Remote sensing techniques can be used to estimate water quality variables such as chlorophyll\mbi a, total suspended particles, and water transparency. This paper describes empirical algorithms for the estimation of these variables using Landsat Thematic Mapper (TM) data. Ground data were taken from several Spanish lakes covering a variety of trophic statuses, ranging from oligotrophic to hypereutrophic. The studied lakes were the Albufera de Valencia and lakes and ponds of the Southeast Regional Park in Madrid. Empirical equations were obtained to estimate chlorophyll\mbi a from the ratio in reflectance values between bands 2 and 4 of TM ( R ² \mmb = 0.66, p\lt 0.001), transparency [Secchi disk (SD)] from reflectance in band 2 ( R ² \mmb = 0.80, pbf \lt 0.001), and total suspended particles from reflectance in band 4 ( R ² \mmb = 0.92, p\lt 0.001). The spectral equivalence between TM and the recent satellite Deimos-1 was also tested. By applying the proposed algorithms to this new sensor, the temporal resolution is improved by up to 3 days, which increases spatial resolution to 22 m. The algorithms were validated using three Deimos-1 scenes of the Albufera de Valencia together with ground measurements. Results of this validation showed root-mean-square errors (RMSEs) of 40\nbspmg·m ^\mmb-3 for Chl-\mbi a (data range: 32\mmb - 238\nbspmg·m ^-3 ), 10\nbspmg·L ^{\mmb -1} for total suspended solid (TSS) (data range: 25\mmb -89\nbspmg·L ^{\mmb -1} ), and 0.10 m for SD (data range: 0.17-0.40 m). In any case, these results show the potential of Deimos-1 as a substitute of TM in water quality monitoring in small/medium water bodies, providing continuity to three decades of TM imagery.

Previous work has shown that the emissivity of crude oil is lower than that of the seawater in the thermal-infrared (TIR) spectrum. Thus, oil slicks cause an emissivity decrease relative to the seawater in that region. The aim of this paper was to carry out experimental measurements to characterize the spectral and angular variations of crude oil and seawater emissivities. The results showed that the crude oil emissivity is lower than the seawater emissivity and that it is essentially flat in the atmospheric window of 8-13 μm. The crude oil emissivity has a marked emissivity decrease with the angle (from 0.956 ± 0.005 at 15 ° to 0.873 ± 0.007 at 65 °), which is even higher than that of the seawater, and thus, the seawater-crude emissivity difference increases with the angle (from +0.030 ±0.007 at close-to-nadir angles up to +0.068 ±0.010 in average at 65 °). In addition, the experimental results were checked by using the dual-angle viewing capability of the Environmental Satellite Advanced Along-Track Scanning Radiometer (ENVISAT-AATSR) images (i.e., 0 °-22 ° and 53 °-55 ° for the nadir and forward views, respectively), with the data acquired during the BP Deepwater Horizon oil slick in 2010. The objective was to explore its applicability to satellite observations. Nadir-forward emissivity differences of +0.028 and +0.017 were obtained for the oil slick and the surrounding clean seawater, respectively. The emissivity differences between the seawater and the oil slick were +0.035 and +0.046 for the nadir and forward views, respectively, which was in agreement with the experimental data. The increase in the seawater-crude emissivity difference with the angle gives significant differences for off-nadir observation angles, showing a new chance of crude oil slick identification from satellite TIR data.

The Biosphere Reserve of La Mancha Húmeda is a wetland-rich area located in central Spain. This reserve comprises a set of temporary lakes, often saline, where water level fluctuates seasonally. Water inflows come mainly from direct precipitation and runoff of small lake watersheds. Most of these lakes lack surface outlets and behave as endorheic systems, where water withdrawal is mainly due to evaporation, causing salt accumulation in the lake beds. Remote sensing was used to estimate the temporal variation of the flooded area in these lakes and their associated hydrological patterns related to the seasonality of precipitation and evapotranspiration. Landsat 7 ETM+ satellite images for the reference period 2013–2015 were jointly used with ground-truth datasets. Several inverse modeling methods, such as two-band and multispectral indices, single-band threshold, classification methods, artificial neural network, support vector machine and genetic programming, were applied to retrieve information on the variation of the flooded areas. Results were compared to ground-truth data, and the classification errors were evaluated by means of the kappa coefficient. Comparative analyses demonstrated that the genetic programming approach yielded the best results, with a kappa value of 0.98 and a total error of omission-commission of 2%. The dependence of the variations in the water-covered area on precipitation and evaporation was also investigated. The results show the potential of the tested techniques to monitor the hydrological patterns of temporary lakes in semiarid areas, which might be useful for management strategy-linked lake conservation and specifically to accomplish the goals of both the European Water Framework Directive and the Habitats Directive.

This work aims to validate the wide use of an algorithm developed using genetic programing (GP) techniques allowing to discern between water and non-water pixels using the near infrared band and different thresholds. A total of 34 wetlands and shallow lakes of 18 ecological types were used for validation. These include marshes, salt ponds, and saline and freshwater, temporary and permanent shallow lakes. Furthermore, based on the spectral matching between Landsat and Sentinel-2 sensors, this methodology was applied to Sentinel-2 imagery, improving the spatial and temporal resolution. When compared to other techniques, GP showed better accuracy (over 85% in most cases) and acceptable kappa values in the estimation of water pixels (κ ≥ 0.7) in 10 of the 18 assayed ecological types evaluated with Landsat-7 and Sentinel-2 imagery. The improvements were especially achieved for temporary lakes and wetlands, where existing algorithms were scarcely reliable. This shows that GP algorithms applied to remote sensing satellite imagery can be a valuable tool to monitor water coverage in wetlands and shallow lakes where multiple factors cause a low resolution by commonly used water indices. This allows the reconstruction of hydrological series showing their hydrological behaviors during the last three decades, being useful to predict how their hydrological pattern may behave under future global change scenarios.