Statistical modeling of radiometric error propagation in support of hyperspectral imaging inversion and optimized ground sensor network design

A method is presented that attempts to isolate the relative magnitudes of various error sources present in common algorithms for inverting the effects of atmospheric scattering and absorption on solar irradiance and determine in what ways, if any, operational ground truth measurement systems can be employed to reduce the overall error in retrieved reflectance factor. Error modeling and propagation methodology is developed for each link in the imaging chain, and representative values are determined for the purpose of exercising the model and observing the system behavior in response to a wide variety of inputs. Three distinct approaches to modelbased atmospheric inversion are compared in a common reflectance error space, where each contributor to the overall error in retrieved reflectance is examined in relation to the others. The modeling framework also allows for performance predictions resulting from the incorporation of operational ground truth measurements. Regimes were identified in which uncertainty in water vapor and aerosols were each found to dominate error contributions to final retrieved reflectance. Cloud cover was also shown to be a significant contributor, while state-of-the-industry hyperspectral sensors were confirmed to not be error drivers. Accordingly, instruments for measuring water vapor, aerosols, and downwelled sky radiance were identified as key to improving reflectance retrieval beyond current performance by current inversion algorithms

Matrix-assisted laser desorption ionization--time of flight mass spectrometry: an emerging tool for the rapid identification of mosquito vectors.

BACKGROUND: The identification of mosquito vectors is typically based on morphological characteristics using morphological keys of determination, which requires entomological expertise and training. The use of protein profiling by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS), which is increasingly being used for the routine identification of bacteria, has recently emerged for arthropod identification. METHODS: To investigate the usefulness of MALDI-TOF-MS as a mosquito identification tool, we tested protein extracts made from mosquito legs to create a database of reference spectra. The database included a total of 129 laboratory-reared and field-caught mosquito specimens consisting of 20 species, including 4 Aedes spp., 9 Anopheles spp., 4 Culex spp., Lutzia tigripes, Orthopodomyia reunionensis and Mansonia uniformis. For the validation study, blind tests were performed with 76 specimens consisting of 1 to 4 individuals per species. A cluster analysis was carried out using the MALDI-Biotyper and some spectra from all mosquito species tested. RESULTS: Biomarker mass sets containing 22 and 43 masses have been detected from 100 specimens of the Anopheles, Aedes and Culex species. By carrying out 3 blind tests, we achieved the identification of mosquito vectors at the species level, including the differentiation of An. gambiae complex, which is possible using MALDI-TOF-MS with 1.8 as the cut-off identification score. A cluster analysis performed with all available mosquito species showed that MALDI-Biotyper can distinguish between specimens at the subspecies level, as demonstrated for An gambiae M and S, but this method cannot yet be considered a reliable tool for the phylogenetic study of mosquito species. CONCLUSIONS: We confirmed that even without any specific expertise, MALDI-TOF-MS profiling of mosquito leg protein extracts can be used for the rapid identification of mosquito vectors. Therefore, MALDI-TOF-MS is an alternative, efficient and inexpensive tool that can accurately identify mosquitoes collected in the field during entomological surveys