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Evapotranspiration Model Based on Remote Sensing and Ground Station Observation Data and Its Application in Agriculture

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: 14 September 2024 | Viewed by 3843

Special Issue Editors

Dr. Liang Sun

E-Mail Website
Guest Editor
Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
Interests: multi-source remote sensing data fusion algorithm and application; crop classification and yield estimation; surface evapotranspiration and crop drought monitoring
Special Issues, Collections and Topics in MDPI journals
Dr. Yun Yang

E-Mail Website
Guest Editor
Department of Forestry, College of Forest Resources, Forest and Wildlife Research Center, Mississippi State University, Starkville, MS 39759, USA
Interests: impacts of climate change and human activities on the interaction between surface water and groundwater; field-scale evapotranspiration mapping using remotely sensed data with cloud computing; multi-sensor data fusion for improved spatiotemporal sampling; vegetation health monitoring for agriculture and natural resource management
Special Issues, Collections and Topics in MDPI journals
Dr. Sibo Duan

E-Mail Website
Guest Editor
Key Laboratory of Agricultural Remote Sensing, Ministry of Agriculture and Rural Affairs, Institute of Agricultural Resources and Regional Planning, Academy of Agricultural Sciences, Beijing 100081, China
Interests: thermal infrared remote sensing; land surface temperature; land surface emissivity; radiative transfer modeling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Evapotranspiration (ET) is a critical component of the water cycle and plays a vital role in water resource management and crop growth in agricultural ecosystems. Remote sensing and ground station observation data have proven to be valuable tools for estimating ET, which can provide valuable insights into crop water use and help optimize irrigation management in agricultural production. However, due to the complexity of factors influencing the process, including soil properties, weather conditions, vegetation growth, and irrigation practices, there are still challenges in accurately modeling ET.

By gathering innovative research articles on ET modeling methods that integrate remote sensing and ground station data for agricultural applications, this Special Issue aims to advance the understanding of the complex factors influencing the ET process and provide valuable insights into crop water use and irrigation management in agricultural production.

This Special Issue seeks to gather research articles on innovative ET modeling methods that integrate remote sensing and ground station data for agricultural applications. The contributions may include (but are not limited to) the following topics:

  • New methods and algorithms for estimating ET using remote sensing data;
  • Advances in ground-based ET measurement techniques and data assimilation;
  • Applications of ET modeling in precision irrigation management, drought monitoring, and water resource management;
  • Evaluation of the accuracy and uncertainty of ET models and data products;
  • Use of ET modeling for predicting crop yield and growth under different environmental and management conditions.

Dr. Liang Sun
Dr. Yun Yang
Dr. Sibo Duan
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 (ET)
  • water resource management
  • environmental monitoring
  • crop growth
  • irrigation management
  • drought monitoring
  • precision agriculture
  • crop yield prediction

Published Papers (3 papers)

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Research

16 pages, 5413 KiB  
Article
Evaluation and Drivers of Four Evapotranspiration Products in the Yellow River Basin
by Lei Jin, Shaodan Chen, Haibo Yang and Chengcai Zhang
Remote Sens. 2024, 16(11), 1829; https://doi.org/10.3390/rs16111829 - 21 May 2024
Viewed by 336
Abstract
Evapotranspiration is a key driver of water and energy exchanges between terrestrial surfaces and the atmosphere, significantly influencing ecosystem balances. This study focuses on the Yellow River Basin (YRB), where evapotranspiration impacts both ecological dynamics and human activities. By analyzing actual evapotranspiration data [...] Read more.
Evapotranspiration is a key driver of water and energy exchanges between terrestrial surfaces and the atmosphere, significantly influencing ecosystem balances. This study focuses on the Yellow River Basin (YRB), where evapotranspiration impacts both ecological dynamics and human activities. By analyzing actual evapotranspiration data from 1982 to 2017, this research provides insights into its spatial and temporal patterns within the YRB. Furthermore, a comprehensive assessment and comparative analysis were performed on four distinct evapotranspiration product datasets: GLDAS-Noah, ERA5-Land, GLEAM v3.8a, and MOD16A2. Employing the Geodetector model, the research identified seven key influencing factors—the digital elevation model (DEM), slope, aspect, precipitation, temperature, soil moisture, and normalized difference vegetation index (NDVI)—and analyzed their impact on evapotranspiration variations, yielding the following insights: (1) Based on the monthly-scale actual evapotranspiration dataset from 1982 to 2017, the annual average evapotranspiration in the YRB fluctuated between 375 and 473 mm, with an average value of 425 mm. A declining trend in the region’s overall evapotranspiration was discerned using the Theil–Sen median slope estimator and Mann–Kendall trend test. (2) The datasets from GLDAS-Noah, ERA5-Land, and GLEAM exhibited the highest correlation with the observed datasets, all exceeding a correlation coefficient of 0.96. In contrast, the MOD16A2 dataset showed the least favorable performance. The ERA5-Land dataset was particularly noteworthy for its close alignment with observational benchmarks, as evidenced by the lowest recorded root mean square error (RMSE) of 5.09 mm, indicative of its outstanding precision. (3) Employing the Geodetector model, a thorough analysis was conducted of the interactions between evapotranspiration and seven critical determinants. The findings revealed that precipitation and the NDVI were the most significant factors influencing evapotranspiration, with q-values of 0.59 and 0.42 in 2010, and 0.71 and 0.59 in 2015, respectively. These results underscore their pivotal role as the main drivers of evapotranspiration variability within the YRB. Conversely, the q-values for slope in 2010 and 2015 were only 0.01 and nearly zero, respectively, indicating their minimal impact on the dynamics of evapotranspiration in the YRB. Full article
(This article belongs to the Special Issue Evapotranspiration Model Based on Remote Sensing and Ground Station Observation Data and Its Application in Agriculture)
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20 pages, 10265 KiB  
Article
A Comparison of Different Machine Learning Methods to Reconstruct Daily Evapotranspiration Time Series Estimated by Thermal–Infrared Remote Sensing
by Gengle Zhao, Lisheng Song, Long Zhao and Sinuo Tao
Remote Sens. 2024, 16(3), 509; https://doi.org/10.3390/rs16030509 - 29 Jan 2024
Viewed by 924
Abstract
Remote sensing-based models usually have difficulty in generating spatio-temporally continuous terrestrial evapotranspiration (ET) due to cloud cover and model failures. To overcome this problem, machine learning methods have been widely used to reconstruct ET. Therefore, studies comparing and evaluating the accuracy and effectiveness [...] Read more.
Remote sensing-based models usually have difficulty in generating spatio-temporally continuous terrestrial evapotranspiration (ET) due to cloud cover and model failures. To overcome this problem, machine learning methods have been widely used to reconstruct ET. Therefore, studies comparing and evaluating the accuracy and effectiveness of reconstruction among different machine learning methods at the basin scale are necessary. In this study, four popular machine learning methods, including deep forest (DF), deep neural network (DNN), random forest (RF) and extreme gradient boosting (XGB), were used to reconstruct the ET product, addressing gaps resulting from cloud cover and model failure. The ET reconstructed by the four methods was evaluated and compared for Heihe River Basin. The results showed that the four methods performed well for Heihe River Basin, but the RF method was particularly robust. It not only performed well compared with ground measurements (R = 0.73) but also demonstrated the ability to fully reconstruct gaps generated by the TSEB model across the entire basin. Validation based on ground measurements showed that the DNN and XGB models performed well (R > 0.70). However, some gaps still existed in the desert after reconstruction using the DNN and XGB models, especially for the XGB model. The DF model filled these gaps throughout the basin, but this model had lower consistency compared with ground measurements (R = 0.66) and yielded many low values. The results of this study suggest that machine learning methods have considerable potential in the reconstruction of ET at the basin scale. Full article
(This article belongs to the Special Issue Evapotranspiration Model Based on Remote Sensing and Ground Station Observation Data and Its Application in Agriculture)
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41 pages, 10735 KiB  
Article
A Thorough Evaluation of 127 Potential Evapotranspiration Models in Two Mediterranean Urban Green Sites
by Nikolaos Proutsos, Dimitris Tigkas, Irida Tsevreni, Stavros G. Alexandris, Alexandra D. Solomou, Athanassios Bourletsikas, Stefanos Stefanidis and Samuel Chukwujindu Nwokolo
Remote Sens. 2023, 15(14), 3680; https://doi.org/10.3390/rs15143680 - 23 Jul 2023
Cited by 4 | Viewed by 1982
Abstract
Potential evapotranspiration (PET) is a particularly important parameter for understanding water interactions and balance in ecosystems, while it is also crucial for assessing vegetation water requirements. The accurate estimation of PET is typically data demanding, while specific climatic, geographical and local factors may [...] Read more.
Potential evapotranspiration (PET) is a particularly important parameter for understanding water interactions and balance in ecosystems, while it is also crucial for assessing vegetation water requirements. The accurate estimation of PET is typically data demanding, while specific climatic, geographical and local factors may further complicate this task. Especially in city environments, where built-up structures may highly influence the micrometeorological conditions and urban green sites may occupy limited spaces, the selection of proper PET estimation approaches is critical, considering also data availability issues. In this study, a wide variety of empirical PET methods were evaluated against the FAO56 Penman–Monteith benchmark method in the environment of two Mediterranean urban green sites in Greece, aiming to investigate their accuracy and suitability under specific local conditions. The methods under evaluation cover all the range of empirical PET estimations: namely, mass transfer-based, temperature-based, radiation-based, and combination approaches, including 112 methods. Furthermore, 15 locally calibrated and adjusted models have been developed based on the general forms of the mass transfer, temperature, and radiation equations, improving the performance of the original models for local application. Among the 127 (112 original and 15 adjusted) evaluated methods, the radiation-based methods and adjusted models performed overall better than the temperature-based and the mass transfer methods, whereas the data-demanding combination methods received the highest ranking scores. The adjusted models seem to give accurate PET estimates for local use, while they might be applied in sites with similar conditions after proper validation. Full article
(This article belongs to the Special Issue Evapotranspiration Model Based on Remote Sensing and Ground Station Observation Data and Its Application in Agriculture)
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