P. Lewis

15.7k total citations · 6 hit papers
124 papers, 8.2k citations indexed

About

P. Lewis is a scholar working on Ecology, Global and Planetary Change and Environmental Engineering. According to data from OpenAlex, P. Lewis has authored 124 papers receiving a total of 8.2k indexed citations (citations by other indexed papers that have themselves been cited), including 83 papers in Ecology, 59 papers in Global and Planetary Change and 58 papers in Environmental Engineering. Recurrent topics in P. Lewis's work include Remote Sensing in Agriculture (83 papers), Remote Sensing and LiDAR Applications (43 papers) and Atmospheric and Environmental Gas Dynamics (24 papers). P. Lewis is often cited by papers focused on Remote Sensing in Agriculture (83 papers), Remote Sensing and LiDAR Applications (43 papers) and Atmospheric and Environmental Gas Dynamics (24 papers). P. Lewis collaborates with scholars based in United Kingdom, United States and Australia. P. Lewis's co-authors include Mathias Disney, David P. Roy, Chris Justice, José Gómez‐Dans, Crystal Schaaf, Tristan Quaife, Peter M. Atkinson, Yufang Jin, Jan‐Peter Müller and Peter North and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of Geophysical Research Atmospheres and Remote Sensing of Environment.

In The Last Decade

P. Lewis

120 papers receiving 7.9k citations

Hit Papers

5 papers align trajectories

What are hit papers?

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.

Fast Automatic Precision Tree Models from Terrestrial Laser Scanner Data

2013 606 citations Pasi Raumonen, Markku Åkerblom et al. Remote Sensing profile →
Retrieval and global assessment of terrestrial chlorophyll fluorescence from GOSAT space measurements

2012 447 citations Luis Guanter, P. Lewis et al. Remote Sensing of Environment profile →
Multi-temporal MODIS–Landsat data fusion for relative radiometric normalization, gap filling, and prediction of Landsat data

2008 440 citations David P. Roy, P. Lewis et al. Remote Sensing of Environment profile →
Assimilation of remote sensing into crop growth models: Current status and perspectives

2019 366 citations José Gómez‐Dans, Qingling Wu et al. Agricultural and Forest Meteorology profile →
Quantifying Vegetation Biophysical Variables from Imaging Spectroscopy Data: A Review on Retrieval Methods

2018 365 citations Jochem Verrelst, Zbyněk Malenovský et al. Surveys in Geophysics profile →
A general method to normalize Landsat reflectance data to nadir BRDF adjusted reflectance

2016 280 citations David P. Roy, José Gómez‐Dans et al. Remote Sensing of Environment profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
P. Lewis United Kingdom	43	5.4k	4.4k	3.9k	1.3k	1.3k	124	8.2k
Michel M. Verstraete Italy	53	5.7k 1.0×	6.0k 1.4×	4.0k 1.0×	2.8k 2.1×	1.1k 0.9×	136	9.8k
Thomas Hilker United States	43	4.5k 0.8×	3.6k 0.8×	3.9k 1.0×	899 0.7×	586 0.5×	83	7.4k
Guangjian Yan China	43	4.0k 0.7×	4.1k 0.9×	6.6k 1.7×	2.9k 2.2×	1.1k 0.8×	238	9.8k
Forrest G. Hall United States	35	4.0k 0.7×	4.2k 1.0×	2.6k 0.7×	1.3k 1.0×	787 0.6×	65	6.5k
J. Cihlar Canada	55	6.2k 1.1×	5.8k 1.3×	3.5k 0.9×	2.6k 2.0×	1.3k 1.0×	141	10.5k
Jean‐Philippe Gastellu‐Etchegorry France	43	4.4k 0.8×	3.1k 0.7×	3.9k 1.0×	1.0k 0.8×	1.3k 1.0×	218	6.6k
Steven E. Franklin Canada	48	5.6k 1.0×	3.3k 0.8×	3.5k 0.9×	1.3k 1.0×	549 0.4×	155	8.7k
F. G. HALL United States	27	4.7k 0.9×	5.4k 1.2×	3.0k 0.8×	2.3k 1.7×	940 0.7×	56	8.5k
J.G.P.W. Clevers Netherlands	49	6.6k 1.2×	3.6k 0.8×	3.4k 0.9×	1.3k 1.0×	2.5k 2.0×	215	9.3k
Ruiliang Pu United States	51	4.5k 0.8×	2.9k 0.7×	2.8k 0.7×	1.5k 1.1×	1.7k 1.4×	157	7.6k

Countries citing papers authored by P. Lewis

Since Specialization

Citations

This map shows the geographic impact of P. Lewis'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 P. Lewis with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites P. Lewis more than expected).

Fields of papers citing papers by P. Lewis

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by P. Lewis. 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 P. Lewis. The network helps show where P. Lewis may publish in the future.

Co-authorship network of co-authors of P. Lewis

This figure shows the co-authorship network connecting the top 25 collaborators of P. Lewis. A scholar is included among the top collaborators of P. Lewis 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 P. Lewis. P. Lewis is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Lewis, P., et al.. (2022). Bayesian atmospheric correction over land: Sentinel-2/MSI and Landsat 8/OLI. Geoscientific model development. 15(21). 7933–7976. 39 indexed citations

Gómez‐Dans, José, P. Lewis, Dilys S. MacCarthy, et al.. (2022). Location, biophysical and agronomic parameters for croplands in northern Ghana. Earth system science data. 14(12). 5387–5410. 7 indexed citations

Brennan, James K., José Gómez‐Dans, Mathias Disney, & P. Lewis. (2019). Theoretical uncertainties for global satellite-derived burned area estimates. Biogeosciences. 16(16). 3147–3164. 14 indexed citations

Feng, Yin, et al.. (2019). The Sensor Independent Atmospheric Correction (SIAC) approach applied to Sentinel-2 and Landsat-8 data. EGU General Assembly Conference Abstracts. 11269. 1 indexed citations

MacBean, Natasha, Fabienne Maignan, Cédric Bacour, et al.. (2018). Strong constraint on modelled global carbon uptake using solar-induced chlorophyll fluorescence data. Scientific Reports. 8(1). 1973–1973. 93 indexed citations

Calders, Kim, Niall Origo, Andrew Burt, et al.. (2018). Realistic Forest Stand Reconstruction from Terrestrial LiDAR for Radiative Transfer Modelling. Remote Sensing. 10(6). 933–933. 126 indexed citations

Verrelst, Jochem, Zbyněk Malenovský, Christiaan van der Tol, et al.. (2018). Quantifying Vegetation Biophysical Variables from Imaging Spectroscopy Data: A Review on Retrieval Methods. Surveys in Geophysics. 40(3). 589–629. 365 indexed citations breakdown →

Calders, Kim, Andrew Burt, Niall Origo, et al.. (2016). Large-area virtual forests from terrestrial laser scanning data. Ghent University Academic Bibliography (Ghent University). 1765–1767. 12 indexed citations

Danson, F. Mark, Rachel Gaulton, Richard Armitage, et al.. (2014). Developing a dual-wavelength full-waveform terrestrial laser scanner to characterize forest canopy structure. Agricultural and Forest Meteorology. 198-199. 7–14. 101 indexed citations

10.

Gómez‐Dans, José, et al.. (2013). Validation Of The Earth Observation Land Data Assimilation System By The Field Data Of ESA SPARC Field Campaign. 722. 74. 1 indexed citations

11.

Kauwe, Martin G. De, Mathias Disney, Tristan Quaife, P. Lewis, & Mathew Williams. (2010). An assessment of the MODIS collection 5 leaf area index product for a region of mixed coniferous forest. Remote Sensing of Environment. 115(2). 767–780. 114 indexed citations

12.

Román, Miguel O., Crystal Schaaf, P. Lewis, et al.. (2009). Assessing the coupling between surface albedo derived from MODIS and the fraction of diffuse skylight over spatially-characterized landscapes. Remote Sensing of Environment. 114(4). 738–760. 152 indexed citations

13.

Lewis, P., Tristan Quaife, José Gómez‐Dans, et al.. (2009). Modelling the impact of wildfire on spectral reflectance. 37. IV–1019. 2 indexed citations

14.

Huang, Dong, Yuri Knyazikhin, Robert E. Dickinson, et al.. (2006). Canopy spectral invariants for remote sensing and model applications. Remote Sensing of Environment. 106(1). 106–122. 128 indexed citations

15.

Roy, David P., Yufang Jin, P. Lewis, & Chris Justice. (2005). Prototyping a global algorithm for systematic fire-affected area mapping using MODIS time series data. Remote Sensing of Environment. 97(2). 137–162. 444 indexed citations

16.

Roy, David P., et al.. (2003). Multi-year southern Africa MODIS burned area product generation and validation. AGU Fall Meeting Abstracts. 2003. 2 indexed citations

17.

Saich, P., et al.. (2002). Comparison of Hymap/E-SAR data with models for optical reflectance and microwave scattering from vegetation canopies. UCL Discovery (University College London). 1 indexed citations

18.

Roy, David P., P. Lewis, & Chris Justice. (2002). Burned area mapping using multi-temporal moderate spatial resolution data—a bi-directional reflectance model-based expectation approach. Remote Sensing of Environment. 83(1-2). 263–286. 293 indexed citations

19.

Lucht, Wolfgang & P. Lewis. (2000). Theoretical noise sensitivity of BRDF and albedo retrieval from the EOS-MODIS and MISR sensors with respect to angular sampling. International Journal of Remote Sensing. 21(1). 81–98. 131 indexed citations

20.

Lewis, P., Mathias Disney, M. J. Barnsley, & Jan‐Peter Müller. (1999). Deriving albedo maps for HAPEX-Sahel from ASAS data using kernel-driven BRDF models. Hydrology and earth system sciences. 3(1). 1–11. 34 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.

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