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.
GLEAM v3: satellite-based land evaporation and root-zone soil moisture
20171.8k citationsBrecht Martens, Diego G. Miralles et al.Geoscientific model developmentprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by Diego Fernández‐Prieto
Since
Specialization
Citations
This map shows the geographic impact of Diego Fernández‐Prieto'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 Diego Fernández‐Prieto with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Diego Fernández‐Prieto more than expected).
Fields of papers citing papers by Diego Fernández‐Prieto
This network shows the impact of papers produced by Diego Fernández‐Prieto. 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 Diego Fernández‐Prieto. The network helps show where Diego Fernández‐Prieto may publish in the future.
Co-authorship network of co-authors of Diego Fernández‐Prieto
This figure shows the co-authorship network connecting the top 25 collaborators of Diego Fernández‐Prieto.
A scholar is included among the top collaborators of Diego Fernández‐Prieto 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 Diego Fernández‐Prieto. Diego Fernández‐Prieto is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Martens, Brecht, Diego G. Miralles, Hans Lievens, et al.. (2017). GLEAM v3: satellite-based land evaporation and root-zone soil moisture. Geoscientific model development. 10(5). 1903–1925.1774 indexed citations breakdown →
Martens, Brecht, Diego G. Miralles, Hans Lievens, et al.. (2016). GLEAM v3: updated land evaporation and root-zone soil moisture datasets. EGUGA.2 indexed citations
11.
Sabia, Roberto, Marlene Klockmann, Diego Fernández‐Prieto, & Craig Donlon. (2015). Air-sea fluxes and satellite-based estimation of water masses formation. EGU General Assembly Conference Abstracts. 13074.1 indexed citations
12.
Jeu, Richard de, Wouter Dorigo, Robert Parinussa, et al.. (2012). Sidebar 2.2: building a climate record of soil moisture from historical satellite observations. Bulletin of the American Meteorological Society. 93(7).4 indexed citations
13.
Fernández‐Prieto, Diego, Claude Duguay, Yves Gauthier, et al.. (2012). ESA STSE North Hydrology: Development of multi-mission satellite data products in support of atmospheric and hydrological modeling of cold regions. EGU General Assembly Conference Abstracts. 12505.2 indexed citations
14.
Font, Jordi, R. Sabia, Gary Lagerloef, et al.. (2012). Derivation of AN Experimental Satellite-Based T-S Diagram. DIGITAL.CSIC (Spanish National Research Council (CSIC)). 2012.1 indexed citations
Marconcini, Mattia, et al.. (2010). ALANIS: a joint ESA–iLEAPS atmosphere–land interaction study over boreal Eurasia. NERC Open Research Archive (Natural Environment Research Council). 688. 32.1 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.