基于改进最大值法合成NDVI的夏玉米物候期遥感监测

利用遥感技术监测农作物物候期,能够及时有效地评估作物生长趋势、提高农情信息化管理水平。本研究利用2016年MODIS 8天合成数据,提出改进的最大值合成法,结合S-G滤波和Logistic函数拟合重构夏玉米生长曲线,最后利用曲率法提取夏玉米的拔节期和成熟期,利用动态阈值法提取夏玉米的出苗期和抽雄期。结果表明:采用本文提取的夏玉米物候期与实测物候期相比,平均误差为2.76 d,其中在抽雄期的绝对误差为1.06 d,运用改进的最大值合成提取作物NDVI时序数据可有效去除连续云雾对植被指数的影响,提高监测作物物候期的准确性,为精准农业提供技术支撑。

Summer maize phenology monitoring based on normalized difference vegetation index reconstructed with improved maximum value composite

Crop phenology period is an important feature of the agriculture eco-system. Using remote sensing technology to monitor crop phenology accurately and timely, which plays an important supporting role in effective assessment of crop growth trends, improving the information management level of agricultural conditions and providing technical support for precision agriculture. Normalized difference vegetation index (NDVI) can well describe the growth process of different types of vegetation, which is the most frequently used data in crop phenology. In this paper, NDVI is extracted from 8-day synthetic data of MODIS in 2016. The time series of NDVI is not continuous in time and space due to the influence of air pollution, it is necessary to smooth the remote sensing time series data which represent the vegetation growth process before the phenology study. Then according to the characteristics of vegetation growth curve, the phenology information is extracted after removing noise of time series data. Maximum value composite (MVC) is widely used in the initial de-noising process of NDVI because of its simple calculation and convenient use, but it is prone to large errors for continuous multi-day cloudy weather. An improved MVC is proposed for the preprocessing of MODIS NDVI time series data in this paper, which is very convenient and does not require additional parameters. The new NDVI time series data can be constructed by extracting NDVI from positive and reverse sequence on growth time series in a fixed interval and then synthesizing it. The reconstructed NDVI time series data are filtered by S-G filter to further eliminate the noise, and then the growth curve of summer maize is reconstructed by logistic function fitting. Finally, the jointing and maturity stages of summer maize are extracted by curvature, and the emergence and tasseling stages of summer maize are extracted by dynamic threshold. Compared with the observed results, the absolute errors of different phenology starting time of summer maize obtained by improved MVC method is less than that of traditional MVC method. Especially the emergence stage, the absolute error of improved MVC method is reduced by 4.5 d. The absolute errors of phenology on summer maize using improved MVC are 3.72, 5, 1.06 and 1.26 d at emergence, jointing, tasseling and maturity stages, respectively. The absolute errors of that by traditional MVC method in subsequent phenology periods aere 8.22, 5.72, 2.78 and 5 d, respectively. From the spatial distribution maps of different phenology periods of summer maize, it can be seen that the starting time of summer maize emergence stage in the study area is relatively concentrated, generally starting from 166 d to 170 d base on the day of year (DOY), namely 15 June to 18 June. Jointing stage generally starts from 185 d to 190 d, namely 3 July to 8 July. Most of the areas in the study area enter the stage of tasseling from 208 d to 212 d, that is, from 25 July to 29 July. A large area of summer maize begins to enter the maturity stage from around 251 d, that is, on September 7. In spatial distribution, the northwest of the canal head in the study area is slightly ahead of the southeast on phenology period. It can be said that using improved MVC to extract NDVI time series data of crops can effectively remove the impact of continuous cloud and fog on vegetation index, improving the accuracy of monitoring crop phenology, providing support for precision agriculture.