Abstract
The overarching aim of this research was to develop a method for deriving crop maps from a time series of Sentinel-2 images between 2017 and 2018 to address global challenges in agriculture and food security. This study is the first step towards improving crop mapping based on phenological features retrieved from an object-based time series on a national scale. Five main crops in Israel were classified: wheat, barley, cotton, carrot, and chickpea. To optimize the object-based classification process, different characteristics and inputs of the mean shift segmentation algorithm were tested, including vegetation indices, three-band combinations, and high/low emphasis on the spatial and spectral characteristics. Four known vegetation indices (VIs)-based time series were tested. Additionally, we compared two widely used machine learning methods for crop classification, support vector machine (SVM) and random forest (RF), in addition to a newer classifier, extreme gradient boosting (XGBoost). Lastly, we examined two accuracy measures—overall accuracy (OA) and area under the curve (AUC)—in order to optimally estimate the accuracy in the case of imbalanced class representation. Mean shift best performed when emphasizing both the spectral and spatial characteristics while using the green, red, and near-infrared (NIR) bands as input. Both accuracy measures showed that RF and XGBoost classified different types of crops with significantly greater success than achieved by SVM. Nevertheless, AUC was better able to represent the significant differences between the classification algorithms than OA was. None of the VIs showed a significantly higher contribution to the classification. However, normalized difference infrared index (NDII) with XGBoost classifier showed the highest AUC results (88%). This study demonstrates that the short-wave infrared (SWIR) band with XGBoost improves crop type classification results. Furthermore, the study emphasizes the importance of addressing imbalanced classification datasets by using a proper accuracy measure. Since object-based classification and phenological features derived from a VI-based time series are widely used to produce crop maps, the current study is also relevant for operational agricultural management and informatics at large scales.
Highlights
normalized difference vegetation index (NDVI), optimized soil-adjusted vegetation index (OSAVI), normalized difference red edge index (NDRE), and normalized difference infrared index (NDII) time-series-based phenology features were used as input for the random forest (RF), support vector machine (SVM), and XGBoost classifiers
One-way analysis of variance (ANOVA) showed that there was no significant difference between different vegetation indices (VIs) both for area under the curve (AUC) results (F (3, 8) = 0.41, MSE = 12, p = 0.74) and overall accuracy (OA) results (F (3, 8) = 1.34, MSE = 10, p = 0.33)
The results showed that for both inputs, the number of segments resulting from a high emphasis on the spectral and spatial characteristics was similar to the number of segments obtained when using a high emphasis on the spectral characteristic and low emphasis spatial characteristic
Summary
Agriculture plays a crucial role in both water management and food security. Irrigated agriculture is one of the most significant water consumers [1], and agricultural production is key to food supply. Due to the impact of agriculture on the world’s resources, it must be optimally managed to secure our future. Identifying which crop is grown where and when is essential for better decision-making regarding water and fertilization. Crop maps can facilitate crop-specific water consumption estimates [2,3] and provide a basis for regional yield forecasting [4]. Decision-makers may rely on crop maps as a reliable tool to facilitate better agricultural management
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