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Advances in the Remote Sensing of Terrestrial Evaporation II
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Advances in the Remote Sensing of Terrestrial Evaporation II

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: closed (30 June 2023) | Viewed by 6466

Special Issue Editors

Dr. Yun Yang

E-Mail Website
Guest Editor
Hydrology and Remote Sensing Laboratory, USDA-ARS, Beltsville, MD 20705, USA
Interests: evapotranspiration; hydrology; remote sensing; water resources
Dr. Martha Anderson

E-Mail Website
Guest Editor
Hydrology and Remote Sensing Laboratory, USDA-ARS, 10300 Baltimore Avenue, Beltsville, MD 20705, USA
Interests: evapotranspiration; hydrology; remote sensing; drought
Dr. Milan Fischer

E-Mail Website
Guest Editor
Global Change Research Institute of the Czech Academy of Sciences, Brno, the Czech Republic
Interests: evapotranspiration; micrometeorology; hydrology; remote sensing

Special Issue Information

Dear Colleagues,

There are ever increasing and competing demands for water resources globally under conditions of changing climate and growing population. To facilitate effective water resource management and better adaptation to future climate conditions, there is a critical need for robust assessment of terrestrial water fluxes, including consumptive water use through evapotranspiration (ET). Advances in remote sensing capabilities, as well as innovative retrieval methods and platforms, are providing new opportunities and challenges.

In this Special Issue, we seek to explore advances in remote sensing technologies, energy and water flux mapping, model platforms, and also new applications of evapotranspiration data.

Submissions relevant to this issue might include efforts related, but not limited to, aspects such as:

  1. multi-scale/multi-sensor ET retrieval or data fusion;
  2. the application of very high spatial resolution data in ET retrieval;
  3. innovative approaches and platforms towards retrieval, evaluation, and assessment, including but not limited to cloud computing, machine learning and artificial intelligence;
  4. new applications of ET data in water resource, watershed, and agricultural management.

We look forward to showcasing your research.

Dr. Yun Yang
Dr. Martha Anderson
Dr. Milan Fischer
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. 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
  • remote sensing
  • cloud computing
  • earth observation
  • hydrology

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (3 papers)

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Research

21 pages, 9782 KiB  
Article
Ecological Water Requirement of Vegetation and Water Stress Assessment in the Middle Reaches of the Keriya River Basin
by Ranran Wang, Abudoukeremujiang Zayit, Xuemin He, Dongyang Han, Guang Yang and Guanghui Lv
Remote Sens. 2023, 15(18), 4638; https://doi.org/10.3390/rs15184638 - 21 Sep 2023
Cited by 4 | Viewed by 1412
Abstract
Desert oases are vital for maintaining the ecological balance in arid regions’ inland river basins. However, fine-grained assessments of water stress in desert oasis ecosystems are limited. In our study, we aimed to evaluate the water stress in desert oasis ecosystems in the [...] Read more.
Desert oases are vital for maintaining the ecological balance in arid regions’ inland river basins. However, fine-grained assessments of water stress in desert oasis ecosystems are limited. In our study, we aimed to evaluate the water stress in desert oasis ecosystems in the middle reaches of the Keriya River Basin, with a specific focus on their ecological functions and optimizing water resource management. We hypothesized that evapotranspiration has significant effects on ecological water consumption. First, we estimated the actual evapotranspiration (ET) and potential evapotranspiration (PET) based on the SEBS (surface energy balance system) model and remote sensing downscaling model. Then, the ecological water requirement (EWR) and ecological water stress (EWS) index were constructed to evaluate the ecological water resource utilization. Finally, we explored the influencing factors and proposed coping strategies. It was found that regions with higher ET values were mainly concentrated along the Keriya River and its adjacent farmland areas, while the lower values were observed in bare land or grassland areas. The total EWR exhibited the sequence of grassland > cropland > forest, while the EWR per unit area followed the opposite order. The grassland’s EWS showed a distinct seasonal response, with severe, moderate, and mild water shortages and water plenitude corresponding to spring, summer, autumn, and winter, respectively. In contrast, the land use types with the lowest EWS were water areas that remained in a state of water plentitude grade (0.08–0.20) throughout the year. Temperature and vegetation index were identified as the primary influencing factors. Overall, this study provides a reliable method for evaluating the EWR and EWS values of basin scale vegetation, which can serve as a scientific basis for formulating water resource management and regulation policies in the region. Full article
(This article belongs to the Special Issue Advances in the Remote Sensing of Terrestrial Evaporation II)
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24 pages, 36195 KiB  
Article
Multi-Temporal Remote Sensing Inversion of Evapotranspiration in the Lower Yangtze River Based on Landsat 8 Remote Sensing Data and Analysis of Driving Factors
by Enze Song, Xueying Zhu, Guangcheng Shao, Longjia Tian, Yuhao Zhou, Ao Jiang and Jia Lu
Remote Sens. 2023, 15(11), 2887; https://doi.org/10.3390/rs15112887 - 1 Jun 2023
Cited by 4 | Viewed by 1710
Abstract
Analysis of the spatial and temporal variation patterns of surface evapotranspiration is important for understanding global climate change, promoting scientific deployment of regional water resources, and improving crop yield and water productivity. Based on Landsat 8 OIL_TIRS data and remote sensing image data [...] Read more.
Analysis of the spatial and temporal variation patterns of surface evapotranspiration is important for understanding global climate change, promoting scientific deployment of regional water resources, and improving crop yield and water productivity. Based on Landsat 8 OIL_TIRS data and remote sensing image data of the lower Yangtze River urban cluster for the same period of 2016–2021, combined with soil and meteorological data of the study area, this paper constructed a multiple linear regression (MLR) model and an extreme learning machine (ELM) inversion model with evapotranspiration as the target and, based on the model inversion, quantitatively and qualitatively analyzed the spatial and temporal variability in surface evapotranspiration in the study area in the past six years. The results show that both models based on feature factors and spectral indices obtained a good inversion accuracy, with the fusion of feature factors effectively improving the inversion ability of the model for ET. The best model for ET in 2016, 2017, and 2021 was MLR, with an R2 greater than 0.8; the best model for ET in 2018–2019 was ELM, with an R2 of 0.83 and 0.62, respectively. The inter-annual ET in the study area showed a “double-peak” dynamic variation, with peaks in 2018 and 2020; the intra-annual ET showed a single-peak cycle, with peaks in July–August. Seasonal differences were obvious, and spatially high-ET areas were mainly found in rural areas north of the Yangtze River and central and western China where agricultural land is concentrated. The net solar radiation, soil heat flux, soil temperature and humidity, and fractional vegetation cover all had significant positive effects on ET, with correlation coefficients ranging from 0.39 to 0.94. This study can provide methodological and scientific support for the quantitative and qualitative estimation of regional ET. Full article
(This article belongs to the Special Issue Advances in the Remote Sensing of Terrestrial Evaporation II)
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Figure 1

20 pages, 6585 KiB  
Article
MODIS Evapotranspiration Downscaling Using a Deep Neural Network Trained Using Landsat 8 Reflectance and Temperature Data
by Xianghong Che, Hankui K. Zhang, Qing Sun, Zutao Ouyang and Jiping Liu
Remote Sens. 2022, 14(22), 5876; https://doi.org/10.3390/rs14225876 - 19 Nov 2022
Cited by 3 | Viewed by 2731
Abstract
The MODIS 8-day composite evapotranspiration (ET) product (MOD16A2) is widely used to study large-scale hydrological cycle and energy budgets. However, the MOD16A2 spatial resolution (500 m) is too coarse for local and regional water resource management in agricultural applications. In this study, we [...] Read more.
The MODIS 8-day composite evapotranspiration (ET) product (MOD16A2) is widely used to study large-scale hydrological cycle and energy budgets. However, the MOD16A2 spatial resolution (500 m) is too coarse for local and regional water resource management in agricultural applications. In this study, we propose a Deep Neural Network (DNN)-based MOD16A2 downscaling approach to generate 30 m ET using Landsat 8 surface reflectance and temperature and AgERA5 meteorological variables. The model was trained at a 500 m resolution using the MOD16A2 ET as reference and applied to the Landsat 8 30 m resolution. The approach was tested on 15 Landsat 8 images over three agricultural study sites in the United States and compared with the classical random forest regression model that has been often used for ET downscaling. All evaluation sample sets applied to the DNN regression model had higher R2 and lower root-mean-square deviations (RMSD) and relative RMSD (rRMSD) (the average values: 0.67, 2.63 mm/8d and 14.25%, respectively) than the random forest model (0.64, 2.76 mm/8d and 14.92%, respectively). Spatial improvement was visually evident both in the DNN and the random forest downscaled 30 m ET maps compared with the 500 m MOD16A2, while the DNN-downscaled ET appeared more consistent with land surface cover variations. Comparison with the in situ ET measurements (AmeriFlux) showed that the DNN-downscaled ET had better accuracy, with R2 of 0.73, RMSD of 5.99 mm/8d and rRMSD of 48.65%, than the MOD16A2 ET (0.65, 7.18 and 50.42%, respectively). Full article
(This article belongs to the Special Issue Advances in the Remote Sensing of Terrestrial Evaporation II)
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