Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Copernicus Global Land Cover Layers—Collection 2
2020535 citationsMarcel Buchhorn, Myroslava Lesiv et al.Remote Sensingprofile →
GEOV1: LAI and FAPAR essential climate variables and FCOVER global time series capitalizing over existing products. Part1: Principles of development and production
2013440 citationsFrédéric Baret, Marie Weiss et al.Remote Sensing of Environmentprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of Bruno Smets's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Bruno Smets with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Bruno Smets more than expected).
This network shows the impact of papers produced by Bruno Smets. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Bruno Smets. The network helps show where Bruno Smets may publish in the future.
Co-authorship network of co-authors of Bruno Smets
This figure shows the co-authorship network connecting the top 25 collaborators of Bruno Smets.
A scholar is included among the top collaborators of Bruno Smets based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Bruno Smets. Bruno Smets is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Buchhorn, Marcel, Myroslava Lesiv, Nandin‐Erdene Tsendbazar, et al.. (2019). Copernicus Global Land Cover Service - Elastic, Operational Land Cover Mapping at Global Scale using Time Series Analysis. AGU Fall Meeting Abstracts. 2019.1 indexed citations
Camacho, Fernando, Roselyne Lacaze, Marie Weiss, et al.. (2016). Validating GEOV3 LAI, FAPAR and vegetation cover estimates derived from PROBA-V observations at 333m over Europe. EGU General Assembly Conference Abstracts.3 indexed citations
14.
Camacho, Fernando, et al.. (2015). Preliminary validation of Albedo, FAPAR and LAI Essential Climate Variables products derived from PROBA-V observations in the Copernicus Global Land Service. EGUGA. 2345.1 indexed citations
Lacaze, Roselyne, Bruno Smets, Jean‐Christophe Calvet, et al.. (2015). Sentinel-3 for the Copernicus Global Land Service: Monitoring the Continental Ecosystems at Global Scale. 734. 32.1 indexed citations
17.
Lacaze, Roselyne, Bruno Smets, Isabel F. Trigo, et al.. (2013). the copernicus global land service: present and future. DIAL (Catholic University of Leuven). 15.3 indexed citations
18.
Baret, Frédéric, Marie Weiss, Roselyne Lacaze, et al.. (2013). GEOV1: LAI and FAPAR essential climate variables and FCOVER global time series capitalizing over existing products. Part1: Principles of development and production. Remote Sensing of Environment. 137. 299–309.440 indexed citations breakdown →
19.
Verger, Aleixandre, Frédéric Baret, M. Weiß, et al.. (2012). LAI, FAPAR and FCOVER products derived from AVHRR long time series: principles and evaluation. EGU General Assembly Conference Abstracts. 7844.4 indexed citations
20.
Lacaze, Roselyne, Gianpaolo Balsamo, Frédéric Baret, et al.. (2010). GEOLAND2 - Towards an operational GMES land monitoring core service; first results of the biogeophysical parameter core mapping service. Digital Access to Libraries (Université catholique de Louvain (UCL), l'Université de Namur (UNamur) and the Université Saint-Louis (USL-B)). 354–359.8 indexed citations
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
bibliographic database. While OpenAlex provides broad and valuable coverage of the global
research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
delays in data updates. As a result, some metrics and network relationships displayed in
Rankless may not fully capture the entirety of a scholar's output or impact.