Improving the Accuracy of Multiple Algorithms for Crop Classification by Integrating Sentinel-1 Observations with Sentinel-2 Data
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In this article, the authors evaluated the performance of 22 nonparametric classifiers on which most of these algorithms had not been tested before with SAR data and found that the best performing scenario was the one integrating VH and VV with normalized difference vegetation index (NDVI) and cubic support vector machine (SVM) as the classifier with the highest accuracy among all those tested.Abstract:
The availability of an unprecedented amount of open remote sensing data, such as Sentinel-1 and -2 data within the Copernicus program, has boosted the idea of combining the use of optical and radar data to improve the accuracy of agricultural applications such as crop classification. Sentinel-1’s Synthetic Aperture Radar (SAR) provides co- and cross-polarized backscatter, which offers the opportunity to monitor agricultural crops using radar at high spatial and temporal resolution. In this study, we assessed the potential of integrating Sentinel-1 information (VV and VH backscatter and their ratio VH/VV with Sentinel-2A data (NDVI) to perform crop classification and to define which are the most important input data that provide the most accurate classification results. Further, we examined the temporal dynamics of remote sensing data for cereal, horticultural, and industrial crops, perennials, deciduous trees, and legumes. To select the best SAR input feature, we tried two approaches, one based on classification with only SAR features and one based on integrating SAR with optical data. In total, nine scenarios were tested. Furthermore, we evaluated the performance of 22 nonparametric classifiers on which most of these algorithms had not been tested before with SAR data. The results revealed that the best performing scenario was the one integrating VH and VV with normalized difference vegetation index (NDVI) and cubic support vector machine (SVM) (the kernel function of the classifier is cubic) as the classifier with the highest accuracy among all those tested.read more
Citations
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From parcel to continental scale – A first European crop type map based on Sentinel-1 and LUCAS Copernicus in-situ observations
Raphaël d'Andrimont,Astrid Verhegghen,Guido Lemoine,Pieter Kempeneers,Michele Meroni,Marijn van der Velde +5 more
TL;DR: In this paper, the authors presented the first continental crop type map at 10m spatial resolution for the EU based on S1A and S1B Synthetic Aperture Radar observations for the year 2018.
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Mapping Crop Types of Germany by Combining Temporal Statistical Metrics of Sentinel-1 and Sentinel-2 Time Series with LPIS Data
Sarah Asam,Ursula Gessner,Roger Almengor González,Martina Wenzl,Jennifer Kriese,Claudia Kuenzer +5 more
TL;DR: In this article , an approach combining multispectral and Synthetic Aperture Radar (SAR) time series for the classification of 17 crop classes at 10 m spatial resolution for Germany in the year 2018 was presented.
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Delineation of field boundary from multispectral satellite images through U-Net segmentation and template matching
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TL;DR: Identification of seasonal changes and their impact using collected geospatial images within a specific time frame to predict land utilized for agriculture.
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Fusion of time-series optical and SAR images using 3D convolutional neural networks for crop classification
TL;DR: In this paper , a 3D-CNN framework was proposed for classifying crops that is based on the fusion of radar and optical time series and also fully exploits 3D spatial-temporal information.
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Early identification of crop types using Sentinel-2 satellite images and an incremental multi-feature ensemble method (Case study: Shahriar, Iran)
TL;DR: In this article , a multi-feature ensemble classifier was proposed to identify the cultivated products (wheat, barley, alfalfa, and rapeseed) in Shahriar's farmlands before cropping season ends using time-series of Sentinel-2 satellite images.
References
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TL;DR: The potential of Sentinel-1 VV and VH backscatter and their ratio VH/VV, the cross ratio (CR), to monitor crop conditions is assessed and demonstrates the large potential of microwave indices for vegetation monitoring of VWC and phenology.
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