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Remote Sens. 2018, 10(11), 1820; https://doi.org/10.3390/rs10111820

Object-Based Plastic-Mulched Landcover Extraction Using Integrated Sentinel-1 and Sentinel-2 Data

1,*
,
1
and
2,*
1
School of Earth Sciences, Zhejiang University, Hangzhou 310027, China
2
Center for Spatial Information Science and Systems, George Mason University, Fairfax, VA 22030, USA
*
Authors to whom correspondence should be addressed.
Received: 27 September 2018 / Revised: 12 November 2018 / Accepted: 14 November 2018 / Published: 16 November 2018
(This article belongs to the Special Issue Selected Papers from Agro-Geoinformatics 2018)
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Abstract

Plastic mulching on farmland has been increasing worldwide for decades due to its superior advantages for improving crop yields. Monitoring Plastic-Mulched Land-cover (PML) can provide essential information for making agricultural management decisions and reducing PML’s eco-environmental impacts. However, mapping PML with remote sensing data is still challenging and problematic due to its complicated and mixed characteristics. In this study, a new Object-Based Image Analysis (OBIA) approach has been proposed to investigate the potential for combined use of Sentinel-1 (S1) SAR and Sentinel-2 (S2) Multi-spectral data to extract PML. Based on the ESP2 tool (Estimation of Scale Parameter 2) and ED2 index (Euclidean Distance 2), the optimal Multi-Resolution Segmentation (MRS) result is chosen as the basis of following object-based classification. Spectral and backscattering features, index features and texture features from S1 and S2 are adopted in classifying PML and other land-cover types. Three machine-learning classifiers known as the—Classification and Regression Tree (CART), the Random Forest (RF) and the Support Vector Machine (SVM) are carried out and compared in this study. The best classification result with an overall accuracy of 94.34% is achieved by using spectral, backscattering, index and textural information from integrated S1 and S2 data with the SVM classifier. Texture information is demonstrated to contribute positively to PML classifications with SVM and RF classifiers. PML mapping using SAR information alone has been greatly improved by the object-based approach to an overall accuracy of 87.72%. By adding SAR data into optical data, the accuracy of object-based PML classifications has also been improved by 1–3%. View Full-Text
Keywords: plastic-mulched landcover (PML); Sentinel-1; Sentinel-2; object-based classification; multi-resolution segmentation (MRS); SVM; RF; CART plastic-mulched landcover (PML); Sentinel-1; Sentinel-2; object-based classification; multi-resolution segmentation (MRS); SVM; RF; CART
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Lu, L.; Tao, Y.; Di, L. Object-Based Plastic-Mulched Landcover Extraction Using Integrated Sentinel-1 and Sentinel-2 Data. Remote Sens. 2018, 10, 1820.

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