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

Yingpin Yang,Xiaodong Hu,Tianjun Wu,Wen Dong,Jiancheng Luo,Qiting Huang,Wei Wu,Lijing Gao

doi:10.3390/rs9121298

Abstract

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.

Highlights

With the development of agricultural management, agro-ecology studies and agricultural policy making, especially in precision agriculture, there have been increasing demands for crop distribution information on a land plot scale
Reed et al [1] derived a suite of 12 metrics from 4 years of Advanced Very High Resolution Radiometer (AVHRR) normalized difference vegetation index (NDVI) time-series, and they found that the metrics were strongly consistent with various land cover types and their predicted phenological characteristics
Hill et al [2] disaggregated quantitative metrics from AVHRR NDVI time-series for 8 years, analyzed the temporal and spatial patterns of key NDVI metrics, and made classifications

Summary

Introduction

With the development of agricultural management, agro-ecology studies and agricultural policy making, especially in precision agriculture, there have been increasing demands for crop distribution information on a land plot scale. Reed et al [1] derived a suite of 12 metrics from 4 years of AVHRR normalized difference vegetation index (NDVI) time-series, and they found that the metrics were strongly consistent with various land cover types and their predicted phenological characteristics. Hird et al [10] selected six kinds of NDVI filtering methods for a quantitative comparative analysis and explained that the purpose of filtering was to maximize the elimination of abnormal impacts while maintaining true values. These existing methods have been applied to smooth VI time-series and eliminate unnecessary fluctuations, especially for crop single growth seasons. Our study presents an adaptive smoothing method for identifying crop rotations

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