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Editorial Board Members’ Collection Series: Advanced Integration of Eddy Covariance Measurements with Remote Sensing, Climate, and Ground-Truth Data

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

Deadline for manuscript submissions: 26 May 2024 | Viewed by 1114

Special Issue Editor

Prof. Dr. Alfredo Huete

E-Mail Website
Guest Editor
School of Life Sciences, University of Technology Sydney, Ultimo, NSW 2007, Australia
Interests: biophysical remote sensing; terrestrial ecohydrology; land surface phenology; carbon and water fluxes; geostationary and low earth observations; time series analyses; climate change impacts; vegetation health and ecosystem resilience; ecological forecasting
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The main goal of the Editorial Board Members’ Collection Series is to showcase articles that emphasize the advanced integration of ground-based measurements covering vegetation, soil, climate, evapotranspiration (ET), carbon dioxide (CO2) fluxes, and additional greenhouse gas (GHG) fluxes with remote sensing data, analysis methods, and modeling. Relevant papers dealing with the link between CO2 fluxes and ET, as well as water-use efficiency, will also be considered.

Prof. Dr. Alfredo Huete
Guest Editor

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 submissions that pass pre-check are 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 2700 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.

Keywords

  • evapotranspiration
  • greenhouse gas emissions
  • water-use efficiency
  • predictive modeling/forecasting
  • remote sensing
  • machine learning
  • ecosystem functioning
  • vegetation stress
  • landscape carbon source/sink analyses

Published Papers (1 paper)

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Research

20 pages, 4768 KiB  
Article
Uncertainty Analysis and Data Fusion of Multi-Source Land Evapotranspiration Products Based on the TCH Method
by Zilong Cui, Yuan Zhang, Anzhi Wang, Jiabing Wu and Chunbo Li
Remote Sens. 2024, 16(1), 28; https://doi.org/10.3390/rs16010028 - 20 Dec 2023
Viewed by 732
Abstract
Evapotranspiration (ET) is a very important variable in the global water cycle, carbon cycle, and energy cycle. However, there are still some uncertainties in existing ET products. Therefore, this paper evaluates the uncertainty of three widely used global ET products (ERA5-Land, GLDAS-Noah, and [...] Read more.
Evapotranspiration (ET) is a very important variable in the global water cycle, carbon cycle, and energy cycle. However, there are still some uncertainties in existing ET products. Therefore, this paper evaluates the uncertainty of three widely used global ET products (ERA5-Land, GLDAS-Noah, and MERRA-2) based on the three-cornered hat (TCH) method, and generates a new ET product based on this. The new product is a long-series global monthly ET dataset with a spatial resolution of 0.25° × 0.25° and a time span of 21 years. The results show that ERA5-Land (8.46 mm/month) has the lowest uncertainty among the three ET products, followed by GLDAS-Noah (8.81 mm/month) and MERRA-2 (11.78 mm/month). The new product (TCH) captures ET trends in different regions as well as validating against in situ flux observations, and it exhibits better performance than the re-analysis dataset (ERA5-Land) in vegetation classifications such as evergreen needle-leaf forest, grassland, open shrubland, savanna, and woody savanna. The linear trend analysis of the new product shows a significant decreasing trend in south-eastern South America and southwestern parts of Africa, and an increasing trend in almost all other regions, including eastern North America, north-eastern South America, western Europe, north-central Africa, southern Asia, and south-eastern Oceania. Full article
(This article belongs to the Special Issue Editorial Board Members’ Collection Series: Advanced Integration of Eddy Covariance Measurements with Remote Sensing, Climate, and Ground-Truth Data)
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