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Spatially and Temporally Continuous Leaf Area Index Mapping for Crops through Assimilation of Multi-resolution Satellite Data

Finer Classification of Crops by Fusing UAV Images and Sentinel-2A Data

by 1,2, 1,2,*, 1,2, 3, 1,2 and 4
Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
Key Laboratory of Agricultural Remote Sensing, Ministry of Agriculture, Beijing 100081, China
Center for Systems Integration and Sustainability, Michigan State University, East Lansing, MI 48823, USA
Department of Restoration Ecology and Built Environment, Faculty of Environmental Studies, Tokyo City University, 3-3-1 Ushikubo-nishi, Tsuzuki-ku, yokohama, Kanagawa 224-8551, Japan
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(24), 3012;
Received: 19 October 2019 / Revised: 4 December 2019 / Accepted: 11 December 2019 / Published: 14 December 2019
(This article belongs to the Special Issue Remote Sensing for Crop Mapping)
Accurate crop distribution maps provide important information for crop censuses, yield monitoring and agricultural insurance assessments. Most existing studies apply low spatial resolution satellite images for crop distribution mapping, even in areas with a fragmented landscape. Unmanned aerial vehicle (UAV) imagery provides an alternative imagery source for crop mapping, yet its spectral resolution is usually lower than satellite images. In order to produce more accurate maps without losing any spatial heterogeneity (e.g., the physical boundary of land parcel), this study fuses Sentinel-2A and UAV images to map crop distribution at a finer spatial scale (i.e., land parcel scale) in an experimental site with various cropping patterns in Heilongjiang Province, Northeast China. Using a random forest algorithm, the original, as well as the fused images, are classified into 10 categories: rice, corn, soybean, buckwheat, other vegetations, greenhouses, bare land, water, roads and houses. In addition, we test the effect of UAV image choice by fusing Sentinel-2A with different UAV images at multiples spatial resolutions: 0.03 m, 0.10 m, 0.50 m, 1.00 m and 3.00 m. Overall, the fused images achieved higher classification accuracies, ranging between 10.58% and 16.39%, than the original images. However, the fused image based on the finest UAV image (i.e., 0.03 m) does not result in the highest accuracy. Instead, the 0.10 m spatial resolution UAV image produced the most accurate map. When the spatial resolution is less than 0.10 m, accuracy decreases gradually as spatial resolution decreases. The results of this paper not only indicate the possibility of combining satellite images and UAV images for land parcel level crop mapping for fragmented landscapes, but it also implies a potential scheme to exploit optimal choice of spatial resolution in fusing UAV images and Sentinel-2A, with little to no adverse side-effects. View Full-Text
Keywords: Sentinel-2A; UAV; image fusion; classification; random forests; resolution Sentinel-2A; UAV; image fusion; classification; random forests; resolution
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MDPI and ACS Style

Zhao, L.; Shi, Y.; Liu, B.; Hovis, C.; Duan, Y.; Shi, Z. Finer Classification of Crops by Fusing UAV Images and Sentinel-2A Data. Remote Sens. 2019, 11, 3012.

AMA Style

Zhao L, Shi Y, Liu B, Hovis C, Duan Y, Shi Z. Finer Classification of Crops by Fusing UAV Images and Sentinel-2A Data. Remote Sensing. 2019; 11(24):3012.

Chicago/Turabian Style

Zhao, Licheng, Yun Shi, Bin Liu, Ciara Hovis, Yulin Duan, and Zhongchao Shi. 2019. "Finer Classification of Crops by Fusing UAV Images and Sentinel-2A Data" Remote Sensing 11, no. 24: 3012.

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