黄淮海多熟种植农业区作物历遥感检测与时空特征

多熟种植是高强度农业土地利用的重要特征,但由于缺乏在空间和时间上清晰描述农业多熟种植和作物种植历时空分布的数据,使得区域尺度农田生态系统碳动态估计、农田生产力监测与模拟等有很大的不确定性。黄淮海农业区是以冬小麦-夏玉米二熟制为主的我国粮食主产区,冬小麦和夏玉米分别为光合作用途径为C3和C4的作物,已有研究证明如果在估算生态系统生产力时不考虑一年两季作物及其光能利用率的差异则会导致生产力估算结果过低。研究结合农业气象站点地面作物物候观测数据和空间分辨率500m、8d合成的MOD IS时间序列数据,分析研究区二熟制作物的生长过程、物候特征和作物历的空间差异,发展基于EVI和LSWI时间序列曲线检测多熟区各季作物种植历的方法,获取黄淮海农业区空间表述清晰的熟制和各季作物的生长开始与结束时间数据,并应用农业气象站点数据对方法和所获取的作物历数据进行了比较验证。论述的方法和提取的各季作物的作物历时空数据将能够应用于区域尺度农田生产力估算、生物地球化学循环模拟和农业生态系统监测。

Satellite observed crop calendar and its spatio-temporal characteristics in multiple cropping area of Huang-Huai-Hai Plain

Multiple cropping is one of the most influenced intensified agricultural land use activity in China because of the increased pressure on water, ecosystems and environment. However, due to the lack of spatial and temporal explicitly data of multiple cropping and crop calendar, there were significant uncertainty in agricultural productivity and Carbon dynamic monitoring, modeling and evaluation on regional to larger scale. In Huang-Huai-Hai Plain, an important agricultural region in China, more than 70% of total cropland land area was planted with winter-wheat and maize double cropping system. It has been proved that identifying multiple cropping and crop calendar and assigning appropriate light use efficiency to C3 and C4 crops could substantially improve our ability to model and evaluate the seasonal dynamics of carbon flux in such winter-wheat and maize rotation system. In this study, we analyze spatial and temporal patterns of crop growth process and crop calendar in the winter-wheat and maize double cropping system using multi-temporal satellite images from the Moderate Resolution Imaging Spectral radiometer (MODIS) and in-situ observation of key crop phenological transition dates, and explore a method to examine and extract crop calendar of each crop season in Huang-Huai-Hai plain from MODIS Enhanced Vegetation Index (EVI) and MODIS Land Surface Water Index (LSWI) time series curves by combining agro-meteorological observation data and MODIS data with a resolution of 500m at 8-day intervals. Multiple cropping distribution, temporal transition characteristics and heterogeneity of the start and the end time of each crop season were examined and analyzed, and the method also was validated by comparing with in-situ observed start date and end date records of winter-wheat and maize. The method and crop calendar products discussed in this paper could be applied in agricultural productivity estimation, biogeochemical cycle modeling and agricultural ecosystem monitoring.