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Open AccessArticle

Geo-Parcel Based Crop Identification by Integrating High Spatial-Temporal Resolution Imagery from Multi-Source Satellite Data

1
Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China
2
University of Chinese Academy of Sciences, Beijing 100049, China
3
Agricultural Science and Technology Information Research Institute, Guangxi Academy of Agricultural Sciences, Nanning 530007, China
4
College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310014, China
5
Department of Mathematics and Information Science, College of Science, Chang’an University, Xi’an 710064, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2017, 9(12), 1298; https://doi.org/10.3390/rs9121298
Received: 21 October 2017 / Revised: 8 December 2017 / Accepted: 8 December 2017 / Published: 12 December 2017
(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)
Geo-parcel based crop identification plays an important role in precision agriculture. It meets the needs of refined farmland management. This study presents an improved identification procedure for geo-parcel based crop identification by combining fine-resolution images and multi-source medium-resolution images. GF-2 images with fine spatial resolution of 0.8 m provided agricultural farming plot boundaries, and GF-1 (16 m) and Landsat 8 OLI data were used to transform the geo-parcel based enhanced vegetation index (EVI) time-series. In this study, we propose a piecewise EVI time-series smoothing method to fit irregular time profiles, especially for crop rotation situations. Global EVI time-series were divided into several temporal segments, from which phenological metrics could be derived. This method was applied to Lixian, where crop rotation was the common practice of growing different types of crops, in the same plot, in sequenced seasons. After collection of phenological features and multi-temporal spectral information, Random Forest (RF) was performed to classify crop types, and the overall accuracy was 93.27%. Moreover, an analysis of feature significance showed that phenological features were of greater importance for distinguishing agricultural land cover compared to temporal spectral information. The identification results indicated that the integration of high spatial-temporal resolution imagery is promising for geo-parcel based crop identification and that the newly proposed smoothing method is effective. View Full-Text
Keywords: crop identification; spatial-temporal collaboration; multi-sources; time series; phenology crop identification; spatial-temporal collaboration; multi-sources; time series; phenology
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MDPI and ACS Style

Yang, Y.; Huang, Q.; Wu, W.; Luo, J.; Gao, L.; Dong, W.; Wu, T.; Hu, X. Geo-Parcel Based Crop Identification by Integrating High Spatial-Temporal Resolution Imagery from Multi-Source Satellite Data. Remote Sens. 2017, 9, 1298.

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