Special Issue "Irrigation Mapping Using Satellite Remote Sensing"

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

Deadline for manuscript submissions: 31 July 2021.

Special Issue Editors

Dr. Mehrez Zribi
E-Mail Website
Guest Editor
Université de Toulouse, Toulouse, France
Interests: airborne instrumentation for land surfaces; microwave remote sensing; GNSS-R; GNSS; land surfaces; spatial hydrology
Special Issues and Collections in MDPI journals
Dr. Nicolas N. Baghdadi
E-Mail Website
Co-Guest Editor
INRAE, Montpellier, France
Interests: Sentinel-1; irrigation mapping; agriculture; biomass
Dr. Valerie Demarez
E-Mail Website
Co-Guest Editor
Universite Paul Sabatier Toulouse III, Toulouse, France
Interests: Sentinel-1; irrigation; agriculture
Special Issues and Collections in MDPI journals

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

Manuscript Submission Information

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Keywords

  • irrigation
  • water resources
  • agriculture
  • optical sensors
  • radar sensors

Published Papers (6 papers)

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Research

Article
Identifying Seasonal Groundwater-Irrigated Cropland Using Multi-Source NDVI Time-Series Images
Remote Sens. 2021, 13(10), 1960; https://doi.org/10.3390/rs13101960 - 18 May 2021
Viewed by 325
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. [...] Read more.
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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Article
Comparison of Thermal Infrared-Derived Maps of Irrigated and Non-Irrigated Vegetation in Urban and Non-Urban Areas of Southern California
Remote Sens. 2020, 12(24), 4102; https://doi.org/10.3390/rs12244102 - 15 Dec 2020
Viewed by 818
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 [...] Read more.
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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Article
Semantic Segmentation of Sentinel-2 Imagery for Mapping Irrigation Center Pivots
Remote Sens. 2020, 12(23), 3937; https://doi.org/10.3390/rs12233937 - 01 Dec 2020
Viewed by 636
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 [...] Read more.
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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Article
Detection of Irrigated and Rainfed Crops in Temperate Areas Using Sentinel-1 and Sentinel-2 Time Series
Remote Sens. 2020, 12(18), 3044; https://doi.org/10.3390/rs12183044 - 17 Sep 2020
Cited by 4 | Viewed by 1428
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) [...] Read more.
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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Article
Potential for the Detection of Irrigation Events on Maize Plots Using Sentinel-1 Soil Moisture Products
Remote Sens. 2020, 12(10), 1621; https://doi.org/10.3390/rs12101621 - 19 May 2020
Cited by 6 | Viewed by 1554
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 [...] Read more.
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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Article
Near Real-Time Irrigation Detection at Plot Scale Using Sentinel-1 Data
Remote Sens. 2020, 12(9), 1456; https://doi.org/10.3390/rs12091456 - 04 May 2020
Cited by 5 | Viewed by 1258
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 [...] Read more.
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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