Assessing the suitability of data from Sentinel-1A and 2A for crop classification
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TLDR
In this paper, six crop types (beans, beetroot, grass, maize, potato, and winter wheat) were identified using five C-SAR images and one MSI image acquired during the 2016 growing season.Abstract:
Sentinel-1A C-SAR and Sentinel-2A MultiSpectral Instrument (MSI) provide data applicable to the remote identification of crop type. In this study, six crop types (beans, beetroot, grass, maize, potato, and winter wheat) were identified using five C-SAR images and one MSI image acquired during the 2016 growing season. To assess the potential for accurate crop classification with existing supervised learning models, the four different approaches namely kernel-based extreme learning machine (KELM), multilayer feedforward neural networks, random forests, and support vector machine were compared. Algorithm hyperparameters were tuned using Bayesian optimization. Overall, KELM yielded the highest performance, achieving an overall classification accuracy of 96.8%. Evaluation of the sensitivity of classification models and relative importance of data types using data-based sensitivity analysis showed that the set of VV polarization data acquired on 24 July (Sentinel-1A) and band 4 data (Sentinel-2A) had the greate...read more
Figures
Table 1. Characteristics of the satellite data used in this study 618 (a) Sentinel-1A 619 
Figure 8. Data-based sensitivity analysis (DSA) results for each classification algorithm. 675 
Table 2. Spatial and spectral resolution of MSI data. 624 
Table 6. Summary of overall accuracy in reviewed studies 646 
Table 5. Comparison of accuracy for six crop types achieved by KELM using three 641 different satellite dataset. PA: producer’s accuracy; UA: user’s accuracy; OA: overall 642 accuracy; AD: allocation disagreement; QD: quantity disagreement 643 
Figure 4. Boxplots of reflectance for each crop in (a) band 2, (b) band 3, (c) band 4, (d) 661 band 5, (e) band 6, (f) band 7, (g) band 8, (h) band 8a, (i) band 11, and (j) band 12. The 662 data for these plots were obtained from Sentinel-2A, taken on 11 August 2016. 663
Citations
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Extreme Learning Machine 기반 퍼지 패턴 분류기 설계
TL;DR: In this article, the Extreme Learning Machine (ELM) was used to train a classifier for learning to solve problems in the real world, and the results showed that the classifier achieved good performance.
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How much does multi-temporal Sentinel-2 data improve crop type classification?
TL;DR: It is concluded that the multi-temporal crop type classification efficiently mitigates negative effects observed when using single-date acquisition within sub-optimal temporal windows.
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Mapping cropland extent of Southeast and Northeast Asia using multi-year time-series Landsat 30-m data using a random forest classifier on the Google Earth Engine Cloud
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TL;DR: This work states that existing cropland extent maps over large areas are derived from coarse resolution imagery and have many limitations such as missing fragmented and small farms with mixed signatures from different crop types and farming practices that can be, confused with other land cover.
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Recent Applications of Landsat 8/OLI and Sentinel-2/MSI for Land Use and Land Cover Mapping: A Systematic Review
TL;DR: This systematic review presents trends, potentialities, challenges, actual gaps, and future possibilities for the use of L8/OLI and S2/MSI for LULC mapping and change detection, and highlights the possibility of using medium-resolution time series and multispectral optical data provided by the harmonization between these sensors and data cube architectures for analysis-ready data that are permeated by publicizations, open data policies, and open science principles.
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Crop type classification using a combination of optical and radar remote sensing data: a review
TL;DR: For many years, crop type classification and monitoring has been an important data source for agricultural monitoring and food security assessment studies as discussed by the authors, and reliable and accurate crop classification maps are an important source of information.
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