Hyperspectral remote sensing of cyanobacteria in turbid productive water using optically active pigments,chlorophyll a and phycocyanin

Nuisance blue-green algal blooms contribute to aesthetic degradation of water resources by means of accelerated eutrophication, taste and odor problems, and the production of toxins that can have serious adverse human health effects. Current field-based methods for detecting blooms are costly and time consuming, delaying management decisions. Methods have been developed for estimating phycocyanin concentration, the accessory pigment unique to freshwater blue-green algae, in productive inland water. By employing the known optical properties of phycocyanin, researchers have evaluated the utility of field-collected spectral response patterns for determining concentrations of phycocyanin pigments and ultimately blue-green algal abundance. The purpose of this research was to evaluate field spectroscopy as a rapid cyanobacteria bloom assessment method. In-situ field reflectance spectra were collected at 54 sampling sites on two turbid reservoirs on September 6th and 7th in Indianapolis, Indiana using ASD Fieldspec (UV/VNIR) spectroradiometers. Surface water samples were analyzed for in-vitro pigment concentrations and other physical and chemical water quality parameters. Semi-empirical algorithms by Simis et al. [Simis, S., Peters, S., Gons, H. (2005). Remote sensing of the cyanobacterial pigment phycocyanin in turbid inland water. American Society of Limnology and Oceanography 50(11): 237–245] were applied to the field spectra to predict chlorophyll a and phycocyanin absorption at 665 nm and 620 nm, respectively. For estimation of phycocyanin concentration, a specific absorption coefficient of 0.0070 m² mg PC^-1 for phycocyanin at 620 nm, a_PC^?(620), was employed, yielding an r² value of 0.85 (n = 48, p < 0.0001), mean relative residual value of 0.51 (σ = 1.41) and root mean square error (RMSE) of 19.54 ppb. Results suggest this algorithm could be a robust model for estimating phycocyanin. Error is highest in water with phycocyanin concentrations of less than 10 ppb and where phycocyanin abundance is low relative to chlorophyll a. A strong correlation between measured phycocyanin concentrations and biovolume measurements of cyanobacteria was also observed (r = 0.89), while a weaker relationship (r = 0.66) resulted between chlorophyll a concentration and cyanobacterial biovolume.     

MERIS satellite chlorophyll mapping of oligotrophic and eutrophic waters in the Laurentian Great Lakes

Chlorophyll-a (Chla) concentrations and ‘water-leaving’ reflectance were assessed along transects in Keweenaw Bay (Lake Superior) and in Green Bay (Lake Michigan) (two of the Laurentian Great Lakes, USA), featuring oligotrophic (0.4–0.8 mg Chla m^? 3) and eutrophic to hyper-eutrophic waters (11–131 mg Chla m^? 3), respectively. A red-to-NIR band Chla retrieval algorithm proved to be applicable to Green Bay, but gave mostly negative values for Keweenaw Bay. An alternative algorithm could be based on Chla fluorescence, which in Keweenaw Bay was indicated by enhanced reflectance near 680 nm. Bands 7, 8 and 9 of the Medium Resolution Imaging Spectrometer (MERIS) have been specifically designed to detect phytoplankton fluorescence in coastal waters. A quite strong linear relationship was found between Chla concentration and fluorescence line height (FLH) computed with these MERIS bands. The same relationship held for observations on oligotrophic waters elsewhere, but not for Green Bay, where the FLH diminished to become negative as Chla increased. The remote sensing application of the algorithms could be tested because a MERIS scene was acquired coinciding with the day of the field observations in Keweenaw Bay and one day after those in Green Bay. For Green Bay the pixel values from the red-to-NIR band algorithm compared well to the steep Chla gradient in situ. This result is very positive from the perspective of satellite use in monitoring eutrophic inland and coastal waters in many parts of the world. Implementation of the FLH relationship in the scene of Keweenaw Bay produced highly variable pixel values. The FLH in oligotrophic inland waters like Lake Superior appears to be very close to or below the MERIS detection limit. An empirical algorithm incorporating three MERIS bands in the blue-to-green spectral region might be used as an alternative, but its applicability to other regions and seasons remains to be verified. Moreover, none of the algorithms will be suitable for mesotrophic water bodies. The results indicate that Chla mapping in oligotrophic and mesotrophic areas of the Great Lakes remains problematic for the current generation of satellite sensors.     

Influence of phytoplankton pigment composition on remote sensing of cyanobacterial biomass

An extensive field campaign was carried out for the validation of a previously published reflectance ratio-based algorithm for quantification of the cyanobacterial pigment phycocyanin (PC). The algorithm uses band settings of the Medium Resolution Imaging Spectrometer (MERIS) onboard ENVISAT, and should accurately retrieve the PC concentration in turbid, cyanobacteria-dominated waters. As algae and cyanobacteria often co-occur, the algorithm response to varying phytoplankton composition was explored. Remote sensing reflectance and reference pigment measurements were obtained in the period 2001-2005 in Spain and the Netherlands using field spectroradiometry and various pigment extraction methods. Additional field data was collected in Spain in May 2005 to allow intercalibration of spectroradiometry and pigment assessment methods. Two methods for extraction of PC from concentrated water samples, and in situ measured PC fluorescence, compared well. Reflectance measurements with different field spectroradiometers used in Spain and the Netherlands also gave similar results. Residual analysis of PC predicted by the algorithm showed that overestimation of PC mainly occurred in the presence of chlorophylls b and c, and phaeophytin. The errors were strongest at low PC relative to Chl a concentrations. A correction applied for absorption by Chl b markedly improved the prediction. Without such a correction, the quality of the PC prediction still increased markedly with estimates > 50 mg PC m^− 3, allowing monitoring of the cyanobacterial status of eutrophic waters. The threshold concentration may be lowered when a high intracellular PC:Chl a ratio or cyanobacterial dominance is expected. Below the limit, predicted PC concentrations should be considered as the highest estimate. We evaluated that remote sensing of both PC and Chl a would allow assessment of cyanobacterial risk to water quality and public health in over 70% of our cases.     

A simple semi-analytical model for remote estimation of chlorophyll-a in turbid waters: Validation

Accurate assessment of phytoplankton chlorophyll-a (chla) concentrations in turbid waters by means of remote sensing is challenging due to the optical complexity of case 2 waters. We have applied a recently developed model of the form [R_rs^? 1(λ₁) ? R_rs^? 1(λ₂)] × R_rs(λ₃) where R_rs(λ_i) is the remote-sensing reflectance at the wavelength λ_i, for the estimation of chla concentrations in turbid waters. The objectives of this paper are (a) to validate the three-band model as well as its special case, the two-band model R_rs^? 1(λ₁) × R_rs(λ₃), using datasets collected over a considerable range of optical properties, trophic status, and geographical locations in turbid lakes, reservoirs, estuaries, and coastal waters, and (b) to evaluate the extent to which the three-band model could be applied to the Medium Resolution Imaging Spectrometer (MERIS) and two-band model could be applied to the Moderate Resolution Imaging Spectroradiometer (MODIS) to estimate chla in turbid waters.The three-band model was calibrated and validated using three MERIS spectral bands (660–670 nm, 703.75–713.75 nm, and 750?757.5 nm), and the 2-band model was tested using two MODIS spectral bands (λ₁ = 662–672, λ₃ = 743–753 nm). We assessed the accuracy of chla prediction in four independent datasets without re-parameterization (adjustment of the coefficients) after initial calibration elsewhere. Although the validation data set contained widely variable chla (1.2 to 236 mg m^? 3), Secchi disk depth (0.18 to 4.1 m), and turbidity (1.3 to 78 NTU), chla predicted by the three-band algorithm was strongly correlated with observed chla (r² > 0.96), with a precision of 32% and average bias across data sets of ? 4.9% to 11%. Chla predicted by the two-band algorithm was also closely correlated with observed chla (r² > 0.92); however, the precision declined to 57%, and average bias across the data sets was 18% to 50.3%. These findings imply that, provided that an atmospheric correction scheme for the red and NIR bands is available, the extensive database of MERIS and MODIS imagery could be used for quantitative monitoring of chla in turbid waters.     

Spectral and chemical analysis of tropical forests: Scaling from leaf to canopy levels

Variation in the foliar chemistry of humid tropical forests is poorly understood, and airborne imaging spectroscopy could provide useful information at leaf and canopy scales. However, variation in canopy structure affects our ability to estimate foliar properties from airborne spectrometer data, yet these structural affects remain poorly quantified. Using leaf spectral (400–2500 nm) and chemical data collected from 162 Australian tropical forest species, along with partial least squares (PLS) analysis and canopy radiative transfer modeling, we determined the strength of the relationship between canopy reflectance and foliar properties under conditions of varying canopy structure.At the leaf level, chlorophylls, carotenoids and specific leaf area (SLA) were highly correlated with leaf spectral reflectance (r = 0.90–0.91). Foliar nutrients and water were also well represented by the leaf spectra (r = 0.79–0.85). When the leaf spectra were incorporated into the canopy radiative transfer simulations with an idealistic leaf area index (LAI) = 5.0, correlations between canopy reflectance spectra and leaf properties increased in strength by 4–18%. The effects of random LAI (= 3.0–6.5) variation on the retrieval of leaf properties remained minimal, particularly for pigments and SLA (r = 0.92–0.93). In contrast, correlations between leaf nitrogen (N) and canopy reflectance estimates decreased from r = 0.87 at constant LAI = 5 to r = 0.65 with randomly varying LAI = 3.0–6.5. Progressive increases in the structural variability among simulated tree crowns had relatively little effect on pigment, SLA and water predictions. However, N and phosphorus (P) were more sensitive to canopy structural variability. Our modeling results suggest that multiple leaf chemicals and SLA can be estimated from leaf and canopy reflectance spectroscopy, and that the high-LAI canopies found in tropical forests enhance the signal via multiple scattering. Finally, the two factors we found to most negatively impact leaf chemical predictions from canopy reflectance were variation in LAI and viewing geometry, which can be managed with new airborne technologies and analytical methods.     

Bounds for the Roots of Lacunary Polynomials

A polynomial P(X)  = X^d + a_d − 1X^d − 1 + ⋯ is called lacunary when a_d − 1 =  0. We give bounds for the roots of such polynomials with complex coefficients. These bounds are much smaller than for general polynomials.     

Photonic crystal slabs with hexagonal air holes fabricated by selective area metal organic vapor phase epitaxy

It is shown that the photonic crystal slab (PCS) with hexagonal air holes has band gaps in the guided mode spectrum, which can be compared to that of the PCS with circular air holes, thus it is also a good candidate to be used for the PC devices. The PC with hexagonal air holes and a = 0.5 μm and r = 0.15 μm was fabricated successfully by selective area metal organic vapor phase epitaxy (SA-MOVPE). The vertical and smooth sidewalls are formed and the uniformity is very good. The same process was also used to fabricate a hexagonal air hole array with the width of 0.1 μm successfully. The air-bridge PCS with hexagonal air holes and a = 0.3 μm and r = 0.09 μm was also fabricated successfully by SA-MOVPE. Further optimization of the growth conditions for the sacrificial layer and the selective etching of the GaAs cap layer is also needed. Our experimental results indicate that SA-MOVPE is a promising method for fabricating PC devices and photonic nanostructures.     

Development of an optical fibre reflectance sensor for copper (II) detection based on immobilised salicylic acid

Immobilized salicylic acid onto XAD-2 (styrene–divinylbenzene cross-linked copolymer) has been attempted in this study as a reagent phase for the development of an optical fibre copper (II) sensor. The measurements were carried out at a given wavelength of 690.27 nm since it yielded the largest divergence different in reflectance spectra before and after reaction with the analyte element. The optimum response was obtained at pH 5.0. The linear dynamic range of Cu(II) was found within the concentration range of 1.0–2.0 mmol L⁻¹ with its LOD of 0.5 mmol L⁻¹. The sensor response from different probes (n = 9) gave an R.S.D. of 8.4% at 0.55 mmol L⁻¹ Cu(II). The effect of interfered ions at 1:1 molar ratio of Cu(II):foreign ion was also studied in this work.     

Linking physiological responses,chlorophyll fluorescence and hyperspectral imagery to detect salinity stress using the physiological reflectance index in the coastal shrub,Myrica cerifera

Measurements of physiology, chlorophyll fluorescence and hyperspectral reflectance were used to detect salinity stress in the evergreen coastal shrub, Myrica cerifera on Hog Island, Virginia. Two experimental sites were used in our study, the oceanside of a M. cerifera thicket, which is exposed to sea spray, and the protected, leeside of the thicket. Using the physiological reflectance index (PRI), we were able to detect stress at both the canopy and landscape level. Monthly variations in stomatal conductance, photosynthesis, and relative water content indicated a strong summer drought response that was not apparent in chlorophyll fluorescence or in the water band index (WBI) derived from canopy and airborne reflectance measurements. In contrast, there were significant differences in both physiological measurements and tissue chlorides between the two sites used in the study, indicating salinity stress. This was reflected in measurements of PRI. There was a positive relationship between PRI measured at the canopy-level and light-adapted fluorescence (ΔF/F′_m; r² = 0.69). PRI was significantly lower on the oceanside of the Myrica cerifera thicket. PRI was not significantly related to NDVI (r² = 0.01) at the canopy-level and only weakly related (r² = 0.04) at the landscape-level, suggesting that the indices are independent. The chlorophyll index (CI) did not show any significant changes between the two sites. Frequency histograms of pixels sampled from airborne hyperspectral imagery revealed that the distribution of PRI was shifted to the right on the backside of the thicket relative to the oceanside and there was a significant difference between sites. These results suggest that PRI may be used for early identification of salt-stress and to identify areas across the landscape where community structure may change due to sea-level rise.     

Spectral discrimination of phytoplankton colour groups: The effect of suspended particulate matter and sensor spectral resolution

Remote sensing has been used extensively to provide quantitative information on the distribution of phytoplankton in inland waters through the surrogate mapping of chlorophyll a, but as chlorophyll a is common to almost all species of phytoplankton it cannot provide any information on the taxonomic composition of phytoplankton communities. However, the varied optical properties of phytoplankton taxa may present a means to their discrimination via remote sensing data. This paper presents the results of an experimental study in which the spectral dissimilarities of brown, green, blue-green and red algae were examined with a view to establishing a basis upon which broad changes in phytoplankton communities might be monitored through remote sensing. Pseudo phytoplankton communities were simulated in a series of mesocosm experiments from which spectral reflectance measurements were acquired. The results demonstrated that the phytoplankton colour groups examined were indeed spectrally dissimilar. The spectral distinction between colour groups was noted to be greatest at high concentrations of chlorophyll a and between pseudo-communities dominated by a single species; spectral differences were lower in mixed pseudo-communities with co-dominant species. Moreover, it proved possible to quantify the concentration of two potential biomarker pigments, fucoxanthin and C-phycocyanin, through the derivation of simple spectral indices. The coincidental presence of varying concentrations of SPM (SPIM and SPOM) caused significant attenuation of the spectral response of the pseudo-communities and affected the accuracy of biomarker pigment estimation. It is considered that the realisation of a remote sensing technique for the discrimination of phytoplankton taxa in inland waters would be an extremely useful tool for limnological research and water resource management and thus the future application of this research to inland waters is also discussed.     

Calibration and validation of hyperspectral indices for the estimation of broadleaved forest leaf chlorophyll content,leaf mass per area,leaf area index and leaf canopy biomass

This article aims at finding efficient hyperspectral indices for the estimation of forest sun leaf chlorophyll content (CHL, µg cm_leaf^? 2), sun leaf mass per area (LMA, g_{dry matter} m_leaf^? 2), canopy leaf area index (LAI, m²_leaf m_soil^? 2) and leaf canopy biomass (B_leaf, g_{dry matter} m_soil^? 2). These parameters are useful inputs for forest ecosystem simulations at landscape scale. The method is based on the determination of the best vegetation indices (index form and wavelengths) using the radiative transfer model PROSAIL (formed by the newly-calibrated leaf reflectance model PROSPECT coupled with the multi-layer version of the canopy radiative transfer model SAIL). The results are tested on experimental measurements at both leaf and canopy scales. At the leaf scale, it is possible to estimate CHL with high precision using a two wavelength vegetation index after a simulation based calibration. At the leaf scale, the LMA is more difficult to estimate with indices. At the canopy scale, efficient indices were determined on a generic simulated database to estimate CHL, LMA, LAI and Bleaf in a general way. These indices were then applied to two Hyperion images (50 plots) on the Fontainebleau and Fougères forests and portable spectroradiometer measurements. They showed good results with an RMSE of 8.2 µg cm^? 2 for CHL, 9.1 g m^? 2 for LMA, 1.7 m² m^? 2 for LAI and 50.6 g m^? 2 for Bleaf. However, at the canopy scale, even if the wavelengths of the calibrated indices were accurately determined with the simulated database, the regressions between the indices and the biophysical characteristics still had to be calibrated on measurements. At the canopy scale, the best indices were: for leaf chlorophyll content: ND_chl = (ρ₉₂₅ ? ρ₇₁₀)/(ρ₉₂₅ + ρ₇₁₀), for leaf mass per area: ND_LMA = (ρ₂₂₆₀ ? ρ₁₄₉₀)/(ρ₂₂₆₀ + ρ₁₄₉₀), for leaf area index: D_LAI = ρ₁₇₂₅ ? ρ₉₇₀, and for canopy leaf biomass: ND_Bleaf = (ρ₂₁₆₀ ? ρ₁₅₄₀)/(ρ₂₁₆₀ + ρ₁₅₄₀).     

First-order piezoresistance coefficients in heavily doped p-type silicon crystals

The first-order piezoresistance coefficients π₁₁, π₁₂, π₄₄ were calculated for heavily doped p-type silicon crystals. The analytical calculation was carried out within the framework of the three-band model taking into account effect of anisotropy and influence of the spin-orbit split-off band (here the SO band) on shape of heavy and light hole bands as well as direct contribution of the SO band into magnitude of the piezoresistance coefficients. The letter is negligible for shear piezoresistance coefficient π₄₄ but for values of piezoresistance coefficients π₁₁ and π₁₂ its contribution reaches 30%. In correspondence with experimental data the calculated values of piezoresistance coefficients π₁₁ and π₁₂ have opposite signs for scattering by acoustic phonons or by ionized impurities.There is a good agreement between our numerical results (for scattering by ionized impurities) and experimental data for shear piezoresistance coefficient π₄₄ at temperatures 190–375 K. It has been obtained in wide range of impurity concentrations (5 × 10¹⁹–2 × 10²¹ cm⁻³).     

Simulation of solid thermal explosion and liquid thermal explosion of dicumyl peroxide using calorimetric technique

Dicumyl peroxide (DCPO), is produced by cumene hydroperoxide (CHP) process, is utilized as an initiator for polymerization, a prevailing source of free radicals, a hardener, and a linking agent. DCPO has caused several thermal explosion and runaway reaction accidents in reaction and storage zone in Taiwan because of its unstable reactive property. Differential scanning calorimetry (DSC) was used to determine thermokinetic parameters including 700 J g^–1 of heat of decomposition (ΔH_d), 110 °C of exothermic onset temperature (T₀), 130 kJ mol^–1 of activation energy (E_a), etc., and to analyze the runaway behavior of DCPO in a reaction and storage zone. To evaluate thermal explosion of DCPO with storage equipment, solid thermal explosion (STE) and liquid thermal explosion (LTE) of thermal safety software (TSS) were applied to simulate storage tank under various environmental temperatures (T_e). T_e exceeding the T₀ of DCPO can be discovered as a liquid thermal explosion situation. DCPO was stored under room temperature without sunshine and was prohibited exceeding 67 °C of self-accelerating decomposition temperature (SADT) for a tank (radius = 1 m and height = 2 m). SADT of DCPO in a box (width, length and height = 1 m, respectively) was determined to be 60 °C. The TSS was employed to simulate the fundamental thermal explosion behavior in a large tank or a drum. Results from curve fitting demonstrated that, even at the earlier stage of the reaction in the experiments, ambient temperature could elicit exothermic reactions of DCPO. To curtail the extent of the risk, relevant hazard information is quite significant and must be provided in the manufacturing process.     

Assessment of C-band synthetic aperture radar data for mapping and monitoring Coastal Plain forested wetlands in the Mid-Atlantic Region,U.S.A.

Multi-temporal C-band SAR data (C-HH and C-VV), collected by ERS-2 and ENVISAT satellite systems, are compared with field observations of hydrology (i.e., inundation and soil moisture) and National Wetland Inventory maps (U.S. Fish and Wildlife Service) of a large forested wetland complex adjacent to the Patuxent and Middle Patuxent Rivers, tributaries of the Chesapeake Bay. Multi-temporal C-band SAR data were shown to be capable of mapping forested wetlands and monitoring hydroperiod (i.e., temporal fluctuations in inundation and soil moisture) at the study site, and the discrimination of wetland from upland was improved with 10 m digital elevation data. Principal component analysis was used to summarize the multi-temporal SAR data sets and to isolate the dominant temporal trend in inundation and soil moisture (i.e., relative hydroperiod). Significant positive, linear correlations were found between the first principal component and percent area flooded and soil moisture. The correlation (r²) between the first principal component (PC1) of multi-temporal C-HH SAR data and average soil moisture was 0.88 (p = < .0001) during the leaf-off season and 0.87 (p = < .0001) during the leaf-on season, while the correlation between PC1 and average percent area inundated was 0.82 (p = < .0001) and 0.47 (p = .0016) during the leaf-off and leaf-on seasons, respectively. When compared to field data, the SAR forested wetland maps identified areas that were flooded for 25% of the time with 63–96% agreement and areas flooded for 5% of the time with 44–89% agreement, depending on polarization and time of year. The results are encouraging and justify further studies to attempt to quantify the relative SAR-derived hydroperiod classes in terms of physical variables and also to test the application of SAR data to more diverse landscapes at a broader scale. The present evidence suggests that the SAR data will significantly improve routine wooded wetland mapping.     

Chromium(III)-selective sensor based on tri-o-thymotide in PVC matrix

Tri-o-thymotide (I) has been used as an electroactive material in PVC (poly(vinyl chloride)) matrix for fabrication of chromium(III)-selective sensor. The membrane containing tri-o-thymotide, sodium tetraphenyl borate (NaTPB), dibutyl phthalate (DBP) and PVC in the optimum ratio 5:1:75:100 (w/w) exhibits a working concentration range of 4.0 × 10⁻⁶ to 1.0 × 10⁻¹ M with a Nernstian slope of 20.0 ± 0.1 mV/decade of activity in the pH range of 2.8–5.1. The detection limit of this sensor is 2.0 × 10⁻⁷ M. The electrode exhibits a fast response time of 15 s, shows good selectivity towards Cr³⁺ over a number of mono-, bi- and trivalent cations and can also be used in partially non-aqueous medium (up to 15%, v/v) also. The assembly has been successfully used as an indicator electrode in the potentiometric titration of chromium(III) against EDTA and also to determine Cr(III) quantitatively in electroplating industry waste samples.     

On the influence of the number of algorithms,problems, and independent runs in the comparison of evolutionary algorithms

When conducting a comparison between multiple algorithms on multiple optimisation problems it is expected that the number of algorithms, problems and even the number of independent runs will affect the final conclusions. Our question in this research was to what extent do these three factors affect the conclusions of standard Null Hypothesis Significance Testing (NHST) and the conclusions of our novel method for comparison and ranking the Chess Rating System for Evolutionary Algorithms (CRS4EAs). An extensive experiment was conducted and the results were gathered and saved of k = 16 algorithms on N = 40 optimisation problems over n = 100 runs. These results were then analysed in a way that shows how these three values affect the final results, how they affect ranking and which values provide unreliable results. The influence of the number of algorithms was examined for values k = {4, 8, 12, 16}, number of problems for values N = {5, 10, 20, 40}, and number of independent runs for values n = {10, 30, 50, 100}. We were also interested in the comparison between both methods – NHST's Friedman test with post-hoc Nemenyi test and CRS4EAs – to see if one of them has advantages over the other. Whilst the conclusions after analysing the values of k were pretty similar, this research showed that the wrong value of N can give unreliable results when analysing with the Friedman test. The Friedman test does not detect any or detects only a small number of significant differences for small values of N and the CRS4EAs does not have a problem with that. We have also shown that CRS4EAs is an appropriate method when only a small number of independent runs n are available.     

Irregular Primes and Cyclotomic Invariants to 12 Million

Computations of irregular primes and associated cyclotomic invariants were extended to all primes up to 12 million using multisectioning/convolution methods and a novel approach which originated in the study of Stickelberger codes Shokrollahi (1996). The latter idea reduces the problem to that of finding zeros of a polynomial overF_pof degree  <  (p −  1) / 2 among the quadratic nonresidues mod p. Use of fast polynomial gcd-algorithms gives anO (p log²p loglog p)-algorithm for this task. A more efficient algorithm, with comparable asymptotic running time, can be obtained by using Schönhage–Strassen integer multiplication techniques and fast multiple polynomial evaluation algorithms; this approach is particularly efficient when run on primes p for whichp −  1 has small prime factors. We also give some improvements on previous implementations for verifying the Kummer–Vandiver conjecture and for computing the cyclotomic invariants of a prime.     

Modelling damping ratio and shear modulus of sand–mica mixtures using genetic programming

This study presents two Genetic Programming (GP) models for damping ratio and shear modulus of sand–mica mixtures based on experimental results. The experimental database used for GP modelling is based on a laboratory study of dynamic properties of saturated coarse rotund sand and mica mixtures with various mix ratios under different effective stresses. In the tests, shear modulus, and damping ratio of the geomaterials have been measured for a strain range of 0.001% up to 0.1% using a Stokoe resonant column testing apparatus. The input variables in the developed NN models are the mica content, effective stress and strain, and the outputs are damping ratio and shear modulus. The performance of accuracies of proposed NN models are quite satisfactory (R² = 0.95 for damping ratio and R² = 0.98 for shear modulus).     

Monitoring land degradation risk using ASTER data: The non-evaporative fraction as an indicator of ecosystem function

There is a need to develop operational land degradation indicators for large regions to prevent losses of biological and economic productivity. Disturbance events press ecosystems beyond resilience and modify the associated hydrological and surface energy balance. Therefore, new indicators for water-limited ecosystems can be based on the partition of the surface energy into latent (λE) and sensible heat flux (H).In this study, a new methodology for monitoring land degradation risk for regional scale application is evaluated in a semiarid area of SE Spain. Input data include ASTER surface temperature and reflectance products, and other ancillary data. The methodology employs two land degradation indicators, one related to ecosystem water use derived from the non-evaporative fraction (NEF = H / (λE + H)), and another related to vegetation greenness derived from the NDVI. The surface energy modeling approach used to estimate the NEF showed errors within the range of similar studies (R² = 0.88; RMSE = 0.18 (22%)).To create quantitative indicators suitable for regional analysis, the NEF and NDVI were standardized between two possible extremes of ecosystem status: extremely disturbed and undisturbed in each climatic region to define the NEFS (NEF Standardized) and NDVIS (NDVI Standardized). The procedure was successful, as it statistically identified ecosystem status extremes for both indicators without supervision. Evaluation of the indicators at disturbed and undisturbed (control) sites, and intermediate surface variables such as albedo or surface temperature, provided insights on the main surface energy status controls following disturbance events. These results suggest that ecosystem functional indicators, such as the NEFS, can provide information related to the surface water deficit, including the role of soil properties.     

Machine learning regression algorithms for biophysical parameter retrieval: Opportunities for Sentinel-2 and -3

ESA's upcoming satellites Sentinel-2 (S2) and Sentinel-3 (S3) aim to ensure continuity for Landsat 5/7, SPOT-5, SPOT-Vegetation and Envisat MERIS observations by providing superspectral images of high spatial and temporal resolution. S2 and S3 will deliver near real-time operational products with a high accuracy for land monitoring. This unprecedented data availability leads to an urgent need for developing robust and accurate retrieval methods. Machine learning regression algorithms may be powerful candidates for the estimation of biophysical parameters from satellite reflectance measurements because of their ability to perform adaptive, nonlinear data fitting.By using data from the ESA-led field campaign SPARC (Barrax, Spain) we have compared the utility of four state-of-the-art machine learning regression algorithms and four different S2 and S3 band settings to assess three important biophysical parameters: leaf chlorophyll content (Chl), leaf area index (LAI) and fractional vegetation cover (FVC). The tested Sentinel configurations were: S2-10 m (4 bands), S2-20 m (8 bands), S2-60 m (10 bands) and S3-300 m (19 bands), and the tested methods were: neural networks (NN), support vector regression (SVR), kernel ridge regression (KRR), and Gaussian processes regression (GPR).GPR outperformed the other retrieval methods for the majority of tested configurations and was the only method that reached the 10% precision required by end users in the estimation of Chl. Also, although validated with an RMSE accuracy around 20%, GPR yielded optimal LAI and FVC estimates at highest S2 spatial resolution of 10 m with only four bands. In addition to high accuracy values, GPR also provided confidence intervals of the estimates and insight in relevant bands, which are key advantages over the other methods. Given all this, GPR proved to be a fast and accurate nonlinear retrieval algorithm that can be potentially implemented for operational monitoring applications.