2022
DOI: 10.1016/j.advwatres.2022.104130
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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

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Cited by 24 publications
(23 citation statements)
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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%
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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%
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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%