Special Issue "Remote Sensing-Based Evapotranspiration Models"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".

Deadline for manuscript submissions: 31 December 2021.

Special Issue Editors

Dr. Vivek Sharma
E-Mail Website
Guest Editor
Agricultural and Biological Engineering Department, Institute of Food and Agricultural Sciences, University of Florida, Gainesville, FL 32611, USA
Interests: precision water management; soil and water conservation; irrigation scheduling; evapotranspiration and surface energy balance fluxes; soil water and crop dynamics; crop water productivity; remote sensing
Special Issues, Collections and Topics in MDPI journals
Dr. Aditya Singh
E-Mail Website
Guest Editor
Department of Agricultural and Biological Engineering, University of Florida, Gainesville, FL 32611, USA
Interests: remote sensing; forestry; spectroscopy; water quality; wildlife habitat use
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Evapotranspiration (ET) plays a significant role in local, regional, and global climate by impacting relationships between land-use/land cover change and microclimate/climate energy balance in the hydrological cycle and has important applications in agriculture and natural system. Over the years, various remote sensing-based techniques have been developed to understand and estimate ET and its interactions over local to regional spatial scales. This special issue aims to provide a forum of discussion for recent developments and advances in Remote Sensing-based ET models and their applications in diverse ecosystems and agrometeorological conditions. The special issue aims at targeting studies related to the advances of large-scale remote sensing-based ET modeling, model and algorithm validation, uncertainty analysis, and calibration aiming at improvements of surface energy and water vapor fluxes computations under different climate and land-use scenarios. Specific topics include but are not limited to:

  • Development, validation, and inter-comparison of new and improved remote sensing-based ET models in diverse ecosystems and agrometeorological conditions.
  • Integration remote sensing model ET with hydrological and crop models, and machine learning.
  • Downscaling and data fusion techniques to improve spatio-temporal resolution of remote sensing-based ET products.
  • Application of remote sensing-based ET models in agriculture, natural, and urban environment, food security, water resources management under irrigated and rainfed settings.
Dr. Vivek Sharma
Dr. Aditya Singh
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 2400 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
  • Surface energy balance models
  • land-use/land cover change
  • Water resources management
  • Water use efficiency
  • Data fusion
  • Machine learning
  • Crop and watershed modelling

Published Papers (1 paper)

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Research

Article
Mapping Daily Evapotranspiration at Field Scale Using the Harmonized Landsat and Sentinel-2 Dataset, with Sharpened VIIRS as a Sentinel-2 Thermal Proxy
Remote Sens. 2021, 13(17), 3420; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13173420 - 28 Aug 2021
Cited by 1 | Viewed by 674
Abstract
Accurate and frequent monitoring of evapotranspiration (ET) at sub-field scales can provide valuable information for agricultural water management, quantifying crop water use and stress toward the goal of increasing crop water use efficiency and production. Using land-surface temperature (LST) data retrieved from Landsat [...] Read more.
Accurate and frequent monitoring of evapotranspiration (ET) at sub-field scales can provide valuable information for agricultural water management, quantifying crop water use and stress toward the goal of increasing crop water use efficiency and production. Using land-surface temperature (LST) data retrieved from Landsat thermal infrared (TIR) imagery, along with surface reflectance data describing albedo and vegetation cover fraction, surface energy balance models can generate ET maps down to a 30 m spatial resolution. However, the temporal sampling by such maps can be limited by the relatively infrequent revisit period of Landsat data (8 days for combined Landsats 7 and 8), especially in cloudy areas experiencing rapid changes in moisture status. The Sentinel-2 (S2) satellites, as a good complement to the Landsat system, provide surface reflectance data at 10–20 m spatial resolution and 5 day revisit period but do not have a thermal sensor. On the other hand, the Visible Infrared Imaging Radiometer Suite (VIIRS) provides TIR data on a near-daily basis with 375 m resolution, which can be refined through thermal sharpening using S2 reflectances. This study assesses the utility of augmenting the Harmonized Landsat and Sentinel-2 (HLS) dataset with S2-sharpened VIIRS as a thermal proxy source on S2 overpass days, enabling 30 m ET mapping at a potential combined frequency of 2–3 days (including Landsat). The value added by including VIIRS-S2 is assessed both retrospectively and operationally in comparison with flux tower observations collected from several U.S. agricultural sites covering a range of crop types. In particular, we evaluate the performance of VIIRS-S2 ET estimates as a function of VIIRS view angle and cloud masking approach. VIIRS-S2 ET retrievals (MAE of 0.49 mm d−1 against observations) generally show comparable accuracy to Landsat ET (0.45 mm d−1) on days of commensurate overpass, but with decreasing performance at large VIIRS view angles. Low-quality VIIRS-S2 ET retrievals linked to imperfect VIIRS/S2 cloud masking are also discussed, and caution is required when applying such data for generating ET timeseries. Fused daily ET time series benefited during the peak growing season from the improved multi-source temporal sampling afforded by VIIRS-S2, particularly in cloudy regions and over surfaces with rapidly changing vegetation conditions, and value added for real-time monitoring applications is discussed. This work demonstrates the utility and feasibility of augmenting the HLS dataset with sharpened VIIRS TIR imagery on S2 overpass dates for generating high spatiotemporal resolution ET products. Full article
(This article belongs to the Special Issue Remote Sensing-Based Evapotranspiration Models)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Evaluating sources of error for mapping evapotranspiration from remotely piloted aircrafts
Authors: Logan Austin Ebert; Alex J Chisholm; Jacob Prater; Samuel C Zipper; Ammara Talib; Ankur R Desai; Mallika Arudi Nocco
Affiliation: University of California Davis
Abstract: The next step in bridging precision irrigation research and application is through real-time monitoring of evapotranspiration (ET) and crop water stress. Eddie Covariance towers and lysimeters are vetted methods of monitoring ET and water stress but can lack field-size domains, spatial resolution. Remotely sensed data offers a solution to this problem, and at less of a time and money cost. Recent advancements in remotely piloted aircrafts (RPAs) have made frequent, low-flying optical and thermal imagery collection more economical and feasible than ever before. However, the ability to fly on your own schedule sometimes results in flying during unfavorable conditions. We are motivated to determine the greatest source of error. The goal of our project was to evaluate the sources of error associated with our evapotranspiration model parameters. The optical and thermal data were collected from a commercially irrigated potato field in the Wisconsin Central Sands during the 2019 growing season. A total of eight mission sets were flown. Each mission set was flown using a quadcopter RPA system and combined multispectral/thermal camera. Mission sets included flights at 90, 60, and 30 m above ground level. Ground reference measurements of surface temperature and soil moisture were collected throughout the domain within 15 minutes of each RPA mission set. ET values were modeled from the flight data using the High-Resolution Mapping of Evapotranspiration (HRMET) model. HRMET-derived ET maps are compared to ET estimates from an Eddy Covariance system within the flight domain. Error maps of the model were made for each flight using the Monte Carlo approach. Ongoing results will be used to develop best practices and assess tradeoffs for integrating RPAs as decision support tools for irrigation and water management.

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