Special Issue "Remote Sensing of Land Surface and Earth System Modelling"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Biogeosciences Remote Sensing".

Deadline for manuscript submissions: 30 June 2020.

Special Issue Editors

Dr. Patricia De Rosnay
E-Mail Website
Guest Editor
European Center For Medium-Range Weather Forecasts, UK
Interests: Land surface data assimilation; coupled assimilation; Earth system modelling; Land surface observations; Forward modelling
Special Issues and Collections in MDPI journals
Dr. Clement Albergel
E-Mail Website
Guest Editor
Affiliation: Météo-France/ Centre National de Recherches Météorologiques (CNRS), France
Interests: land surface modelling; climate change; hydrology; data analysis
Special Issues and Collections in MDPI journals
Dr. Sujay Kumar
E-Mail Website
Guest Editor
Hydrological Sciences Lab, NASA Goddard Space Flight Center, 8800 Greenbelt Rd, Greenbelt, MD, 21042, USA
Interests: land surface modelling; hydrology; data assimilation; remote sensing; Optimization
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Land surface observations are increasingly used to constrain climate models, weather prediction systems and hydrological forecast and flood alert systems. Current satellites provide relevant information on hydrology (e.g., soil moisture, snow depth and cover, terrestrial water storage, inland water extent and temperature), vegetation (e.g., LAI, NDVI, FAPAR, biomass) and energy (e.g., LST, albedo).

This Special Issue aims at documenting most recent progress in using land surface remote sensing observations (soil moisture, vegetation, snow extent and water equivalent, lakes and land surface temperature, land surface albedo, flooded areas) for Earth system modelling applications, including weather forecasts, climate modelling and hydrological forecasts. We welcome studies related to land surface data assimilation, land surface re-analysis, as well as land surface forward modelling (VIS/IR/MW), inverse modelling and machine learning. The Special Issue also encourages studies that investigate land surface parameter retrieval, coupled assimilation in land-hydrology-atmosphere systems as well as intercomparison studies.

Dr. Patricia De Rosnay
Dr. Sujay Kumar
Dr. Clement Albergel
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.


  • Land surface observations
  • Land surface data assimilation Land surface monitoring
  • Coupled land–atmosphere data assimilation
  • Retrieval of surface parameters
  • Land surface forward modelling

Published Papers (1 paper)

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Open AccessArticle
Cold Bias of ERA5 Summertime Daily Maximum Land Surface Temperature over Iberian Peninsula
Remote Sens. 2019, 11(21), 2570; https://doi.org/10.3390/rs11212570 - 01 Nov 2019
Land surface temperature (LST) is a key variable in surface-atmosphere energy and water exchanges. The main goals of this study are to (i) evaluate the LST of the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim and ERA5 reanalyses over Iberian Peninsula using [...] Read more.
Land surface temperature (LST) is a key variable in surface-atmosphere energy and water exchanges. The main goals of this study are to (i) evaluate the LST of the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim and ERA5 reanalyses over Iberian Peninsula using the Satellite Application Facility on Land Surface Analysis (LSA-SAF) product and to (ii) understand the main drivers of the LST errors in the reanalysis. Simulations with the ECMWF land-surface model in offline mode (uncoupled) were carried out over the Iberian Peninsula and compared with the reanalysis data. Several sensitivity simulations were performed in a confined domain centered in Southern Portugal to investigate potential sources of the LST errors. The Copernicus Global Land Service (CGLS) fraction of green vegetation cover (FCover) and the European Space Agency’s Climate Change Initiative (ESA-CCI) Land Cover dataset were explored. We found a general underestimation of daytime LST and slightly overestimation at night-time. The results indicate that there is still room for improvement in the simulation of LST in ECMWF products. Still, ERA5 presents an overall higher quality product in relation to ERA-Interim. Our analysis suggested a relation between the large daytime cold bias and vegetation cover differences between (ERA5 and CGLS FCocver) with a correlation of −0.45. The replacement of the low and high vegetation cover by those of ESA-CCI provided an overall reduction of the large Tmax biases during summer. The increased vertical resolution of the soil at the surface, has a positive impact, but much smaller when compared with the vegetation changes. The sensitivity of the vegetation density parameter, that currently depends on the vegetation type, provided further proof for a needed revision of the vegetation in the model, as there is a reasonable correlation between this parameter and the Tmax mean errors when using the ESA-CCI vegetation cover (while the same correlation cannot be reproduced with the original model vegetation). Our results support the hypothesis that vegetation cover is one of the main drivers of the LST summertime cold bias in ERA5 over Iberian Peninsula. Full article
(This article belongs to the Special Issue Remote Sensing of Land Surface and Earth System Modelling)
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