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Article

Generating Up-to-Date Crop Maps Optimized for Sentinel-2 Imagery in Israel

1
Agricultural Research Organization, Volcani Institute, Institute of Soil, Water and Environmental Sciences, HaMaccabim Road 68, P.O. Box 15159, Rishon LeZion 7528809, Israel
2
The Robert H. Smith Institute for Plant Sciences and Genetics in Agriculture, The Hebrew University of Jerusalem, P.O. Box 12, Rehovot 7610001, Israel
*
Author to whom correspondence should be addressed.
Academic Editors: James Murphy and Colin Olson
Remote Sens. 2021, 13(17), 3488; https://doi.org/10.3390/rs13173488
Received: 26 June 2021 / Revised: 28 August 2021 / Accepted: 30 August 2021 / Published: 2 September 2021
(This article belongs to the Special Issue Theory and Application of Machine Learning in Remote Sensing)
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. View Full-Text
Keywords: remote sensing; Sentinel-2; classification; segmentation; phenology feature; accuracy assessment; machine learning; vegetation indices; crop mapping; agriculture remote sensing; Sentinel-2; classification; segmentation; phenology feature; accuracy assessment; machine learning; vegetation indices; crop mapping; agriculture
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MDPI and ACS Style

Goldberg, K.; Herrmann, I.; Hochberg, U.; Rozenstein, O. Generating Up-to-Date Crop Maps Optimized for Sentinel-2 Imagery in Israel. Remote Sens. 2021, 13, 3488. https://doi.org/10.3390/rs13173488

AMA Style

Goldberg K, Herrmann I, Hochberg U, Rozenstein O. Generating Up-to-Date Crop Maps Optimized for Sentinel-2 Imagery in Israel. Remote Sensing. 2021; 13(17):3488. https://doi.org/10.3390/rs13173488

Chicago/Turabian Style

Goldberg, Keren, Ittai Herrmann, Uri Hochberg, and Offer Rozenstein. 2021. "Generating Up-to-Date Crop Maps Optimized for Sentinel-2 Imagery in Israel" Remote Sensing 13, no. 17: 3488. https://doi.org/10.3390/rs13173488

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