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Hydrology
Special Issues
GIS Modelling of Evapotranspiration with Remote Sensing
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GIS Modelling of Evapotranspiration with Remote Sensing

A special issue of Hydrology (ISSN 2306-5338). This special issue belongs to the section "Hydrology–Climate Interactions".

Deadline for manuscript submissions: 30 November 2024 | Viewed by 7984

Special Issue Editors

Dr. Sonam Sandeep Dash

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Guest Editor
Postdoctoral Researcher, School of Civil Engineering, University College Dublin, Dublin, Ireland
Interests: hydrological modeling; evapotranspiration; climate change; drought; multivariate analysis
Dr. Pooja P. Preetha

E-Mail Website
Guest Editor
Department of Civil Engineering, Alabama A&M University, Huntsville, AL 35782, USA
Interests: water quality modeling; groundwater systems; GIS; remote sensing
Dr. Han Chen

E-Mail Website
Guest Editor
Postdoctoral Researcher, School of Civil Engineering, Nankai University, Tianjin, China
Interests: evapotranspiration; eddy covariance; agriculture; precision; irrigation; hydrological modeling

Special Issue Information

Dear Colleagues,

Evapotranspiration (ET) can be estimated from the complex surface energy balance equations. This process plays a decisive role in various water resource management activities, including the required irrigation water, vegetation–atmosphere interactions, and terrestrial ecosystem productivity over a range of spatial and temporal domains. However, the reliable estimation of ET, characterized by complex vegetation–atmosphere interactions, is limited by scarce data availability and a lack of expertise in conceptualizing the real field scenario. Several remote sensing-based ET estimation approaches, particularly those used to estimate sensible heat flux within a smaller spatial domain, are discussed in this Special Issue. This includes Mapping EvapoTranspiration at High Resolution using Internalized Calibration (METRIC) and Surface Energy Balance Algorithm for Land (SEBAL) (Allen et al., 2011). The introduction of a Moderate Resolution Imaging Spectroradiometer (MODIS) satellite, a device that onboards the aqua and terra sensors, provided continuous ET estimates at 250 m spatial and 8-day temporal resolutions. This was performed with the objective of improved irrigation scheduling at an 8-day timescale, in congruence with the general water stress-sensitive period for major crops.

This Special Issue provides an opportunity for budding researchers to publish their research outcomes related to remote sensing applications in evapotranspiration mapping. This Special Issue invites research articles including but not limited to:

  1. Catchment-scale Evapotranspiration monitoring
  2. MODIS ET product for vegetation monitoring
  3. GIS-based crop planning
  4. Remote sensing-based hydrological water balance assessment
  5. Spatiotemporal vegetation health monitoring
  6. Evapotranspiration modeling under scarce data availability scenario
  7. Modeling evapotranspiration with soil moisture estimates

Dr. Sonam Sandeep Dash
Dr. Pooja P. Preetha
Dr. Han Chen
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 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. Hydrology is an international peer-reviewed open access monthly 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.

Keywords

  • evapotranspiration
  • remote sensing
  • leaf area index (LAI)
  • geographic information system
  • crop monitoring
  • watershed modeling
  • vegetation indices
  • evaporation
  • transpiration

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Published Papers (4 papers)

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15 pages, 4339 KiB  
Article
Estimation of Evapotranspiration in South Eastern Afghanistan Using the GCOM-C Algorithm on the Basis of Landsat Satellite Imagery
by Emal Wali, Masahiro Tasumi and Otto Klemm
Hydrology 2024, 11(7), 95; https://doi.org/10.3390/hydrology11070095 - 30 Jun 2024
Viewed by 969
Abstract
This study aims to assess the performance of the Global Change Observation Mission—Climate (GCOM-C) ETindex estimation algorithm to estimate the actual evapotranspiration (ETa) in southeastern Afghanistan. Here, the GCOM-C ETindex algorithm was adopted to estimate the monthly ETa for the period [...] Read more.
This study aims to assess the performance of the Global Change Observation Mission—Climate (GCOM-C) ETindex estimation algorithm to estimate the actual evapotranspiration (ETa) in southeastern Afghanistan. Here, the GCOM-C ETindex algorithm was adopted to estimate the monthly ETa for the period from November 2016 to October 2017 using a series of Landsat 8, Thermal Infrared Sensor (TIRS) Band 10 satellite imagery. The estimation accuracy was evaluated by comparing the results with other estimates of ETa, namely the mapping evapotranspiration with the internalized calibration (METRIC) model, the MODIS Global Evapotranspiration Project (MOD16), the surface energy balance system (SEBS) tools, and with the crop evapotranspiration under standard conditions (ETc) as estimated by the FAO-56 procedure. The evaluation was made for irrigated wheat, maize, rice, and orchards and for non-irrigated bare soil land. The comparison of ETa values showed good correlation among the GCOM-C, METRIC, and FAO-56, while the MOD16 and SEBS showed significantly lower values of ETa. The agreement with the METRIC ETa implies that the simple GCOM-C algorithm successfully estimated the ETa in the region and that the precision was similar to that of the METRIC. This study provides the first high-quality evapotranspiration data with the spatial resolution of Landsat Band 10 data for the southeastern part of Afghanistan. The estimation procedure is straightforward, and its results are anticipated to enhance the understanding of regional hydrology. Full article
(This article belongs to the Special Issue GIS Modelling of Evapotranspiration with Remote Sensing)
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20 pages, 8660 KiB  
Article
Performance Evaluation of Regression-Based Machine Learning Models for Modeling Reference Evapotranspiration with Temperature Data
by Maria J. Diamantopoulou and Dimitris M. Papamichail
Hydrology 2024, 11(7), 89; https://doi.org/10.3390/hydrology11070089 - 21 Jun 2024
Cited by 1 | Viewed by 1117
Abstract
In this study, due to their flexibility in forecasting, the capabilities of three regression-based machine learning models were explored, specifically random forest regression (RFr), generalized regression neural network (GRNN), and support vector regression (SVR). The above models were assessed for their suitability in [...] Read more.
In this study, due to their flexibility in forecasting, the capabilities of three regression-based machine learning models were explored, specifically random forest regression (RFr), generalized regression neural network (GRNN), and support vector regression (SVR). The above models were assessed for their suitability in modeling daily reference evapotranspiration (ETo), based only on temperature data (Tmin, Tmax, Tmean), by comparing their daily ETo results with those estimated by the conventional FAO 56 PM model, which requires a broad range of data that may not be available or may not be of reasonable quality. The RFr, GRNN, and SVR models were subjected to performance evaluation by using statistical criteria and scatter plots. Following the implementation of the ETo models’ comparisons, it was observed that all regression-based machine learning models possess the capability to accurately estimate daily ETo based only on temperature data requirements. In particular, the RFr model outperformed the others, achieving the highest R value of 0.9924, while the SVR and GRNN models had R values of 0.9598 and 0.9576, respectively. Additionally, the RFr model recorded the lowest values in all error metrics. Once these regression-based machine learning models have been successfully developed, they will have the potential to serve as effective alternatives for estimating daily ETo, under current and climate change conditions, when temperature data are available. This information is crucial for effective water resources management and especially for predicting agricultural production in the context of climate change. Full article
(This article belongs to the Special Issue GIS Modelling of Evapotranspiration with Remote Sensing)
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27 pages, 5087 KiB  
Article
Evapotranspiration Assessment by Remote Sensing in Brazil with Focus on Amazon Biome: Scientometric Analysis and Perspectives for Applications in Agro-Environmental Studies
by Daniela Castagna, Luzinete Scaunichi Barbosa, Charles Campoe Martim, Rhavel Salviano Dias Paulista, Nadja Gomes Machado, Marcelo Sacardi Biudes and Adilson Pacheco de Souza
Hydrology 2024, 11(3), 39; https://doi.org/10.3390/hydrology11030039 - 8 Mar 2024
Viewed by 2181
Abstract
The Amazon biome plays a crucial role in the hydrological cycle, supplying water vapor for the atmosphere and contributing to evapotranspiration (ET) that influences regional humidity across Brazil and South America. Remote sensing (RS) has emerged as a valuable tool for measuring and [...] Read more.
The Amazon biome plays a crucial role in the hydrological cycle, supplying water vapor for the atmosphere and contributing to evapotranspiration (ET) that influences regional humidity across Brazil and South America. Remote sensing (RS) has emerged as a valuable tool for measuring and estimating ET, particularly in the data-scarce Amazon region. A scientometric analysis was conducted to identify the most used RS-based ET product or model in Brazil and its potential application in the Amazon. Scientometrics allows for the quantitative analysis of scientific output; this study identified the most widely used RS product in the Amazon biome. Articles published in Web of Science, Scielo, and Scopus databases up to 2022 were searched using the keywords “Evapotranspiration”, “Remote Sensing”, and “Brazil”. After initial screening, 140 relevant articles were subjected to scientometric analysis using the Bibliometrix library in RStudio 2023.06.1+524. These articles, published between 2001 and 2022, reveal a collaborative research landscape involving 600 authors and co-authors from 245 institutions, with most studies originating from Brazil’s Southeast and North (Amazon) regions. Notably, within the 12 studies focusing on ET by RS in the Amazon biome, applications were diverse, encompassing river basins, climate change, El Niño, and deforestation, with the MOD16 product being the most frequently employed. Full article
(This article belongs to the Special Issue GIS Modelling of Evapotranspiration with Remote Sensing)
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23 pages, 15596 KiB  
Article
Geospatial Insights into Aridity Conditions: MODIS Products and GIS Modeling in Northeast Brazil
by Jhon Lennon Bezerra da Silva, Marcos Vinícius da Silva, Alexandre Maniçoba da Rosa Ferraz Jardim, Pabrício Marcos Oliveira Lopes, Henrique Fonseca Elias de Oliveira, Josef Augusto Oberdan Souza Silva, Márcio Mesquita, Ailton Alves de Carvalho, Alan Cézar Bezerra, José Francisco de Oliveira-Júnior, Maria Beatriz Ferreira, Iara Tamires Rodrigues Cavalcante, Elania Freire da Silva and Geber Barbosa de Albuquerque Moura
Hydrology 2024, 11(3), 32; https://doi.org/10.3390/hydrology11030032 - 26 Feb 2024
Cited by 2 | Viewed by 2356
Abstract
Northeast Brazil (NEB), particularly its semiarid region, represents an area highly susceptible to the impacts of climate change, including severe droughts, and intense anthropogenic activities. These stresses may be accelerating environmental degradation and desertification of soil in NEB. The main aim of this [...] Read more.
Northeast Brazil (NEB), particularly its semiarid region, represents an area highly susceptible to the impacts of climate change, including severe droughts, and intense anthropogenic activities. These stresses may be accelerating environmental degradation and desertification of soil in NEB. The main aim of this study was to gain geospatial insights into the biophysical parameters of surface energy balance and actual evapotranspiration on a multi-temporal scale, aiming to detect and analyze the spectral behavioral patterns of areas vulnerable to degradation processes, based on thematic maps at the surface, for NEB and mainly the semiarid region of NEB from 2000 to 2019. Geospatial data from 8-day MODIS sensor products were used, such as surface reflectance (Terra/MOD09A1 and Aqua/MYD09A1), surface temperature (Terra/MOD11A2 and Aqua/MYD11A2), and actual evapotranspiration (Terra/MOD16A2 and Aqua/MYD16A2), version 6. Therefore, in this study, pixel-to-pixel values were processed by calculating the average pixel statistics for each year. From the reflectance product, digital processing of the surface albedo and spectral vegetation indices was also carried out, using computational programming scripts and machine learning algorithms developed via the Google Earth Engine (GEE) platform. The study also presents a seasonal analysis of these components and their relationships over 20 years. Through vegetation indices and statistical correlations, a new predictive model of actual evapotranspiration was developed. The quantitative and spatiotemporal spectral patterns of the parameters were assessed through descriptive statistics, measures of central tendency and dispersion, and statistical error analyses and correlation indices. Thematic maps highlighted the pixel-to-pixel results, with patterns of high temperature distribution mainly in the central and northeastern part of NEB and the semiarid region of NEB, highlighting the formation of persistent heat islands over time. Meanwhile, in these areas, the maps of actual evapotranspiration showed a drastic reduction due to the lesser availability of energy. Over time, the semiarid region of NEB presented areas with little and/or no vegetation cover, which were highly well-defined between the years 2012 and 2019, confirming that these areas are extremely vulnerable to degradation and desertification processes due to significant loss of vegetative and water resilience. The components of energy balance were highly interconnected to climatological and environmental conditions, showing the severe results of drought and accentuation of the water deficit in NEB, presenting a greater condition of aridity in the semiarid region of NEB over time. Full article
(This article belongs to the Special Issue GIS Modelling of Evapotranspiration with Remote Sensing)
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