An assessment of Planet satellite imagery for county-wide mapping of rice planting areas in Jiangsu Province, China with one-class classification approaches. Issue 19 (2nd October 2021)

• Record Type: Journal Article
• Authors: Zhang, Xue; Yang, Gaoxiang; Xu, Xinjie; Yao, Xia; Zheng, Hengbiao; Zhu, Yan; Cao, Weixing; Cheng, Tao
• Is Part Of: International journal of remote sensing. Volume 42:Issue 19(2021)
• Page Start: 7610
• Page End: 7635
• Publication Date: 2021-10-02
• Subjects: Remote sensing -- Periodicals; Télédétection -- Périodiques; 621.3678
• DOI: 10.1080/01431161.2021.1964710
• Languages: English
• ISSNs: 0143-1161

Abstract

ABSTRACT: There is a growing demand of the spatial distribution of major crops at the field level in recent years for the precision management of farmlands in developing countries. However, most previous studies used low or medium resolution images (e.g. Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat) to map the planting areas of rice in various regions of China. As a new source of satellite imagery, the 3 m Planet satellite imagery with daily revisit frequency has rarely been used for mapping crop types. In particular, it is still unclear how well the Planet imagery would perform in mapping the rice planting areas in the Lower Reaches of the Yangtze River Plain of China where image acquisition is strongly limited by rainy and cloudy weather conditions. To overcome the spectral confusion between rice and non-rice vegetation types, this study proposed one-class classification approaches with Planet imagery from the early and late seasons of rice crops over three counties (Guanyun, Rugao and Wujiang) across Jiangsu Province in eastern China. Apart from the traditional use of mono-temporal imagery from the late season (Strategy I), we developed two strategies to make use of the bi-temporal imagery from the early and late seasons, with one being direct classification using stacked images (Strategy II) and the other being stepwise classification (Strategy III). All classifications were performed with the popular machine learning classifier one-class support vector machine (OCSVM) and evaluated from the aspects of classification accuracy and area estimation accuracy. The results showed Strategy I could achieve an overall classification accuracy of 83.02% and kappa coefficient ( ƙ ) of 0.65. The overall accuracy (OA) can be significantly improved by Strategy II (OA = 84.44% and ƙ = 0.69) and further by Strategy III (OA = 90.42% and ƙ = 0.81). In terms of area estimation, the estimates from Strategy III were consistent with the government reported statistical data with a relative error (RE) within 10% at the county level. At the town level, the correspondence between Planet-derived rice planting areas and the Landsat-8 derived reference data was significantly improved with Strategy III as compared to the other two for Guanyun and Wujiang. The stepwise classification approach (Strategy III) developed with Planet imagery has great values in monitoring rice paddies over the regions with fragmentized distribution of rice fields.