遥感数据时空融合技术在农作物监测中的适应性研究

受卫星回访周期及云的影响,大范围研究区同一时期的Landsat卫星数据很难获取,因而国内外学者提出了遥感影像时空融合技术。以石河子为实验区,利用STARFM(Spatial and Temporal Adaptive Reflectance Fusion Model)模型融合生成了高时空分辨率TM影像,对不同作物类型真实反射率与融合影像反射率作相关性分析,分析了遥感数据时空融合技术在新疆农作物监测中的适用性。结果表明:利用STARFM模型模拟得到的融合影像与真实影像间的相关性较高,但当地物类型发生变化时,融合影像与真实影像间将存在明显的差异。地物类型变化作物融合影像反射率与真实影像反射率间的相关性较小。

Adaptability Research of Spatial and Temporal Remote Sensing Data Fusion Technology in Crop Monitoring

The application of Landsat satellite image in crop-growth monitoring is limited by the 16-day revisit cycle and frequent cloud contamination.One solution is to combine the high-spatial information of fine-resolution sensors,such as Landsat with the high-frequency temporal information of coarse-resolution sensors,Moderate Resolution Imaging Spectroradiometer(MODIS).In the present paper,we studied the adaptability of Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM),which is a spatial and temporal fusion technology in monitoring crop in Shihezi of Xinjiang Uygur Autonomous Region.We analyzed the correlation between fusion result and observed data.The result showed that the correlation between fusion image and real image was high,however,due to the change of land cover which did not meet the assumption of STARFM,and the differences were still significant,especially for the reflectance of fusion image and real image.The correlation between fusion image and real image was low,when feature type changes.