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Irrigation Mapping Using Satellite Remote Sensing

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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 July 2021) | Viewed by 51165

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Dr. Mehrez Zribi

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Guest Editor

CESBIO, University of Toulouse, CNRS/UT3/IRD/CNES/INRAE, 31400 Toulouse, France
Interests: remote sensing; synthetic aperture radar; GNSS-R; airborne microwave instrumentation; soil moisture; vegetation; hydrology; water resources
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Dr. Nicolas N. Baghdadi

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Co-Guest Editor

INRAE, Montpellier, France
Interests: Sentinel-1; irrigation mapping; agriculture; biomass

Dr. Valerie Demarez

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Co-Guest Editor

Universite Paul Sabatier Toulouse III, Toulouse, France
Interests: Sentinel-1; irrigation; agriculture
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Special Issue Information

Dear Colleagues,

In a context of scarcity of water resources and high consumption of resources by agriculture, irrigation becomes a major scientific and societal challenge. The scientific community has so far mainly used optical remote sensing for monitoring irrigation. The arrival of new free and open access optical and radar sensors (such as Copernicus Sentinel missions) with very good spatial and temporal resolutions has made it possible to intensify the work of mapping irrigation and water management with remote sensing data. In this issue, the main objective is to highlight the scientific works related to irrigation:

- Mapping of irrigated areas using optical and radar remote sensing

- Assimilation of satellite data in irrigation models for monitoring water consumption

- Estimation of land surface flows for better irrigation management

Dr. Mehrez Zribi
Dr. Nicolas N. Baghdadi
Dr. Valérie Demarez
Guest Editors

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Keywords

irrigation
water resources
agriculture
optical sensors
radar sensors

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Irrigation Mapping Using Satellite Remote Sensing: 2nd Edition in Remote Sensing (3 articles)

Published Papers (9 papers)

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Research

Jump to: Review

26 pages, 6037 KiB

Open AccessArticle

Irrigation Amounts and Timing Retrieval through Data Assimilation of Surface Soil Moisture into the FAO-56 Approach in the South Mediterranean Region

by Nadia Ouaadi, Lionel Jarlan, Saïd Khabba, Jamal Ezzahar, Michel Le Page and Olivier Merlin

Remote Sens. 2021, 13(14), 2667; https://doi.org/10.3390/rs13142667 - 7 Jul 2021

Cited by 19 | Viewed by 3760

Abstract

Agricultural water use represents more than 70% of the world’s freshwater through irrigation water inputs that are poorly known at the field scale. Irrigation monitoring is thus an important issue for optimizing water use in particular with regards to the water scarcity that the semi-arid regions are already facing. In this context, the aim of this study is to develop and evaluate a new approach to predict seasonal to daily irrigation timing and amounts at the field scale. The method is based on surface soil moisture (SSM) data assimilated into a simple land surface (FAO-56) model through a particle filter technique based on an ensemble of irrigation scenarios. The approach is implemented in three steps. First, synthetic experiments are designed to assess the impact of the frequency of observation, the errors on SSM and the a priori constraints on the irrigation scenarios for different irrigation techniques (flooding and drip). In a second step, the method is evaluated using in situ SSM measurements with different revisit times (3, 6 and 12 days) to mimic the available SSM product derived from remote sensing observation. Finally, SSM estimates from Sentinel-1 are used. Data are collected on different wheat fields grown in Morocco, for both flood and drip irrigation techniques in addition to rainfed fields used for an indirect evaluation of the method performance. Using in situ data, accurate results are obtained. With an observation every 6 days to mimic the Sentinel-1 revisit time, the seasonal amounts are retrieved with R > 0.98, RMSE < 32 mm and bias < 2.5 mm. Likewise, a good agreement is observed at the daily scale for flood irrigation as more than 70% of the detected irrigation events have a time difference from actual irrigation events shorter than 4 days. Over the drip irrigated fields, the statistical metrics are R = 0.74, RMSE = 24.8 mm and bias = 2.3 mm for irrigation amounts cumulated over 15 days. When using SSM products derived from Sentinel-1 data, the statistical metrics on 15-day cumulated amounts slightly dropped to R = 0.64, RMSE = 28.7 mm and bias = 1.9 mm. The metrics on the seasonal amount retrievals are close to assimilating in situ observations with R = 0.99, RMSE = 33.5 mm and bias = −18.8 mm. Finally, among four rainfed seasons, only one false event was detected. This study opens perspectives for the regional retrieval of irrigation amounts and timing at the field scale and for mapping irrigated/non irrigated areas. Full article

(This article belongs to the Special Issue Irrigation Mapping Using Satellite Remote Sensing)

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28 pages, 5559 KiB

Open AccessArticle

An Operational Framework for Mapping Irrigated Areas at Plot Scale Using Sentinel-1 and Sentinel-2 Data

by Hassan Bazzi, Nicolas Baghdadi, Ghaith Amin, Ibrahim Fayad, Mehrez Zribi, Valérie Demarez and Hatem Belhouchette

Remote Sens. 2021, 13(13), 2584; https://doi.org/10.3390/rs13132584 - 1 Jul 2021

Cited by 25 | Viewed by 3648

Abstract

In this study, we present an operational methodology for mapping irrigated areas at plot scale, which overcomes the limitation of terrain data availability, using Sentinel-1 (S1) C-band SAR (synthetic-aperture radar) and Sentinel-2 (S2) optical time series. The method was performed over a study site located near Orléans city of north-central France for four years (2017 until 2020). First, training data of irrigated and non-irrigated plots were selected using predefined selection criteria to obtain sufficient samples of irrigated and non-irrigated plots each year. The training data selection criteria is based on two irrigation metrics; the first one is a SAR-based metric derived from the S1 time series and the second is an optical-based metric derived from the NDVI (normalized difference vegetation index) time series of the S2 data. Using the newly developed irrigation event detection model (IEDM) applied for all S1 time series in VV (Vertical-Vertical) and VH (Vertical-Horizontal) polarizations, an irrigation weight metric was calculated for each plot. Using the NDVI time series, the maximum NDVI value achieved in the crop cycle was considered as a second selection metric. By fixing threshold values for both metrics, a dataset of irrigated and non-irrigated samples was constructed each year. Later, a random forest classifier (RF) was built for each year in order to map the summer agricultural plots into irrigated/non-irrigated. The irrigation classification model uses the S1 and NDVI time series calculated over the selected training plots. Finally, the proposed irrigation classifier was validated using real in situ data collected each year. The results show that, using the proposed classification procedure, the overall accuracy for the irrigation classification reaches 84.3%, 93.0%, 81.8%, and 72.8% for the years 2020, 2019, 2018, and 2017, respectively. The comparison between our proposed classification approach and the RF classifier built directly from in situ data showed that our approach reaches an accuracy nearly similar to that obtained using in situ RF classifiers with a difference in overall accuracy not exceeding 6.2%. The analysis of the obtained classification accuracies of the proposed method with precipitation data revealed that years with higher rainfall amounts during the summer crop-growing season (irrigation period) had lower overall accuracy (72.8% for 2017) whereas years encountering a drier summer had very good accuracy (93.0% for 2019). Full article

(This article belongs to the Special Issue Irrigation Mapping Using Satellite Remote Sensing)

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21 pages, 132270 KiB

Open AccessArticle

Identifying Seasonal Groundwater-Irrigated Cropland Using Multi-Source NDVI Time-Series Images

by Amit Kumar Sharma, Laurence Hubert-Moy, Sriramulu Buvaneshwari, Muddu Sekhar, Laurent Ruiz, Hemanth Moger, Soumya Bandyopadhyay and Samuel Corgne

Remote Sens. 2021, 13(10), 1960; https://doi.org/10.3390/rs13101960 - 18 May 2021

Cited by 7 | Viewed by 3652

Abstract

Groundwater has become a major source of irrigation in the past few decades in India, but as it comes from millions of individual borewells owned by smallholders irrigating small fields, it is difficult to quantify the actual irrigated area across seasons and years. This study’s main goal was to monitor seasonal irrigated cropland using multiple optical satellite images. The proposed research was performed over the Berambadi watershed, an experimental site in southern peninsular India. While cloud cover during crop growth is the greatest obstacle to optical remote sensing in tropical regions, the cloud-free images from multiple optical satellite platforms (Landsat-8 (OLI), EO1 (ALI), IRS-P6 (LISS3 and LISS4), and Spot5Take5 (HRG2)) were used to fill data gaps during crop growth periods. The seasonal cumulative normalized difference vegetation index (NDVI) was calculated and resampled at 5 m spatial resolution for various cropping seasons. The support vector machine (SVM) classification was applied to seasonal cumulative NDVI images for irrigated cropland area classification. Validation of the classified irrigated cropland was performed by calculating kappa coefficients for three cropping seasons (summer, kharif, and rabi) from 2014–2016 using ground observations. Kappa coefficients ranged from 0.81–0.96 for 2014–2015 and 0.62–0.89 for 2015–2016, except for summer 2016, when it was 1.00. Groundwater irrigation in the watershed ranged from 4.6% to 16.5% of total cropland during these cropping seasons. These results showed that multi-source optical satellite data are relevant for quantifying areas under groundwater irrigation in tropical regions. Full article

(This article belongs to the Special Issue Irrigation Mapping Using Satellite Remote Sensing)

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19 pages, 4260 KiB

Open AccessArticle

Comparison of Thermal Infrared-Derived Maps of Irrigated and Non-Irrigated Vegetation in Urban and Non-Urban Areas of Southern California

by Red Willow Coleman, Natasha Stavros, Glynn Hulley and Nicholas Parazoo

Remote Sens. 2020, 12(24), 4102; https://doi.org/10.3390/rs12244102 - 15 Dec 2020

Cited by 13 | Viewed by 4271

Abstract

It is important to understand the distribution of irrigated and non-irrigated vegetation in rapidly expanding urban areas that are experiencing climate-induced changes in water availability, such as Los Angeles, California. Mapping irrigated vegetation in Los Angeles is necessary for developing sustainable water use practices and accurately accounting for the megacity’s carbon exchange and water balance changes. However, pre-existing maps of irrigated vegetation are largely limited to agricultural regions and are too coarse to resolve heterogeneous urban landscapes. Previous research suggests that irrigation has a strong cooling effect on vegetation, especially in semi-arid environments. The July 2018 launch of the ECOsystem Spaceborne Thermal Radiometer on Space Station (ECOSTRESS) offers an opportunity to test this hypothesis using retrieved land surface temperature (LST) data in complex, heterogeneous urban/non-urban environments. In this study, we leverage Landsat 8 optical imagery and 30 m sharpened afternoon summertime ECOSTRESS LST, then apply very high-resolution (0.6–10 m) vegetation fraction weighting to produce a map of irrigated and non-irrigated vegetation in Los Angeles. This classification was compared to other classifications using different combinations of sensors in order to offer a preliminary accuracy and uncertainty assessment. This approach verifies that ECOSTRESS LST data provides an accurate map (98.2% accuracy) of irrigated urban vegetation in southern California that has the potential to reduce uncertainties in regional carbon and hydrological cycle models. Full article

(This article belongs to the Special Issue Irrigation Mapping Using Satellite Remote Sensing)

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19 pages, 2489 KiB

Open AccessArticle

Semantic Segmentation of Sentinel-2 Imagery for Mapping Irrigation Center Pivots

by Lukas Graf, Heike Bach and Dirk Tiede

Remote Sens. 2020, 12(23), 3937; https://doi.org/10.3390/rs12233937 - 1 Dec 2020

Cited by 13 | Viewed by 4067

Abstract

Estimating the number and size of irrigation center pivot systems (CPS) from remotely sensed data, using artificial intelligence (AI), is a potential information source for assessing agricultural water use. In this study, we identified two technical challenges in the neural-network-based classification: Firstly, an effective reduction of the feature space of the remote sensing data to shorten training times and increase classification accuracy is required. Secondly, the geographical transferability of the AI algorithms is a pressing issue if AI is to replace human mapping efforts one day. Therefore, we trained the semantic image segmentation algorithm U-NET on four spectral channels (U-NET SPECS) and the first three principal components (U-NET principal component analysis (PCA)) of ESA/Copernicus Sentinel-2 images on a study area in Texas, USA, and assessed the geographic transferability of the trained models to two other sites: the Duero basin, in Spain, and South Africa. U-NET SPECS outperformed U-NET PCA at all three study areas, with the highest f1-score at Texas (0.87, U-NET PCA: 0.83), and a value of 0.68 (U-NET PCA: 0.43) in South Africa. At the Duero, both models showed poor classification accuracy (f1-score U-NET PCA: 0.08; U-NET SPECS: 0.16) and segmentation quality, which was particularly evident in the incomplete representation of the center pivot geometries. In South Africa and at the Duero site, a high rate of false positive and false negative was observed, which made the model less useful, especially at the Duero test site. Thus, geographical invariance is not an inherent model property and seems to be mainly driven by the complexity of land-use pattern. We do not consider PCA a suited spectral dimensionality reduction measure in this. However, shorter training times and a more stable training process indicate promising prospects for reducing computational burdens. We therefore conclude that effective dimensionality reduction and geographic transferability are important prospects for further research towards the operational usage of deep learning algorithms, not only regarding the mapping of CPS. Full article

(This article belongs to the Special Issue Irrigation Mapping Using Satellite Remote Sensing)

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19 pages, 10210 KiB

Open AccessFeature PaperArticle

Detection of Irrigated and Rainfed Crops in Temperate Areas Using Sentinel-1 and Sentinel-2 Time Series

by Yann Pageot, Frédéric Baup, Jordi Inglada, Nicolas Baghdadi and Valérie Demarez

Remote Sens. 2020, 12(18), 3044; https://doi.org/10.3390/rs12183044 - 17 Sep 2020

Cited by 43 | Viewed by 6114

Abstract

The detection of irrigated areas by means of remote sensing is essential to improve agricultural water resource management. Currently, data from the Sentinel constellation offer new possibilities for mapping irrigated areas at the plot scale. Until now, few studies have used Sentinel-1 (S1) and Sentinel-2 (S2) data to provide approaches for mapping irrigated plots in temperate areas. This study proposes a method for detecting irrigated and rainfed plots in a temperate area (southwestern France) jointly using optical (Sentinel-2), radar (Sentinel-1) and meteorological (SAFRAN) time series, through a classification algorithm. Monthly cumulative indices calculated from these satellite data were used in a Random Forest classifier. Two data years have been used, with different meteorological characteristics, allowing the performance of the method to be analysed under different climatic conditions. The combined use of the whole cumulative data (radar, optical and weather) improves the irrigated crop classifications (Overall Accuary (OA) ≈ 0.7) compared to the classifications obtained using each data separately (OA < 0.5). The use of monthly cumulative rainfall allows a significant improvement of the Fscore of irrigated and rainfed classes. Our study also reveals that the use of cumulative monthly indices leads to performances similar to those of the use of 10-day images while considerably reducing computational resources. Full article

(This article belongs to the Special Issue Irrigation Mapping Using Satellite Remote Sensing)

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22 pages, 2995 KiB

Open AccessArticle

Potential for the Detection of Irrigation Events on Maize Plots Using Sentinel-1 Soil Moisture Products

by Michel Le Page, Lionel Jarlan, Marcel M. El Hajj, Mehrez Zribi, Nicolas Baghdadi and Aaron Boone

Remote Sens. 2020, 12(10), 1621; https://doi.org/10.3390/rs12101621 - 19 May 2020

Cited by 45 | Viewed by 5398

Abstract

Although the real timing and flow rates used for crop irrigation are controlled at the scale of individual plots by the irrigator, they are not generally known by the farm upper management. This information is nevertheless essential, not only to compute the water balance of irrigated plots and to schedule irrigation, but also for the management of water resources at regional scales. The aim of the present study was to detect irrigation timing using time series of surface soil moisture (SSM) derived from Sentinel-1 radar observations. The method consisted of assessing the direction of change of surface soil moisture (SSM) between observations and a water balance model, and to use thresholds to be calibrated. The performance of the approach was assessed on the F-score quantifying the accuracy of the irrigation event detections and ranging from 0 (none of the irrigation timing is correct) to 100 (perfect irrigation detection). The study focused on five irrigated and one rainfed plot of maize in South-West France, where the approach was tested using in situ measurements and surface soil moisture (SSM) maps derived from Sentinel-1 radar data. The use of in situ data showed that (1) irrigation timing was detected with a good accuracy (F-score in the range (80–83) for all plots) and (2) the optimal revisit time between two SSM observations was 2–4 days. The higher uncertainties of microwave SSM products, especially when the crop is well developed (normalized difference of vegetation index (NDVI) > 0.7), degraded the score (F-score = 69), but various possibilities of improvement were discussed. This paper opens perspectives for the irrigation detection at the plot scale over large areas and thus for the improvement of irrigation water management. Full article

(This article belongs to the Special Issue Irrigation Mapping Using Satellite Remote Sensing)

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32 pages, 7658 KiB

Open AccessArticle

Near Real-Time Irrigation Detection at Plot Scale Using Sentinel-1 Data

by Hassan Bazzi, Nicolas Baghdadi, Ibrahim Fayad, Mehrez Zribi, Hatem Belhouchette and Valérie Demarez

Remote Sens. 2020, 12(9), 1456; https://doi.org/10.3390/rs12091456 - 4 May 2020

Cited by 39 | Viewed by 4978

Abstract

In the context of monitoring and assessment of water consumption in the agricultural sector, the objective of this study is to build an operational approach capable of detecting irrigation events at plot scale in a near real-time scenario using Sentinel-1 (S1) data. The proposed approach is a decision tree-based method relying on the change detection in the S1 backscattering coefficients at plot scale. First, the behavior of the S1 backscattering coefficients following irrigation events has been analyzed at plot scale over three study sites located in Montpellier (southeast France), Tarbes (southwest France), and Catalonia (northeast Spain). To eliminate the uncertainty between rainfall and irrigation, the S1 synthetic aperture radar (SAR) signal and the soil moisture estimations at grid scale (10 km × 10 km) have been used. Then, a tree-like approach has been constructed to detect irrigation events at each S1 date considering additional filters to reduce ambiguities due to vegetation development linked to the growth cycle of different crops types as well as the soil surface roughness. To enhance the detection of irrigation events, a filter using the normalized differential vegetation index (NDVI) obtained from Sentinel-2 optical images has been proposed. Over the three study sites, the proposed method was applied on all possible S1 acquisitions in ascending and descending modes. The results show that 84.8% of the irrigation events occurring over agricultural plots in Montpellier have been correctly detected using the proposed method. Over the Catalonian site, the use of the ascending and descending SAR acquisition modes shows that 90.2% of the non-irrigated plots encountered no detected irrigation events whereas 72.4% of the irrigated plots had one and more detected irrigation events. Results over Catalonia also show that the proposed method allows the discrimination between irrigated and non-irrigated plots with an overall accuracy of 85.9%. In Tarbes, the analysis shows that irrigation events could still be detected even in the presence of abundant rainfall events during the summer season where two and more irrigation events have been detected for 90% of the irrigated plots. The novelty of the proposed method resides in building an effective unsupervised tool for near real-time detection of irrigation events at plot scale independent of the studied geographical context. Full article

(This article belongs to the Special Issue Irrigation Mapping Using Satellite Remote Sensing)

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Review

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26 pages, 2000 KiB

Open AccessReview

A Review of Irrigation Information Retrievals from Space and Their Utility for Users

by Christian Massari, Sara Modanesi, Jacopo Dari, Alexander Gruber, Gabrielle J. M. De Lannoy, Manuela Girotto, Pere Quintana-Seguí, Michel Le Page, Lionel Jarlan, Mehrez Zribi, Nadia Ouaadi, Mariëtte Vreugdenhil, Luca Zappa, Wouter Dorigo, Wolfgang Wagner, Joost Brombacher, Henk Pelgrum, Pauline Jaquot, Vahid Freeman, Espen Volden, Diego Fernandez Prieto, Angelica Tarpanelli, Silvia Barbetta and Luca Brocca Show full author list Hide full author list

Remote Sens. 2021, 13(20), 4112; https://doi.org/10.3390/rs13204112 - 14 Oct 2021

Cited by 92 | Viewed by 11433

Abstract

Irrigation represents one of the most impactful human interventions in the terrestrial water cycle. Knowing the distribution and extent of irrigated areas as well as the amount of water used for irrigation plays a central role in modeling irrigation water requirements and quantifying the impact of irrigation on regional climate, river discharge, and groundwater depletion. Obtaining high-quality global information about irrigation is challenging, especially in terms of quantification of the water actually used for irrigation. Here, we review existing Earth observation datasets, models, and algorithms used for irrigation mapping and quantification from the field to the global scale. The current observation capacities are confronted with the results of a survey on user requirements on satellite-observed irrigation for agricultural water resources’ management. Based on this information, we identify current shortcomings of irrigation monitoring capabilities from space and phrase guidelines for potential future satellite missions and observation strategies. Full article

(This article belongs to the Special Issue Irrigation Mapping Using Satellite Remote Sensing)

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