Investigating the Kokhanovsky snow reflectance model in closerange spectral imaging

The internal structure of the snow and its reflectance function play a major contribution in its appearance. We investigate the snow reflectance model introduced by Kokhanovsky and Zege in a close-range imaging scale. By monitoring the evolution of melting snow through time using hyperspectral cameras in a laboratory, we estimate snow grain sizes from 0.24 to 8.49 mm depending on the grain shape assumption chosen. Using our experimental results, we observe differences in the reconstructed reflectance spectra with the model regarding the spectra's shape or magnitude. Those variations may be due to our data or to the grain shape assumption of the model. We introduce an effective parameter describing both the snow grain size and the snow grain shape, to give us the opportunity to select the adapted assumption. The computational technique is ready, but more ground truths are required to validate the model.

Thin Film Reflectance Model for Trap Detector

The physical model of the spectral responsivity of trap detector consists of multiple parameters such as the internal quantum efficiency and the spectral reflectance. In some measurement models, the spectral reflectance of the trap detector is approximated by fitting a wavelength dependence equation which does not consider the effect of the oxide thickness of the silicon photodiode. To analyse the uncertainty due to the oxide thickness variation, a thin film reflectance model is set up in the Standards and Calibration Laboratory (SCL) for the evaluation of the spectral reflectance of the trap detectors. The model is based on the Fresnel coefficients of a 3-layer thin film structure which consists of air and a thin film oxide layer on a silicon substrate. The reflectance model was implemented as user-defined functions to calculate the spectral reflectance at different oxide thickness. It was also integrated with the SCL’s MCM program to evaluate the uncertainty of the spectral responsivity of trap detectors.

Estimating Fractional Vegetation Cover of Row Crops from High Spatial Resolution Image

With high spatial resolution remote sensing images being increasingly used in precision agriculture, more details of the row structure of row crops are captured in the corresponding images. This phenomenon is a challenge for the estimation of the fractional vegetation cover (FVC) of row crops. Previous studies have found that there is an overestimation of FVC for the early growth stage of vegetation in the current algorithms. When the row crops are a form in the early stage of vegetation, their FVC may also have overestimation. Therefore, developing an algorithm to address this problem is necessary. This study used World-View 3 images as data sources and attempted to use the canopy reflectance model of row crops, coupling backward propagation neural networks (BPNNs) to estimate the FVC of row crops. Compared to the prevailing algorithms, i.e., empirical method, spectral mixture analysis, and continuous crop model coupling BPNNs, the results showed that the calculated accuracy of the canopy reflectance model of row crops coupling with BPNNs is the highest performing (RMSE = 0.0305). Moreover, when the structure is obvious, we found that the FVC of row crops was about 0.5–0.6, and the relationship between estimated FVC of row crops and NDVI presented a strong exponential relationship. The results reinforced the conclusion that the canopy reflectance model of row crops coupled with BPNNs is more suitable for estimating the FVC of row crops in high-resolution images.

Performance Evaluation of Four Ocean Reflectance Model

Phytoplankton are the main factors influencing light under the sea surface in Case Ι water. The ocean reflectance model (ORM), which takes into account the chlorophyll a concentration data, can calculate the remote sensing reflectance of Case Ι water. In this study, we examined the differences and performance of four ORMs, including Morel and Maritorena (2001, MM01), Morel and Gentili (2007, MG07), Mobley (2014, MO14), and Hydrolight Abcase1 Lookup Tables. The differences between the four ORMs in terms of their absorption and backscattering coefficients were evaluated. Preformation of the four ORMs was compared using the NASA bio-Optical Marine Algorithm Dataset and in situ data from the South China Sea. The results showed that preformation of MM01 was the best.

Contribution of woody elements to tree level reflectance in boreal forests

Spectral mixture analysis was used to estimate the contribution of woody elements to tree level reflectance from airborne hyperspectral data in boreal forest stands in Finland. Knowledge of the contribution of woody elements to tree or forest reflectance is important in the context of lea area index (LAI) estimation and, e.g., in the estimation of defoliation due to insect outbreaks, from remote sensing data. Field measurements from four Scots pine ( L.), five Norway spruce ( (L.) Karst.) and four birch ( Roth and Ehrh.) dominated plots, spectral measurements of needles, leaves, bark, and forest floor, airborne hyperspectral as well as airborne laser scanning data were used together with a physically-based forest reflectance model. We compared the results based on simple linear combinations of measured bark and needle/leaf spectra to those obtained by accounting for multiple scattering of radiation within the canopy using a physically-based forest reflectance model. The contribution of forest floor to reflectance was additionally considered. The resulted mean woody element contribution estimates varied from 0.140 to 0.186 for Scots pine, from 0.116 to 0.196 for birches and from 0.090 to 0.095 for Norway spruce, depending on the model used. The contribution of woody elements to tree reflectance had a weak connection to plot level forest variables.Pinus sylvestrisPicea abiesBetula pendulaBetula pubescens

Effect of Surface Finish Reflectance on Energy Consumed by Lighting

Efforts from both spatial and energy engineers were conceived in order to reduce the total running costs of electric consumption in buildings. An often-overlooked energy and money saving opportunity for the built environment lies in lighting. This study investigates the effect of room interior finish on electrical lighting energy consumption. Walls, ceiling, and floor finish, in accordance to light reflectance values, were taken at low reflectance model (LRM), medium reflectance model (MRM), and high reflectance model (HRM). Various occupied spaces were classified in accordance to physical dimensions and capacity in order to cover a wide range of space usage and standard illuminance requirements. It was found that the HRM reduced power consumption in lighting by about 40.62 % compared to the LRM in the case for medium museum halls, with energy saved rating at about 2.32 GWh annually; other occupied spaces show a saving potential between 22.00 % and 40.00 %.