Technique for validating remote sensing products of water quality

Remote sensing of water quality is initiated as an additional part of the on going activities of the EAGLE2006 project. Within this context intensive in-situ and airborne measurements campaigns were carried out over the Wolderwijd and Veluwemeer natural waters. However, in-situ measurements and image acquisitions were not simultaneous. This poses some constraints on validating air/space-borne remote sensing products of water quality. Nevertheless, the detailed insitu measurements and hydro-optical model simulations provide a bench mark for validating remote sensing products. That is realized through developing a stochastic technique to quantify the uncertainties on the retrieved aquatic inherent optical properties (IOP). The output of the proposed technique is applied to validate remote sensing products of water quality. In this processing phase, simulations of the radiative transfer in the coupled atmosphere-water system are performed to generate spectra at-sensor-level. The upper and the lower boundaries of perturbations, around each recorded spectrum, are then modelled as function of residuals between simulated and measured spectra. The perturbations are parameterized as a function of model approximations/inversion, sensor-noise and atmospheric residual signal. All error sources are treated as being of stochastic nature. Three scenarios are considered: spectrally correlated (i.e. wavelength dependent) perturbations, spectrally uncorrelated perturbations and a mixed scenario of the previous two with equal probability of occurrence. Uncertainties on the retrieved IOP are quantified with the relative contribution of each perturbation component to the total error budget of the IOP. This technique can be used to validate earth observation products of water quality in remote areas where few or no in– situ measurements are available

Vakdidactiek wiskunde in een Community of Learners (CoL)

Vakdidactiek op de lerarenopleiding in een Community of Learners (CoL) was stimulerend voor docenten in opleiding (DIO’s). Zij leerden door wetenschappelijke artikelen over de didactiek van de wiskunde aan el-kaar te presenteren en samen problemen op te lossen. De vertaling van de opleidingssituatie naar de schoolsituatie stond voortdurend centraal. In de voorbereiding was er in alle gevallen sprake van integratie van de aangereikte theorie. In de uitvoering bracht de schoolpraktijk echter zo’n schokeffect teweeg dat DIO’s niet in staat waren de geplande onderwijsleergesprekken met het oog op de ontwikkeling van wis-kundige begrippen daadwerkelijk te houden. In de evaluatie lag het accent op het keurslijf van het boek, de sommencultuur, de studiewijzer en de cultuur van zelfstandig werken, en niet op de inbedding van de theorie. De aanbeveling is om inrichting van een CoL te verrijken met de concrete deelname van docenten die lesgeven op school

Retrieval of canopy component temperatures through Bayesian inversion of directional thermal measurements

Evapotranspiration is usually estimated in remote sensing from single temperature value representing both soil and vegetation. This surface temperature is an aggregate over multiple canopy components. The temperature of the individual components can differ significantly, introducing errors in the evapotranspiration estimations. The temperature aggregate has a high level of directionality. An inversion method is presented in this paper to retrieve four canopy component temperatures from directional brightness temperatures. The Bayesian method uses both a priori information and sensor characteristics to solve the ill-posed inversion problem. The method is tested using two case studies: 1) a sensitivity analysis, using a large forward simulated dataset, and 2) in a reality study, using two datasets of two field campaigns. The results of the sensitivity analysis show that the Bayesian approach is able to retrieve the four component temperatures from directional brightness temperatures with good success rates using multi-directional sensors (Srspectra˜0.3, Srgonio˜0.3, and SrAATSR˜0.5), and no improvement using mono-angular sensors (Sr˜1). The results of the experimental study show that the approach gives good results for high LAI values (RMSEgrass=0.50 K, RMSEwheat=0.29 K, RMSEsugar beet=0.75 K, RMSEbarley=0.67 K); but for low LAI values the results were unsatisfactory (RMSEyoung maize=2.85 K). This discrepancy was found to originate from the presence of the metallic construction of the setup. As these disturbances, were only present for two crops and were not present in the sensitivity analysis, which had a low LAI, it is concluded that using masked thermal images will eliminate this discrepanc

Good, better, engaged? The effect of company-initiated customer engagement behavior on shareholder value

In today’s connected world, customer engagement behaviors are very important. Many companies launch initiatives to stimulate customer engagement. However, despite evidence that customer engagement behavior also matters to share-holders, academic research on the firm value consequences of customer engagement campaigns is limited. This study is the first to investigate the value-related consequences of firm-initiated customer engagement behaviors, using shareholder evaluations of the public announcements of such initiatives. We find that companies’ customer engagement initiatives, on average, decrease market value, which is likely because the shareholders are sensitive to the risk of these initiatives backfiring.Nevertheless, initiatives that stimulate word-of-mouth are viewed less negatively than initiatives that solicit customer feedback, as are initiatives that are supported by social media. Companies that operate in a competitive environment or do not advertise much can create value by stimulating customer engagement, while companies with a strong corporate reputation are likely to not benefit from it

Estimating forest parameters from top-of-atmosphere radiance measurements using coupled radiative transfer models

The canopy and atmosphere radiative transfer models SLC and MODTRAN were coupled to simulate top-of-atmosphere (TOA) radiance data for 3 Norway spruce stands in Eastern Czech Republic. The simulations fitted the near-nadir CHRIS radiance data well. A sensitivity analysis based on the singular value decomposition of the Jacobian matrix provided useful information for building the look up tables needed to estimate needle and canopy parameters. Canopy cover, fraction of bark in the plant area index, needle chlorophyll and dry matter content were estimated using the TOA CHRIS radiance. For comparison, the simulations, sensitivity analysis and parameter estimations were also conducted for the top of canopy (TOC) level, using atmospherically corrected CHRIS reflectance data. The results showed that the TOA approach performs as good as the TOC approach and allowed decreasing the ill-posedness for at least one stand

Stochastic User Equilibrium Traffic Assignment with Price-sensitive Demand: Do Methods matter (much)?

We compare three stochastic user equilibrium traffic assignment models multinomial probit, nested logit, and generalized nested logit), using a congestible transport network. We test the models in two situations: one in which they have theoretically equivalent coefficients, and one in which they are calibrated to have similar traffic flows. In each case, we examine the differences in traffic flows between the SUE models, and use them to evaluate policy decisions, such as profit-maximizing tolling or second-best socially optimal tolling. We then investigate how the optimal tolls, and their performance, depend on the model choice, and hence, how important the differences between models are. We show that the differences between models are small, as a result of the congestibility of the network, and that a better calibration does not always lead to better traffic flow predictions. As the outcomes are so similar, it may be better to use computationally more efficient logit models instead of probit models, in at least some applications, even if the latter is preferable from a conceptual viewpoint

Impact of the representation of the infiltration on the river flow during intense rainfall events in JULES

Intense rainfall can lead to flash flooding and may cause disruption, damage and loss of life. Since flooding from intense rainfall (FFIR) events are of a short duration and occur within a limited area, they are generally poorly predicted by Numerical Weather Prediction (NWP) models. This is because of the high spatio-temporal resolution required and because of the way the convective rainfall is described in the model. Moreover, the hydrological process descriptions of Land Surface Models (LSMs) are not necessarily suitable to deal with cases of intense rainfall. In this study different representations of infiltration into the soil were developed in the JULES land surface scheme with the aim of improving prediction of the amount of surface runoff, and thus ultimately river flow. Infiltration and surface runoff are explored in a test case of intense rainfall with a variable maximum infiltration. The modelled hydraulic conductivity profile is modified with depth to reduce the rate of outgoing fluxes. The new infiltration scheme is then applied to different UK catchments. The resulting river flow is evaluated against a benchmark river flow calculated using default infiltration in JULES and also observations. The results demonstrate improved representation of the highest flows with this new variable maxiumum infiltration scheme in some catchments but limited improvement elsewhere. This scheme shows best improvement in the wettest areas of the UK where the annual mean precipitation is above 1200 mm. This work highlights the requirement for substantial further work on the hydrological process representation in JULES

Satellite estimates of wide-range suspended sediment concentrations in Changjiang (Yangtze) estuary using MERIS data

The Changjiang (Yangtze) estuarine and coastal waters are characterized by suspended sediments over a wide range of concentrations from 20 to 2,500 mg l-1. Suspended sediment plays important roles in the estuarine and coastal system and environment. Previous algorithms for satellite estimates of suspended sediment concentration (SSC) showed a great limitation in that only low to moderate concentrations (up to 50 mg l-1) could be reliably estimated. In this study, we developed a semi-empirical radiative transfer (SERT) model with physically based empirical coefficients to estimate SSC from MERIS data over turbid waters with a much wider range of SSC. The model was based on the Kubelka–Munk two-stream approximation of radiative transfer theory and calibrated using datasets from in situ measurements and outdoor controlled tank experiments. The results show that the sensitivity and saturation level of remote-sensing reflectance to SSC are dependent on wavelengths and SSC levels. Therefore, the SERT model, coupled with a multi-conditional algorithm scheme adapted to satellite retrieval of wide-range SSC, was proposed. Results suggest that this method is more effective and accurate in the estimation of SSC over turbid water

Application of Sentinel-2A data for pasture biomass monitoring using a physically based radiative transfer model

A large proportion of the global land surface is covered by pasture. The advent of the Sentinel satellites program provides free datasets with good spatiotemporal resolution that can be a valuable source of information for monitoring pasture resources. We combined optical remote sensing data (proximal hyperspectral and Sentinel 2A) with a radiative transfer model (PROSAIL) to estimate leaf area index (LAI), and biomass, in a dairy farming context. Three sites in Southern England were used: two pasture farms that differed in pasture type and management, and a set of small agronomy trial plots with different mixtures of grasses, legumes and herbs, as well as pure perennial ryegrass. The proximal and satellite spectral data were used to retrieve LAI via PROSAIL model inversion, which were compared against field observations of LAI. The potential of bands of Sentinel 2A that corresponded with a 10 m resolution was studied by convolving narrow spectral bands (from a handheld hyperspectral sensor) into Sentinel 2A bands (10 m). Retrieved LAI, using these spectrally resampled S2A data, compared well with measured LAI, for all sites, even for those with mixed species cover (although retrieved LAI was somewhat overestimated for pasture mixtures with high LAI). This proved the suitability of 10 m Sentinel 2A spectral bands for capturing LAI dynamics for different types of pastures. We also found that inclusion of 20 m bands in the inversion scheme did not lead to any further improvement in retrieved LAI. Sentinel 2A image based retrieval yielded good agreement with LAI measurements obtained for a typical perennial ryegrass based pasture farm. LAI retrieved in this way was used to create biomass maps (that correspond to indirect biomass measurements by Rising Plate Meter (RPM)), for mixed-species paddocks for a farm for which limited field data were available. These maps compared moderately well with farmer-collected RPM measurements for this farm. We propose that estimates of paddock-averaged and within-paddock variability of biomass are more reliably obtained from a combined Sentinel 2A-PROSAIL approach, rather than by manual RPM measurements. The physically based radiative transfer model inversion approach outperformed the Normalised Difference Vegetation Index based retrieval method, and does not require site specific calibrations of the inversion scheme