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Irrigation Estimates and Management from Remote Sensing and Hydrological Modelling

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 54633

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Special Issue Editors

Dr. Chiara Corbari

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Guest Editor

Department of Civil and Environmental Engineering, Politecnico di Milano, Milano, Italy
Interests: hydrology; water resources; remote sensing; flood; irrigation

Dr. Kamal Labbassi

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Guest Editor

Department of Earth Sciences, Chouaib Doukkali University, El Jadida, Morocco
Interests: environment; hydrogeology; remote sensing; geology; soil

Dr. Francesco Vuolo

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Guest Editor

Institute of Geomatics, University of Natural Resources and Life Sciences, Vienna (BOKU), Austria
Interests: irrigation; evapotranspiration; agriculture crop; digital farming; remote sensing
Special Issues, Collections and Topics in MDPI journals

Dr. Kaniska Mallick

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Guest Editor

Remote Sensing and Natural Resources Modeling, Department ERIN Luxembourg Institute of Science and Technology (LIST) 41, rue du Brill L-4422 Belvaux, Grand-duchy of Luxembourg
Interests: remote sensing; evapotranspiration; water balance; irrigation; soil; Environment

Special Issue Information

Dear Colleagues,

This Special Issue will focus on the use of remote sensing data to estimate irrigation volumes and timing; management of irrigation using hydrological modeling combined with satellite data; improving irrigation water use efficiency based on remote sensing vegetation indices, hydrological modeling, satellite soil moisture or land surface temperature data; precision farming with high-resolution satellite data or drones; farm and irrigation district irrigation management; improving the performance of irrigation schemes; irrigation water needs estimates from ground and satellite data; and ICT tools for real-time irrigation management with remote sensing and ground data coupled with hydrological modeling.

Dr. Chiara Corbari
Dr. Kamal Labbassi
Dr. Francesco Vuolo
Dr. Kaniska Mallick
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 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
Remote sensing
Hydrological modeling
Soil moisture
ICT-tools

Published Papers (10 papers)

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Research

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18 pages, 44725 KiB

Open AccessArticle

Proximal Gamma-Ray Spectroscopy: An Effective Tool to Discern Rain from Irrigation

by Andrea Serafini, Matteo Albéri, Michele Amoretti, Stefano Anconelli, Enrico Bucchi, Stefano Caselli, Enrico Chiarelli, Luca Cicala, Tommaso Colonna, Mario De Cesare, Salvatore Gentile, Enrico Guastaldi, Tommaso Letterio, Andrea Maino, Fabio Mantovani, Michele Montuschi, Gabriele Penzotti, Kassandra Giulia Cristina Raptis, Filippo Semenza, Domenico Solimando and Virginia Strati Show full author list Hide full author list

Remote Sens. 2021, 13(20), 4103; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13204103 - 13 Oct 2021

Cited by 4 | Viewed by 2387

Abstract

Proximal gamma-ray spectroscopy is a consolidated technology for a continuous and real-time tracing of soil water content at field scale. New developments have shown that this method can also act as an unbiased tool for remotely distinguishing rainwater from irrigation without any meteorological support information. Given a single detector, the simultaneous observation in a gamma spectrum of a transient increase in the ²¹⁴Pb signal, coupled with a decrease in the ⁴⁰K signal, acts as an effective proxy for rainfall. A decrease in both ²¹⁴Pb and ⁴⁰K signals is, instead, a reliable fingerprint for irrigation. We successfully proved this rationale in two data-taking campaigns performed on an agricultural test field with different crop types (tomato and maize). The soil moisture levels were assessed via the ⁴⁰K gamma signal on the basis of a one-time setup calibration. The validation against a set of gravimetric measurements showed excellent results on both bare and vegetated soil conditions. Simultaneously, the observed rain-induced increase in the ²¹⁴Pb signal permitted to identify accurately the rain and irrigation events occurred in the 8852 h of data taking. Full article

(This article belongs to the Special Issue Irrigation Estimates and Management from Remote Sensing and Hydrological Modelling)

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20 pages, 5688 KiB

Open AccessArticle

Detection and Quantification of Irrigation Water Amounts at 500 m Using Sentinel-1 Surface Soil Moisture

by Luca Zappa, Stefan Schlaffer, Bernhard Bauer-Marschallinger, Claas Nendel, Beate Zimmerman and Wouter Dorigo

Remote Sens. 2021, 13(9), 1727; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13091727 - 29 Apr 2021

Cited by 28 | Viewed by 4876

Abstract

Detailed information about irrigation timing and water use at a high spatial resolution is critical for monitoring and improving agricultural water use efficiency. However, neither statistical surveys nor remote sensing-based approaches can currently accommodate this need. To address this gap, we propose a novel approach based on the TU Wien Sentinel-1 Surface Soil Moisture product, characterized by a spatial sampling of 500 m and a revisit time of 1.5–4 days over Europe. Spatiotemporal patterns of soil moisture are used to identify individual irrigation events and estimate irrigation water amounts. To retrieve the latter, we include formulations of evapotranspiration and drainage losses to account for vertical fluxes, which may significantly influence sub-daily soil moisture variations. The proposed approach was evaluated against field-scale irrigation data reported by farmers at three sites in Germany with heterogeneous field sizes, crop patterns, irrigation systems and management. Our results show that most field-scale irrigation events can be detected using soil moisture information (mean F-score = 0.77). Irrigation estimates, in terms of temporal dynamics as well as spatial patterns, were in agreement with reference data (mean Pearson correlation = 0.64) regardless of field-specific characteristics (e.g., crop type). Hence, the proposed approach has the potential to be applied over large regions with varying cropping systems. Full article

(This article belongs to the Special Issue Irrigation Estimates and Management from Remote Sensing and Hydrological Modelling)

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24 pages, 8570 KiB

Open AccessArticle

Evapotranspiration Estimates at High Spatial and Temporal Resolutions from an Energy–Water Balance Model and Satellite Data in the Capitanata Irrigation Consortium

by Chiara Corbari, Drazen Skokovic Jovanovic, Luigi Nardella, Josè Sobrino and Marco Mancini

Remote Sens. 2020, 12(24), 4083; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12244083 - 13 Dec 2020

Cited by 15 | Viewed by 2560

Abstract

The feasibility of combining remotely sensed land surface temperature data (LST) and an energy–water balance model for improving evapotranspiration estimates over time distributed in space in the Capitanata irrigation consortium is analysed. The energy–water balance FEST-EWB model (flash flood event-based spatially distributed rainfall–runoff transformation—energy–water balance model) computes continuously in time and is distributed in space soil moisture (SM) and evapotranspiration (ET) fluxes solving for a land surface temperature that closes the energy–water balance equations. The comparison between modelled and observed LST was used to calibrate the model soil parametres with a newly developed pixel to pixel calibration procedure. The effects of the calibration procedure were analysed against ground measures of soil moisture and evapotranspiration. The FEST-EWB model was run at 30 m of spatial resolution for the period between 2013 and 2018. Absolute errors of 2.5 °C were obtained for LST estimates against satellite data; while RMSE around 0.06 and 40 Wm⁻² are found for ground measured soil moisture and latent heat flux, respectively. Full article

(This article belongs to the Special Issue Irrigation Estimates and Management from Remote Sensing and Hydrological Modelling)

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25 pages, 7084 KiB

Open AccessArticle

Irrigation and Precipitation Hydrological Consistency with SMOS, SMAP, ESA-CCI, Copernicus SSM1km, and AMSR-2 Remotely Sensed Soil Moisture Products

by Nicola Paciolla, Chiara Corbari, Ahmad Al Bitar, Yann Kerr and Marco Mancini

Remote Sens. 2020, 12(22), 3737; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12223737 - 13 Nov 2020

Cited by 7 | Viewed by 3170

Abstract

Numerous Surface Soil Moisture (SSM) products are available from remote sensing, encompassing different spatial, temporal, and radiometric resolutions and retrieval techniques. Notwithstanding this variety, all products should be coherent with water inputs. In this work, we have cross-compared precipitation and irrigation with different SSM products: Soil Moisture Ocean Salinity (SMOS), Soil Moisture Active Passive (SMAP), European Space Agency (ESA) Climate Change Initiative (ESA-CCI) products, Copernicus SSM1km, and Advanced Microwave Scanning Radiometer 2 (AMSR2). The products have been analyzed over two agricultural sites in Italy (Chiese and Capitanata Irrigation Consortia). A Hydrological Consistency Index (HCI) is proposed as a means to measure the coherency between SSM and precipitation/irrigation. Any time SSM is available, a positive or negative consistency is recorded, according to the rainfall registered since the previous measurement and the increase/decrease of SSM. During the irrigation season, some agreements are labeled as “irrigation-driven”. No SSM dataset stands out for a systematic hydrological coherence with the rainfall. Negative consistencies cluster just below 50% in the non-irrigation period and lose 20–30% in the irrigation period. Hybrid datasets perform better (+15–20%) than single-technology measurements, among which active data provide slightly better results (+5–10%) than passive data. Full article

(This article belongs to the Special Issue Irrigation Estimates and Management from Remote Sensing and Hydrological Modelling)

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28 pages, 6206 KiB

Open AccessArticle

Evaluation of Remote Sensing-Based Irrigation Water Accounting at River Basin District Management Scale

by Jesús Garrido-Rubio, Alfonso Calera, Irene Arellano, Mario Belmonte, Lorena Fraile, Tatiana Ortega, Raquel Bravo and José González-Piqueras

Remote Sens. 2020, 12(19), 3187; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12193187 - 29 Sep 2020

Cited by 9 | Viewed by 3356

Abstract

The Water Framework Directive in Europe requires extending metering and water abstraction controls to accurately satisfy the necessary water resource requirements. However, in situ measurement instruments are inappropriate for large irrigation surface areas, considering the high investment and maintenance service costs. In this study, Remote Sensing-based Irrigation Water Accounting (RS-IWA) (previously evaluated for commercial plots, water user associations, and groundwater water management scales) was applied to over 11 Spanish river basin districts during the period of 2014–2018. Using the FAO56 methodology and incorporating remote sensing basal crop coefficient time series to simulate the Remote Sensing-based Soil Water Balance (RS-SWB), we were able to provide spatially and temporally distributed net irrigation requirements. The results were evaluated against the irrigation water demands estimated by the Hydrological Planning Offices and published in the River Basin Management Plans applying the same spatial (Agricultural Demand Units and Exploitation Systems) and temporal (annual and monthly) water management scales used by these public water managers, ultimately returning ranges of agreement (r² and d_r) (Willmott refined index) of 0.79 and 0.99, respectively. Thus, this paper presents an operational tool for providing updated spatio-temporal maps of RS-IWA over large and diverse irrigation surface areas, which is ready to serve as a complementary irrigation water monitoring and management tool. Full article

(This article belongs to the Special Issue Irrigation Estimates and Management from Remote Sensing and Hydrological Modelling)

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22 pages, 6633 KiB

Open AccessArticle

Exploiting High-Resolution Remote Sensing Soil Moisture to Estimate Irrigation Water Amounts over a Mediterranean Region

by Jacopo Dari, Luca Brocca, Pere Quintana-Seguí, María José Escorihuela, Vivien Stefan and Renato Morbidelli

Remote Sens. 2020, 12(16), 2593; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12162593 - 12 Aug 2020

Cited by 48 | Viewed by 7068

Abstract

Despite irrigation being one of the main sources of anthropogenic water consumption, detailed information about water amounts destined for this purpose are often lacking worldwide. In this study, a methodology which can be used to estimate irrigation amounts over a pilot area in Spain by exploiting remotely sensed soil moisture is proposed. Two high-resolution DISPATCH (DISaggregation based on Physical And Theoretical scale CHange) downscaled soil moisture products have been used: SMAP (Soil Moisture Active Passive) and SMOS (Soil Moisture and Ocean Salinity) at 1 km. The irrigation estimates have been obtained through the SM2RAIN algorithm, in which the evapotranspiration term has been improved to adequately reproduce the crop evapotranspiration over irrigated areas according to the FAO (Food and Agriculture Organization) model. The experiment exploiting the SMAP data at 1 km represents the main work analyzed in this study and covered the period from January 2016 to September 2017. The experiment with the SMOS data at 1 km, for which a longer time series is available, allowed the irrigation estimates to be extended back to 2011. For both of the experiments carried out, the proposed method performed well in reproducing the magnitudes of the irrigation amounts that actually occurred in four of the five pilot irrigation districts. The SMAP experiment, for which a more detailed analysis was performed, also provided satisfactory results in representing the spatial distribution and the timing of the irrigation events. In addition, the investigation into which term of the SM2RAIN algorithm plays the leading role in determining the amount of water entering into the soil highlights the importance of correct representation of the evapotranspiration process. Full article

(This article belongs to the Special Issue Irrigation Estimates and Management from Remote Sensing and Hydrological Modelling)

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23 pages, 27135 KiB

Open AccessArticle

IrrMapper: A Machine Learning Approach for High Resolution Mapping of Irrigated Agriculture Across the Western U.S.

by David Ketchum, Kelsey Jencso, Marco P. Maneta, Forrest Melton, Matthew O. Jones and Justin Huntington

Remote Sens. 2020, 12(14), 2328; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12142328 - 20 Jul 2020

Cited by 31 | Viewed by 13265

Abstract

High frequency and spatially explicit irrigated land maps are important for understanding the patterns and impacts of consumptive water use by agriculture. We built annual, 30 m resolution irrigation maps using Google Earth Engine for the years 1986–2018 for 11 western states within the conterminous U.S. Our map classifies lands into four classes: irrigated agriculture, dryland agriculture, uncultivated land, and wetlands. We built an extensive geospatial database of land cover from each class, including over 50,000 human-verified irrigated fields, 38,000 dryland fields, and over 500,000 km

^{2}

of uncultivated lands. We used 60,000 point samples from 28 years to extract Landsat satellite imagery, as well as climate, meteorology, and terrain data to train a Random Forest classifier. Using a spatially independent validation dataset of 40,000 points, we found our classifier has an overall binary classification (irrigated vs. unirrigated) accuracy of 97.8%, and a four-class overall accuracy of 90.8%. We compared our results to Census of Agriculture irrigation estimates over the seven years of available data and found good overall agreement between the 2832 county-level estimates (r

^{2}

= 0.90), and high agreement when estimates are aggregated to the state level (r

^{2}

= 0.94). We analyzed trends over the 33-year study period, finding an increase of 15% (15,000 km

^{2}

) in irrigated area in our study region. We found notable decreases in irrigated area in developing urban areas and in the southern Central Valley of California and increases in the plains of eastern Colorado, the Columbia River Basin, the Snake River Plain, and northern California. Full article

(This article belongs to the Special Issue Irrigation Estimates and Management from Remote Sensing and Hydrological Modelling)

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20 pages, 2923 KiB

Open AccessArticle

A New Low-Cost Device Based on Thermal Infrared Sensors for Olive Tree Canopy Temperature Measurement and Water Status Monitoring

by Miguel Noguera, Borja Millán, Juan José Pérez-Paredes, Juan Manuel Ponce, Arturo Aquino and José Manuel Andújar

Remote Sens. 2020, 12(4), 723; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12040723 - 22 Feb 2020

Cited by 28 | Viewed by 4751

Abstract

In recent years, many olive orchards, which are a major crop in the Mediterranean basin, have been converted into intensive or super high-density hedgerow systems. This configuration is more efficient in terms of yield per hectare, but at the same time the water requirements are higher than in traditional grove arrangements. Moreover, irrigation regulations have a high environmental (through water use optimization) impact and influence on crop quality and yield. The mapping of (spatio-temporal) variability with conventional water stress assessment methods is impractical due to time and labor constraints, which often involve staff training. To address this problem, this work presents the development of a new low-cost device based on a thermal infrared (IR) sensor for the measurement of olive tree canopy temperature and monitoring of water status. The performance of the developed device was compared to a commercial thermal camera. Furthermore, the proposed device was evaluated in a commercially managed olive orchard, where two different irrigation treatments were established: a full irrigation treatment (FI) and a regulated deficit irrigation (RDC), aimed at covering 100% and 50% of crop evapotranspiration (ETc), respectively. Predawn leaf water potential (Ψ_PD) and stomatal conductance (g_s), two widely accepted indicators for crop water status, were regressed to the measured canopy temperature. The results were promising, reaching a coefficient of determination R² ≥ 0.80. On the other hand, the crop water stress index (CWSI) was also calculated, resulting in a coefficient of determination R² ≥ 0.79. The outcomes provided by the developed device support its suitability for fast, low-cost, and reliable estimation of an olive orchard’s water status, even suppressing the need for supervised acquisition of reference temperatures. The newly developed device can be used for water management, reducing water usage, and for overall improvements to olive orchard management. Full article

(This article belongs to the Special Issue Irrigation Estimates and Management from Remote Sensing and Hydrological Modelling)

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Review

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37 pages, 4727 KiB

Open AccessReview

Remote Sensing for Plant Water Content Monitoring: A Review

by Carlos Quemada, José M. Pérez-Escudero, Ramón Gonzalo, Iñigo Ederra, Luis G. Santesteban, Nazareth Torres and Juan Carlos Iriarte

Remote Sens. 2021, 13(11), 2088; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13112088 - 26 May 2021

Cited by 23 | Viewed by 5627

Abstract

This paper reviews the different remote sensing techniques found in the literature to monitor plant water status, allowing farmers to control the irrigation management and to avoid unnecessary periods of water shortage and a needless waste of valuable water. The scope of this paper covers a broad range of 77 references published between the years 1981 and 2021 and collected from different search web sites, especially Scopus. Among them, 74 references are research papers and the remaining three are review papers. The different collected approaches have been categorized according to the part of the plant subjected to measurement, that is, soil (12.2%), canopy (33.8%), leaves (35.1%) or trunk (18.9%). In addition to a brief summary of each study, the main monitoring technologies have been analyzed in this review. Concerning the presentation of the data, different results have been obtained. According to the year of publication, the number of published papers has increased exponentially over time, mainly due to the technological development over the last decades. The most common sensor is the radiometer, which is employed in 15 papers (20.3%), followed by continuous-wave (CW) spectroscopy (12.2%), camera (10.8%) and THz time-domain spectroscopy (TDS) (10.8%). Excluding two studies, the minimum coefficient of determination (R²) obtained in the references of this review is 0.64. This indicates the high degree of correlation between the estimated and measured data for the different technologies and monitoring methods. The five most frequent water indicators of this study are: normalized difference vegetation index (NDVI) (12.2%), backscattering coefficients (10.8%), spectral reflectance (8.1%), reflection coefficient (8.1%) and dielectric constant (8.1%). Full article

(This article belongs to the Special Issue Irrigation Estimates and Management from Remote Sensing and Hydrological Modelling)

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33 pages, 3402 KiB

Open AccessReview

Dynamic Crop Models and Remote Sensing Irrigation Decision Support Systems: A Review of Water Stress Concepts for Improved Estimation of Water Requirements

by Massimo Tolomio and Raffaele Casa

Remote Sens. 2020, 12(23), 3945; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12233945 - 02 Dec 2020

Cited by 19 | Viewed by 5973

Abstract

Novel technologies for estimating crop water needs include mainly remote sensing evapotranspiration estimates and decision support systems (DSS) for irrigation scheduling. This work provides several examples of these approaches, that have been adjusted and modified over the years to provide a better representation of the soil–plant–atmosphere continuum and overcome their limitations. Dynamic crop simulation models synthetize in a formal way the relevant knowledge on the causal relationships between agroecosystem components. Among these, plant–water–soil relationships, water stress and its effects on crop growth and development. Crop models can be categorized into (i) water-driven and (ii) radiation-driven, depending on the main variable governing crop growth. Water stress is calculated starting from (i) soil water content or (ii) transpiration deficit. The stress affects relevant features of plant growth and development in a similar way in most models: leaf expansion is the most sensitive process and is usually not considered when planning irrigation, even though prolonged water stress during canopy development can consistently reduce light interception by leaves; stomatal closure reduces transpiration, directly affecting dry matter accumulation and therefore being of paramount importance for irrigation scheduling; senescence rate can also be increased by severe water stress. The mechanistic concepts of crop models can be used to improve existing simpler methods currently integrated in irrigation management DSS, provide continuous simulations of crop and water dynamics over time and set predictions of future plant–water interactions. Crop models can also be used as a platform for integrating information from various sources (e.g., with data assimilation) into process-based simulations. Full article

(This article belongs to the Special Issue Irrigation Estimates and Management from Remote Sensing and Hydrological Modelling)

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