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Remotely-Sensed Evapotranspiration and Soil Moisture: What Lies Ahead?

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors".

Deadline for manuscript submissions: closed (1 November 2020) | Viewed by 2869

Special Issue Editor


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Guest Editor
Royal Meteorological Institute (KMI), Ringlaan 3, B-1180 Brussels, Belgium
Interests: terrestrial and atmospheric remote sensing data; terrestrial water and carbon cycles; air pollution (NOX, ozone, aerosols); allergenic pollen; chemistry transport models
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

An increased anthropogenic demand for water for satisfying the manifold of agricultural, industrial, and recreational activities in combination with the increased awareness for climate and the environment prompts the further development of monitoring and modeling of water flow between the surface and the lower atmosphere both in space as well as in time. The rich history of remote sensing measurements from space, air, and ground has shown a lot of potential to estimate and monitor soil moisture and evapotranspiration. Combined with the 20th anniversary in 2020 since the launch of this journal, Sensors, this may foster the discussion on the maturity and advancement of our current knowledge on monitoring and modeling the water loss from the earth’s surface with attention to the lessons that we have learned or have failed to learn.

To celebrate 20 years of Sensors, this Special Issue requests papers presenting strategies, methodologies or approaches leading to an enhanced knowledge of soil moisture and evapotranspiration and its inevitable uncertainties in space and time derived from remote sensing observations of all kind. This Special Issue aims to bring together scientific papers on remote sensing techniques, products, and model development and applications leading to strategies with a higher impact on the stability and sustainability of the Earth’s water management, for the benefit of humanity and its next generations.

This Special Issue solicits papers focusing on the following topics on this non-exhaustive list on deriving evapotranspiration and soil moisture from remote sensing observations:

  • Novel approaches for estimating remotely-sensed soil moisture and/or evapotranspiration;
  • Water balance models integrating remote sensing (RS) observations;
  • Position papers on water demand for next generation;
  • Review papers: lessons learned from remote sensing;
  • Uncertainty analysis on remotely-sensed evapotranspiration and soil moisture products;
  • Passive and thermal optical RS for soil and plant water fluxes;
  • Passive and active microwave RS;
  • Other remote sensors (smart dust) applications;
  • Automated imagery processing to produce evapotranspiration and soil moisture maps;
  • The effect of soil moisture and/or evapotranspiration on climate;
  • The impact of plant water limitation in agriculture, irrigation, and drainage;
  • Water availability effects on vegetation growth, sun-induced fluorescence, carbon sequestration, allergic pollen production, surface ozone damage, and public health (asthma, rhinitis);
  • Strategies for making available water at the right time at the right place;
  • Other applications.

Dr. Willem W. Verstraeten
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. Sensors 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 2600 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.

Published Papers (1 paper)

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Research

21 pages, 6932 KiB  
Article
Estimation of Daily Terrestrial Latent Heat Flux with High Spatial Resolution from MODIS and Chinese GF-1 Data
by Xiangyi Bei, Yunjun Yao, Lilin Zhang, Yi Lin, Shaomin Liu, Kun Jia, Xiaotong Zhang, Ke Shang, Junming Yang, Xiaowei Chen and Xiaozheng Guo
Sensors 2020, 20(10), 2811; https://doi.org/10.3390/s20102811 - 15 May 2020
Cited by 11 | Viewed by 2400
Abstract
Reliable estimates of terrestrial latent heat flux (LE) at high spatial and temporal resolutions are of vital importance for energy balance and water resource management. However, currently available LE products derived from satellite data generally have high revisit frequency or fine spatial resolution. [...] Read more.
Reliable estimates of terrestrial latent heat flux (LE) at high spatial and temporal resolutions are of vital importance for energy balance and water resource management. However, currently available LE products derived from satellite data generally have high revisit frequency or fine spatial resolution. In this study, we explored the feasibility of the high spatiotemporal resolution LE fusion framework to take advantage of the Moderate Resolution Imaging Spectroradiometer (MODIS) and Chinese GaoFen-1 Wide Field View (GF-1 WFV) data. In particular, three-fold fusion schemes based on Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) were employed, including fusion of surface reflectance (Scheme 1), vegetation indices (Scheme 2) and high order LE products (Scheme 3). Our results showed that the fusion of vegetation indices and further computing LE (Scheme 2) achieved better accuracy and captured more detailed information of terrestrial LE, where the determination coefficient (R2) varies from 0.86 to 0.98, the root-mean-square error (RMSE) ranges from 1.25 to 9.77 W/m2 and the relative RSME (rRMSE) varies from 2% to 23%. The time series of merged LE in 2017 using the optimal Scheme 2 also showed a relatively good agreement with eddy covariance (EC) measurements and MODIS LE products. The fusion approach provides spatiotemporal continuous LE estimates and also reduces the uncertainties in LE estimation, with an increment in R2 by 0.06 and a decrease in RMSE by 23.4% on average. The proposed high spatiotemporal resolution LE estimation framework using multi-source data showed great promise in monitoring LE variation at field scale, and may have value in planning irrigation schemes and providing water management decisions over agroecosystems. Full article
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