Abstract. The temporal behaviour of precipitable water vapour (PWV) retrieved from GPS delay data is analysed in a number of case studies of intense precipitation in the Lisbon area, in the period 2010-2012 and in a continuous annual cycle of 2012 observations. Such behaviour is found to correlate positively with the probability of precipitation, especially in cases of severe rainfall. The evolution of the GPS PWV in a few stations is analysed by a least-squares fitting of a broken line tendency, made by a temporal sequence of ascents and descents over the data. It is found that most severe rainfall events occur in descending trends after a long ascending period and that the most intense events occur after steep ascents in PWV. A simple algorithm, forecasting rain in the 6 h after a steep ascent of the GPS PWV in a single station, is found to produce reasonable forecasts of the occurrence of precipitation in the nearby region, without significant misses in what concerns larger rain events, but with a substantial amount of false alarms. It is suggested that this method could be improved by the analysis of 2-D or 3-D time-varying GPS PWV fields or by its joint use with other meteorological data relevant to nowcast precipitation.

This paper presents the multi-modal BigEarthNet (BigEarthNet-MM) benchmark archive made up of 590,326 pairs of Sentinel-1 and Sentinel-2 image patches to support the deep learning (DL) studies in multi-modal multi-label remote sensing (RS) image retrieval and classification. Each pair of patches in BigEarthNet-MM is annotated with multi-labels provided by the CORINE Land Cover (CLC) map of 2018 based on its thematically most detailed Level-3 class nomenclature. Our initial research demonstrates that some CLC classes are challenging to be accurately described by only considering (single-date) BigEarthNet-MM images. In this paper, we also introduce an alternative class-nomenclature as an evolution of the original CLC labels to address this problem. This is achieved by interpreting and arranging the CLC Level-3 nomenclature based on the properties of BigEarthNet-MM images in a new nomenclature of 19 classes. In our experiments, we show the potential of BigEarthNet-MM for multi-modal multi-label image retrieval and classification problems by considering several state-of-theart DL models. We also demonstrate that the DL models trained from scratch on BigEarthNet-MM outperform those pretrained on ImageNet, especially in relation to some complex classes, including agriculture and other vegetated and natural environments. We make all the data and the DL models publicly available at https://bigearth.net, offering an important resource to support studies on multi-modal image scene classification and retrieval problems in RS.

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Portugal is building a land cover monitoring system to deliver land cover products annually for its mainland territory. This paper presents the methodology developed to produce a prototype relative to 2018 as the first land cover map of the future annual map series (COSsim). A total of thirteen land cover classes are represented, including the most important tree species in Portugal. The mapping approach developed includes two levels of spatial stratification based on landscape dynamics. Strata are analysed independently at the higher level, while nested sublevels can share data and procedures. Multiple stages of analysis are implemented in which subsequent stages improve the outputs of precedent stages. The goal is to adjust mapping to the local landscape and tackle specific problems or divide complex mapping tasks in several parts. Supervised classification of Sentinel-2 time series and post-classification analysis with expert knowledge were performed throughout four stages. The overall accuracy of the map is estimated at 81.3% (±2.1) at the 95% confidence level. Higher thematic accuracy was achieved in southern Portugal, and expert knowledge significantly improved the quality of the map.