a b s t r a c tRadiometric correction is a prerequisite for generating high-quality scientific data, making it possible to discriminate between product artefacts and real changes in Earth processes as well as accurately produce land cover maps and detect changes. This work contributes to the automatic generation of surface reflectance products for Landsat satellite series. Surface reflectances are generated by a new approach developed from a previous simplified radiometric (atmospheric + topographic) correction model. The proposed model keeps the core of the old model (incidence angles and cast-shadows through a digital elevation model [DEM], Earth-Sun distance, etc.) and adds new characteristics to enhance and automatize ground reflectance retrieval. The new model includes the following new features: (1) A fitting model based on reference values from pseudoinvariant areas that have been automatically extracted from existing reflectance products (Terra MODIS MOD09GA) that were selected also automatically by applying quality criteria that include a geostatistical pattern model. This guarantees the consistency of the internal and external series, making it unnecessary to provide extra atmospheric data for the acquisition date and time, dark objects or dense vegetation.(2) A spatial model for atmospheric optical depth that uses detailed DEM and MODTRAN simulations. (3) It is designed so that large time-series of images can be processed automatically to produce consistent Landsat surface reflectance time-series. (4) The approach can handle most images, acquired now or in the past, regardless of the processing system, with the exception of those with extremely high cloud coverage. The new methodology has been successfully applied to a series of near 300 images of the same area including MSS, TM and ETM+ imagery as well as to different formats and processing systems (LPGS and NLAPS from the USGS; CEOS from ESA) for different degrees of cloud coverage (up to 60%) and SLC-off. Reflectance products have been validated with some example applications: time series robustness (for a pixel in a pseudoinvariant area, deviations are only 1.04% on average along the series), spectral signatures generation (visually coherent with the MODIS ones, but more similar between dates), and classification (up to 4 percent points better than those obtained with the original manual method or the CDR products). In conclusion, this new approach, that could also be applied to other sensors with similar band configurations, offers a fully automatic and reasonably good procedure for the new era of long time-series of spatially detailed global remote sensing data.

Abstract:The use of Pseudoinvariant Areas (PIA) makes it possible to carry out a reasonably robust and automatic radiometric correction for long time series of remote sensing imagery, as shown in previous studies for large data sets of Landsat MSS, TM, and ETM+ imagery. In addition, they can be employed to obtain more coherence among remote sensing data from different sensors. The present work validates the use of PIA for the radiometric correction of pairs of images acquired almost simultaneously (Landsat-7 (ETM+) or Landsat-8 (OLI) and Sentinel-2A (MSI)). Four pairs of images from a region in SW Spain, corresponding to four different dates, together with field spectroradiometry measurements collected at the time of satellite overpass were used to evaluate a PIA-based radiometric correction. The results show a high coherence between sensors (r 2 = 0.964) and excellent correlations to in-situ data for the MiraMon implementation (r 2 > 0.9). Other methodological alternatives, ATCOR3 (ETM+, OLI, MSI), SAC-QGIS (ETM+, OLI, MSI), 6S-LEDAPS (ETM+), 6S-LaSRC (OLI), and Sen2Cor-SNAP (MSI), were also evaluated. Almost all of them, except for SAC-QGIS, provided similar results to the proposed PIA-based approach. Moreover, as the PIA-based approach can be applied to almost any image (even to images lacking of extra atmospheric information), it can also be used to solve the robust integration of data from new platforms, such as Landsat-8 or Sentinel-2, to enrich global data acquired since 1972 in the Landsat program. It thus contributes to the program's continuity, a goal of great interest for the environmental, scientific, and technical community.

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<p>This paper details the process of generating the 2017 Land Use/Land Cover Map of Catalonia (MUCSC) using automatic classification of satellite imagery and auxiliary cartographic and remote sensing data. A total of 60 images (6 dates for each of the 10 tiles covering Catalonia) captured by the Sentininel-2A and Sentinel-2B satellites were used. These images as well as texture variables, terrain models derived from lidar processing, and vegetation and wetness indices were classified using the k-Nearest Neighbor algorithm (kNN) to obtain a map with 25 categories. The categories related to urbanized areas (urban areas, urbanizations and industrial zones/ commercial areas), road infrastructures and burned areas were edited using official cartographic datasets of the Catalan Government [Generalitat]. The results have an overall accuracy greater than 98 %, which was evaluated with a set of more than 8.6 million independent test pixels. This work represents an important milestone in terms of the computational effort it involves due to the territorial extension (32 000 km²), the spatial detail of between 2 and 20 m, the use of up to 58 variables, the relative completeness of the legend and the level of success achieved. The MUCSC 2017, which is part of a 30-year quinquennial series beginning in 1987, can be downloaded in different formats (also in MMZX: new ISO 19165-2) and at resolutions of 10 m and 30 m pixel side from the Ministry of Territory and Sustainability website of the Catalan Government.</p>

Farmland abandonment has been a widespread land-use change in the Iberian Peninsula since the second half of the 20th century, leading to the establishment of secondary forests across the region. In this study, we aimed to address changes in the recent (1985–2014) emergence patterns of these forests and examine how environmental factors affected their growth by considering differences in leaf-habit types. We used a combination of Landsat-derived land-cover maps and aboveground biomass (AGB) maps from the European Space Agency to assess the secondary forest establishment and growth, respectively, in the study region. We also obtained a set of topographic, climatic and landscape variables from diverse GIS layers and used them for determining changes over time in the environmental drivers of forest establishment and AGB using general linear models. The results highlight that secondary forest cover was still increasing in the Iberian Peninsula at a rate above the European average. Yet, they also indicate a directional change in the emergence of secondary forests towards lower and less steep regions with higher water availability (mean rainfall and SPEI) and less forest cover but are subjected to greater drought events. In addition, these environmental factors differentially affect the growth of forests with different leaf-habit types: i.e., needleleaf secondary forests being less favoured by high temperature and precipitation, and broadleaf deciduous forests being most negatively affected by drought. Finally, these spatial patterns of forest emergence and the contrasting responses of forest leaf-habits to environmental factors explained the major development of broadleaf evergreen compared to broadleaf deciduous forests and, especially, needleleaf secondary forests. These results will improve the knowledge of forest dynamics that have occurred in the Iberian Peninsula in recent decades and provide an essential tool for understanding the potential effects of climate warming on secondary forest growth.