Next Article in Journal
Rapid Coastal Forest Decline in Florida’s Big Bend
Next Article in Special Issue
Estimating Evapotranspiration in a Post-Fire Environment Using Remote Sensing and Machine Learning
Previous Article in Journal
An Extension of Phase Correlation-Based Image Registration to Estimate Similarity Transform Using Multiple Polar Fourier Transform
Previous Article in Special Issue
Sensitivity of Evapotranspiration Components in Remote Sensing-Based Models
Article Menu
Issue 11 (November) cover image

Export Article

Open AccessArticle
Remote Sens. 2018, 10(11), 1720; https://doi.org/10.3390/rs10111720

Towards Estimating Land Evaporation at Field Scales Using GLEAM

1
Laboratory of Hydrology and Water Management—Ghent University; Coupure links 653, 9000 Gent, Belgium
2
VanderSat BVBA, Wilhelminastraat 43a, 2011 VK Haarlem, The Netherlands
3
Royal HaskoningDHV, Laan 1914 35, 3818 EX Amersfoort, The Netherlands
*
Author to whom correspondence should be addressed.
Received: 27 September 2018 / Revised: 24 October 2018 / Accepted: 29 October 2018 / Published: 31 October 2018
(This article belongs to the Special Issue Advances in the Remote Sensing of Terrestrial Evaporation)
Full-Text   |   PDF [17318 KB, uploaded 31 October 2018]   |  

Abstract

The evaporation of water from land into the atmosphere is a key component of the hydrological cycle. Accurate estimates of this flux are essential for proper water management and irrigation scheduling. However, continuous and qualitative information on land evaporation is currently not available at the required spatio-temporal scales for agricultural applications and regional-scale water management. Here, we apply the Global Land Evaporation Amsterdam Model (GLEAM) at 100 m spatial resolution and daily time steps to provide estimates of land evaporation over The Netherlands, Flanders, and western Germany for the period 2013–2017. By making extensive use of microwave-based geophysical observations, we are able to provide data under all weather conditions. The soil moisture estimates from GLEAM at high resolution compare well with in situ measurements of surface soil moisture, resulting in a median temporal correlation coefficient of 0.76 across 29 sites. Estimates of terrestrial evaporation are also evaluated using in situ eddy-covariance measurements from five sites, and compared to estimates from the coarse-scale GLEAM v3.2b, land evaporation from the Satellite Application Facility on Land Surface Analysis (LSA-SAF), and reference grass evaporation based on Makkink’s equation. All datasets compare similarly with in situ measurements and differences in the temporal statistics are small, with correlation coefficients against in situ data ranging from 0.65 to 0.95, depending on the site. Evaporation estimates from GLEAM-HR are typically bounded by the high values of the Makkink evaporation and the low values from LSA-SAF. While GLEAM-HR and LSA-SAF show the highest spatial detail, their geographical patterns diverge strongly due to differences in model assumptions, model parameterizations, and forcing data. The separate consideration of rainfall interception loss by tall vegetation in GLEAM-HR is a key cause of this divergence: while LSA-SAF reports maximum annual evaporation volumes in the Green Heart of The Netherlands, an area dominated by shrubs and grasses, GLEAM-HR shows its maximum in the national parks of the Veluwe and Heuvelrug, both densely-forested regions where rainfall interception loss is a dominant process. The pioneering dataset presented here is unique in that it provides observational-based estimates at high resolution under all weather conditions, and represents a viable alternative to traditional visible and infrared models to retrieve evaporation at field scales. View Full-Text
Keywords: terrestrial evaporation; root-zone soil moisture; microwave remote sensing; GLEAM; LSA-SAF terrestrial evaporation; root-zone soil moisture; microwave remote sensing; GLEAM; LSA-SAF
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Martens, B.; de Jeu, R.A.M.; Verhoest, N.E.C.; Schuurmans, H.; Kleijer, J.; Miralles, D.G. Towards Estimating Land Evaporation at Field Scales Using GLEAM. Remote Sens. 2018, 10, 1720.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top