不同时空分辨率遥感数据融合估算冬小麦叶面积指数

高时空分辨率叶面积指数(leaf area index,LAI)数据能反映作物的长势动态变化,为作物长势评估和产量预测提供有效的生长指标依据。该文综合利用混合像元线性分解与数据同化算法,以高空间分辨率SPOT-5数据反演的LAI修正高时间分辨率HJ-CCD数据反演的LAI序列,生成了覆盖冬小麦主要生育期的高空间分辨率LAI序列,并结合SPOT-5反演的LAI和实测LAI值分析了像元纯度、高空间分辨率遥感数据同化景数对融合效果的影响。结果表明,采用数据融合方法生成的LAI与检验LAI具有较高的一致性,但像元纯度对融合效果影响较大;基于2景SPOT-5影像能够提高LAI序列估测精度,且优于基于1景SPOT-5影像的融合效果。该研究结果可为冬小麦生长监测提供技术支撑。

Estimation of winter wheat leaf area index by fusing different spatial and temporal resolution remote sensing data

Leaf area index (LAI) with high spatial and temporal resolutions can reflect the dynamic change of crop growth, and be served as a key parameter for crop growth evaluation and yield prediction. By combining the techniques of linear pixel unmixing and data assimilation, the LAI based on SPOT-5 image with high spatial resolution was used to adjust the time-series LAI based on HJ-CCD image with high temporal resolution, and LAI series covering the whole winter wheat growth period and with high spatial and temporal resolutions were generated. The effects of pixel purity and the number of high spatial image on the performance of fusing method were analyzed by comparing the LAI with fusing method and LAI from SPOT-5 image or observed LAI. The results showed that the estimated LAI with fusing method has high consistency with observed LAI and the pixel purity is main obstacle factor. The fusion results based on two scenes of SPOT-5 images are better than that based on one image. These results can provide an important technical support for monitoring of growth in winter wheat.