thickness of leaves upon leaf water indices. All leaf water content indices examined exhibited basic correlations with the relative water content (RWC) of leaves, while the R ₁₃₀₀/R ₁₄₅₀ leaf water index also demonstrated a high signal strength and low variability (R ²>0.94). All examined leaf reflectance ratios could also be correlated with leaf thickness. The thickness of leaves, however, was not independent of leaf RWC but appeared to decrease substantially as a result of leaf dehydration.     

Using hyperspectral data to quantify water-quality parameters in the Wabash River and its tributaries,Indiana

A hand-held spectrometer was used to collect above-water spectral measurements for measuring optically active water-quality characteristics of the Wabash River and its tributaries in Indiana. Water sampling was undertaken concurrent with spectral measurements to estimate concentrations of chlorophyll (chl) and total suspended solids (TSS). A method for removing sky and Sun glint from field spectra for turbid inland waters was developed and tested. Empirical models were then developed using the corrected field spectra and in situ chl and TSS data. A subset of the field measurements was used for model development and the rest for model validation. Spectral characteristics indicative of waters dominated by different inherent optical properties (IOPs) were identified and used as the basis of selecting bands for empirical model development. It was found that the ratio of the reflectance peak at the red edge (704 nm) with the local minimum caused by chl absorption at 677 nm was a strong predictor of chl concentrations (coefficient of determination (R²) = 0.95). The reflectance peak at 704 nm was also a good predictor for TSS estimation (R² = 0.75). In addition, we also found that reflectance within the near-infrared (NIR) wavelengths (700–890 nm) all showed a strong correlation (0.85–0.91) with TSS concentrations and generated robust models. Results suggest that hyperspectral information provided by field spectrometer can be used to distinguish and quantify water-quality parameters under complex IOP conditions.     

基于蚁群优化算法的遥感器波段设置探究

卫星载荷研制发射后其光谱和空间观测模式固定,无法根据复杂地表的多样化需求进行实时灵活调整,且目前遥感器波段设置尚不完善还存在优化空间.引进基于蚁群优化算法的波段选择方法(AntColonyOptimization basedBandSelection,ACOBS),结合北美区域３３景AVIRIS航空高光谱图像,开展了不同区域、不同地表覆盖类型的高光谱波段优选研究,发现各地表类型优选波段组合存在一定差异,其中４波段组合中红光、近红外波段为２个共同入选波段,６波段组合中绿光、红光、短波红外波段为３个共有波段,８波段组合中紫光、绿光、红光、红边、近红外１、近红外２、短波红外１、短波红外２为８个共有入选波段,其他入选波段与地表覆盖类型有关.在此基础上,进一步开展了多光谱卫星波段设置评价研究,发现:４波段优化方案中,绿光、红光、近红外波段１ (７７０~８９５nm)、近红外波段２(９００~１３５０nm)为最优波段组合;６波段优化方案中,绿、红、红边、近红外１(７７０~８９５nm)、近红外２(９００~１３５０nm)、短波红外１(１５６０~１６６０nm)为最优波段组合;８波段优化方案中,蓝、绿、红、红边、近红外１(７７０~８９５nm)、近红外２(９００~１３５０nm)、短波红外１(１５６０~１６６０nm)和短波红外２(２１００~２３００nm)为最优波段组合.研究结果表明Land satTM OLI、SPOT等陆地资源遥感器波段设置还存在一定优化调整空间,特别是红边波段在目前传感器波段设置中没有得到足够重视.     

Feasibility of estimating leaf water content using spectral indices from WorldView-3’s near-infrared and shortwave infrared bands

A recently-launched high-resolution commercial satellite, DigitalGlobe’s WorldView-3, has 8 bands in the shortwave infrared (SWIR) wavelength region, which may be capable of estimating canopy water content at 3.7-m spatial resolution. WorldView-3 also has 8 multispectral bands at 1.24-m resolution with two bands in the near-infrared (NIR). The relative spectral response functions for WorldView-3 were provided by DigitalGlobe, Inc., and band reflectances were determined for reflectance spectra of PROSPECT model simulations and leaf data from maize, trees, grasses, and broadleaf herbaceous eudicots. For laboratory measurements, the range of leaf water contents was extended by including drying leaves and leaf stacks of corn, soybean, oaks, and maples. Correlations between leaf water content and spectral indices from model simulations suggested that indices using SWIR band 1 (center wavelength 1210 nm) had low variability with respect to leaf water content, but also low sensitivity. Other indices using SWIR band 5 (2165 nm) had the highest sensitivity, but also had high variability caused by different values of the leaf structure parameter in PROSPECT. Indices using SWIR bands 2, 3 and 4 (1570, 1660, and 1730 nm, respectively) had high correlations and intermediate variability from the leaf structure parameter. Spectral indices calculated from the leaf data had the same overall patterns as the simulations for variation and sensitivity; however, indices using SWIR band 1 had low correlations, and the best correlations were from indices that used SWIR bands 2, 3 and 4. Spectral indices for maize, grasses, and herbaceous crops and weeds had similar responses to leaf water content; tree leaves had higher index values and saturated at lower leaf water contents. The specified width of NIR band 2 (860–1040 nm) overlaps the water absorption feature at 970 nm wavelength; however, the normalized difference of NIR band 1 and 2 was insensitive to water content because NIR band 2’s spectral response was most heavily weighted to wavelengths less than 930 nm. The high spatial resolution of the WorldView-3 SWIR data will help analyze how variation among plant species and functional groups affects spectral responses to differences in canopy water content.