Accurate estimation of above-ground biomass (AGB) is essential to understanding carbon stocks and flows, monitoring forest health, assessing biodiversity, and tracking ecological disturbances, which together help to inform climate policies. Imminent global satellite biomass missions (such as ESA’s BIOMASS and NASA-ISRO’s NISAR satellites) will offer valuable environmental monitoring, but their low spatial resolution limits their application in detailed local assessments. In this study, we present BiomSHARP (Biomass Super-resolution for High Accuracy Prediction), a deep learning (DL) model that extends the Hierarchical Attention Transformer (HAT) architecture adapting it to enhance coarse-resolution biomass maps by fusing them with high-resolution multispectral data from sensors such as Sentinel-2 or Landsat. BiomSHARP achieves 25-meter biomass predictions—four times the spatial resolution of the input—bridging the gap between global-scale monitoring and local-scale applications. In a first set of experiments, conducted in a local area in Europe, we demonstrate that BiomSHARP outperforms both traditional interpolation methods and state-of-the-art DL interpolation and prediction approaches for high-resolution AGB estimation across all evaluated metrics (MAE, MSE, RMSE, PSNR and SSIM), while using a comparable/lower number of parameters. Moreover, the model exhibits strong global-scale generalization, as demonstrated by its ability to accurately estimate biomass across diverse climatic regions despite being trained on a limited subset of data. Furthermore, the model presents strong temporal generalization, achieving improved performance in estimating AGB from 2020 data even when trained solely on 2010 data. We also analyze the impact of different combinations of spectral bands on biomass estimation, identifying optimal subsets that reduce redundancy and improve computational efficiency. BiomSHARP represents a promising approach to advance global environmental assessments and support improved climate strategies. The code and models are publicly available at https://github.com/laiaalbors/biomsharp.

Coastal island ecosystems are unique and fragile environments and very sensitive to climate change and direct anthropogenic impact. The use of remote sensing offers the advantage of monitoring these valuable areas in an accessible and cost-effective manner. The main objective of this research, linked to the sustainable management of littoral areas, is the generation of knowledge that is materialized in the implementation of a robust image processing methodology to generate accurate bathymetry and benthic high-resolution maps in coastal shallow waters using remote sensing satellite multispectral sensors (WorldView-2/3). So, this paper presents a methodology for the monitoring of two protected ecosystems in Canary Islands (Spain): Las Canteras beach (Gran Canaria Island) and the channel of La Graciosa-Lanzarote Island. In addition, a multitemporal study is presented where the usefulness of the technology in the monitoring of marine ecosystems is presented.

The reliable detection of vegetation disease and plant stress are challenges in forest ecosystems. To address this problem, remote sensing existing methods of detection mostly rely on vegetation indices, however, in dense forest, the spectral saturation must be considered to select the most appropriate index. In this work, after a revision of the state of the art, a total of 20 vegetation indices were preliminary selected to perform a thorough statistical analysis with the aim to identify the disease and devitalization phenomena in a complex laurel forest. Multisensor very high resolution imagery, from the same month, with a time difference of a decade have been used. A robust methodology has been implemented to generate accurate vigor maps and to identify the forest areas that have experienced a degradation in plant health after 10 years.

Remote spectral imaging of coastal areas can provide valuable information for their sustainable management and conservation of their biodiversity. Unfortunately, such areas are very sensitive to changes due to human activity, natural phenomenon, introduction of non-native species, and climate change. Thus, the main objective of this research is the implementation of a robust image processing methodology to produce accurate bathymetry maps in shallow coastal waters using high-resolution multispectral WorldView-2/3 satellite imagery for the monitoring at the maximum spatial and spectral resolutions. Two different island ecosystems have been selected for the assessment, since they stand out for their richness in endemic species and they are more vulnerable to climate change: Cabrera National Park and Maspalomas Natural Protected area, located in the Balearic and Canary Islands, Spain, respectively. In addition, a third example to show the applicability of the mapping methodology to monitor the construction of a new port in Granadilla (Canary Islands) is presented. Contributions of this work focus on improving the preprocessing methodology and, mainly, on the proposal and assessment of new satellite-derived regression and machine learning bathymetric models, which have been validated and compared with respect to measured reference bathymetry. After a thorough analysis of nine techniques, using visual and quantitative statistical parameters, ensemble learning approaches have demonstrated excellent performance, even in challenging scenarios up to 35-m depth, with mean RMSE values around 2 m.

Many remote sensing applications require high spatial resolution images, but the elevated cost of these images makes some studies unfeasible. Single-image super-resolution algorithms can improve the spatial resolution of a lowresolution image by recovering feature details learned from pairs of low-high resolution images. In this work, several configurations of ESRGAN, a state-of-the-art algorithm for image super-resolution, are tested. We make a comparison between several scenarios, with different modes of upsampling and channels involved. The best results are obtained training a model with RGB-IR channels and using progressive upsampling.

Coastal and inner water lagoon ecosystems are essential due to their high biodiversity and primary production. However, they are extremely complex. Remote sensing can be very useful due to the spatial and spectral improvement of satellites and the availability of airborne or drone hyperspectral sensors. Unfortunately, the mapping of coastal and inner lakes areas is challenging due to the low SNR received at the sensor, as a consequence of the minimum reflectivity of the water surface and the atmospheric disturbances. In this context, the goal of this work is to obtain a robust image processing methodology to generate accurate water quality maps in shallow waters using multiplatform imagery: WorldView-2 (High Resolution Satellite Multispectral Sensor), AHS (Airborne Hyperspectral Scanner) and Pika-L (Drone Hyperspectral Scanner) images. Maspalomas (Gran Canaria, Spain) inner water lagoon ecosystems was studied due to its complexity and the presence of important chlorophyll concentration.

Remote sensing of coastal areas requires multispectral satellite images with high spatial resolution. In this sense, WorldView-2 is a very high resolution satellite, which provides an advanced multispectral sensor with eight narrow bands, allowing the proliferation of new environmental monitoring and mapping applications in shallow coastal ecosystems. The problem of estimating water depths using a radiative model has yielded good results as it considers the physical phenomena of water absorption-backscattering and the relationship between the albedo of the seafloor and the reflectivity of the shallow waters. The sophisticated model developed and evaluated in this study expands the ratio algorithm model allowing for the increased amount of information provided in WorldView-2 imagery to be included in the retrieval of water depth of shallow coastal waters.

Climate change and anthropogenic pressure are causing an indisputable decline in biodiversity; therefore, the need of environmental knowledge is important to develop the appropriate management plans. In this context, remote sensing and, specifically, hyperspectral imagery (HSI) can contribute to the generation of vegetation maps for ecosystem monitoring. To properly obtain such information and to address the mixed pixels inconvenience, the richness of the hyperspectral data allows the application of unmixing techniques. In this sense, a problem found by the traditional linear mixing model (LMM), a fully constrained least squared unmixing (FCLSU), is the lack of ability to account for spectral variability. This paper focuses on assessing the performance of different spectral unmixing models depending on the quality and quantity of endmembers. A complex mountainous ecosystem with high spectral changes was selected. Specifically, FCLSU and 3 approaches, which consider the spectral variability, were studied: scaled constrained least squares unmixing (SCLSU), Extended LMM (ELMM) and Robust ELMM (RELMM). The analysis includes two study cases: 1) robust endmembers and 2) nonrobust endmembers. Performances were computed using the reconstructed root-mean-square error (RMSE) and classification maps taking the abundances maps as inputs. It was demonstrated that advanced unmixing techniques are needed to address the spectral variability to get accurate abundances estimations. RELMM obtained excellent RMSE values and accurate classification maps with very little knowledge of the scene and minimum effort in the selection of endmembers, avoiding the curse of dimensionality problem found in HSI.

The use of cutting-edge geospatial technologies to monitor ecosystems and the development of tailored tools for assessing such natural areas is a fundamental task. In this context, the growing availability of hyperspectral (HS) imagery from satellite and aerial platforms can provide valuable information for the sustainable management of ecosystems. However, in some cases, the spectral richness provided by HS sensors is at the expense of spatial quality. To alleviate this inconvenience, which can be critical to monitor some heterogeneous and mixed natural areas, a number of HS sharpening techniques have been developed to increase the spatial resolution while trying to preserve the spectral content. This image processing field has attracted the interest of the scientific community, and many research studies have been conducted to assess the performance of different HS sharpening algorithms. In the last decade, however, many comparative studies rely upon simulated data. In this work, the challenging application of sharpening methods in real situations using multiplatform or multisensor data is also addressed. Thus, experiments with real data have been conducted, in addition to a thorough assessment of HS sharpening techniques using simulated imagery in scenarios with different spatial resolution ratios and registration errors. In particular, airborne and satellite HS imageries have been pansharpened with drone, orthophotos, and satellite high spatial resolution data evaluating 11 fusion algorithms. After a comprehensive analysis, considering different visual and quantitative quality indicators, the algorithm characteristics have been summarized and the methods with higher performance and robustness have been identified.

Hyperspectral imagery (HSI) has become an important tool in ecosystem conservation due to its capability to perform accurate spectral unmixing, for vegetation mapping and ecosystem monitoring. An issue to be solved is the spectral variability of endmembers that can be induced by sensor noise and topographic changes. This spectral variability is considered by the Extended Linear Mixing Model (ELMM), which is applied to a mountainous ecosystem with high spectral variability and radiometric changes in each swath. The results obtained are very satisfactory, achieving reasonable abundance estimations and accurate characterization of the variability within the scene. ELMM allows studying the features of each pixel, including additional information about the characterization of the mixed pixels, in HSI by taking spectral variability into account. Moreover, it is observed that ELMM is robust to the absence of pure pixels as well as to noise.

Ecosystem management and monitoring are essential to preserve natural resources. Hyperspectral imagery (HSI) is a useful tool to obtain accurate classification maps, providing significant level of detail. Thus, traditional and novel methodologies based on pixel and object classification approaches are compared and evaluated in a homogeneous and mixed vulnerable ecosystem. Considering the challenging ecosystem, all classifications successfully resulted in high OA (higher than 82%), showing that HSI is very useful providing accurate vegetation maps to evaluate and monitor the ecosystems in a faster and economic way.

Coastal ecosystems are essential due to their high biodiversity and primary production, however they are extremely complex and with high spatial and temporal variability. Thus, to properly manage them it is necessary a systematic monitoring. Remote sensing can be very useful due to the spatial and spectral improvement of satellites and the availability of airborne or drone hyperspectral sensors. Unfortunately, the mapping of coastal areas is challenging due to the low SNR received at the sensor, as a consequence of the minimum reflectivity of the seafloor and the atmospheric and water column disturbances. In this context, the goal of this work is to obtain a robust classification methodology to generate accurate benthic habitat maps applying object-oriented and pixel-based classification methods in shallow waters using WorldView-2 and AHS (Airborne Hyperspectral Scanner) images. Maspalomas (Gran Canaria, Spain) was studied due to its complexity and the presence of important seagrass meadows.

Land cover information is a fundamental parameter in a wide range of applications like urban growth, land degradation, climate change, food security, environmental sustainability, etc. In this context, remote sensing satellites can provide valuable data to allow the generation of thematic maps. On the other hand, the recent availability of hyperspectral sensors on board aircrafts and drones offers an opportunity to improve the resolution and accuracy of land cover maps. In island territories, where land is usually a scarce resource, the need of very high spatial resolution (VHR) is essential. In this context, we have generated VHR land cover maps using multispectral Worldview data and hyperspectral airborne CASI information. In particular, after corrections and pansharpening enhancements, we have analyzed pixel-based and object-based classification approaches using different input band combinations. We have compared the performance when using multispectral or hyperspectral imagery and its robustness depending on the quality of the training samples considered.

Satellite sensors usually provide two types of data: panchromatic and multispectral images which are characterized by their high spatial resolution and high spectral resolution respectively. In this context, the fusion techniques or pansharpening consist of merging these different aspects to obtain a fused (or pan-sharpened) image with high spatial and spectral resolutions. In this paper, a new quality assessment scheme for pan-sharpened remote sensing imagery is proposed. The methodology described extracts the segments of the images to constitute the basic elements of the measuring quality methodology. This new strategy overcomes traditional pixel-based perspectives, approaching an evaluation by human observers. The results of its application to a set of fused images show that an object-based assessment is consistent in terms of quality determination of both the spectral and spatial properties of remote sensing images.

In order to understand and quantify the environmental impacts caused by the eruption of the El Hierro Island submarine volcano, a regular multidisciplinary monitoring was carried out. In this context, we performed the systematic processing of MODIS and MERIS imagery. Thanks to the volcanic tracer, detailed studies could be undertaken using ocean color imagery allowing, for the first time, to monitor the process of filamentation and axisymmetrization. In our work, a novel 2-steps segmentation methodology has been developed. In this context, this natural tracer release has been detected using an advanced methodology based on an initial structure detection algorithm and a structure growing technique. This detection approach has been validated over a database of MERIS and MODIS oceanographic products and it has demonstrated an excellent performance and robustness.

The singular characteristics of the Canarian archipelago (Spain) have allowed the development of a unique biological richness. Almost half of its territory is protected to preserve the natural environment. In this paper, different approaches to consider fusion of multi-sensor data are considered and corresponding methodologies described. The application to real datasets over Canarian islands is undergoing and fusion maps will be presented at the conference while preliminary classification results with multispectral data are described here.

The analysis of the seafloor in shallow waters using remote sensing imagery at very high spatial resolution is a very challenging topic due to the minimum signal level received; the presence of noisy contributions from the atmosphere, solar reflection, foam, turbidity and water column; and the limited spectral information available for the classification at such depths that impedes, for example, the extraction of vegetation indices. In this complex scenario we have developed a mapping methodology that involves the precise application of pre-processing techniques and the use of efficient classification algorithms. In particular, after a detailed assessment, support vector machines achieved the best performance using the appropriate kernel and parameters. Two natural areas located at the Canary Islands (Spain) have been selected for their benthic habitats richness and specially for their preservation of highly protected seagrass regions.

Coastlines, shoals, and reefs are some of the most dynamic and constantly changing regions of the globe. The emergence of high-resolution satellites with new spectral channels, such as the WorldView-2, increases the amount of data available, thereby improving the determination of coastal management parameters. Water-leaving radiance is very difficult to determine accurately, since it is often small compared to the reflected radiance from other sources such as atmospheric and water surface scattering. Hence, the atmospheric correction has proven to be a very important step in the processing of high-resolution images for coastal applications. On the other hand, specular reflection of solar radiation on nonflat water surfaces is a serious confounding factor for bathymetry and for obtaining the seafloor albedo with high precision in shallow-water environments. This paper describes, at first, an optimal atmospheric correction model, as well as an improved algorithm for sunglint removal based on combined physical and image processing techniques. Then, using the corrected multispectral data, an efficient multichannel physics-based algorithm has been implemented, which is capable of solving through optimization the radiative transfer model of seawater for bathymetry retrieval, unmixing the water intrinsic optical properties, depth, and seafloor albedo contributions. Finally, for the mapping of benthic features, a supervised classification methodology has been implemented, combining seafloor-type normalized indexes and support vector machine techniques. Results of atmospheric correction, remote bathymetry, and benthic habitat mapping of shallow-water environments have been validated with in situ data and available bionomic profiles providing excellent accuracy.

This paper provides an objective evaluation of six supervised classification techniques and three state of the art features, with the objective of obtaining a single combination of them that provides both robustness and objective performance improvements. As a conclusion, a simple procedure for obtaining LULC maps with four targeted classes is proposed.

The island off the Atlantic coast of North Africa, El Hierro (Canary Island), has been rocked by thousands of tremors and earthquakes since July 2011, and an underwater volcanic eruption 300 meters below sea level started on October 10, 2011. Thanks to this natural tracer release, low resolution satellite images obtained from MODIS and MERIS sensors have been processed for monitoring the volcanic plume evolution and it has provided a unique and outstanding source of tracer that may allow us to study submesoscale front-like and filament-like structures. These structures have been monitored and detected using an advanced methodology based on an initial structure segmentation algorithm combined by a structure growing technique.

Land use and land cover information are key information for Governments in developing countries. In this context, remote sensing satellites like Landsat or SPOT can provide valuable data covering several decades. We have developed a methodology to generate land cover maps with the aim to analyze changes in the last 25 years in the NW region of Senegal. In particular, we have applied the radiometric and atmospheric corrections prior to the classification algorithm or to the generation of vegetation indexes and, finally, to analyze the spatial and temporal variability, post-classification change detection techniques have been applied, providing valuable quantitative information.

The evaluation of the spatial quality is one of the factors that determine the performance of pansharpening algorithms for remote sensing images. However, the number of studies that focus on the functioning of these measures is not extensive. This paper addresses the evaluation of the performance of spatial assessments of pansharpened images. For this, a test of affine transformations that distorts the image used as reference is designed and implemented. This test is applied to the images of a database created for this purpose. Thus the behavior of different selected spatial indices is obtained. As well as the results of a proposed new spatial index based on the discrete cosine transform. Additionally, spatial quality measurements have been carried out between the panchromatic and pansharpened images in order to test the performance of the spatial quality indices.

Along with the launch of a number of very high-resolution satellites in the last decade, efforts have been made to increase the spatial resolution of the multispectral bands using the panchromatic information. Quality evaluation of pixel-fusion techniques is a fundamental issue to benchmark and to optimize different algorithms. In this letter, we present a thorough analysis of the spatial and spectral distortions produced by eight pan sharpening techniques. The study was conducted using real data from different types of land covers and also a synthetic image with different colors and spatial structures for comparison purposes. Several spectral and spatial quality indexes and visual information were considered in the analysis. Experimental results have shown that fusion methods cannot simultaneously incorporate the maximum spatial detail without degrading the spectral information. Atrous_IHS, Atrous_PCA, IHS, and eFIHS algorithms provide the best spatial-spectral tradeoff for wavelet-based and algebraic or component substitution methods. Finally, inconsistencies between some quality indicators were detected and analyzed.

The use of weather forecasting models in energy applications has proven to be an effective tool in the management of renewable energy sources in power distribution networks which requires precise predictions and high-resolution of solar radiation and wind speed data. Therefore, the use of data from meteorological satellites such as Meteosat second Generation (MSG) can produce improvements in weather forecasting results. The integration of satellite data in weather prediction models is a developing field, because the assimilated data for these types of models mainly proceed from in-situ data at multiple stations, with low spatial resolution. The objective of this work has been the integration of data from the MSG satellite in the assimilation of meteorological model Weather Research and Forecasting (WRF-ARW), trying to improve results in meteorology predictions used in renewable power applications.

Nowadays, high spatial resolution remote sensing systems collect simultaneously a low resolution multispectral image plus a high resolution panchromatic image. The objective of pixel level fusion methods is to combine this information in order to obtain a new multispectral image that exhibits the spectral characteristics of the multispectral image and the spatial resolution of the panchromatic image. There are many image pansharpening methods that have been proposed in the literature however, in general, none of them exhibits the best performance for all type of images. Thus, in this paper we present a comparative analysis of different fusion methods applied over a database of Geoeye-1 images having different types of land covers. We have analyzed qualitatively and quantitatively the pansharpened images in order to select those methods providing the best performance to each particular type of cover.

In this letter, a region-based fusion methodology is presented for joint classification and hierarchical segmentation of specific ground cover classes from high-spatial-resolution remote sensing images. Multispectral information is fused at the partition level using nonlinear techniques, which allows the different relevance of the various bands to be fully exploited. A hierarchical segmentation is performed for each individual band, and the ensuing segmentation results are fused in an iterative and cooperative way. At each iteration, a consensus partition is obtained based on information theory and is combined with a specific ground cover classification. Here, the proposed approach is applied to the extraction and segmentation of vegetation areas. The result is a hierarchy of partitions with the most relevant information of the vegetation areas at different levels of resolution. This system has been tested for vegetation analysis in high-spatial-resolution images from the QuickBird and GeoEye satellites.

Sea surface salinity is a key oceanographic parameter that can be measured by means of L-band microwave radiometry. The measured brightness temperatures over the ocean are influenced by the sea state, which can entirely mask the salinity signature. Sea-state corrections parameterized in terms of wind speed and/or significant wave height have proven not to be fully satisfactory. In 2003, it was proposed to use reflectometry using navigation opportunity signals [Global Navigation Satellite System Reflectometer (GNSS-R)] for sea-state determination and correction of the measured L-band brightness temperature changes associated to the sea state. The novelty of the approach relies in the measurement of the whole Delay-Doppler Map that captures the scattering of the GNSS signals in the whole glistening zone. In this framework, the “Advanced L-BAnd emissiviTy and Reflectivity Observations of the Sea Surface” (ALBATROSS) field experiments were undertaken in 2008 and 2009, collecting an extensive data set of collocated radiometric and reflectometric measurements over the Atlantic Ocean, as well as oceanographic and meteorological data. In this paper, the experimental results and conclusions of the ALBATROSS 2009 field experiment are compiled and presented, showing the great potential of this technique to perform the necessary corrections in future salinity missions. Empirical relationships are derived among measured brightness temperature variations due to the sea-state effect and direct GNSS-R observables, and the sea surface correlation time at L1 band, a key parameter for GNSS-R data processing since it determines the maximum coherent integration time, was experimentally determined.

Global Navigation Satellite Systems (GNSS) signals can be used to infer geophysical data related to the surface where they scatter. When dealing with the sea surface, its state influences the GNSS scattered signals and, therefore, the GNSS reflectometry (GNSS-R) observables. The aim of the Advanced L-band Emissivity and Reflectivity Observations of the Sea Surface 2008 field experiment was to gather experimental data to study the relationship of the GNSS-R delay-Doppler maps (DDMs) and the sea state. This work describes the field campaign and the main results obtained, where among them is the use of the DDM volume as a roughness descriptor weakly affected by the GPS satellite geometry.

A new region-based methodology for the automated extraction and hierarchical segmentation of vegetation areas into high spatial resolution images is proposed. This approach is based on the iterative and cooperative fusion of the independent segmentation results of equal or different resolution spectral bands, combined with an unsupervised classification into vegetation and no-vegetation regions. The result is a hierarchy of partitions with most relevant information at different levels of resolution of the vegetation areas. In addition, the high flexibility of the scheme allows different configurations depending on the final purpose. For instance, considering the size of the vegetation areas into the hierarchy, or prioritizing the information into the high resolution panchromatic band to improve the accuracy of both vegetation extraction and segmentation. This general tool for vegetation analysis is tested into high spatial resolution images from IKONOS and QuickBird satellites.

Determination of atmospheric dynamic characteristics from remote sensing imagery is fundamental in weather and climate studies. The SEVIRI radiometer, on board the MSG, with its 12 bands and 15 minutes sensing capability provides an important amount of information for cloud tracking. In this work, we have first conducted a detailed evaluation of twelve region matching techniques in order to select those providing the best results. For this performance evaluation, databases of synthetic and real sequences have been used. Next, the best metrics have been incorporated in a new methodology that includes a preliminary stage that segments cloudy structures to initialize the optimum motion estimation parameters (template size and search window dimensions). Also a study region mask is generated to disable the application of the motion estimation algorithm in unreliable areas, thus, eliminating erroneous vectors and decreasing the computation times.

Changes in the coastal upwelling ecosystems need to be accounted for as these structures are responsible for an important percentage of the global fish catch, for the primary and secondary productivity and for the atmosphere-ocean exchange. In this sense, our work aims to assess its impact in the coastal upwelling regions located in the northwest African coast (latitudes 5° to 36°N and longitudes 5° to 30°W). This area is one of the major upwelling regions in the world, so, it is important its study to predict the variability that measured parameters may have in the future. Upwelling Index obtained from Sea Surface Temperature images for the period 1987-2006 and remote sensing wind stress have been used to analyze the coastal upwelling region off Northwest Africa. Upwelling Index shows an intensification of the upwelling during the 20 years period.

The ocean involves a complex set of physical, chemical, biological, and geological processes, interacting with each other to influence our climate and natural environment. One of the most important disciplines in oceanography is the study of the ocean dynamics and, particularly, the ocean surface circulation. One can estimate this by the automated tracking of thermal infrared features in pairs of sequential satellite imagery. In this context, an extensive analysis of different motion estimation techniques has been performed by employing databases with synthetic sequences, real sequences, andinsitumeasurements. Four region- based metrics and two differential algorithms are proposed to estimate surface currents in multitemporal and multisensor AVHRR and MODIS image sequences. Once the appropriate motion estimation techniques have been selected, a new methodology to compute ocean currents is proposed. It includes a preliminary step to precisely segment the oceanographic structures and a second step to track its motion using additional modules (initialization, preprocessing, and postprocessing) to increase effectiveness. The information provided by the segmentation step reduces computing times, initializes the motion estimation parameters with appropriate values, and increases the overall performance. In summary, this two-stage approach combines image processing tools and physical oceanography knowledge to achieve a good ocean current estimation.

The ocean involves a complex set of physical, chemical, biological and geological processes, interacting each other to influence our climate and natural environment. One of the most important disciplines in oceanography is the study of the ocean dynamics. Particularly, ocean surface circulation can be recovered by the automated tracking of thermal features (coastal upwellings, filaments and eddies) in pairs of sequential satellite imagery. This paper presents a new methodology for the automatic estimation of ocean surface currents using AVHRR and MODIS imagery. The technique is based on a two-step process: precise structure detection followed by the structure tracking. The precise detection methodology is, as well, composed by two modules. The first one with the goal to obtain a coarse structure segmentation and the second achieving the maximum detail of the structure. The tracking methodology estimates motion fields using region matching and differential techniques with additional modules to guarantee the optimum performance. Twelve matching metrics and four differential algorithms were implemented and tested. Information from the previous structure segmentation stage is of fundamental importance to initialize the optimum motion estimation parameters and to generate the corresponding study area for each sequence. The proposed methodology, combining the segmentation and tracking steps, has been extensively tested and it has demonstrated an adequate performance in the estimation of flow fields in multitemporal and multisensorial sequences of the NW African coast.

The study of the ocean circulation is the central core of all dynamical oceanography. The routine derivation of sea surface temperature or infrared brightness temperatures has been used to estimate the surface circulation by calculating the motion of the thermal features (coastal upwellings, filaments and eddies) in successive images. To that respect, a number of authors have developed different methodologies to recover the motion field, but the most straightforward methods match patterns (points, borders or regions) in all possible subwindows of one image with those in the next image. The maximization of the normalized cross- correlation coefficient, known as the Maximum Cross-Correlation (MCC) technique, is the most popular region-based matching metric applied to compute ocean circulation. In this paper a careful analysis of different region matching techniques has been conducted and the performance achieved for each approach is presented. The assessment methodology uses a database of synthetic sequences, real sequences and in-situ speed measurements. After the qualitative and quantitative analysis, we can conclude that the best performance is achieved by ZSAD, ZSSD, NZSSD and NCC metrics. These metrics achieve, when applied to synthetic sequences, mean angular errors around 30deg and magnitude errors around 30% for the worst case. In general the flow field recovered by the 4 previous metrics, perfectly models the motion of the structures in real sequences. Finally, results obtained with comparison with ground-truth data suggest an underestimation in the computed velocity between 35%-45% but with a higher angular accuracy, achieving global errors around 30deg-50deg. To conclude, it is important to emphasize that the prevalent MCC method provides acceptable results but with more errors when compared with the four previous metrics over the three databases.

The upward movement of cool and nutrient-rich waters toward the surface leads to horizontal alterations in the distribution of the physical, chemical, and biological properties. Remote sensing is being extensively applied to detect such coastal upwellings; however, the enormous amount of data daily generated obliges to develop automatic detection and prediction tools. The problem of identifying oceanographic mesoscale structures has been studied using a variety of image processing techniques; however, the outstanding difficulties encountered in the traditional approaches are the presence of noise, the fact that gradients are weak, the strong morphological variation, and the absence of a valid analytical model for the structures. In this context, the proposed automatic upwelling extraction methodology overcomes the preceding detection inconveniences and achieves a highly accurate structure extraction. This automatic technique is based on a coarse-segmentation methodology followed by a fine-detail growing process. The complete system has been validated over a database of 378 multisensorial images of years 2000 to 2003, and it has been applied to the detection and feature extraction of coastal upwellings and filaments in three areas with different characteristics, such as the Canary Islands, Cape Ghir, and the Alboran Sea, using imagery from the Advanced Very High Resolution Radiometer 2 and 3 sensors, the Sea-viewing Wide Field-of-view Sensor, and the Moderate Resolution Imaging Spectroradiometer sensor, demonstrating its effectiveness and robustness in a wide variety of climate conditions.

Spatial registration of multidate or multisensor images is required for many applications in remote sensing. Automatic image registration, which has been extensively studied in other areas of image processing, is still a complex problem in the framework of remote sensing. This work explores an alternative strategy for a fully automatic and operational registration system capable of registering multitemporal and multisensor remote sensing satellite images with high accuracy and avoiding the use of ground control points, exploiting the maximum reliable information in both images (coastlines not occluded by clouds). The automatic feature-based approach is summarized as follows: (i) reference image coastline extraction; (ii) sensed image gradient energy map estimation and (iii) contour matching, mapping function estimation and transformation of the sensed images. Several experimental results for single sensor imagery (AVHRR/3) and multisensor imagery (AVHRR/3-SeaWiFS-MODIS-ATSR) from different viewpoints and dates have verified the robustness and accuracy of the proposed automatic registration algorithm, demonstrating its capability of registering satellite images of coastal areas within one pixel.

Sea surface temperature (SST) is one of the main factors in the understanding of the interaction between the oceans and the atmosphere, so the development of algorithms for the production of reliable SST data sets from space borne infrared radiometers has been pursued since late 1960's. Unfortunately the thermal structure in the upper 10 m of the ocean is complex and highly variable, so SST may be significantly different depending on the vertical depth of the in situ measurement, the local time of day, local conditions at the air-sea interface and the instrument used. In this context, the validation of the AVHRR/3 and MODIS atmospheric correction algorithms for the retrieval of sea surface temperature from the Canary Islands-Azores-Gibraltar area is performed by using in situ temperature measurements, derived from the ARGO data collection system and by the oceanographic service of the University of Las Palmas Gran Canaria, over the period from December 2000 to September 2003. The improvements and restrictions introduced in the systematic procedure to generate the match-up database have lead to a very high quality comparison data set. Finally, error analysis shows that SST can be retrieved with accuracies better than 0.6degC and, specifically, the MODIS mid-infrared algorithm achieves an excellent performance at night-time with accuracies to the order of 0.35degC

The upward movement of cool and nutrient-rich waters towards the surface leads to horizontal alterations in the distribution of physical, chemical and biological properties. Remote sensing is being extensively applied to detect such coastal upwellings; however, the enormous amount of data daily generated obliges to develop automatic detection and prediction tools. The problem of identifying oceanographic mesoscale structures has been studied using a variety of image processing techniques, however, the outstanding difficulties encountered in the traditional approaches are the presence of noise, mainly due to the clouds and other atmospheric phenomena; the fact that gradients are weak and provide excess of information; the strong morphological variation that impedes an accurate geometric representation and the absence of a valid analytical model for the structures. In this context, the proposed automatic upwelling extraction methodology overcomes the preceding detection inconveniences and achieves a highly accurate structure detection and identification. This automatic technique has been applied to the detection and feature extraction of coastal upwellings and filaments in the northwest African coast, the Alboran Sea and Cape Ghir using imagery from the AVHRR/2&3, SeaWiFS and MODIS sensors. The system has proven to be very effective and robust in a wide variety of climate conditions.

Mesoscale processes such as upwellings, eddies, or thermal fronts are very energetic and their knowledge is very important not only to study oceanic circulation but also areas of applications that include acoustic propagation anomalies, fisheries management and exploitation, coastal monitoring and offshore or ocean oil detection and exploitation. A variety of techniques and algorithms have been developed to detect such structures. The foremost difficulties encountered in the preceding approaches are the presence of noise, mainly due to clouds and other atmospheric phenomena. In this context, the proposed methodology, due to its region-based nature, overcomes the edge detection inconveniences and obtains the proper structure identification. Moreover and in order to perform an exhaustive analysis of the structure dynamics, it is necessary to compare image sequences. In this context, the use of spatial registration techniques is necessary to achieve that pixels in different images correspond to the same geographic region. An automatic contour based approach for high accuracy registration of multisensor and multitemporal remote sensing images is presented. It avoids the use of ground control points, while exploiting the maximum reliable information in both images. These automatic tools, that combine structures detection techniques and multitemporal and multisensoral registration, have been applied to AVHRR, SeaWiFS and MODIS images of the Canary Island and Alboran Sea areas and have demonstrated that it can be a fundamental tool to validate marine and coastal dynamic studies using remote sensing data.

We assess the use of an approximation to the Bayes factor for objectively assessing spatial segmentation models. The Bayes factor allows us to automatically determine thresholds, in multidimensional feature space, for such objectives as cloud mask definition. We compare our results with a cloud map currently provided as a data product.

An automatic approach for high accuracy registration of multisensor and multitemporal remote sensing images is presented. It avoids the use of ground control points, while exploiting the maximum reliable information in both images. Features to be used for image registration are those contours in both images that have been classified as coastline (reliable information). The automatic contour-based approach is summarized by the following steps: (i) reference image coastline extraction; (ii) sensed image gradient energy map estimation; (iii) contour matching, mapping function estimation and transformation of the sensed images. The algorithm proposed is automatic and of significant value in an operational context. Several experimental results for single sensor imagery (AVHRR) from different viewpoints and dates as well as multisensor imagery (AVHRR-SeaWiFS) have verified the robustness and accuracy of the proposed automatic registration algorithm, demonstrating its capability of registering satellite images of coastal areas within one pixel.

The retrieval of the sea surface temperature (SST) from space is limited by radiometer noise and window placement, in-flight calibration quality, viewing geometry and, specially, by the atmospheric correction. Thus, the potential for highly accurate SST measurements has led to considerable interest within the research community in the development and assessment of new multi-channel atmospheric correction algorithms. The essential objective in this work is to optimize the split-window algorithm coefficients obtained for the AVHRR/2 sensors, for the retrieval of SST in the Canary Islands-Azores-Gibraltar area from AVHRR/3 instruments. The proposed AVHRR/3 split-window algorithm has been compared with the NESDIS operational multichannel algorithms (MCSST) and, finally, the MODIS SST algorithms have been assessed in our oceanographic area. In both cases, the residues obtained demonstrated the ability to achieve good results in sea surface temperature recovery.

The problem of identifying mesoscale structures has been studied using a variety of image processing techniques, mainly, texture analysis, edge detection, mathematical morphology, neural networks and wavelet transform. The foremost difficulties encountered in the preceding approaches are the presence of noise, mainly due to clouds and other atmospheric phenomena; the fact that gradients are weak and provide excess of information; the strong morphological variation that impedes an accurate geometric representation and the absence of a valid analytical model for the structures. In this context, the proposed methodology, due to its region-based nature, overcomes the edge detection inconveniences and obtains the proper structure identification. This automatic technique has been applied to the detection and feature extraction of coastal upwellings and filaments in the northwest African coast and the Alboran Sea using imagery from the AVHRR/2&3, SeaWiFS and MODIS sensors. The system has proven to be very effective and robust in a wide variety of climate conditions.

This paper presents a technique for a fully automatic and operational geometric correction system capable of georeferencing satellite images with high accuracy. A simple Keplerian orbital satellite model is considered and mean orbital elements are given as input from ephemeris data. To correct the systematic errors caused by these simplifications, nonzero values for the spacecraft roll, pitch and yaw and failures in the satellite internal clock, an automatic global contour matching approach is proposed. It has three main steps: (i) estimation of the gradient energy map (edges) and detection of the cloudless (reliable) areas; (ii) initialization of the contour positions; (iii) obtaining the transformation parameters (affine model) by means of a global contour optimization approach. Three different algorithms are proposed for optimization. The performance of the overall technique is assessed using AVHRR and multisensor AVHRR-SeaWiFS imagery.