Double-scale analysis on the detectability of irrigation signals from remote sensing soil moisture over an area with complex topography in central Italy

Dari, Jacopo; Brocca, Luca; Quintana‐Seguí, Pere; Casadei, Stefano; Escorihuela, Maria-José; Stefan, Vivien; Morbidelli, Renato

doi:10.1016/j.advwatres.2022.104130

Cited by 24 publications

(23 citation statements)

References 58 publications

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“…The classification of irrigated and rain-fed areas proposed by Dari et al [37] relies mainly on two soil moisture indices derived from soil moisture time series data using the temporal stability theorem introduced by Vachaud et al [42] initially developed to optimize soil moisture monitoring. These two soil moisture indices describe the spatio-temporal dynamics of the soil moisture.…”

Section: Spatial and Temporal Soil Moisture Anomaliesmentioning

confidence: 99%

“…The proposed framework, thereby referred to S 2 IM (Sentinel-1/Sentinel-2 Irrigation Mapping), was tested over a study site located in north-central France for four years having different climatic conditions. Dari et al [22,36,37] also proposed an unsupervised approach for irrigated area mapping based on the K-means clustering algorithm using satellite derived soil moisture products. The proposed approach is based on deriving spatio-temporal indices describing the soil moisture dynamics and classifies irrigated/rain-fed plots by applying the K-means clustering using the derived soil moisture metrics.…”

Section: Introductionmentioning

confidence: 99%

“…Dari et al [36] first tested the soil moisture based approach using satellite soil moisture derived from the SMAP, SMOS and ASCAT and Sentinel-1 at 1 Km scale over an arid area in Catalonia, Spain. Later on, Dari et al [37] adapted the proposed soil moisture-based approach using a plot scale soil moisture product provided by the French Data and Services center for continental surfaces (Theia) over a semi-arid area of central Italy to map irrigated areas at 100 m × 100 m scale.…”

Section: Introductionmentioning

confidence: 99%

“…The objective of this paper is to present a comparative analysis between the efficacity of both models proposed by Bazzi et al [20] (S 2 IM) and Dari et al [37] (later referred to as Dari model) to map irrigated areas at plot scale. Two different climatic contexts, Mediterranean and semi-oceanic, were considered in this study in order to assess the robustness of each method with respect to the climatic conditions that may highly alter the classification accuracies.…”

Section: Introductionmentioning

confidence: 99%

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Comparative Analysis between Two Operational Irrigation Mapping Models over Study Sites in Mediterranean and Semi-Oceanic Regions

Bazzi

Baghdadi

Zribi

2022

Water

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Accurate information about the irrigated surface is essential to help assess the impact of irrigation on water consumption, the hydrological cycle and regional climate. In this study, we compare recently developed operational and spatially transferrable classification models proposed for irrigation mapping. The first model suggests the use of spatio-temporal soil moisture indices derived from the Sentinel-1/2 soil moisture product (S2MP) at plot scale to map irrigated areas using the unsupervised K-means clustering algorithm (Dari model). The second model called the Sentinel-1/2 Irrigation mapping (S2IM) is a classification model based on the use the Sentinel-1 (S1) and Sentinel-2 (S2) time series data. Five study cases were examined including four studied years in a semi-oceanic area in north-central France (between 2017 and 2020) and one year (2020) in a Mediterranean context in south France. Main results showed that the soil-moisture based model using K-means clustering (Dari model) performs well for irrigation mapping but remains less accurate than the S2IM model. The overall accuracy of the Dari model ranged between 72.1% and 78.4% across the five study cases. The Dari model was found to be limited over humid conditions as it fails to correctly distinguish rain-fed plots from irrigated plots with an accuracy of the rain-fed class reaching 24.2% only. The S2IM showed the best accuracy in the five study cases with an overall accuracy ranging between 72.8% and 93.0%. However, for humid climatic conditions, the S2IM had an accuracy of the rain-fed class reaching 62.0%. The S2IM is thus superior in terms of accuracy but with higher complexity for application than the Dari model that remains simple yet effective for irrigation mapping.

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Section: Spatial and Temporal Soil Moisture Anomaliesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Comparative Analysis between Two Operational Irrigation Mapping Models over Study Sites in Mediterranean and Semi-Oceanic Regions

Bazzi

Baghdadi

Zribi

2022

Water

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“…In contrast, others have used unsupervised decision tree-based classification [4], achieving a good overall performance of over 80% accuracy. Other studies have achieved comparable accuracy with a 𝑘-means clustering algorithm [15,16] and a deep learning approach of a convolution neural network (CNN) architecture [3]. These studies, however, apply these models to irrigation-related indices such as the Normalized Difference Vegetation Index (NDVI) to generate large-scale irrigation maps without distinguishing the type of irrigation method.…”

Section: Related Workmentioning

confidence: 99%

Diesel GenSat

Lukuyu

Bensch

Conlon

et al. 2022

Proceedings of the Thirteenth ACM International Conference on Future Energy Systems

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In Sub-Saharan Africa, electricity access is progressing, but electricity use for economic growth remains stagnant. Powering economies sustainably is vital to enhancing livelihoods and is particularly challenging in agriculture-led rural economies. The financial viability of electrification hinges on identifying potential sources of demand to ensure sustainable revenues for utilities, which in turn provides economic benefits to consumers.This paper presents a technique for identifying areas with dieselpowered irrigation activity in Ethiopia based on remotely sensed data. We develop and evaluate a supervised classification model based on data collected in the Western Ethiopia Highlands on irrigation practices. We find that a feature-based multivariate time series classification approach combined with a 𝑘-Nearest Neighbors model accurately predicts about 75% of areas with diesel-powered irrigation activity. Our results suggest that our technique could be valuable in identifying areas in Ethiopia with potential anchor loads for electricity grid extension by replacing existing diesel pumps for irrigation with electric pumps. Guidance on financially-viable areas to expand electricity networks, especially those with economicallyvibrant activities like irrigation, is crucial for enabling electricity service providers to recover costs and expand access to more communities more quickly. CCS CONCEPTS• General and reference → Empirical studies; • Computing methodologies → Supervised learning by classification.

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Evolution of freshwater availability in a climate‐changing Mediterranean context: The case of Umbria region, central Italy

Dari,

Flammini,

Morbidelli

et al. 2023

Hydrological Processes

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Freshwater availability is the litmus test of the ongoing global warming, but local effects in relation to larger scale behaviours are often ambiguous. In this study, 19 indicators derived by historical rainfall and temperature records have been used to reconstruct climatic trends over the Umbria region (central Italy). The scenario emerging from observations of the last 70–100 years consists in increasing air temperatures and decreasing yearly precipitation depths, which are differently distributed over the 12 months with respect to the past. As a second step, the response in terms of freshwater availability has been assessed by analysing runoff and groundwater records. A clear reduction in yearly water volumes flowing through the outlet stations of five pilot basins during the last century (1927–2020) has been found, quantified in a median loss rate of equivalent surface water thickness of −1.62 mm/year. Monthly mean groundwater levels show a slight increasing trend for most of the monitoring stations, but such an analysis has been carried out over a limited and more recent period (2006–2020). Hence, it can be affected by the uncertainty commonly associated to the interpretation of trend analyses referring to short temporal windows. This last critical point is strengthened by an analysis of the runoff volume limited to the period 2006–2020 from which, in contrast with the results provided by the aforementioned long‐term investigation, an increasing trend has been derived.

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Double-scale analysis on the detectability of irrigation signals from remote sensing soil moisture over an area with complex topography in central Italy

Cited by 24 publications

References 58 publications

Comparative Analysis between Two Operational Irrigation Mapping Models over Study Sites in Mediterranean and Semi-Oceanic Regions

Comparative Analysis between Two Operational Irrigation Mapping Models over Study Sites in Mediterranean and Semi-Oceanic Regions

Diesel GenSat

Evolution of freshwater availability in a climate‐changing Mediterranean context: The case of Umbria region, central Italy

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