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Advances in Crop Evapotranspiration and Soil Water Content
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Advances in Crop Evapotranspiration and Soil Water Content

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Water, Agriculture and Aquaculture".

Deadline for manuscript submissions: closed (20 February 2025) | Viewed by 1341

Special Issue Editors

Dr. Swatantra Kumar Dubey

E-Mail Website
Guest Editor
Department of Environmental Engineering, Seoul National University of Science and Technology, Seoul 01811, Republic of Korea
Interests: crop water requirement; water productivity; crop modeling; climate change; water used efficiency; PET
Special Issues, Collections and Topics in MDPI journals
Dr. Prakash Kumar Jha

E-Mail Website
Guest Editor
Department of Plant and Soil Sciences, Mississippi State University, Starkville, MS, USA
Interests: agriculture; crop water requirement; crop modeling; climate change; water used efficiency; evapotranspiration
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue on advancements in crop evapotranspiration (ET) and soil water content research aims to enhance agricultural productivity and sustainability. It focuses on developing precise measurement and modeling techniques for ET and soil moisture to improve irrigation management and water conservation. This research spans field-level studies to global analyses, utilizing technologies such as remote sensing, machine learning and sensor networks. The primary goal is to optimize water usage, reduce wastage and boost crop yields, ultimately contributing to food security and environmental sustainability. Additionally, these advancements support the creation of decision support systems for farmers, helping them make informed irrigation and water management choices.

In the context of existing literature, this Special Issue addresses critical agricultural challenges such as water scarcity, climate change and the necessity for sustainable practices. It highlights the importance of precise measurement techniques and real-time monitoring to enhance irrigation efficiency. By integrating advanced technologies, the research improves crop resilience against climate-related stresses and boosts productivity. These innovations not only support efficient water use, but also tackle broader issues of food security and environmental sustainability, providing practical solutions to contemporary agricultural problems.

Dr. Swatantra Kumar Dubey
Dr. Prakash Kumar Jha
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. Water 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 2600 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

  • crop evapotranspiration (ET)
  • soil water content
  • irrigation management
  • water conservation
  • sustainable agriculture

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Published Papers (2 papers)

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Research

20 pages, 9678 KiB  
Article
Precipitation Spatio-Temporal Forecasting in China via DC-CNN-BiLSTM
by Peng Shu, Xiaoqi Duan, Chenming Shao, Jie Liu, Youliang Tian and Sheng Li
Water 2025, 17(9), 1381; https://doi.org/10.3390/w17091381 (registering DOI) - 4 May 2025
Abstract
Accurate and reliable precipitation prediction remains a significant challenge due to an incomplete understanding of regional meteorological dynamics and limitations in forecasting routine weather events. To overcome these challenges, we propose a novel model, DC-CNN-BiLSTM, which integrates a dilation causal convolutional neural network [...] Read more.
Accurate and reliable precipitation prediction remains a significant challenge due to an incomplete understanding of regional meteorological dynamics and limitations in forecasting routine weather events. To overcome these challenges, we propose a novel model, DC-CNN-BiLSTM, which integrates a dilation causal convolutional neural network (DC-CNN) with a Bidirectional Long Short-Term Memory (BiLSTM) network. The DC-CNN component, by fusing causal and dilated convolutions, extracts multi-scale spatial features from time series data. In parallel, the BiLSTM module leverages bidirectional memory cells to capture long-term temporal dependencies. This integrated approach effectively links localized meteorological inputs with broader hydrological responses. Experimental evaluation demonstrates that the DC-CNN-BiLSTM model significantly outperforms traditional models. Specifically, the model improves the Root Mean Square Error (RMSE) by 9.05% compared to ConvLSTM and by 32.3% compared to ConvGRU, particularly in forecasting medium- to long-term precipitation. In conclusion, our results validate the benefits of incorporating advanced spatio-temporal feature extraction techniques for precipitation forecasting, ultimately improving disaster preparedness and resource management. Full article
(This article belongs to the Special Issue Advances in Crop Evapotranspiration and Soil Water Content)
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39 pages, 18138 KiB  
Article
Evaluation of Micrometeorological Models for Estimating Crop Evapotranspiration Using a Smart Field Weighing Lysimeter
by Phathutshedzo Eugene Ratshiedana, Mohamed A. M. Abd Elbasit, Elhadi Adam and Johannes George Chirima
Water 2025, 17(2), 187; https://doi.org/10.3390/w17020187 - 11 Jan 2025
Cited by 1 | Viewed by 850
Abstract
Accurate estimation of crop water use, which is expressed as evapotranspiration (ET) is an important task for effective irrigation and agricultural water management. Although direct field measurement of actual evapotranspiration (ETa) is the most reliable method, practical and economic limitations often make it [...] Read more.
Accurate estimation of crop water use, which is expressed as evapotranspiration (ET) is an important task for effective irrigation and agricultural water management. Although direct field measurement of actual evapotranspiration (ETa) is the most reliable method, practical and economic limitations often make it difficult to acquire, especially in developing countries. Consequently, crop evapotranspiration (ETc) is calculated using reference evapotranspiration (ETo) and crop-specific coefficients (Kc) to support irrigation water management practices. Several ETo models have been developed to address varying environmental conditions; however, their transferability to new environments often leads to under or over estimation of ETo, which has an impact on ETc estimation. This study evaluated the accuracy of 30 ETo micrometeorological models to estimate ETc under different seasonal and micro-climatic conditions using ETa data directly measured using a smart field weighing lysimeter as a benchmark. Local Kc values were derived from field-based measurements, while statistical metrics were applied for the evaluation process. A cumulative ranking approach was used to assess the accuracy and consistency of the models across four cropping seasons. Results demonstrated the Penman–Monteith model to be the most consistent model in estimating ETc, which outperformed other models across all cropping seasons. The performance of alternative models differed significantly with seasonal conditions, indicating their susceptibility to seasonality. The findings demonstrated the Penman–Monteith model as the most reliable approach for estimating ETc, which justifies its application role as a benchmark for validating other ETo models in data-limited areas. The study emphasizes the importance of site-specific validation and calibration of ETo models to improve their accuracy, applicability, and reliability in diverse environmental conditions. Full article
(This article belongs to the Special Issue Advances in Crop Evapotranspiration and Soil Water Content)
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