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Crop Evapotranspiration: Accurate Ground and Remote Sensing Observations and Computational Tools

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A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "New Sensors, New Technologies and Machine Learning in Water Sciences".

Deadline for manuscript submissions: closed (31 January 2023) | Viewed by 17741

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Prof. Dr. Luis Santos Pereira

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Research Center on Landscape, Environment, Agriculture and Food (LEAF), School of Agriculture (ISA), University of Lisbon, 1649-004 Lisbon, Portugal
Interests: reference and crop evapotranspiration; crop water and irrigation requirements; irrigation management; coping with water scarcity; droughts characterization and management; performance of irrigation methods
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Dr. Paula Paredes

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Research Center on Landscape, Environment, Agriculture and Food (LEAF), School of Agriculture (ISA), University of Lisbon, Lisbon, Portugal
Interests: crop and reference evapotranspiration; crop water requirements; irrigation management; modelling; water–yield relations; coping with water scarcity
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Evapotranspiration (ET) plays a main role in the water balance at various scales, from the plant and the field to the watershed or the irrigation system. ET allows understanding the hydrological behavior of natural and cropped ecosystems and adopting improved water resource planning and management. With the increasing competition for water and decreasing water resource availability, the need to cope with water scarcity and climate change increases the importance of accurate knowledge and estimation of ET from annual and perennial crops and landscapes. Improved use of ET information allows enhancing water use in agriculture at various scales. Therefore, there is a great need for an accurate scrutiny of the ET measurements, of weather data, and of data processing procedures.

This Special Issue encourages the submission of review and application research articles which contribute to accurate measurement and estimation of crop evapotranspiration in a broad sense (plants, trees, natural, and human-made landscapes) aiming at improved crop and irrigation management. Potential topics include emerging fields such as the use of Internet of Things (IoT), big data, cloud computing, Artificial intelligence (AI), and advanced sensing technologies oriented to crop and irrigation management in agriculture.

Prof. Dr. Luis Santos Pereira
Dr. Paula Paredes
Guest Editors

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Keywords

crop evapotranspiration (ET_c)
accurate ET measuring and estimation
proximal and remote sensing
thermal sensing
plant and soil sensors
data processing, big data, cloud computing, artificial intelligence
IoT and multiuser application solutions
simulation models, irrigation requirements, and precise irrigation
assessing advection impacts
using saline and treated wastewater

Published Papers (8 papers)

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Research

24 pages, 5114 KiB

Open AccessArticle

Adaptation of a Scientific Decision Support System to the Productive Sector—A Case Study: MOPECO Irrigation Scheduling Model for Annual Crops

by Alfonso Domínguez, José Antonio Martínez-López, Hacib Amami, Radhouan Nsiri, Fadi Karam and Maroua Oueslati

Water 2023, 15(9), 1691; https://doi.org/10.3390/w15091691 - 26 Apr 2023

Cited by 2 | Viewed by 1471

Abstract

Despite the great number of models developed in research projects, only a small percentage have been successfully transferred to the productive sector. The PRIMA programme supported by Horizon 2020, the European Union Framework Programme for Research and Innovation, aims to reverse this situation. The SUPROMED project funded by PRIMA sought to develop an online platform composed of several models adapted to the requirements of end users for increasing the economic and environmental sustainability of Mediterranean agricultural systems. MOPECO, in its research version, was designed to maximize the profitability of irrigated farms in water-scarce regions. A simplified version of this model (MOPECO irrigation scheduling) was included in the SUPROMED platform for improving irrigation efficiency, providing farmers with a useful irrigation scheduling software. This paper shows the approach to adapt and transfer MOPECO to the productive sector. The tool was validated in three different demosite areas across the Mediterranean, involving local stakeholders in the design, validation, and dissemination of the software. The simplified tool reached similar or higher yields than farmers using less water. Thus, the average water saved was around 16%, while the average yield increased around 10% in the plots located in the three demosites of the project (Eastern Mancha in Spain, Bekaa valley in Lebanon, and Sidi Bouzid in Tunisia). This fact decreased the water footprint and increased the profitability of farms. The high applicability of the tool has generated interest among many technicians, farmers, and advisory enterprises. Furthermore, regional and national governmental extension services have shown interest in spreading the use of the tool across their territories, validating the methodology used for adapting and transferring a scientific model to the productive sector. Full article

(This article belongs to the Special Issue Crop Evapotranspiration: Accurate Ground and Remote Sensing Observations and Computational Tools)

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16 pages, 4546 KiB

Open AccessArticle

Improvement of Hargreaves–Samani Reference Evapotranspiration Estimates in the Peruvian Altiplano

by Apolinario Lujano, Miguel Sanchez-Delgado and Efrain Lujano

Water 2023, 15(7), 1410; https://doi.org/10.3390/w15071410 - 05 Apr 2023

Viewed by 2844

Abstract

The FAO 56 Penman–Monteith equation (PM) is considered the most accurate method for estimating reference evapotranspiration (ETo). However, PM requires a large amount of data that is not always available. Thus, the objective of this study is to improve the Hargreaves–Samani (HS) reference evapotranspiration estimates in the Peruvian Altiplano (PA) by calibrating the radiation coefficient K_RS. The results show modified HS (HSM) ETo estimates at validation after K_RS calibration, revealing evident improvements in accuracy with Nash–Sutcliffe efficiency (NSE) between 0.58 and 0.93, percentage bias (PBIAS) between −0.58 and 1.34%, mean absolute error (MAE) between −0.02 and 0.05 mm/d, and root mean square error (RMSE) between 0.14 and 0.25 mm/d. Consequently, the multiple linear regression (MLR) model was used to regionalize the K_RS for the PA. It is concluded that, in the absence of meteorological data, the HSM equation can be used with the new values of K_RS instead of HS for the PA. Full article

(This article belongs to the Special Issue Crop Evapotranspiration: Accurate Ground and Remote Sensing Observations and Computational Tools)

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22 pages, 15568 KiB

Open AccessArticle

Estimation of the Evapotranspiration of Irrigated Açaí (Euterpe oleracea M.), through the Surface Energy Balance Algorithm for Land—SEBAL, in Eastern Amazonia

by Paulo Jorge de Oliveira Ponte de Souza, Ewelyn Regina Rocha Silva, Bernardo Barbosa da Silva, Thomás Rocha Ferreira, Denis de Pinho Sousa, Denilson Barreto da Luz, Marcos Adami, Adriano Marlison Leão de Sousa, Hildo Giuseppe Garcia Caldas Nunes, Gabriel Siqueira Tavares Fernandes, João Vitor de Nóvoa Pinto, Vivian Dielly da Silva Farias, Israel Alves de Oliveira, Sandra Andrea Santos da Silva, José Farias Costa, Matheus Lima Rua, Deborah Luciany Pires Costa, Vandeilson Belfort Moura, Marcus José Alves de Lima, Jannaylton Everton Oliveira Santos, Antonio José da Silva Sousa and Samuel Ortega-Farias Show full author list Hide full author list

Water 2023, 15(6), 1073; https://doi.org/10.3390/w15061073 - 10 Mar 2023

Cited by 1 | Viewed by 1804

Abstract

The culture of açaí (Euterpe oleraceae M.), originating from floodplain areas, was planted on dry land in many properties in Pará, Brazil, making necessary the use of irrigation. To irrigate adequately with less waste, it is necessary that studies aim at increasing efficiency in the use of water in this sector, and one of the ways to do so is to estimate evapotranspiration (ET). The objective of this study was to estimate the actual daily evapotranspiration using the Surface Energy Balance Algorithm for Land (SEBAL) in eastern Amazonia. Six images from the Landsat 8 satellite were used, and the estimates of evapotranspiration with the SEBAL algorithm showed good agreement with the results obtained by the Bowen ratio method in the area of açaí planting, including the mean absolute error (MAE), mean relative error (MRE), root of mean square error (RMSE), and the concordance index (d index) equal to 0.45 mm day⁻¹, 4.23%, 0.52 mm day⁻¹, and 0.80, respectively. SEBAL showed the ability to distinguish the soil cover, demonstrating the sensitivity of the model, which provided the mapping of the components analyzed. The use of the algorithm helps in decision making regarding irrigation management and reducing costs and water losses. Full article

(This article belongs to the Special Issue Crop Evapotranspiration: Accurate Ground and Remote Sensing Observations and Computational Tools)

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20 pages, 2584 KiB

Open AccessArticle

CSM-CERES-Wheat Sensitivity to Evapotranspiration Modeling Frameworks under a Range of Wind Speeds

by Milad Nouri, Gerrit Hoogenboom, Mohammad Bannayan and Mehdi Homaee

Water 2022, 14(19), 3023; https://doi.org/10.3390/w14193023 - 26 Sep 2022

Cited by 2 | Viewed by 1449

Abstract

Crop modeling uncertainty is expected to be high under weather data limitations; thus, jeopardizing decision-making on food-water security. Missing near-surface wind speed (u₂) data required to accurately estimate reference evapotranspiration (ET_o) seemed to significantly affect both the potential evapotranspiration (ET_P) and yield simulations for data-scarce windy regions. In this study, the uncertainty in crop modeling based on different ET_P approaches was assessed. In this regard, wheat yield and evapotranspiration were simulated with the CSM-CERES-Wheat model using either the Priestley-Taylor/Ritchie (PT) or the Penman-Monteith DSSAT (PM) methods under “rain-fed, low-nitrogen stress”, “rain-fed, high nitrogen stress”, “full irrigation, low nitrogen stress”, and “full irrigation, high nitrogen stress” scenarios for a u₂ range from 0.8 to 3.5 m s⁻¹. The daily weather data required to run the model were retrieved from 18 semi-arid areas located in western Iran. The statistically significant differences in mean yield and cumulative distribution were determined by the non-parametric Wilcoxon signed-rank and the Kolmogorov-Smirnov tests, respectively. The deviation in evaporation and transpiration simulated by applying PT and PM was lower under rain-fed condition. Under “rain-fed, low-nitrogen stress”, the PT-simulated yield deviated significantly (p < 0.05) from PM-simulated yield by more than 26% for the sites with u₂ above 3 m s⁻¹. The deviation in ET_P estimates did not, however, lead to statistically significant difference in yield distribution curves for almost all sites and scenarios. Nitrogen deficiency resulted in a smaller difference in yield for rain-fed condition. The yield results showed a deviation below 6% under full irrigation condition. Under windy rain-fed condition, high deviation in leaf area index (LAI) and ET_P estimates caused a large difference in the actual transpiration to potential transpiration ratio (T_a/T_P), and yield. However, the deviation between PT- and PM-simulated LAI and T_a/T_P for the full irrigation scenarios was less than 6%. Overall, the results from this study indicate that when soil moisture is depleted, resembling rain-fed condition, simulation of yield appears to be highly sensitive to the estimation of ET_P for windy areas. Full article

(This article belongs to the Special Issue Crop Evapotranspiration: Accurate Ground and Remote Sensing Observations and Computational Tools)

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28 pages, 7028 KiB

Open AccessFeature PaperArticle

Assessing Spatio-Temporal Dynamics of Deep Percolation Using Crop Evapotranspiration Derived from Earth Observations through Google Earth Engine

by Antónia Ferreira, João Rolim, Paula Paredes and Maria do Rosário Cameira

Water 2022, 14(15), 2324; https://doi.org/10.3390/w14152324 - 27 Jul 2022

Cited by 3 | Viewed by 2092

Abstract

Excess irrigation may result in deep percolation and nitrate transport to groundwater. Furthermore, under Mediterranean climate conditions, heavy winter rains often result in high deep percolation, requiring the separate identification of the two sources of deep percolated water. An integrated methodology was developed to estimate the spatio-temporal dynamics of deep percolation, with the actual crop evapotranspiration (ET_{c act}) being derived from satellite images data and processed on the Google Earth Engine (GEE) platform. GEE allowed to extract time series of vegetation indices derived from Sentinel-2 enabling to define the actual crop coefficient (K_{c act}) curves based on the observed lengths of crop growth stages. The crop growth stage lengths were then used to feed the soil water balance model ISAREG, and the standard K_c values were derived from the literature; thus, allowing the estimation of irrigation water requirements and deep drainage for independent Homogeneous Units of Analysis (HUA) at the Irrigation Scheme. The HUA are defined according to crop, soil type, and irrigation system. The ISAREG model was previously validated for diverse crops at plot level showing a good accuracy using soil water measurements and farmers’ irrigation calendars. Results show that during the crop season, irrigation caused 11 ± 3% of the total deep percolation. When the hotspots associated with the irrigation events corresponded to soils with low suitability for irrigation, the cultivated crop had no influence. However, maize and spring vegetables stood out when the hotspots corresponded to soils with high suitability for irrigation. On average, during the off-season period, deep percolation averaged 54 ± 6% of the annual precipitation. The spatial aggregation into the Irrigation Scheme scale provided a method for earth-observation-based accounting of the irrigation water requirements, with interest for the water user’s association manager, and at the same time for the detection of water losses by deep percolation and of hotspots within the irrigation scheme. Full article

(This article belongs to the Special Issue Crop Evapotranspiration: Accurate Ground and Remote Sensing Observations and Computational Tools)

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29 pages, 3478 KiB

Open AccessFeature PaperArticle

Searching for Sustainable-Irrigation Issues of Clementine Orchards in the Syrian Akkar Plain: Effects of Irrigation Method and Canopy Size on Crop Coefficients, Transpiration, and Water Use with SIMDualKc Model

by Hanaa Darouich, Razan Karfoul, Tiago B. Ramos, Ali Moustafa and Luis S. Pereira

Water 2022, 14(13), 2052; https://doi.org/10.3390/w14132052 - 27 Jun 2022

Cited by 4 | Viewed by 2087

Abstract

Citrus is one of the most valuable crops in Syria, with the largest production areas in the Tartus and Latakia provinces. Water-saving policies have been adopted to modernize the irrigation systems and increase water productivity. Following dedicated research, this study aimed to evaluate the water balance in clementine trees irrigated with diverse methods and schedules using the SIMDualKc software model. Two experiments are reported: one with 10–14 years old trees irrigated with different methods (2007−2011) and the other with the same trees but now 18−20 years old, irrigated with different schedules (2015−2019). The SIMDualKc model successfully simulated the soil water contents measured in the various field plots, with root mean square error values lower than 0.004 m³ m⁻³ and modeling efficiencies up to 0.83. The model-calibrated standard basal crop coefficients (K_cb) were approximately constant throughout all growing stages, assuming values of 0.54−0.55 for the mature trees having smaller height (h) and fraction of ground cover (f_c), and 0.64 for older trees with larger canopies, i.e., larger h and f_c. With drip irrigation, single K_c had a higher value (1.14) at the end, non-growing, and initial stages, and a lower value (0.75–0.76) during mid-season (K_{c mid}), because precipitation was lesser then, contributing less to soil evaporation. On the other hand, K_c values were nearly constant with micro-sprinkler and surface irrigation techniques because the ground was fully wetted. The K_cb values derived from the fraction of ground cover and height (A&P approach) were similar to those obtained from the model, thus showing that the A&P approach represents a practical alternative to estimate K_cb in the practice of irrigation management. The soil water balance further revealed a large weight of the terms corresponding to the non-beneficial water consumption and non-consumptive water use when the fraction wetted was large and the application efficiencies were low. These terms were reduced, namely, evaporation losses when drip irrigation was used. This study, thus, provides a valuable tool for improving the irrigation management, water saving, and water productivity of Syrian citrus production systems. Full article

(This article belongs to the Special Issue Crop Evapotranspiration: Accurate Ground and Remote Sensing Observations and Computational Tools)

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13 pages, 1751 KiB

Open AccessFeature PaperArticle

Evapotranspiration and Quantitative Partitioning of Spring Maize with Drip Irrigation under Mulch in an Arid Region of Northwestern China

by Chenggong Xuan, Risheng Ding, Jie Shao and Yanshuo Liu

Water 2021, 13(22), 3169; https://doi.org/10.3390/w13223169 - 10 Nov 2021

Cited by 5 | Viewed by 2427

Abstract

To examine evapotranspiration (ET_c), soil evaporation (E_s), and transpiration (T_r), and partitioning of ET_c, a two-year field experiment was carried out in a maize field with drip irrigation under mulch in an arid region of northwestern China in 2017 and 2018. In the experiment we designed two treatments with full irrigation (T1) and growth stage-based strategic regulated deficit irrigation (T2). The applied irrigation of T2 was 40% of the T1 during both late vegetative and reproductive growth stages. Based on the measurements of soil water content (SWC) and T_r, a dual crop coefficient model (SIMDualK_c) was calibrated and validated, and daily ET_c, E_s, and T_r were estimated. The model can simulate well the dynamic variations of SWC and T_r. The calibrated basic crop coefficient at the initial, mid-season, and end growth stages was 0.2, 1.15, and 0.75, respectively. The ET_c was 507.9 and 519.1 mm for the T1 treatment, and 428.9 and 430.9 mm for the T2 treatment. The ratios of T_r to ET_c were higher for the two treatments, ~90%, for two years. Collectively, both drip irrigation under mulch and strategic deficit irrigation after canopy covering of the ground can significantly reduce the ineffective proportion of ET_c and E_s. Full article

(This article belongs to the Special Issue Crop Evapotranspiration: Accurate Ground and Remote Sensing Observations and Computational Tools)

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13 pages, 2093 KiB

Open AccessFeature PaperArticle

Estimation of Evapotranspiration and Crop Coefficient of Chinese Cabbage Using Eddy Covariance in Northwest China

by Jie Ding, Sien Li, Hongshuo Wang, Chunyu Wang, Yunxuan Zhang and Danni Yang

Water 2021, 13(19), 2781; https://doi.org/10.3390/w13192781 - 07 Oct 2021

Cited by 6 | Viewed by 2449

Abstract

Chinese cabbage is a key vegetable crop in northwest China. It is of great significance to study the evapotranspiration (ET) and crop coefficient (K_c) for agricultural water-saving management in this area. Eddy covariance (EC) was used to measure the ET and K_c of Chinese cabbage in northwest China from 1 May to 16 October 2020, in order to analyze the characteristics of these variables under plastic mulch. The results showed that the average K_c of the first crop of cabbage was higher in the middle and late stages, with values of 1.08 and 1.09 during the heading and maturity stages, respectively. The average K_c of the second crop of cabbage was higher in the middle stage, with values of 1.10 and 1.13 during the rosette and heading stages, respectively. The average annual K_c of Chinese cabbage was 0.81. Although K_c was higher in the middle and late periods, the water use efficiency was still 28.96 kg·ha⁻¹·mm⁻¹. The annual ET of Chinese cabbage was 505.3 mm. The study revealed the variation pattern of ET and K_c of Chinese cabbage, which provides an important scientific basis for the irrigation management of Chinese cabbage and is of great significance to guide the practice of water-saving vegetable planting. Full article

(This article belongs to the Special Issue Crop Evapotranspiration: Accurate Ground and Remote Sensing Observations and Computational Tools)

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