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Water Management Using Drones and Satellites in Agriculture

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A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Water Resources Management, Policy and Governance".

Deadline for manuscript submissions: closed (30 December 2018) | Viewed by 78117

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Special Issue Editors

Prof. Dr. Javier J Cancela

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GI-1716, Projects and Planification, Dpto. Ingeniería Agroforestal, Universidad de Santiago de Compostela, Escola Politécnica Superior de Enxeñaría, Rúa Benigno Ledo s/n, 27002 Lugo, Spain
Interests: crop water requirements; soil–water management; irrigation management; soil science; fertility; precision viticulture; remote sensing; unmanned aerial vehicles; satellite imagery
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Dr. Xesús P. González

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GI-1716, Projects and Planification, Dpto. Ingeniería Agroforestal, Universidad de Santiago de Compostela, Escola Politécnica Superior de Enxeñaría, Rúa Benigno Ledo s/n, 27002 Lugo, Spain
Interests: precision agriculture; neuronal networks; software implementation; remote sensing; geographic information systems
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Dr. José Manuel Mirás-Avalos

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Agroforestry Engineering Department. University of Santiago de Compostela
Interests: irrigation; soil science; viticulture; modelling; crop water requirements; sustainability; environment
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Dr. Mar Vilanova

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Misión Biológica de Galicia, Consejo Superior de Investigaciones Científicas (CSIC), El Palacio, Salcedo, 36143 Pontevedra, Spain
Interests: viticulture; grape and wine quality; biochemistry; sustainability
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Special Issue Information

Dear Colleagues,

The use of drones and satellites in agriculture is a reality, although we are currently at the crossroads of quickly and efficiently exploiting the information obtained to render it useful to farmers, selecting the most appropriate and versatile tool for each situation. In this sense, water management is one of the key issues in agriculture in which these new technologies can provide solutions, such as where to manage irrigation water, maximizing its efficiency, adapting crops to climate change, and facilitating the worldwide increase of food production. This improvement of productivity should be linked to the assurance of the quality of the final product, by using less inputs (water and nutrients), thus, maintaining soil and water resources, and vegetal material. The use of aerial images, after being processed through different statistical techniques, combined with ground-based remote sensors, are key lines for future research in which new solutions and applications must be proposed, combining the number of techniques currently available.

Dr. Javier J. Cancela
Dr. Xesús P. González
Dr. José Manuel Mirás-Avalos
Dr. Mar Vilanova
Guest Editors

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Keywords

Precision Agriculture
Irrigation Water Management
Crop Water Requirements
Soil Science
Crop Quality
Sensors
Remote Sensing

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Published Papers (9 papers)

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Editorial

Jump to: Research

4 pages, 199 KiB

Open AccessEditorial

Water Management Using Drones and Satellites in Agriculture

by Javier J. Cancela, Xesús P. González, Mar Vilanova and José M. Mirás-Avalos

Water 2019, 11(5), 874; https://doi.org/10.3390/w11050874 - 26 Apr 2019

Cited by 38 | Viewed by 9047

Abstract

This document intends to be a presentation of the Special Issue “Water Management Using Drones and Satellites in Agriculture”. The objective of this Special Issue is to provide an overview of recent advances in the methodology of using remote sensing techniques for managing water in agricultural systems. Its eight peer-reviewed articles focus on three topics: new equipment for characterizing water bodies, development of satellite-based technologies for determining crop water requirements in order to enhance irrigation efficiency, and monitoring crop water status through proximal and remote sensing. Overall, these contributions explore new solutions for improving irrigation management and an efficient assessment of crop water needs, being of great value for both researchers and advisors. Full article

(This article belongs to the Special Issue Water Management Using Drones and Satellites in Agriculture)

Research

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16 pages, 27770 KiB

Open AccessArticle

Use of Drones for the Topo-Bathymetric Monitoring of the Reservoirs of the Segura River Basin

by Manuel Erena, Joaquín F. Atenza, Sandra García-Galiano, José A. Domínguez and José M. Bernabé

Water 2019, 11(3), 445; https://doi.org/10.3390/w11030445 - 2 Mar 2019

Cited by 50 | Viewed by 9601

Abstract

The Segura River Basin (SRB), located in the South East of Spain, has the lowest percentage of renewable water resources of all the Spanish basins. Therefore, knowledge of the annual rate of water reservoir sedimentation is an important issue to be resolved in one of the most water-stressed regions in the western Mediterranean basin. This paper describes the sensors developed in collaboration with technology-based enterprises (aerial drone, floating drone, and underwater drone), and the methodology for integration of the different types of data acquired to monitor the reservoirs of the SRB. The proposed solution was applied to 21 reservoirs of the SRB. The proposed methodology is based on the use of unmanned aerial vehicles (UAV) for photogrammetry of the reservoir surface area. For each reservoir, two flights were completed, with 20 cm and 5 cm resolution, respectively. Then, a triangular irregular network mesh was generated by GIS techniques. Surface water vehicles (USV) and underwater remote-operated vehicles (ROV) were used to undertake bathymetric surveys. In addition, water quality measurements were made with an ROV device. The main results consist of topographic and bathymetric measurements for each reservoir, obtained by using equipment based on OpenSource technology. According to the results, the annual rate of storage capacity loss of water resources in the SRB´s reservoirs is 0.33%. Full article

(This article belongs to the Special Issue Water Management Using Drones and Satellites in Agriculture)

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15 pages, 4709 KiB

Open AccessCommunication

A Newly Developed Unmanned Aerial Vehicle (UAV) Imagery Based Technology for Field Measurement of Water Level

by Ang Gao, Shiqiang Wu, Fangfang Wang, Xiufeng Wu, Peng Xu, Lei Yu and Senlin Zhu

Water 2019, 11(1), 124; https://doi.org/10.3390/w11010124 - 11 Jan 2019

Cited by 15 | Viewed by 5834

Abstract

Field measurement of water level is important for water conservancy project operation and hydrological forecasting. In this study, we proposed a new measuring technique by integrating the advantages of unmanned aerial vehicle (UAV) photogrammetry and image recognition technology. Firstly, the imagery of water fluctuation process was captured by an UAV airborne camera, and water surface line in the imagery was recognized and extracted using image recognition technology. Subsequently, successive water levels at a measuring section were calculated by parameter calibration. Statistical parameters of water levels, such as maximum, average, and minimum values during the capturing period were also calculated. Additionally, we introduced a correction method to offset the error caused by UAV drift. The newly proposed method was tested in field measurement for Miaowei hydropower station, China, and the results showed that the method is reliable and adoptable. Full article

(This article belongs to the Special Issue Water Management Using Drones and Satellites in Agriculture)

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20 pages, 4339 KiB

Open AccessArticle

Evaluation of Normalized Difference Water Index as a Tool for Monitoring Pasture Seasonal and Inter-Annual Variability in a Mediterranean Agro-Silvo-Pastoral System

by João Serrano, Shakib Shahidian and José Marques da Silva

Water 2019, 11(1), 62; https://doi.org/10.3390/w11010062 - 1 Jan 2019

Cited by 87 | Viewed by 9646

Abstract

Extensive animal production in Iberian Peninsula is based on pastures, integrated within the important agro-silvo-pastoral system, named “montado” in Portugal and “dehesa” in Spain. Temperature and precipitation are the main driving climatic factors affecting agricultural productivity and, in dryland pastures, the hydrological cycle of soil, identified by soil moisture content (SMC), is the main engine of the vegetation development. The objective of this work was to evaluate the normalized difference water index (NDWI) based on Sentinel-2 imagery as a tool for monitoring pasture seasonal dynamics and inter-annual variability in a Mediterranean agro-silvo-pastoral system. Forty-one valid NDWI records were used between January and June 2016 and between January 2017 and June 2018. The 2.3 ha experimental field is located within the “Mitra” farm, in the South of Portugal. Soil moisture content, pasture moisture content (PMC), pasture surface temperature (T_ir), pasture biomass productivity and pasture quality degradation index (PQDI) were evaluated in 12 satellite pixels (10 m × 10 m). The results show significant correlations (p < 0.01) between NDWI and: (i) SMC (R² = 0.7548); (ii) PMC (R² = 0.8938); (iii) T_ir (R² = 0.5428); (iv) biomass (R² = 0.7556); and (v) PQDI (R² = 0.7333). These findings suggest that satellite-derived NDWI can be used in site-specific management of “montado” ecosystem to support farmers’ decision making. Full article

(This article belongs to the Special Issue Water Management Using Drones and Satellites in Agriculture)

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17 pages, 1870 KiB

Open AccessArticle

A Novel ArcGIS Toolbox for Estimating Crop Water Demands by Integrating the Dual Crop Coefficient Approach with Multi-Satellite Imagery

by Juan Miguel Ramírez-Cuesta, José Manuel Mirás-Avalos, José Salvador Rubio-Asensio and Diego S. Intrigliolo

Water 2019, 11(1), 38; https://doi.org/10.3390/w11010038 - 25 Dec 2018

Cited by 30 | Viewed by 7997

Abstract

Advances in information and communication technologies facilitate the application of complex models for optimizing agricultural water management. This paper presents an easy-to-use tool for determining crop water demands using the dual crop coefficient approach and remote sensing imagery. The model was developed using Python as a programming language and integrated into an ArcGIS (geographic information system) toolbox. Inputs consist of images from satellites Landsat 7 and 8, and Sentinel 2A, along with data for defining crop, weather, soil type, and irrigation system. The tool produces a spatial distribution map of the crop evapotranspiration estimates, assuming no water stress, which allows quantifying the water demand and its variability within an agricultural field with a spatial resolution of either 10 m (for Sentinel) or 30 m (for Landsat). The model was validated by comparing the estimated basal crop coefficients (K_cb) of lettuce and peach during an irrigation season with those tabulated as a reference for these crops. Good agreements between K_cb derived from both methods were obtained with a root mean squared error ranging from 0.01 to 0.02 for both crops, although certain underestimations were observed resulting from the uneven crop development in the field (percent bias of −4.74% and −1.80% for lettuce and peach, respectively). The developed tool can be incorporated into commercial decision support systems for irrigation scheduling and other applications that account for the water balance in agro-ecosystems. This tool is freely available upon request to the corresponding author. Full article

(This article belongs to the Special Issue Water Management Using Drones and Satellites in Agriculture)

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22 pages, 6304 KiB

Open AccessArticle

Segmentation of Apples in Aerial Images under Sixteen Different Lighting Conditions Using Color and Texture for Optimal Irrigation

by Sajad Sabzi, Yousef Abbaspour-Gilandeh, Ginés García-Mateos, Antonio Ruiz-Canales and José Miguel Molina-Martínez

Water 2018, 10(11), 1634; https://doi.org/10.3390/w10111634 - 12 Nov 2018

Cited by 20 | Viewed by 4831

Abstract

Due to the changes in the lighting intensity and conditions throughout the day, machine vision systems used in precision agriculture for irrigation management should be prepared for all possible conditions. For this purpose, a complete segmentation algorithm has been developed for a case study on apple fruit segmentation in outdoor conditions using aerial images. This algorithm has been trained and tested using videos with 16 different light intensities from apple orchards during the day. The proposed segmentation algorithm consists of five main steps: (1) transforming frames in RGB to CIE L*u*v* color space and applying thresholds on image pixels; (2) computing texture features of local standard deviation; (3) using intensity transformation to remove background pixels; (4) color segmentation applying different thresholds in RGB space; and (5) applying morphological operators to refine the results. During the training process of this algorithm, it was observed that frames in different light conditions had more than 58% color sharing. Results showed that the accuracy of the proposed segmentation algorithm is higher than 99.12%, outperforming other methods in the state of the art that were compared. The processed images are aerial photographs like those obtained from a camera installed in unmanned aerial vehicles (UAVs). This accurate result will enable more efficient support in the decision making for irrigation and harvesting strategies. Full article

(This article belongs to the Special Issue Water Management Using Drones and Satellites in Agriculture)

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20 pages, 4337 KiB

Open AccessArticle

Monitoring Seasonal Pasture Quality Degradation in the Mediterranean Montado Ecosystem: Proximal versus Remote Sensing

by João Serrano, Shakib Shahidian and José Marques da Silva

Water 2018, 10(10), 1422; https://doi.org/10.3390/w10101422 - 11 Oct 2018

Cited by 41 | Viewed by 4037

Abstract

Montado is an agro-forestry system occupying a large surface in countries of the Mediterranean region. In this system, the natural dryland pasture is the principal source for animal feed in extensive grazing. The climatic seasonality associated with the inter-annual irregularity of precipitation greatly influences the development of pasture and its vegetative cycle. The end of spring is a critical period in terms of animal feed due to the notable reduction in the nutritive value of the plants. The objective of this work was to evaluate, through the correlation between pasture quality indexes (Pasture Quality Degradation Index, PQDI and Normalized Difference Vegetation Index, NDVI), two technological approaches for monitoring the evolution of the quality of a biodiverse pasture in the period of greatest vegetative development (between February and June). The technological approaches consisted of (i) proximal sensing (PS), with the use of an active optical sensor; and (ii) remote sensing (RS), using images captured by a Sentinel-2 satellite. The results of this study show strong and significant correlations between PQDI and NDVI (obtained by PS or RS). These two techniques (PS or RS) can, therefore, be used in a complementary way to identify and anticipate the food supplementation needs for animals and support farmers in decision making. Full article

(This article belongs to the Special Issue Water Management Using Drones and Satellites in Agriculture)

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14 pages, 1870 KiB

Open AccessArticle

Performance Assessment of MOD16 in Evapotranspiration Evaluation in Northwestern Mexico

by Ana L. Aguilar, Héctor Flores, Guillermo Crespo, Ma I. Marín, Isidro Campos and Alfonso Calera

Water 2018, 10(7), 901; https://doi.org/10.3390/w10070901 - 7 Jul 2018

Cited by 45 | Viewed by 7048

Abstract

Evapotranspiration (ET) is the second largest component of the water cycle in arid and semiarid environments, and, in fact, more than 60% of the precipitation on earth is returned to the atmosphere through it. MOD16 represents an operational source of ET estimates with adequate spatial resolution for several applications, such as water resources planning, at a regional scale. However, the use of these estimates in routine applications will require MOD16 evaluation and validation using accurate ground-based measurements. The main objective of this study was to evaluate the performance of the MOD16A2 product by comparing it with eddy covariance (EC) systems. Additional objectives were the analysis of the limitations, uncertainties, and possible improvements of the MOD16-estimated ET. The EC measurements were acquired for five sites and for a variety of land covers in northwestern Mexico. The indicators used for the comparison were: root mean square error (RMSE), bias (BIAS), concordance index (d), and determination coefficient (R²) of the correlation, comparing measured and modelled ET. The best performance was observed in Rayón (RMSE = 0.77 mm∙day⁻¹, BIAS = −0.46 mm∙day⁻¹, d = 0.88, and R² = 0.86); El Mogor and La Paz showed errors and coefficients of determination comparable to each other (RMSE = 0.39 mm·day⁻¹, BIAS = −0.04 mm∙day⁻¹, R² = 0.46 and RMSE = 0.42 mm·day⁻¹, BIAS = −0.18 mm∙day⁻¹, R² = 0.45, respectively). In most cases, MOD16 underestimated the ET values. Full article

(This article belongs to the Special Issue Water Management Using Drones and Satellites in Agriculture)

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14 pages, 1748 KiB

Open AccessEditor’s ChoiceArticle

In Situ Water Quality Measurements Using an Unmanned Aerial Vehicle (UAV) System

by Cengiz Koparan, Ali Bulent Koc, Charles V. Privette and Calvin B. Sawyer

Water 2018, 10(3), 264; https://doi.org/10.3390/w10030264 - 3 Mar 2018

Cited by 140 | Viewed by 15748

Abstract

An unmanned aerial vehicle-assisted water quality measurement system (UAMS) was developed for in situ surface water quality measurement. A custom-built hexacopter was equipped with an open-source electronic sensors platform to measure the temperature, electrical conductivity (EC), dissolved oxygen (DO), and pH of water. Electronic components of the system were coated with a water-resistant film, and the hexacopter was assembled with flotation equipment. The measurements were made at thirteen sampling waypoints within a 1.1 ha agricultural pond. Measurements made by an open-source multiprobe meter (OSMM) attached to the unmanned aerial vehicle (UAV) were compared to the measurements made by a commercial multiprobe meter (CMM). Percent differences between the OSMM and CMM measurements for DO, EC, pH, and temperature were 2.1 %, 3.43 %, 3.76 %, and <1.0 %, respectively. The collected water quality data was used to interpret the spatial distribution of measurements in the pond. The UAMS successfully made semiautonomous in situ water quality measurements from predetermined waypoints. Water quality maps showed homogeneous distribution of measured constituents across the pond. The concept presented in this paper can be applied to the monitoring of water quality in larger surface waterbodies. Full article

(This article belongs to the Special Issue Water Management Using Drones and Satellites in Agriculture)

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