Incluye 1 fichero de datosIn the last decades, several topographic correction methods (TOC) have been proposed, but there is not an agreement on the best method. Furthermore, different evaluation criteria have been used in the past, and there is not any simple and objective evaluation procedure to measure the quality of the correction. Consequently, a multicriteria analysis of widely used topographic correction methods is required that evaluates their performance over different sensors, terrain and temporal configurations. In this work, ten TOC methods were assessed using seven different evaluation strategies. The analysis was carried out for three SPOT5 images acquired over a mountainous area of northern Spain. The images had different acquisition dates and solar angles, so as to evaluate performance under varying illumination conditions. The results obtained showed that Statistic-Empiricalmethod, CCorrection and Sun-Canopy-Sensor+C performed the best, and differences were minor when favorable illumination conditions were considered. For the seven tested evaluation strategies, interquartile range reduction of land covers or the comparison of sunlit and shaded slopes gave very similar results, whereas there were greater contrasts among other criteria.Part of the research presented in this paper is funded by the Spanish Ministry of Economy and Competitiveness in the frame of the ESP2013-48458-C4-2-P project

Permanent crops, such as olive groves, vineyards and fruit trees, are important in European agriculture because of their spatial and economic relevance. Agricultural geographical databases (AGDBs) are commonly used by public bodies to gain knowledge of the extension covered by these crops and to manage related agricultural subsidies and inspections. However, the updating of these databases is mostly based on photointerpretation, and thus keeping this information up-to-date is very costly in terms of time and money. This paper describes a methodology for automatic detection of uprooted orchards (parcels where fruit trees have been eliminated) based on the textural classification of orthophotos with a spatial resolution of 0.25 m. The textural features used for this classification were derived from the grey level co-occurrence matrix (GLCM) and wavelet transform, and were selected through principal components (PCA) and separability analyses. Next, a Discriminant Analysis classification algorithm was used to detect uprooted orchards. Entropy, contrast and correlation were found to be the most informative textural features obtained from the co-occurrence matrix. The minimum and standard deviation in plane 3 were the selected features based on wavelet transform. The classification based on these features achieved a true positive rate (TPR) of over 80% and an accuracy (A) of over 88%. As a result, this methodology enabled reducing the number of fields to photointerpret by 60–85%, depending on the membership threshold value selected. The proposed approach could be easily adopted by different stakeholders and could increase significantly the efficiency of agricultural database updating tasks.

Satellite images in mountainous areas are strongly affected by topography. Different studies demonstrated that the results of semi-empirical topographic correction algorithms improved when a stratification of land covers was carried out first. However, differences in the stratification strategies proposed and also in the evaluation of the results obtained make it unclear how to implement them. The objective of this study was to compare different stratification strategies with a non-stratified approach using several evaluation criteria. For that purpose, Statistic-Empirical and Sun-Canopy-Sensor + C algorithms were applied and six different stratification approaches, based on vegetation indices and land cover maps, were implemented and compared with the non-stratified traditional option. Overall, this study demonstrates that for this particular case study the six stratification approaches can give results similar to applying a traditional topographic correction with no previous stratification. Therefore, the non-stratified correction approach could potentially aid in removing the topographic effect, because it does not require any ancillary information and it is easier to implement in automatic image processing chains. The findings also suggest that the Statistic-Empirical method performs slightly better than the Sun-Canopy-Sensor + C correction, regardless of the stratification approach. In any case, further research is necessary to evaluate other stratification strategies and confirm these results.

A new procedure to assess the quality of topographic correction (TOC) algorithms applied to remote sensing imagery was previously proposed by the authors. This procedure was based on a model that simulated synthetic scenes, representing the radiance an optical sensor would receive from an area under some specific conditions. TOC algorithms were then applied to synthetic scenes and the resulting corrected scenes were compared with a horizontal synthetic scene free of topographic effect. This comparison enabled an objective and quantitative evaluation of TOC algorithms. This approach showed promising results but had some shortcomings that are addressed herein. First, the model, originally built to simulate only broadband panchromatic scenes, is extended to multispectral scenes in the visible, near infrared (NIR), and short wave infrared (SWIR) bands. Next, the model is validated by comparing synthetic scenes with four Satellite pour l'Observation de la Terre 5 (SPOT5) real scenes acquired on different dates and different test areas along the Pyrenees mountain range (Spain). The results obtained show a successful simulation of all the spectral bands. Therefore, the model is deemed accurate enough for its purpose of evaluating TOC algorithms.

In June 2018, the European Commission approved a modification of the Common Agricultural Policy (CAP) that, among other measures, proposed the use of Copernicus data for the verification process of farmers’ declarations. In recent years, several research efforts have been conducted to develop operational tools to accomplish this aim, among this the Interreg-POCTEFA PyrenEOS project. This article describes the methodological strategy proposed in the PyrenEOS project, which is based on the identification of the most probable crop using the Random Forests algorithm. Originally, the strategy builds a training sample from the CAP declarations file based on their NDVI time series. In addition, a series of rules are proposed to establish the level of uncertainty in the classification, and the criteria used to represent each parcel in the verification map with a simple colour coding (traffic light), where green represents correctly declared parcels, red indicates that the declaration is dubious, and orange corresponds to parcels with a high classification uncertainty. This verification strategy has been applied to two Agricultural Regions of Navarre, during an agricultural campaign where valuable field inspections were available, with a sampling intensity of 7% of the declared parcels. The results obtained, report overall accuracies close to 80% when the most probable crop was considered, and 90% when the two most probable crops were considered. This proves it is possible to identify correctly declared parcels (green parcels) with an error below 1%. Orange and red parcels should be considered for further analysis and inspection by technicians from the paying agencies, though they represent a small percentage of declarations (~6% of parcels), and include most of the wrong declarations.

SEOSAT/Ingenio is the future Spanish Earth Observation high spatial resolution mission in the optical domain. While Level 1 products, at-sensor geo-referenced radiances, are in an advanced phase of development under the framework of an industrial contractor, Level 2 products must be developed by the users. This fact limits the use of the satellite images only to the scientific community, restricting their use in other applications. The need to alleviate this limitation motivated this work, developed under the framework of a coordinate project, which aimed at offering a list of Level2 products to the Ingenio/SEOSAT user community. In this paper, we present the different methodologies developed to produce the proposed Level2 products, from surface reflectance at nominal sensor spatial resolution to images with higher spatial resolution or the possibility to create spatial and temporal mosaics. On the one side, for the surface reflectance product, we proposed an atmospheric correction algorithm based on using the spatial information, linked to a cloud screening algorithm and including morphological and topographic shadow corrections. On the other side, to enhance the image spatial resolution, we applied different fusion techniques using the multispectral and the panchromatic band, as well as some of the so-called “super-resolution” techniques. Finally, we provided different tools to develop spatial mosaics and temporal composites, directed to users interested on the exploitation of the Ingenio/ SEOSAT images.