Abstract—

This article presents the use of the Normalized Differences Vegetation Index (NDVI) time series based change detection method for agriculture phenology monitoring. NDVI make use of the multi-spectral remote sensing data band combinations techniques to find out landscape such as agriculture, vegetation, land use/cover, water bodies and forest. Geographic Information System (GIS) technology is becoming an essential tool to combing multiple maps and information from different sources as satellite, field and socio-economic data. Landsat 8 and Sentinel-2 satellite data were used to generate NDVI time series from Sep. 2017 to Nov. 2018. This research work was the procedure by pre-processing, signal filtering and interpolation of monthly NDVI time series that represent a complete crop phonological cycle. NDVI method is applied according to its specialty range from –1 to +1. We divided whole agriculture area into five part according to NDVI Values such as no agriculture, low agriculture, medium agriculture, high agriculture and very high agriculture area. The simulation results show that the NDVI is highly useful in detecting the surface feature of the area, which is extremely beneficial for sustainable development of agriculture and decision making. The methodology of reform NDVI time series had been providing feasible to improve crop phenology mapping.

中文翻译：

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基于遥感卫星的NDVI时间序列农业物候监测-以广东省为例

摘要-

本文介绍了基于归一化差异植被指数（NDVI）时间序列的变化检测方法在农业物候监测中的应用。NDVI利用多光谱遥感数据带组合技术来查找景观，例如农业，植被，土地利用/覆盖，水体和森林。地理信息系统（GIS）技术正在成为组合多种地图和来自不同来源的信息（如卫星，野外和社会经济数据）的重要工具。使用Landsat 8和Sentinel-2卫星数据来生成2017年9月至2018年11月的NDVI时间序列。这项研究工作是通过预处理，信号滤波和内插每月NDVI时间序列（代表完整的作物音系）的程序进行的。周期。NDVI方法根据其从–1到+1的特殊范围而应用。我们根据NDVI值将整个农业区域分为五个部分，如无农业，低农业，中等农业，高农业和极高农业面积。仿真结果表明，NDVI在探测该地区的地表特征方面非常有用，这对于农业的可持续发展和决策制定极为有利。改革NDVI时间序列的方法为改善作物物候图谱提供了可行性。仿真结果表明，NDVI在探测该地区的地表特征方面非常有用，这对于农业的可持续发展和决策制定极为有利。改革NDVI时间序列的方法为改善作物物候图谱提供了可行性。仿真结果表明，NDVI在探测该地区的地表特征方面非常有用，这对于农业的可持续发展和决策制定极为有利。改革NDVI时间序列的方法为改善作物物候图谱提供了可行性。