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Mapping sugarcane plantation dynamics in Guangxi, China, by time series Sentinel-1, Sentinel-2 and Landsat images

机译:在中国广西,中国广西的甘蔗种植园动力学,按时间序列Sentinel-1,Sentinel-2和Landsat图像

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Sugarcane is a major crop for sugar and ethanol production and its area has increased substantially in tropical and subtropical regions in recent decades. Updated and accurate sugarcane maps are critical for monitoring sugarcane area and production and assessing its impacts on the society, economy and the environment. To date, no sugarcane mapping tools are available to generate annual maps of sugarcane at the field scale over large regions. In this study, we developed a pixel- and phenology-based mapping tool to produce an annual map of sugarcane at 10-m spatial resolution by analyzing time-series Landsat-7/8, Sentinel-2 and Sentinel-1 images (LC/ S2/S1) during August 31, 2017 - July 1, 2019 in Guangxi province, China, which accounts for 65% of sugarcane production of China. First, we generated annual maps of croplands and other land cover types in 2018. Second, we delineated the cropping intensity (single, double and triple cropping in a year) for all cropland pixels in 2018. Third, we identified sugarcane fields in 2018 based on its phenological characteristics. The resultant 2018 sugarcane map has producer, user and overall accuracies of 88%, 96% and 96%, respectively. According to the annual sugarcane map in 2018, there was a total of 8940 km(2) sugarcane in Guangxi, which was similar to 1% higher than the estimate from the Guangxi Agricultural Statistics Report. Finally, we identified green-up dates of those sugarcane fields in 2019, which could be used to support the sugarcane planting and management activities. Our study demonstrates the potential of the pixel- and phenology-based sugarcane mapping tool (both the algorithms and the LC/S2/S1 time series images) in identifying croplands, cropping intensity and sugarcane fields in the complex landscapes with diverse crop types, fragmented crop fields and frequent cloudy weather. The resultant annual maps from this study could be used to assist farms and sugarcane mills for sustainable sugarcane production and environment.
机译:甘蔗是糖和乙醇产量的主要作物,近几十年来,它的地区大幅增加了热带和亚热带地区。更新和准确的甘蔗地图对于监测甘蔗面积和生产以及评估其对社会,经济和环境的影响至关重要。迄今为止,没有甘蔗映射工具可用于在大地区的野外规模上产生年度甘蔗的年地图。在这项研究中,我们开发了一种基于像素和诸如诸多的基于酚类的映射工具,通过分析时间序列Landsat-7/8,Sentinel-2和Sentinel-1图像来在10米的空间分辨率下产生甘蔗的年地图(LC / S2 / S1)2017年8月31日 - 2019年7月1日在中国广西省,占中国甘蔗产量的65%。首先,我们在2018年生成了农田和其他土地覆盖类型的年度地图。第二,我们划定了2018年所有农田像素的种植强度(一年,双重和三重裁剪)。第三,我们在2018年确定了甘蔗领域论其鉴生特征。结果2018甘蔗图分别具有88%,96%和96%的生产者,用户和整体准确性。根据2018年的年度甘蔗地图,广西共甘蔗共有8940公里(2)甘蔗,比广西农业统计报告的估算高出1%。最后,我们确定了2019年甘蔗领域的绿色枣,可用于支持甘蔗种植和管理活动。我们的研究表明,基于像素和诸如诸如算法和LC / S2 / S1时间图像的算法和LC / S2 / S1时间序列图像的潜力识别复杂的景观中的农作物,裁剪强度和甘蔗田,分段化庄稼领域和频繁的多云天气。本研究的所得年度地图可用于帮助农场和甘蔗厂进行可持续甘蔗生产和环境。

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