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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 (10 April 2022) | Viewed by 18532

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Special Issue Editor

Dr. Ofer Beeri

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Guest Editor

Manna Irrigation, Gvat, Israel
Interests: geography; remote sensing; agro-meteorology; presicion agriculture
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue calls for studies of remote sensing applications in crop irrigation and estimating the crop evapotranspiration and the corresponding site-specific irrigation (or variable rate irrigation, VRI). Manuscripts on remotely mapping soil moisture or the alternative crop water stress are also welcome. Under these topics, we will appreciate papers presenting how to split and divide a crop field into dynamic management zones, with a focus on the changes during the irrigation season. We will consider a critical review of agriculture applications of models as the SEBAL, METRIC, ALEXI, and others. This issue also welcomes remote sensing applications of water-use-efficiency and soil salinity. Another important contribution could be in the discipline of aquatic agriculture, as water quality or remotely productivity estimations.

Remote sensing platforms are no longer only satellite or airborne based, but also include drones, towers, robots, and any other platform that can produce a map of the entire agriculture field. Further, we are encouraging studies representing applications that integrate different sensors or platforms, or applications that utilize historical images to deliver update information (for example, for cloudy pixels), and even to forecast the near future

The results should be presented in tables and high-quality maps, but also interactive material will be considered. Please, avoid very long papers and prioritize the original outputs.

Dr. Ofer Beeri
Guest Editor

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

Irrigation scheduling
Crop evapotranspiration (ETc)
Remotely soil moisture
Remotely water stress
The crop water stress index (CWSI)
Water use efficiency (WUE)
Dynamic management zones

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Published Papers (5 papers)

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Research

16 pages, 5532 KiB

Open AccessArticle

Utilizing Optical Satellite Imagery to Monitor Temporal and Spatial Changes of Crop Water Stress: A Case Study in Alfalfa

by Ofer Beeri, Rom Tarshish, Ran Pelta and Tal Shilo

Water 2022, 14(11), 1676; https://doi.org/10.3390/w14111676 - 24 May 2022

Cited by 2 | Viewed by 3134

Abstract

Since the 1980s, thermal imagery has been used to assess crop water stress. The increase in the temporal resolution of optical satellite sensors (in the range of 400–2500 nm) and the better spatial resolution compared to the thermal imagery call for the definition of a new way for crop water stress monitoring. Hence, we are suggesting a new method utilizing spectral indices from three subsequent images to address this challenge. This method predicts the current water stress with the two past images and compares it to the current stress: if the existing conditions are better than the predicted stress, the crop is not under stress and has sufficient water for development. To evaluate the suggested method, we downloaded Sentinel-2 images and compared the stress found with that method to the leaf area index, leaf water potential, and yield from seven alfalfa growth cycles. The results outline the ability of the new optical stress index to depict spatial and temporal changes in the alfalfa water stress and especially illustrated the changes in the crop water stress over the growth cycle and after each irrigation. This new method needs to be validated with different crops and satellite sensors to verify its success. Full article

(This article belongs to the Special Issue Applications of Remote Sensing in Agricultural Water Management)

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20 pages, 4851 KiB

Open AccessArticle

Combining Precision Viticulture Technologies and Economic Indices to Sustainable Water Use Management

by Adele Finco, Deborah Bentivoglio, Giulia Chiaraluce, Matteo Alberi, Enrico Chiarelli, Andrea Maino, Fabio Mantovani, Michele Montuschi, Kassandra Giulia Cristina Raptis, Filippo Semenza, Virginia Strati, Filippo Vurro, Edoardo Marchetti, Manuele Bettelli, Michela Janni, Emiliano Anceschi, Carlo Sportolaro and Giorgia Bucci

Water 2022, 14(9), 1493; https://doi.org/10.3390/w14091493 - 6 May 2022

Cited by 19 | Viewed by 4249

Abstract

The scarcity of water due to climate change is endangering worldwide the production, quality, and economic viability of growing wine grapes. One of the main mitigation measures to be adopted in the viticulture sector will be an adequate irrigation strategy. Irrigation involves an increasing demand for water, a natural limited resource with increasing availability problems for the foreseeable future. Therefore, the development of a precision irrigation system, which is able to manage the efficient use of water and to monitor the crop water stress, is an important research topic for viticulture. This paper, through the analysis of a case study, aims to describe the prototype of a software platform that integrates data coming from different innovative remote and proximal sensors to monitor the hydric stress status of the vineyard. In addition, by using a cost analysis of grape cultivation and implementing economic indices, this study examines the conditions by which irrigation strategies may be economically justified, helping the decision-making process. By combining different sensors, the platform makes it possible to assess the spatial and temporal variability of water in vineyards. In addition, the output data of the platforming, matched with the economic indices, support the decision-making process for winemakers to optimize and schedule water use under water-scarce conditions. Full article

(This article belongs to the Special Issue Applications of Remote Sensing in Agricultural Water Management)

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22 pages, 3044 KiB

Open AccessArticle

Estimating the Leaf Water Status and Grain Yield of Wheat under Different Irrigation Regimes Using Optimized Two- and Three-Band Hyperspectral Indices and Multivariate Regression Models

by Salah Elsayed, Salah El-Hendawy, Yaser Hassan Dewir, Urs Schmidhalter, Hazem H. Ibrahim, Mohamed M. Ibrahim, Osama Elsherbiny and Mohamed Farouk

Water 2021, 13(19), 2666; https://doi.org/10.3390/w13192666 - 27 Sep 2021

Cited by 17 | Viewed by 2970

Abstract

Spectral reflectance indices (SRIs) often show inconsistency in estimating plant traits across different growth conditions; thus, it is still necessary to develop further optimized SRIs to guarantee the performance of SRIs as a simple and rapid approach to accurately estimate plant traits. The primary goal of this study was to develop optimized two- and three-band vegetation- and water-SRIs and to apply different multivariate regression models based on these SRIs for accurately estimating the relative water content (RWC), gravimetric water content (GWCF), and grain yield (GY) of two wheat cultivars evaluated under three irrigation regimes (100%, 75%, and 50% of crop evapotranspiration (ETc)) for two seasons. Results showed that the three plant traits and all SRIs showed significant differences (p < 0.05) between the three irrigation treatments for each wheat cultivar. The three-band water-SRIs (NWIs-3b) showed the best performance in estimating the three plant traits for both cultivars (R² > 0.80), and RWC and GWC_F under 75% ETc (R² ≥ 0.65). Four out of six three-band vegetation-SRIs (NDVIs-3b) performed better than any other SRIs for estimating GY under 100% ETc and 50% ETC, and RWC under 100% ETc (R² ≥ 0.60). All types of SRIs demonstrated excellent performance in estimating the three plant traits (R² ≥ 0.70) when the data of all growth conditions were combined and analyzed together. The NWIs-3b coupled with Random Forest models predicted the three plant traits with satisfactory accuracy for the calibration (R² ≥ 0.96) and validation (R² ≥ 0.93) datasets. The overall results of this study elucidate that extracting an optimized NWIs-3b from the full spectrum data and combined with an appropriate regression technique could be a practical approach for managing deficit irrigation regimes of crops through accurately, timely, and non-destructively monitoring the water status and final potential yield. Full article

(This article belongs to the Special Issue Applications of Remote Sensing in Agricultural Water Management)

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19 pages, 8417 KiB

Open AccessEditor’s ChoiceArticle

Soil Moisture Retrieval during the Wheat Growth Cycle Using SAR and Optical Satellite Data

by Min Zhang, Fengkai Lang and Nanshan Zheng

Water 2021, 13(2), 135; https://doi.org/10.3390/w13020135 - 8 Jan 2021

Cited by 15 | Viewed by 3197

Abstract

The objective of this paper is to propose a combined approach for the high-precision mapping of soil moisture during the wheat growth cycle based on synthetic aperture radar (SAR) (Radarsat-2) and optical satellite data (Landsat-8). For this purpose, the influence of vegetation was [...] Read more.

f_{v e g}

) into account. The VV/VH polarization radar backscattering coefficients database was established by a numerical simulation based on the advanced integrated equation model (AIEM) and the cross-polarized ratio of the Oh model. Then the empirical relationship between the bare soil backscattering coefficient and both the soil moisture and the surface roughness was developed by regression analysis. The surface roughness in this paper was described by using the effective roughness parameter and the combined roughness form. The experimental results revealed that using effective roughness as the model input instead of in-situ measured roughness can obtain soil moisture with high accuracy and effectively avoid the uncertainty of roughness measurement. The accuracy of soil moisture inversion could be improved by introducing vegetation fraction on the basis of the water cloud model (WCM). There was a good correlation between the estimated soil moisture and the observed values, with a root mean square error (RMSE) of about 4.14% and the coefficient of determination (

R^{2}

) about 0.7390. Full article

(This article belongs to the Special Issue Applications of Remote Sensing in Agricultural Water Management)

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16 pages, 4884 KiB

Open AccessArticle

Drought Monitoring Based on Remote Sensing in a Grain-Producing Region in the Cerrado–Amazon Transition, Brazil

by Mairon Ânderson Cordeiro Correa de Carvalho, Eduardo Morgan Uliana, Demetrius David da Silva, Uilson Ricardo Venâncio Aires, Camila Aparecida da Silva Martins, Marionei Fomaca de Sousa Junior, Ibraim Fantin da Cruz and Múcio André dos Santos Alves Mendes

Water 2020, 12(12), 3366; https://doi.org/10.3390/w12123366 - 30 Nov 2020

Cited by 11 | Viewed by 3631

Abstract

Drought is a natural disaster that affects a country’s economy and food security. The monitoring of droughts assists in planning assertive actions to mitigate the resulting environmental and economic impacts. This work aimed to evaluate the performance of the standardized precipitation index (SPI) using rainfall data estimated by orbital remote sensing in the monitoring of meteorological drought in the Cerrado–Amazon transition region, Brazil. Historical series from 34 rain gauge stations, in addition to indirect measurements of monthly precipitation obtained by remote sensing using the products CHIRPS-2.0, PERSIANN-CDR, PERSIANN-CCS, PERSIANN, GPM-3IMERGMv6, and GPM-3IMERGDLv6, were used in this study. Drought events detected by SPI were related to a reduction in soybean production. The SPI calculated from the historical rain series estimated by remote sensing allowed monitoring droughts, enabling a high detailing of the spatial variability of droughts in the region, mainly during the soybean development cycle. Indirect precipitation measures associated with SPI that have adequate performance for detecting droughts in the study region were PERSIANN-CCS (January), CHIRPS-2.0 (February and November), and GPM-3IMERGMv6 (March, September, and December). The SPI and the use of precipitation data estimated by remote sensing are effective for characterizing and monitoring meteorological drought in the study region. Full article

(This article belongs to the Special Issue Applications of Remote Sensing in Agricultural Water Management)

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