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Article

Outlier Detection at the Parcel-Level in Wheat and Rapeseed Crops Using Multispectral and SAR Time Series

1
TerraNIS, 12 Avenue de l’Europe, 31520 Ramonville-Saint-Agne, France
2
IRIT/TéSA/INP-ENSEEIHT, University of Toulouse, 2 Rue Charles Camichel, 31000 Toulouse, France
3
IRIT/UPS, University of Toulouse, 118 Route de Narbonne, CEDEX 9, 31062 Toulouse, France
*
Author to whom correspondence should be addressed.
Academic Editor: Kuniaki Uto
Received: 2 February 2021 / Revised: 23 February 2021 / Accepted: 27 February 2021 / Published: 4 March 2021
(This article belongs to the Special Issue Remote Sensing for Precision Agriculture)
This paper studies the detection of anomalous crop development at the parcel-level based on an unsupervised outlier detection technique. The experimental validation is conducted on rapeseed and wheat parcels located in Beauce (France). The proposed methodology consists of four sequential steps: (1) preprocessing of synthetic aperture radar (SAR) and multispectral images acquired using Sentinel-1 and Sentinel-2 satellites, (2) extraction of SAR and multispectral pixel-level features, (3) computation of parcel-level features using zonal statistics and (4) outlier detection. The different types of anomalies that can affect the studied crops are analyzed and described. The different factors that can influence the outlier detection results are investigated with a particular attention devoted to the synergy between Sentinel-1 and Sentinel-2 data. Overall, the best performance is obtained when using jointly a selection of Sentinel-1 and Sentinel-2 features with the isolation forest algorithm. The selected features are co-polarized (VV) and cross-polarized (VH) backscattering coefficients for Sentinel-1 and five Vegetation Indexes for Sentinel-2 (among us, the Normalized Difference Vegetation Index and two variants of the Normalized Difference Water). When using these features with an outlier ratio of 10%, the percentage of detected true positives (i.e., crop anomalies) is equal to 94.1% for rapeseed parcels and 95.5% for wheat parcels. View Full-Text
Keywords: crop monitoring; Sentinel-1; Sentinel-2; isolation forest; anomaly detection; unsupervised; heterogeneity; vigor crop monitoring; Sentinel-1; Sentinel-2; isolation forest; anomaly detection; unsupervised; heterogeneity; vigor
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MDPI and ACS Style

Mouret, F.; Albughdadi, M.; Duthoit, S.; Kouamé, D.; Rieu, G.; Tourneret, J.-Y. Outlier Detection at the Parcel-Level in Wheat and Rapeseed Crops Using Multispectral and SAR Time Series. Remote Sens. 2021, 13, 956. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13050956

AMA Style

Mouret F, Albughdadi M, Duthoit S, Kouamé D, Rieu G, Tourneret J-Y. Outlier Detection at the Parcel-Level in Wheat and Rapeseed Crops Using Multispectral and SAR Time Series. Remote Sensing. 2021; 13(5):956. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13050956

Chicago/Turabian Style

Mouret, Florian, Mohanad Albughdadi, Sylvie Duthoit, Denis Kouamé, Guillaume Rieu, and Jean-Yves Tourneret. 2021. "Outlier Detection at the Parcel-Level in Wheat and Rapeseed Crops Using Multispectral and SAR Time Series" Remote Sensing 13, no. 5: 956. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13050956

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