Research on Soil Moisture and Irrigation

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

Deadline for manuscript submissions: closed (30 May 2024) | Viewed by 5751

Special Issue Editors

College of Agricultural Science and Engineering, Hohai University, Nanjing, China
Interests: irrigation scheduling; irrigation control; automatic irrigation; smart irrigation; soil moisture modelling

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Guest Editor
College of Agricultural Science and Engineering, Hohai University, Nanjing, China
Interests: agricultural water resource allocation; irrigation scheduling; irrigation control; simulation-optimization of irrigation water allocation; irrigation water quality

Special Issue Information

Dear Colleagues,

Irrigation scheduling, especially for real-time applications, is vital for crop growth in districts with insufficient precipitation. Prompt crop water deficit recognition and water supply control are guarantees for higher crop yield and better quality. Involved in various methods for irrigation scheduling or crop water management, soil moisture is the key factor in view of its bridge role in precisely indicating crop water status and controlling irrigation water supply. Therefore, a discussion on soil moisture sensing or prediction, and how it is acted on reflecting crop water status and further for irrigation management is raised in this Special Issue. Topics include, but are not limited to:

(1) Methods, instruments and systems for soil moisture sensing or water status detection in farmland;

(2) Satellite and UAV remote sensing methods for soil moisture monitoring and generation of irrigation prescription chart;

(3) Data fusion methods for temporal-spatial resolution improvement of soil moisture or water status;

(4) Modelling soil moisture variations for irrigations;

(5) Combined application of crop model and machine learning model for irrigation scheduling based on soil moisture;

(6) Irrigation control methods focused on soil water status;

(7) R&D of efficient irrigation systems based on soil moisture;

(8) Effects of soil structures on soil infiltration and soil water status.

Both original research and review papers are welcome.

Dr. Zhe Gu
Prof. Dr. Jiang Li
Guest Editors

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Keywords

  • soil moisture sensing
  • soil moisture prediction
  • irrigation scheduling modelling
  • temporal–spatial resolution
  • irrigation control
  • irrigation system

Published Papers (6 papers)

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Research

21 pages, 4154 KiB  
Article
Using the AIEM and Radarsat-2 SAR to Retrieve Bare Surface Soil Moisture
by Chengshen Yin, Quanming Liu and Yin Zhang
Water 2024, 16(11), 1617; https://doi.org/10.3390/w16111617 - 5 Jun 2024
Viewed by 345
Abstract
Taking the Jiefangzha irrigation area of the Inner Mongolia Autonomous Region as the research area, the response relationships between the backscattering coefficient and radar frequency, radar incidence angle, root-mean-square height, correlation length, and soil water content under different conditions were simulated using advanced [...] Read more.
Taking the Jiefangzha irrigation area of the Inner Mongolia Autonomous Region as the research area, the response relationships between the backscattering coefficient and radar frequency, radar incidence angle, root-mean-square height, correlation length, and soil water content under different conditions were simulated using advanced integral equations. The backscattering characteristics of exposed surfaces in cold and dry irrigation areas were discussed, and the reasons for the different effects were analyzed. Based on this, surface roughness models and statistical regression moisture inversion models were constructed through co-polarized backscatter coefficients and combined surface roughness. The correlation between the inverted surface roughness values and the measured values was R2 = 0.7569. The correlation between the soil moisture simulation values and the measured values was R2 = 0.8501, with an RMSE of 0.04. The findings showed a strong correlation between the values from the regression simulation and the measured data, indicating that the model can be applied to soil moisture inversion and has a good inversion accuracy. Compared with previous studies in the same area, the inversion model proposed in this paper has a higher accuracy and is more suitable for the inversion of soil moisture in the Jiefangzha irrigation area. These findings can support research on the water cycle and water environment assessment in the region. Full article
(This article belongs to the Special Issue Research on Soil Moisture and Irrigation)
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23 pages, 7576 KiB  
Article
Advancing Digital Image-Based Recognition of Soil Water Content: A Case Study in Bailu Highland, Shaanxi Province, China
by Yaozhong Zhang, Han Zhang, Hengxing Lan, Yunchuang Li, Honggang Liu, Dexin Sun, Erhao Wang and Zhonghong Dong
Water 2024, 16(8), 1133; https://doi.org/10.3390/w16081133 - 16 Apr 2024
Viewed by 561
Abstract
Soil water content (SWC) plays a vital role in agricultural management, geotechnical engineering, hydrological modeling, and climate research. Image-based SWC recognition methods show great potential compared to traditional methods. However, their accuracy and efficiency limitations hinder wide application due to their status as [...] Read more.
Soil water content (SWC) plays a vital role in agricultural management, geotechnical engineering, hydrological modeling, and climate research. Image-based SWC recognition methods show great potential compared to traditional methods. However, their accuracy and efficiency limitations hinder wide application due to their status as a nascent approach. To address this, we design the LG-SWC-R3 model based on an attention mechanism to leverage its powerful learning capabilities. To enhance efficiency, we propose a simple yet effective encoder–decoder architecture (PVP-Transformer-ED) designed on the principle of eliminating redundant spatial information from images. This architecture involves masking a high proportion of soil images and predicting the original image from the unmasked area to aid the PVP-Transformer-ED in understanding the spatial information correlation of the soil image. Subsequently, we fine-tune the SWC recognition model on the pre-trained encoder of the PVP-Transformer-ED. Extensive experimental results demonstrate the excellent performance of our designed model (R2 = 0.950, RMSE = 1.351%, MAPE = 0.081, MAE = 1.369%), surpassing traditional models. Although this method involves processing only a small fraction of original image pixels (approximately 25%), which may impact model performance, it significantly reduces training time while maintaining model error within an acceptable range. Our study provides valuable references and insights for the popularization and application of image-based SWC recognition methods. Full article
(This article belongs to the Special Issue Research on Soil Moisture and Irrigation)
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24 pages, 6959 KiB  
Article
Soil-Matric-Potential-Based Irrigation Scheduling to Increase Yield and Water Productivity of Okra
by Arunadevi K., Singh M., Khanna M., Mishra A. K., Prajapati V. K., Denny F., Ramachandran J. and Maruthi Sankar G. R.
Water 2023, 15(24), 4300; https://doi.org/10.3390/w15244300 - 18 Dec 2023
Viewed by 1259
Abstract
A field experiment was conducted on okra (Abelmoschus esculentus L.) for assessing the sustainability of yield with optimum irrigation schedule based on soil moisture depletion. Four irrigation treatments: Irrigation at I1:20%, I2:30%, I3:40% and I4 [...] Read more.
A field experiment was conducted on okra (Abelmoschus esculentus L.) for assessing the sustainability of yield with optimum irrigation schedule based on soil moisture depletion. Four irrigation treatments: Irrigation at I1:20%, I2:30%, I3:40% and I4:50% of soil moisture depletion rate in main plots and three fertilizer treatments: Fertigation at F1:100%, F2:80% and F3:60% of recommended NPK (100:25:40 kg/ha) in subplots were tested. Soil matric potential was recorded continuously using electronic tensiometers. The soil moisture characteristics curve was derived for various soil matric potential value sand the soil water content. The irrigation controller triggered solenoid valves for irrigation when soil moisture depletion reached a prespecified level in each treatment. Soil moisture depletion values were significantly predicted based on a regression model calibrated for each treatment over the crop growing period. The model gave minimum prediction error (PE) for I1, followed by I2, I3 and I4, respectively. Plant growth and yield parameters were significantly influenced by the soil moisture availability under each treatment. It is recommended that irrigation be scheduled at 20% soil moisture depletion rate together with 100% NPK fertilizer application for attaining sustainable yield of okra (12.3 t/ha), apart from maximum WUE (3.5 kg/m3) and plant growth parameters under semiarid inceptisols. Full article
(This article belongs to the Special Issue Research on Soil Moisture and Irrigation)
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16 pages, 8146 KiB  
Article
Regionalization of Root Zone Moisture Estimations from Downscaled Surface Moisture and Environmental Data with the Soil Moisture Analytical Relationship Model
by Yonghao Liu, Taohui Li, Wenxiang Zhang and Aifeng Lv
Water 2023, 15(23), 4133; https://doi.org/10.3390/w15234133 - 29 Nov 2023
Viewed by 919
Abstract
Root-zone soil moisture (RZSM) plays a key role in the hydrologic cycle and regulates water–heat exchange. Although site observations can provide soil profile moisture measurements, they have a restricted representation. Satellites can determine soil moisture on a large scale, yet the depth of [...] Read more.
Root-zone soil moisture (RZSM) plays a key role in the hydrologic cycle and regulates water–heat exchange. Although site observations can provide soil profile moisture measurements, they have a restricted representation. Satellites can determine soil moisture on a large scale, yet the depth of detection is limited. RZSM can be estimated on a large scale using the soil moisture analytical relationship (SMAR) and surface soil moisture (SSM). However, the applicability of the SMAR to different deep-root zones and covariate sources is unclear. This paper investigates the applicability of the SMAR in the Shandian River Basin, upstream of the Luan River in China, by combining site and regional soil moisture, soil properties, and meteorological data. In particular, we first compared the estimation results of the SMAR at different depths (10–20 cm; 10–50 cm) and using covariates from different sources (dataset, SMAR-P1; literature, SMAR-P2) at the site in order to generate SMAR calibration parameters. The parameters were then regionalized based on multiple linear regression by combining the SMAR-P1, SMAR-P2, and SMAR calibration parameters in the 10–50 cm root zone. Finally, the Shandian River RZSM was estimated using regional surface soil moisture and the aforementioned regionalized parameters. At the site scale, diffusion coefficient b obtained in the 10–20 cm root zone at the same depth as the surface layer exceeded the upper limit of the SMAR by one. This is not fit an environment within the site context, and thus the SMAR is not applicable at this particular depth. The opposite is observed for the 10–50 cm root zone. In addition, SMAR-P1 (RMSE = 0.02) outperformed SMAR-P2 (RMSE = 0.04) in the estimation of the RZSM at 10–50 cm. Parameter regionalization analysis revealed the failure of SMAR-P2 to pass the significance test (p > 0.05) for building a multivariate linear model, while SMAR-P1 successfully passed the significance test (p < 0.05) and finished the parameter regionalization process. The median RMSE and median R2adj of the regional RZSM results were determined as 0.12 and 0.3, respectively. The regional RZSM agrees with the spatial trend of the Shandian River. This study examines the suitability of the SMAR model in varying deep-root zones and with diverse covariate sources. The results provide a crucial basis for future utilization of the SMAR. Full article
(This article belongs to the Special Issue Research on Soil Moisture and Irrigation)
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13 pages, 5892 KiB  
Article
Prediction of the Soil–Water Retention Curve of Loess Using the Pore Data from the Mercury Intrusion Technique
by Xiaokun Hou
Water 2023, 15(18), 3273; https://doi.org/10.3390/w15183273 - 15 Sep 2023
Cited by 3 | Viewed by 974
Abstract
The soil–water characteristic curve (SWCC) is a crucial input parameter for describing the distribution of soil moisture and water movement in various environmental and geotechnical challenges. It is widely recognized that the SWCC is controlled by the soil’s pore structure. Attempts to estimate [...] Read more.
The soil–water characteristic curve (SWCC) is a crucial input parameter for describing the distribution of soil moisture and water movement in various environmental and geotechnical challenges. It is widely recognized that the SWCC is controlled by the soil’s pore structure. Attempts to estimate the SWCC using pore data obtained through the mercury intrusion porosity (MIP) technique have been conducted. However, the performance of MIP estimation remains uncertain and requires further validation with experimental data. In this study, the accuracy of MIP estimation is validated using intact and compacted loess samples prepared at different water contents, specifically dry of optimum (8%), optimum (17%), and wet of optimum (19%). The results reveal that intact and dry of optimum specimens exhibit relatively good pore connectivity, with more point-to-point contacts between particles. Conversely, specimens compacted under optimum and wet of optimum conditions exhibit poor pore connectivity, with more isolated pores, particularly in the wet-of-optimum specimen. The SWCC predictions based on MIP data are accurate for intact and dry-of-optimum compacted specimens, but significant errors occur for the optimum and wet-of-optimum specimens. Prediction accuracy using MIP data is closely linked to the soil’s pore connectivity. Despite a tenuous theoretical basis in the high suction range where adsorption forces dominate, a strong consistency between predictions and measured data across a wide suction range (e.g., 10–104 kPa) instills a high level of confidence in using MIP data to predict the wetting SWCC. The contact angle required for the prediction is suggested as a fitting parameter. Full article
(This article belongs to the Special Issue Research on Soil Moisture and Irrigation)
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15 pages, 3481 KiB  
Article
Safe Groundwater Level Estimation in Pingtung Plain, Taiwan
by Yi-Lung Yeh, Ching-Yi Wu, Zhi-Mou Chen and Teng-Pao Chiu
Water 2023, 15(16), 2947; https://doi.org/10.3390/w15162947 - 15 Aug 2023
Viewed by 1091
Abstract
The groundwater resources in the Pingtung Plain are crucial water sources in southern Taiwan. However, they have been significantly impacted by climate change, resulting in changes in groundwater quality and quantity in the region. To effectively manage groundwater extraction, this study utilized runs [...] Read more.
The groundwater resources in the Pingtung Plain are crucial water sources in southern Taiwan. However, they have been significantly impacted by climate change, resulting in changes in groundwater quality and quantity in the region. To effectively manage groundwater extraction, this study utilized runs theory to analyze the safe groundwater levels at six groundwater level observation stations located in the proximal fan, mid fan, and distal fan areas of the Pingtung Plain. The methodology involved dividing the range between the maximum and minimum groundwater levels at each station into 20 equal intervals. The groundwater levels were then sorted in ascending order, and the cumulative frequency percentiles of groundwater levels in each interval were calculated to determine the truncation levels for runs theory. Subsequently, the groundwater over-extraction duration and severity were computed. By comparing the results with the groundwater management levels set by the Water Resources Agency of the Ministry of Economic Affairs, it was found that the safe groundwater levels in the proximal fan and distal fan areas were the average of observation data plus 0.5 times the standard deviation. The over-withdrawn duration for these areas was approximately 8 to 10 months and 8 months, respectively. In the mid fan area, the safe groundwater level was based on the average of observation data, and the over-withdrawn duration ranged from 6 to 9 months. Full article
(This article belongs to the Special Issue Research on Soil Moisture and Irrigation)
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