In the remote sensing community, Land Use Land Cover (LULC) classification with satellite imagery is a main focus of current research activities. Accurate and appropriate LULC classification, however, continues to be a challenging task. In this paper, we evaluate the performance of multi-temporal (monthly time series) compared to mono-temporal (single time step) satellite images for multi-label classification using supervised learning on the RapidAI4EO dataset. As a first step, we trained our CNN model on images at a single time step for multi-label classification, i.e. mono-temporal. We incorporated time-series images using a LSTM model to assess whether or not multi-temporal signals from satellites improves CLC classification. The results demonstrate an improvement of approximately 0.89% in classifying satellite imagery on 15 classes using a multi-temporal approach on monthly time series images compared to the mono-temporal approach. Using features from multi-temporal or mono-temporal images, this work is a step towards an efficient change detection and land monitoring approach.

In the past five years, an international team has been working towards improved detection, characterization and modeling of abrupt changes in dryland ecosystem functioning, EF. This paper collects the recent advances and lessons learnt from the U-TURN project (Belspo SR/00/339, SR/00/366). Specifically abrupt changes in EF were mapped and categorized over global drylands; new 30m resolution time series of land cover maps and cover fractions were created, validated and released open access for the Sahel region; and new physically-based insights into dryland vegetation response to extreme rainfall were derived based on dryland optimized LPJ-GUESS simulations.

Under the sponsorship of the European Union's Horizon 2020 program, RapidAI4EO will establish the foundations for the next generation of Copernicus Land Monitoring Service (CLMS) products. The project aims to provide intensified monitoring of Land Use (LU), Land Cover (LC), and LU change at a much higher level of detail and temporal cadence than it is possible today. Focus is on disentangling phenology from structural change and in providing critical training data to drive advancement in the Copernicus community and ecosystem well beyond the lifetime of this project. To this end we are creating the densest spatiotemporal training sets ever by fusing open satellite data with Planet imagery at as many as 500,000 patch locations over Europe and delivering high resolution daily time series at all locations. We plan to open source these datasets for the benefit of the entire remote sensing community.

Despite the relevance of historical land cover maps for scientists and policy makers, an accurate high resolution record is currently lacking over the Sudano-Sahel. In this study, 30m resolution historically consistent land cover and cover fraction maps are provided over the Sudano-Sahel for the period 1986–2015. These land cover/cover fraction maps are achieved based on the Landsat archive preprocessed on Google Earth Engine and a random forest classification/regression model, while historical consistency is achieved using the hidden Markov model. Using these historical maps, a multitude of variability in the dynamic Sudano-Sahel region over the past 30 years is revealed. These include cropland expansion and the re-greening of the Sahel, forest degradation & the detection of fine-scale changes, such as smallholder or subsistence farming. The historical land cover / cover fraction maps are made available via an open-access platform.

Many land cover changes are a direct threat to nature, especially through its negative effects on ecosystem services and thus, poses a risk to long-term human health and wellbeing. Land cover data is requested by different users, i.e. it is key for many SDG indicators and therefore accurate and continuously updated land cover information is more essential than ever. However, the “let's produce a map and someone will use it” approach is not helpful as users' have their own requirements and needs in terms of detail, quality and repeatability. Hence, developments for mapping approaches and data sharing (satellite imagery as well as reference data) should focus on distributed, flexible, user-oriented approaches. Data providers and especially data distributors should put the users in charge in terms of data and processing access, geographic scope and customization of map products (thematic classes) and next generation land cover services have to offer a much more flexible and targeted land cover characterization framework than in the past.

iCOR is a scene and sensor generic atmospheric correction algorithm which can process images containing land and coastal, inland or transitional water pixels. The tool is adaptable with minimal efforts to hyper- or multi-spectral sensors. iCOR has been extensively validated for Landsat-8 OLI, Sentinel-2 MSI and Proba-V and is now being developed for Sentinel-3 OLCI. It has been developed in such a way that it can be easily integrated in an operational workflow. Three examples of the integration in an operational workflow are presented, the Highroc processor for coastal waters, the Belgian collaborative ground segment TERRASCOPE and the WATCHITGROW service for the potato industry.