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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.

agronomy

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