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Remote Sensing Estimation Methods of Evapotranspiration, Soil Moisture and Plant...
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Remote Sensing Estimation Methods of Evapotranspiration, Soil Moisture and Plant Water Status

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 (30 September 2023) | Viewed by 11329

Special Issue Editors

Prof. Dr. Mladen Todorovic

E-Mail Website
Guest Editor
CIHEAM – Mediterranean Agronomic Institute of Bari, Bari, Italy
Interests: water management in agriculture; irrigation; soil water balance and crop growth modelling; climate change impact; adaptation and mitigation; eco-efficiency
Special Issues, Collections and Topics in MDPI journals
Dr. Nada Mzid

E-Mail Website
Guest Editor
Department of Agriculture Forestry and Nature (DAFNE), University of Tuscia, 01100 Viterbo, Italy
Interests: remote sensing; digital soil mapping; multitemporal satellite; hyperspectral satellite; water stress; irrigation management; soil spectral library; precision agriculture; salinity; modelling
Dr. Vito Cantore

E-Mail Website
Guest Editor
Institute of Sciences of Food Production, National Research Council (CNR-ISPA), Bari, Italy
Interests: evapotranspiration; irrigation management; water stress; salinity stress; water use efficiency; sustainable water management; vegetables; irrigation DSS; sustainable fertilization
Special Issues, Collections and Topics in MDPI journals
Dr. Rossella Albrizio

E-Mail Website1 Website2
Guest Editor
National Research Council of Italy, Institute for Mediterranean Agricultural and Forestry Systems (CNR-ISAFOM), Portici, Italy
Interests: environmental crop eco-physiology and agronomy; crop response to abiotic stresses; gas-exchange measurements at both leaf and canopy scales; evapotranspiration; water use efficiency; water relations; crop modelling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Crop evapotranspiration, soil moisture content and plant water status are mutually connected variables of vital interest for studies related to agrometeorology, hydrology, agricultural water management and climate change. At present, timely and accurate estimations of these phenomena affect a wide range of applications, mainly focusing on irrigation scheduling, hydrological dynamics and modelling, environmental risk assessment monitoring and early warning system applications (i.e., drought, flooding, abiotic stresses), crop growth modelling, water dynamics in the soil–plant–atmosphere continuum, and a more efficient use of resources (water, land, nutrients and energy). With growing requirements for water use and limited water reserve accessibility, a compelling need emerges to enhance the daily monitoring and estimation of soil water balance components and to support a more efficient management of resources. Given the growing potentials in using space-based Earth Observation and other remote sensing techniques (airborne and ground-based), an accurate and timely retrieval of crop evapotranspiration, soil moisture, and plant water status is crucial to support precision agriculture and watershed management. Both fundamental properties are highly correlated with the electromagnetic radiation at various wavelengths, from the visible-near infrared to the thermal infrared spectral regions, up to the radar range.

This Special Issue encourages the submission of review and research articles, with a particular focus on the accurate estimation of crop evapotranspiration, soil moisture and plant water status based on remote sensing datasets (multispectral, hyperspectral and radar). There is great potential in the advancement of this topic that includes evolving domains such as the use of cloud-based access and processing policy (e.g., Google Earth Engine), machine learning algorithms, advanced operating high signal-to-noise ratio earth observation spectrometers, and smart precision farming strategies and policies oriented to sustainable agricultural development and hydrological applications.

Prof. Dr. Mladen Todorovic
Dr. Nada Mzid
Dr. Vito Cantore
Dr. Rossella Albrizio
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

  • multispectral and hyperspectral remote sensing
  • radar sensors
  • soil water balance
  • evapotranspiration estimation
  • machine learning algorithms
  • agricultural water management
  • smart precision farming
  • watershed management
  • early warning systems

Published Papers (4 papers)

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Research

14 pages, 7903 KiB  
Article
Retrieving Soil Moisture in the First-Level Tributary of the Yellow River–Wanchuan River Basin Based on CD Algorithm and Sentinel-1/2 Data
by Xingyu Liu, Xuelu Liu, Xiaodan Li, Xiaoning Zhang, Lili Nian, Xinyu Zhang, Pengkai Wang, Biao Ma, Quanxi Li, Xiaodong Zhang, Caihong Hui, Yonggang Bai, Jin Bao, Xiaoli Zhang, Jie Liu, Jin Sun, Wenting Yu and Li Luo
Water 2023, 15(19), 3409; https://doi.org/10.3390/w15193409 - 28 Sep 2023
Viewed by 653
Abstract
Lanzhou is the only provincial capital city in Northwest China where the main stream of the Yellow River and its tributaries flow through the city. Due to its geographical location and the influence of various factors, it is difficult to evaluate and simulate [...] Read more.
Lanzhou is the only provincial capital city in Northwest China where the main stream of the Yellow River and its tributaries flow through the city. Due to its geographical location and the influence of various factors, it is difficult to evaluate and simulate the climatic, hydrological, and ecological processes of the main stream of the Yellow River and its tributaries in the region. In this study, the Wanchuan River basin, currently undergoing ecological restoration, was selected as the study area. Seasonal backscatter differences generated using Sentinel-1/2 (S1/S2) data and the CD algorithm were used to reduce the effects of surface roughness; vegetation indices, soils, and field measurements were used to jointly characterize the vegetation contribution and soil contribution. Then, SM maps with a grid spacing of 10 m × 10 m were generated in the Wanchuan River basin, covering an area of 1767.78 km2. To validate the results, optimal factors were selected, and a training set and validation set were constructed. The results indicated a high level of the coefficient of determination (R2) of 0.78 and the root mean square error (RMSE) of 0.08 for the comparison of measured and inverted water contents, indicating that the algorithm retrieved the SM values of the study area well. Furthermore, Box line plots with ERA5-Land and GLDAS confirmed that the algorithm is in good agreement with current SM products and feasibility for soil water content inversion work in the Wanchuan River basin. Full article
(This article belongs to the Special Issue Remote Sensing Estimation Methods of Evapotranspiration, Soil Moisture and Plant Water Status)
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18 pages, 15189 KiB  
Article
Characterization of Evapotranspiration in the Orange River Basin of South Africa-Lesotho with Climate and MODIS Data
by Pululu S. Mahasa, Sifiso Xulu and Nkanyiso Mbatha
Water 2023, 15(8), 1501; https://doi.org/10.3390/w15081501 - 12 Apr 2023
Cited by 2 | Viewed by 4318
Abstract
Evapotranspiration (ET) is crucial to the management of water supplies and the functioning of numerous terrestrial ecosystems. To understand and propose planning strategies for water-resource and crop management, it is critical to examine the geo-temporal patterns of ET in drought-prone areas such as [...] Read more.
Evapotranspiration (ET) is crucial to the management of water supplies and the functioning of numerous terrestrial ecosystems. To understand and propose planning strategies for water-resource and crop management, it is critical to examine the geo-temporal patterns of ET in drought-prone areas such as the Upper Orange River Basin (UORB) in South Africa. While information on ET changes is computed from directly observed parameters, capturing it through remote sensing is inexpensive, consistent, and feasible at different space–time scales. Here, we employed the Moderate Resolution Imaging Spectroradiometer (MODIS)-derived spectral indices within Google Earth Engine (GEE) to analyze and characterize patterns of ET over the UORB from 2003 to 2021, in association with various climatic parameters. Our results show spatially consistent ET patterns with the Vegetation Condition Index (VCI), with lower values in the west, increasing toward the eastern section of the basin, over the Lesotho highlands. We noted that the UORB faced significant variability in ET and VCI during pronounced drought episodes. The random forests (RF) model identified precipitation, temperature, Standardized Precipitation Index (SPI)-6, Palmer Drought Severity Index (PDSI), and VCI as variables of high importance for ET variability, while the wavelet analysis confirmed the coherence connectivity between these variables with periodicities ranging from eight to 32 months, suggesting a strong causal influence on ET, except for PDSI, that showed an erratic relationship. Based on the sequential Mann–Kendall test, we concluded that evapotranspiration has exhibited a statistically downward trend since 2011, which was particularly pronounced during the dry periods in 2015–2016, 2019, and 2021. Our study also confirmed the high capacity of the GEE and MODIS-derived indices in mapping consistent geo-temporal ET patterns. Full article
(This article belongs to the Special Issue Remote Sensing Estimation Methods of Evapotranspiration, Soil Moisture and Plant Water Status)
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20 pages, 6413 KiB  
Article
Estimation of Actual Evapotranspiration Using Satellite-Based Surface Energy Balance Derived from Landsat Imagery in Northern Thailand
by Teerawat Suwanlertcharoen, Thodsapol Chaturabul, Thanaporn Supriyasilp and Kobkiat Pongput
Water 2023, 15(3), 450; https://doi.org/10.3390/w15030450 - 22 Jan 2023
Cited by 1 | Viewed by 2880
Abstract
In this study, satellite-based measures of surface energy balance and the mapping evapotranspiration at high resolution with internalized calibration (METRIC) from Landsat imagery were used to estimate the spatiotemporal distribution of actual evapotranspiration (ETa) in northern Thailand, constituting a procedure [...] Read more.
In this study, satellite-based measures of surface energy balance and the mapping evapotranspiration at high resolution with internalized calibration (METRIC) from Landsat imagery were used to estimate the spatiotemporal distribution of actual evapotranspiration (ETa) in northern Thailand, constituting a procedure that has rarely been performed in southeast Asia. Subsequently, we compared the ETa obtained from METRIC with that calculated using the FAO-56 dual-crop coefficient method via the SIMDualKc software and found a strong correlation. An assessment of the accuracy of all the sample plots revealed the R2, Root-Mean-Square Error (RMSE), and mean absolute error (MAE) values to be 0.830, 0.730, and 0.575 mm d−1, respectively. Differences in the cumulative ETa values derived from SIMDualKc and METRIC ranged in magnitude from 0.93–3.57% for rice and 3.08–7.99% for longan. The ETa values for forestland and waterbodies were higher than those for agricultural areas and areas with other forms of land use. The spatiotemporal distribution of the seasonal ETa during the dry season was consistent with the climate, vegetation, and anthropogenic activity. Thus, our results indicate that METRIC is a reliable tool for estimating ETa for water resource management under different environmental conditions and improving water use efficiency over large areas. Full article
(This article belongs to the Special Issue Remote Sensing Estimation Methods of Evapotranspiration, Soil Moisture and Plant Water Status)
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23 pages, 13097 KiB  
Article
Correlation between Ground Measurements and UAV Sensed Vegetation Indices for Yield Prediction of Common Bean Grown under Different Irrigation Treatments and Sowing Periods
by Aleksa Lipovac, Atila Bezdan, Djordje Moravčević, Nevenka Djurović, Marija Ćosić, Pavel Benka and Ružica Stričević
Water 2022, 14(22), 3786; https://doi.org/10.3390/w14223786 - 21 Nov 2022
Cited by 4 | Viewed by 2436
Abstract
The objective of this study is to assess the possibility of using unmanned aerial vehicle (UAV) multispectral imagery for rapid monitoring, water stress detection and yield prediction under different sowing periods and irrigation treatments of common bean (Phaseolus vulgaris, L). The [...] Read more.
The objective of this study is to assess the possibility of using unmanned aerial vehicle (UAV) multispectral imagery for rapid monitoring, water stress detection and yield prediction under different sowing periods and irrigation treatments of common bean (Phaseolus vulgaris, L). The study used a two-factorial split-plot design, divided into subplots. There were three sowing periods (plots; I—mid April, II—end of May/beginning of June, III—third decade of June/beginning of July) and three levels of irrigation (subplots; full irrigation (F)—providing 100% of crop evapotranspiration (ETc), deficit irrigation (R)—providing 80% of ETc, and deficit irrigation (S) providing—60% of ETc). Canopy cover (CC), leaf area index (LAI), transpiration (T) and soil moisture (Sm) were monitored in all treatments during the growth period. A multispectral camera was mounted on a drone on seven occasions during two years of research which provided raw multispectral images. The NDVI (Normalized Difference Vegetation Index), MCARI1 (Modified Chlorophyll Absorption in Reflectance Index), NDRE (Normalized Difference Red Edge), GNDVI (Green Normalized Difference Vegetation Index) and Optimized Soil Adjusted Vegetation Index (OSAVI) were computed from the images. The results indicated that NDVI, MCARI1 and GNDVI derived from the UAV are sensitive to water stress in S treatments, while mild water stress among the R treatments could not be detected. The NDVI and MCARI1 of the II-S treatment predicted yields better (r2 = 0.65, y = 4.01 tha−1; r2 = 0.70, y = 4.28 tha−1) than of III-S (r2 = 0.012, y = 3.54 tha−1; r2 = 0.020, y = 3.7 tha−1). The use of NDVI and MCARI will be able to predict common bean yields under deficit irrigation conditions. However, remote sensing methods did not reveal pest invasion, so good yield predictions require observations in the field. Generally, a low-flying UAV proved to be useful for monitoring crop status and predicting yield and water stress in different irrigation regimes and sowing period. Full article
(This article belongs to the Special Issue Remote Sensing Estimation Methods of Evapotranspiration, Soil Moisture and Plant Water Status)
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