The absorption spectra of photosynthetic algae are characterized by a continuous envelope, which is a result of the overlapping spectra of the indivual pigments. This feature makes it difficult to estimate the contribution of each pigment to the total absorption spectra. Derivative analysis is an objective tool for isolating absorption peaks in phytoplankton. Theoretically, electrons and ions of interacting molecules can be regarded as simple harmonic oscillators in an electromagnetic field, which result in a Lorentzian shape. However, when measured by an optical spectrophotometer the signal is transformed into a Gaussian curve. Thus, a combination of both types of curve provides a realistic approach to the decomposition of absorption spectra. In this study derivative analysis is performed on absorption spectra in order to prove that the method can be successfully used to identify the individual absorption spectra of component pigments. The spectra used are modelled phytoplankton, spectrophotometric measurements of algal cultures and samples from natural waters. A combination of Gaussian-Lorentzian shaped curves, centred on the identified peaks, were compared with the original spectra and showed good agreement.

We applied remote sensing techniques using WorldView-2 images with high spatial resolution and field verification to map the bathymetry and benthic habitats of the Puerto Morelos Reef National Park in the Mexican Caribbean. These images were processed using the standardized physics-based data processing of EOMAP's Modular Inversion and Processing System (MIP). To generate a detailed benthic habitat map we developed a two level-classification scheme based on biological and geomorphologic characteristics. These images showed high effectiveness for mapping benthic habitat. Further improvements will emphasize and focus on the selection of regional specific main spectral components and the automation of the radiometric fine tuning. The main contribution is the generation of a detailed benthic habitat map representing functional classification by combining both maps. These studies serve for the management of marine areas in Mexico and could be extended to the entire Mesoamerican Reef System.

Advanced Very High Resolution Radiometer (AVHRR) National Oceanic and Atmospheric Administration (NOAA)-14 imagery was used to analyse changes in land surface temperature in an area of Central Mexico during the course of each dry season (November–April) for the period 1996–2000. Daily surface temperature was obtained by the split-window method and cloud-free monthly composites were subsequently built. This value was related to maximum air temperatures recorded at meteorological stations and to forest fires detected from night-time images. During 1996–1997 and 1997–1998 (El Niño) dry seasons, monthly surface temperature ranged from 35°C to 46°C and from 33°C to 51°C, respectively; during 1998–1999 (La Niña) and 1999–2000 it was lower, ranging from 28°C to 47°C, and from 28°C to 41°C, respectively. At the end of El Niño, land surface temperatures higher than 50°C were registered, and 730 forest fires were detected, suggesting that this temperature increment also contributed to the vulnerability of vegetation to fire. It is concluded that land surface temperature during the first four months of the dry season can be used as a variable for modelling the probability of forest fire occurrence, in combination with other environmental variables. Similarities between land surface temperature and maximum air temperature suggest the potential use of NOAA-AVHRR imagery for evaluating El Niño/La Niña effects on the continental surface.

This study reports on a combined investigation between in situ measurements of total suspended sediments, collected by ship, and remotely sensed data provided by the Advanced Very High Resolution Radiometer (AVHRR) during the winter-summer period of 1989. It is meant to be a case study stating the problems inherent to coastal waters and proposing a methodology to understand them. Ship and satellite data are compared in order to detect suspended sediments in case 2 waters in the North Sea through a linear regression analysis. The results show a wide range of coefficient of determination (R²) values. The highest values correspond to summer dates, while the lowest belonged to the winter and spring period. It was found that for the summer dates the relatively still atmospheric and water conditions were suitable for comparison with satellite data providing good values of R². In winter and spring, unsettled sea water conditions complicated the comparison of the data. It can be concluded that the seasonal stratification of the water column during summer time allows a better correlation between in situ and remotely sensed data than the typically well-mixed waters during winter.

We conducted high-frequency spatial and temporal water sampling in two small, shallow eutrophic lakes that experience cyanobacterial blooms. The spatial and temporal aspects of the sampling strategy markedly influenced the findings of cyanobacteria monitoring. Spectral analysis confirmed the presence of Microcystis aeruginosa and showed its spatial distribution in the lakes.

This study shows a simplified approach for calculating emissions associated with forest fires in Mexico, based on different satellite observation products: the biomass, burnt area, emission factors, and burning efficiency. Biomass loads were based on a Mexican biomass map, updated with the net primary productivity products. The burning efficiency was estimated from a Random Forest Regression (RFR) model, which considered the fuel, weather and topographical conditions. The burned areas were the downloaded Maryland University MCD64c6 product. The emission factors were obtained from well-known estimations, corrected by a dedicated US Forest Service and Mexican campaign. The uncertainty was estimated from an integrative method. Our method was applied to a four-year period, 2011–2014, in three Mexican ecoregions. The total burned in the study region was 12,898 km² (about 4% of the area), producing 67.5 (±20) Tg of CO₂. Discrepancies of the land cover maps were found to be the main cause of a low correlation between our estimations and the Global Emission Database (GFED). The emissions were clearly associated to precipitation patterns. They mainly affected dry and tropical forests (almost 50% of all emissions). Six priority areas were identified, where prevention or mitigation measures must be implemented.