Estimating suspended sediment concentrations from spectral reflectance data

Abstract

A method for estimating sediment concentrations at different depths in coastal waters from measured spectral reflectance above the sea is proposed. The water backscattering coefficient is obtained by integrating a scattering cross section derived from Mie theory over a Junge type particle-size distribution. The results for turbid water, near an arid coast, with vertical mixing, shows a maximum of particle concentration in the middle layer, while the same method applied to reflectance data for ‘blue’ water, gives a relative concentration of sediment that gradually increases with depth.     

Estimating the irradiance spectrum from measurements in a limited number of spectral bands

Accurate measurement and characterisation of fluctuations in the irradiance environment is important for many areas of optical remote sensing. This paper describes a method of estimating spectral irradiance over the region 400-1000 nm from the radiance of a calibrated reference panel, measured in four narrow spectral bands (FWHM approx. 10 nm). The reproducibility of the method was found to have an average root-mean-squared error of approximately 30 mW m^− 2 nm^− 1 over the region 400-1000 nm when applied to spectra covering a range of clear-sky conditions typical of mid-latitude temperate regions. This was approximately twice as precise as the sequential method, even when the interval between target and reference panel measurements was very short (median interval 23 s). The method provides an alternative to linear interpolation between successive reference panel measurements and is particularly appropriate for conditions when irradiance is varying in a non-systematic way, for example, during the passage of sub-visual clouds.     

Extraction of spectral hemispherical reflectance (albedo) of surfaces from nadir and directional reflectance data

Abstract

A radiative transfer model was used to explore how the error in inferring spectral hemispherical reflectance (p_λ) from nadir reflectance values varies as a function of wavelength, solar zenith angle, leaf area index and leaf orientation distribution. Secondly, a technique using multiple spectral nadir reflectance values to infer p_λ for a single wavelength was tested using field data. In addition, several techniques that use multiple off-nadir view angles taken in azimuth planes (called strings of data) were tested using field data. These latter techniques were very accurate (with errors less than 4 percent of the true value)and are ideally suited to present and future sensor systems that scan in a known azimuth plane (e.g. Advanced Very High Resolution Radiometer (AVHRR) and other scanning radiometers) or view fore and aft in a known azimuth plane (e.g. Advanced Solid-State Array Sensor (ASAS)Moderate Resolution Imaging Spectrometer (MODIS)High Resolution Imaging Spectrometer (HIRIS)), a brief analysis was performed to explore the effects of errors in hemispherical reflectance on terrestrial energy budget and productivity calculations.     

Direct retrieval of the shape of leaf spectral albedo from multiangular hyperspectral Earth observation data

Physically-based retrieval of vegetation canopy properties from remote sensing data presumes a knowledge of the spectral albedo of the basic scattering unit, i.e. leaf. In this paper, we present a novel method to directly retrieve the spectral dependence of leaf single-scattering albedo of a closed broadleaf forest canopy from multiangular hyperspectral satellite imagery. The new algorithm is based on separating the reflected signal into a linear (first-order) and non-linear (diffuse) reflectance component. A limitation of the proposed algorithm is that the leaf single-scattering albedo ω(λ) is retrieved with an accuracy of a structural parameter (called a₀) which, in turn, depends on canopy bidirectional gap probability, ratio of leaf reflectance to transmittance, and distribution of leaf normals. The structural parameter (a₀) was found to depend on tree-level structural parameters, such as tree height and volume of a single crown, but not the amount of leaf area.     

Arctic tundra albedo and its estimation from spectral hemispheric reflectance

We present the results of a field experiment in which the nearly complete bidirectional reflectance distribution function of Alaskan arctic tundra sites early in the growing season is measured by the PARABOLA instrument. The spectral hemispheric reflectances were computed by angular integration of these measurements for three wavebands: red (650-670nm), near-infrared (810-840nm) and shortwave infrared (1620-1690 nm). Total albedo was then estimated by weighting the spectral hemispheric reflectances by the fraction of total solar irradiance in three broadband spectral regions (300-700, 700-1300 and 1300-4000nm) and representing each spectral region by the narrowband PARABOLA measurements. These calculations resulted in albedo estimates with a mean relative error of 15.7 per cent as compared to pyranometer measured albedo. Since vegetation reflectance varies significantly over each of the three broadband regions, additional reflectance weighting factors were computed from a combination of high spectral resolution canopy reflectance data and corresponding computed spectral solar irradiance. This additional reflectance weighting resulted in a reduction in the mean relative error to 7.5 per cent relative to pyranometer measured albedo. It is noted that the three spectral bands of the PARABOLA instrument data reported here are similar to those of the spectral wavebands planned for future Advanced Very High Resolution Radiometer (AVHRR) sensors on National Oceanic and Atmospheric Administration (NOAA) satellites. Therefore the results and techniques presented here may be useful for future global albedo estimation utilizing AVHRR sensors. The analysis presented here may also be applied to albedo estimation from satellite sensors with higher spectral resolution and more complete spectral coverage, such as the future orbiting MODIS sensor, in which the errors of spectral reflectance weighting will be reduced considerably due to a more complete sampling of the reflected spectrum.     

Deriving surface albedo measurements from narrow band satellite data

Abstract

A target calibration procedure for obtaining surface albedo from satellite data is presented. The methodology addresses two key issues, the calibration of remotely-sensed, discrete wavelength, digital data and the derivation of an albedo measurement (defined over the solar short wave spectrum) from spectrally limited observations. Twenty-seven LANDSAT observations, calibrated with urban targets (building roof-tops and parking lots), are used to derive spatial and seasonal patterns of surface reflectance and albedo for four land cover types, city, suburb, farm and forest.     

基于单片机的光谱反射率试验台

介绍了一种由MCS-51单片机实现的数字化后视镜光谱反射率试验台，着重讨论了系统的硬件设计，并研制了光谱反射率试验台。文章最后进行了误差分析，给出的试验数据表明，该设计是合理有效的。     

Correcting atmospheric masking to retrieve the spectral albedo of land surfaces from satellite measurements

A radiative transfer approach to the problem of atmospheric correction of satellite images in the solar spectral range is presented which includes all multiple scattering processes without any approximation. The numerical solution is accepted as satisfying, if the numerical accuracy is better than I per cent. This means that the accuracy of the atmospheric correction depends almost exclusively on the quality of the auxiliary data on the atmospheric state and the surface reflection indicatrix. Byextensivemodel calculations these parameter driven error bounds have been quantified. Thus the calculation results in a corrected albedo image with specified error bounds. This seems to be the first algorithm available for atmospheric correction of real imagery data which relies on a numerical exact treatment of multiple scattering. The program EXACT (EXact Atmospheric Correction Technique) has so far been used with Landsat Thematic Mapper (TM), NOAA AVHRR (National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer) and also with airborne Daedalus ATM images. The algorithm has been validated by comparison of satellite data to ground measurements and between different sensors. Errors of the derived albedos were found to remain below 0·01 for visible and near-infrared sensor channels of this set of radiometers.     

Surface temperature and albedo relationships in Senegal derived from NOAA-7 satellite data

Ground temperature and albedo values were studied in a semiarid, sub-Saharan region of Africa, Senegal, for various days in the period of September 1981 to October 1982. The albedo and ground temperature were found to be positively correlated; i.e., regions of high albedo were regions of high ground temperature, and regions of low albedo were regions of low ground temperature. Highest values of ground temperature and albedo were found in a region characterized by sparse vegetation and in the dry season. Lowest values of ground temperature and albedo were found in regions characterized by dense vegetation and in the wet season. These results suggest that regions of high albedo in the sub-Saharan region of west Africa are regions of high ground temperature. Mechanisms for increasing the ground temperature under high albedo conditions are discussed. The increased ground temperature will increase the long wave radiation emitted to the atmosphere and the sensible heat flux at the surface. Therefore, there can be a net heat gain in the local atmosphere which will result in a general rising motion. These results are in contrast to those proposed by Charney (1975), who suggested that an increased albedo in semidesert regions will cause a net radiation loss, contributing to net cooling, sinking, and drying of the air aloft.     

Theoretical noise sensitivity of BRDF and albedo retrieval from the EOS-MODIS and MISR sensors with respect to angular sampling

The sensitivity of the semiempirical RossThick-LiSparse Ambrals BRDF model to random noise in observed multiangular reflectances was investigated through a study of the impact of angular sampling. The mathematical properties of (linear, additive) kernel-driven BRDF models allow the analytical derivation of so-called weights of determination or noise amplification factors which quantify the uncertainty in retrieved parameters such as nadir-view reflectance or albedo at various solar zenith angles, or in the BRDF model parameters themselves. The study was carried out using simulated angular sampling for the MODIS and MISR instruments to be flown on NASA's Earth Observing System AM-1 platform, as a function of latitude, day of year and sampling period. A similar study was carried out for comparison using the modified RPV BRDF model, a multiplicative model. Results show that the retrieved parameters, reflectance and albedo can be expected to have noise amplification factors that are less than unity, indicating that the retrievals are stable with respect to random noise under the angular sampling schemes occurring. The BRDF model parameters themselves were found to be more susceptible to noise than many of the derived products, especially for the modified RPV model. The effect of different angular sampling regimes on the uncertainty of derived information was further explored. This study provides an indication of the reliability to be expected from the operational BRDF/albedo products from the MODIS and MISR instruments. The findings may qualitatively also apply to AVHRR, SPOT VEGETATION and similar satellite angular sampling regimes.     

Estimating forest parameters using Landsat ETM+ spectral responses and monocultured plantation fieldwork measurements data

Forest parameters, such as mean diameter at breast height (DBH), mean stand height (H) or volume per hectare (V), are imperative for forest resources assessment. Traditional forest inventory that is usually based on fieldwork is often difficult, time-consuming, and expensive to conduct over large areas. Therefore, estimating forest parameters in large areas using a traditional inventory approach combined with satellite data analysis can improve the spatial estimates of forest inventory data, and hence be useful for sustainable forest management and natural resources assessment. However, extracting practical information from satellite imagery for such purpose is a challenging task mainly because of insufficient knowledge linking forest inventory data to satellite spectral response. Here, we present the use of a cost-free Landsat-7 Enhanced Thematic Mapper Plus (ETM+) in order to explore whether it is possible to combine all available optical bands from a specific sensor for improving forest parameter spatial estimates, based on fieldwork at Lahav and Kramim Forests, in the Israeli Northern Negev. A generic strategy, based on morphological structuring element, convex hall and spectral band linear combination algorithms, was developed in order to extract the mathematical dependencies between the forest inventory measurements and linear combination sets of Landsat-7 ETM+ spectral bands, which yields the highest possible correlation with the forest inventory measured data. Using the mathematical dependency functions, we then convert the entire Landsat-7 ETM+ scenes into forest inventory parameter values with sufficient accuracy and tolerance errors needed for sustainable forest management. The root mean square error obtained between the measured and the estimated values for Lahav Forest are 0.70 cm, 0.29 m, and 1.48 m³ ha^?1 for the mean DBH, H, and V, respectively, and for Kramim forest are 0.61 cm, 0.70 m, and 6.31 m³ ha^?1, respectively. Furthermore, the suggested strategy could also be applied with other satellites data sources.     

The ratio of spectral albedo to spectral directed reflection coefficient for a smooth water surface

This communication describes the relation between spectral albedo Aλ and bidirectional spectral reflection coefficient ?λ for a smooth water surface for vertical view directions.     

Estimating floodwater from MODIS time series and SRTM DEM data

Real-time flood mapping with an automatic flood-detection technique is important in emergency response efforts. However, current mapping technology still has limitations in accurately expressing information on flood areas such as inundation depth and extent. For this reason, the authors attempt to improve a floodwater detection method with a simple algorithm for a better discrimination capacity to discern flood areas from turbid floodwater, mixed vegetation areas, snow, and clouds. The purpose of this study was to estimate a flood area based on the spatial distribution of a nationwide flood from the Moderate Resolution Imaging Spectroradiometer (MODIS) time series images (8-day composites, MOD09A1, 500-m resolution) and a digital elevation model (DEM). The results showed the superiority of the developed method in providing instant, accurate flood mapping by using two algorithms, which modified land surface water index from MODIS image and eight-direction tracking algorithm based on DEM data.     

On spectral windows in supervised learning from data

For Tikhonov regularization in supervised learning from data, the effect on the regularized solution of a joint perturbation of the regression function and the data is investigated. Spectral windows in the finite-sample and population cases are compared via probabilistic estimates of the differences between regularized solutions.     

Estimating aerodynamic resistance of rough surfaces using angular reflectance

Current wind erosion and dust emission models neglect the heterogeneous nature of surface roughness and its geometric anisotropic effect on aerodynamic resistance, and over-estimate the erodible area by assuming it is not covered by roughness elements. We address these shortfalls with a new model which estimates aerodynamic roughness length (z₀) using angular reflectance of a rough surface. The new model is proportional to the frontal area index, directional, and represents the geometric anisotropy of z₀. The model explained most of the variation in two sets of wind tunnel measurements of aerodynamic roughness lengths (z₀). Field estimates of z₀ for varying wind directions were similar to predictions made by the new model. The model was used to estimate the erodible area exposed to abrasion by saltating particles. Vertically integrated horizontal flux (F_h) was calculated using the area not covered by non-erodible hemispheres; the approach embodied in dust emission models. Under the same model conditions, F_h estimated using the new model was up to 85% smaller than that using the conventional area not covered. These F_h simulations imply that wind erosion and dust emission models without geometric anisotropic sheltering of the surface, may considerably over-estimate F_h and hence the amount of dust emission. The new model provides a straightforward method to estimate aerodynamic resistance with the potential to improve the accuracy of wind erosion and dust emission models, a measure that can be retrieved using bi-directional reflectance models from angular satellite sensors, and an alternative to notoriously unreliable field estimates of z₀ and their extrapolations across landform scales.     

Estimating the coverage of coral reef benthic communities from airborne hyperspectral remote sensing data: multiple discriminant function analysis and linear spectral unmixing

A staged approach for the application of linear spectral unmixing techniques to airborne hyperspectral remote sensing data of reef communities of the Al Wajh Barrier, Red Sea, is presented. Quantification of the percentage composition of four different reef components (live coral, dead coral, macroalgae and carbonate sand) contained within the ground sampling distance associated with an individual pixel is demonstrated. In the first stage, multiple discriminant function analysis is applied to spectra collected in situ to define an optimal subset combination of derivative and raw image wavebands for discriminating reef benthos. In the second phase, unmixing is applied to a similarly reduced subset of pre-processed image data to accurately determine the relative abundance of the reef benthos (R ² > 0.7 for all four components). The result of a phased approach is an increased signal-to-noise ratio for solution of the linear functions and reduction of processing burdens associated with image unmixing.     

Estimating subpixel fire sizes and temperatures from ASTER using multiple endmember spectral mixture analysis

The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) shortwave infrared subsystem can acquire images of active fires during daytime and night-time from a polar orbit, providing useful data on fire properties at a nominal spatial resolution of 30 m. Binary fire/no-fire counts of ASTER pixels have also been useful in evaluating the performance of widely-used fire products from the Moderate-Resolution Imaging Spectroradiometer (MODIS), which have a nominal spatial resolution of 1 km. However, the ASTER fire pixels are actually mixed pixels that can contain flaming, smouldering and non-burning components, and ASTER fire pixel counts provide no information about the sizes or temperatures of these subpixel components. This paper uses multiple endmember spectral mixture analysis (MESMA) to estimate subpixel fire sizes and temperatures from a night-time ASTER image of a fire in California, USA, demonstrating new methods that can provide information on fires not available from other sources. As a fire's size and its temperature exert strong influences on its gas and aerosol emissions, ecological impact and spreading rates, these MESMA estimates from ASTER imagery could contribute valuable new information towards monitoring, forecasting and understanding the behaviour and impacts of many fires worldwide.     

Retrieval of aerosol spectral optical thickness from AVIRIS data

The ER-2, with the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) onboard, overflew Rapid City, South Dakota, and the United States Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Site, Oklahoma, on 9 August and 15 August 1993, respectively. High contrast natural and artificial surfaces present in the imagery were used as a basis for retrieving aerosol spectral optical depth (SOD) over these two sites. Coincident measurements of spectral optical depth from a surface-based sunphotometer also were obtained and used as a validation of the AVIRIS derived retrievals. The accuracy of the retrievals is discussed as a function of measurement uncertainty and surface contrast. The results indicate that, given sufficiently small sensor errors and spectrally uniform surfaces with a reflectance difference of at least 0.5, aerosol spectral optical depth over clear continental atmospheres can be retrieved from high spatial resolution space-based imagery to an accuracy of approximately 0.1. Although only two cases are reported here and additional tests are required, these preliminary results suggest that background aerosol spectral optical depth (i.e., τ_aerosol, <01) cannot be retrieved with adequate accuracy from space; however, the aerosol spectral optical depth of more polluted atmospheres (i.e., τ_aerosol < 0.2) can be retrieved with adequate accuracy.     

Estimating dry matter content from spectral reflectance for green leaves of different species

Efficient and accurate detection of the temporal dynamics and spatial variations of leaf dry matter content would help monitor key properties and processes in vegetation and the wider ecosystem. However, leaf water content strongly absorbs at shortwave infrared wavelengths, reducing the signal from dry matter. The major objective of this study was to examine relationship between spectral reflectance of fresh leaves and the ratio of leaf dry mass to leaf area, across a wide range of species at the leaf scale. A narrow-band, normalized index combining two distinct wavebands centred at 1649 and 1722 nm achieved the highest overall performance and discriminatory power compared to either single band or first derivatives. The normalized index was evaluated using the PROSPECT (leaf optical properties spectra) simulated reflectance spectra and field measurements from the Leaf Optical Properties Experiment (LOPEX) data set. Both evaluations show that leaf dry matter contents were retrievable with R ² of 0.845 and 0.681 and regression slopes of 0.903 and 0.886. This study suggests that spectral reflectance measurements hold promise for the assessment of dry matter content for green leaves. Further investigation needs to be conducted to evaluate the effectiveness of this normalized index at canopy scales.