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Open AccessArticle

Assessment of Multi-Date Sentinel-1 Polarizations and GLCM Texture Features Capacity for Onion and Sunflower Classification in an Irrigated Valley: An Object Level Approach

1
Center for Applied Research and Development in Computer Science and Telecommunications (CIADE-IT), University Blas Pascal, 5147 Córdoba, Argentina
2
INVAP, Government & Security Division, Av. Colón, 5003 Córdoba, Argentina
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Remote Sensing and SIG Laboratory, Hilario Ascasubi Agricultural Experimental Station, National Institute of Agricultural Technology (INTA), 8142 Hilario Ascasubi, Argentina
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Permanent Observatory of Agro-ecosystems, Climate and Water Institute-National Agricultural Research Centre (ICyA-CNIA), National Institute of Agricultural Technology (INTA), 1686 Buenos Aires, Argentina
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Colorado River Development Corporation (CORFO), Agrarian Development Ministry of Buenos Aires Province, 8148 Pedro Luro, Argentina
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INGTRADUCCIONES, Fructuoso Rivera 44, 5000 Córdoba, Argentina
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Image Processing Laboratory (IPL), University of Valencia, 46980 Valencia, Spain
*
Author to whom correspondence should be addressed.
Agronomy 2020, 10(6), 845; https://doi.org/10.3390/agronomy10060845
Received: 29 April 2020 / Revised: 4 June 2020 / Accepted: 11 June 2020 / Published: 13 June 2020
(This article belongs to the Special Issue Remote Sensing of Agricultural Monitoring)
The objective of this work is to evaluate the capacity of the C-band Synthetic Aperture Radar (SAR) time series imagery, acquired by the European satellite Sentinel-1 (S1), for the agriculture crop classification and its reliability to differentiate onion from sunflower, among others. The work then focused on classifying land cover in intensively cultivated agricultural regions. The study was developed in the Bonaerense Valley of the Colorado River (BVCR), Buenos Aires Province in Argentina, backed up by the field truth of 1634 field samples. In addition to the onion and sunflower crops, there are other crops present in the study area such as cereals, alfalfa, potatoes and maize, which are considered as the image background in the classification process. The field samples database was used for training and supporting a supervised classification with two machine learning algorithms—Random Forest (RF) and Support Vector Machine (SVM)—obtaining high levels of accuracy in each case. Different S1 SAR time-series features were used to assess the performance of S1 crop classification in terms of polarization VH+VV, Grey Level Co-occurrence Matrix (GLCM) image texture and a combination of both. The analysis of SAR data and their features was carried out at OBIA lot level (Object Based Image Analysis) showing an optimal strategy to counteract the effect of the residual and inherent speckle noise of the radar signal. In the process of differentiating onion and sunflower crops, the analysis of the VH+VV stack with the SVM algorithm delivered the best statistical classification results in terms of Overall Accuracy (OA) and Kappa Index, (Kp) when other crops (image background) were not considered (OA = 95.35%, Kp = 0.89). Certainly, the GLCM texture analysis derived from the S1 SAR images is a valuable source of information for obtaining very good classification results. When differentiating sunflower from onion considering also other crops present in the BVCR, the GLCM stack proved to be the most suitable dataset analyzed in this work (OA = 89.98%, Kp = 0.66 for SVM algorithm). This working methodology is applicable to other irrigated valleys in Argentina dedicated to intensive crops. There are also variables inherent to each lot, soil, crop and agricultural producer that differ according to the study area and that should be considered for each case in the future. View Full-Text
Keywords: Sentinel-1; time-series; supervised classification; land cover; onion crop; sunflower crop Sentinel-1; time-series; supervised classification; land cover; onion crop; sunflower crop
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MDPI and ACS Style

Caballero, G.R.; Platzeck, G.; Pezzola, A.; Casella, A.; Winschel, C.; Silva, S.S.; Ludueña, E.; Pasqualotto, N.; Delegido, J. Assessment of Multi-Date Sentinel-1 Polarizations and GLCM Texture Features Capacity for Onion and Sunflower Classification in an Irrigated Valley: An Object Level Approach. Agronomy 2020, 10, 845.

AMA Style

Caballero GR, Platzeck G, Pezzola A, Casella A, Winschel C, Silva SS, Ludueña E, Pasqualotto N, Delegido J. Assessment of Multi-Date Sentinel-1 Polarizations and GLCM Texture Features Capacity for Onion and Sunflower Classification in an Irrigated Valley: An Object Level Approach. Agronomy. 2020; 10(6):845.

Chicago/Turabian Style

Caballero, Gabriel R.; Platzeck, Gabriel; Pezzola, Alejandro; Casella, Alejandra; Winschel, Cristina; Silva, Samanta S.; Ludueña, Emilia; Pasqualotto, Nieves; Delegido, Jesús. 2020. "Assessment of Multi-Date Sentinel-1 Polarizations and GLCM Texture Features Capacity for Onion and Sunflower Classification in an Irrigated Valley: An Object Level Approach" Agronomy 10, no. 6: 845.

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