利用时序植被指数监测作物物候的方法研究

    Methods of monitoring crop phonological stages using time series of vegetation indicator

    • 摘要: 该文是对全国主要产粮县旱地和水田作物的物候期进行遥感监测。在数据预处理中采用最小二乘法和谐函数分解重构方法相结合,去除时序植被指数影像的云噪声影响。基于土地利用数据,通过耕地植被指数加权平均的方法提取区旱地和水田作物生长过程。结合野外观察数据,对一年一熟作物用作物生长过程的最大上升斜率、最大值和最大下降斜率作为作物出苗(返青)期、抽穗期和收获期的遥感识别标志。对一年两熟、多熟县作物物候期依据轮作规律进行了监测。同时进行物候年际间对比和农业灾害监测分析。遥感监测出苗(返青)期和收获期与野外采样照片实测信息有90%的相同率,抽穗期遥感监测与实测信息相同率95%。

       

      Abstract: Crop phonological stages monitoring is an important part of growth monitoring. Crop growth period has relation to not only weather variety, but also to the people's planting habit. Crop phonological stages and growth period vary in different years. The normalised different vegetation index (NDVI) derived from red band and near infrared band of SPOT vegetation sensor is the directly remote sensing indicator that reflects crop growth situation. Each main production county was used as a main research target. To eliminate the cloud effect, least square method and harmonic analysis method was used to reconstruct the time series of imagine. On the foundation that using 1∶100000 land cover mask the non-agricultural land, according to weighted average method, the information with crop growth profiles about the non-irrigated field and irrigated field crop was extracted. This combined with field measured data, and maximum rising slope, maximum value and maximum descending slope were used as remote sensing indications for different phonological stages during the corps growth period including emergence(recovering), heading and harvest for corps which harvest once each year. For corps which harvest more than once. At the same time taking the contrast among different years and analysed the effect of disaster on agriculture. The similarity coefficient of me results from remote sensing monitor emergence and harvest and from GVG photograph information was 90%, and bloom period reached 95%.It is the basis for processing crop classification, crop area change monitoring, crop yield forecast.

       

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