基于ESTARFM模型的区域农田高时空分辨率影像产生与应用

多时相遥感影像特别是关键生育期数据是农业物候、长势及产量监测的重要数据源,然而可见光影像易受云雨干扰,在特定区域关键时间窗口缺少高时空分辨率数据的现实情况下,遥感影像时空数据融合方法变得尤为重要。增强型自适应反射率时空融合模型 ESARFM (Enanced Spaial and emporal Adapive Reflecance Fusion Model)是一种小区域合成高时空分辨率影像的较好方法,该算法在我国不同农业种植区的适应性及应用工作尚未充分展开。本文以河北、黑龙江、新疆典型农区为研究区域进行大面积应用检验分析,基于 MODIS 与 Landsa 影像,利用ESARFM 生成具有高时空特征的 Landsa 模拟影像,将其与真实 Landsa 影像进行对比,并在新疆地区展开ESARFM 算法在 NDVI 方面的应用。结果表明, ESARFM 对 3 个不同区域状况的地区都有较好的影像预测能力,并且在新疆地区可以很好地生成 30 m 空间分辨率的多时相 NDVI,用于作物分类和长势监测。

Generation and application of high temporal and spatial resolution images of regional farmland based on ESTARFM model

Multi-temporal remote sensing images are important data sources for agricultural phenology, growth, and yield monitoring. However, visible light images are vulnerable to cloud and rain, and there is a lack of high temporal and spatial resolution data in reality, the remote sensing image fusion methods have become particularly important. ESTARFM (Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model) is used to synthesize high spatial-temporal resolution images in small areas. The adaptability and application of the algorithm in different agricultural growing areas in China have not yet fully developed. In this paper, the large area application test analysis was performed in the Hebei, Heilongjiang, and Xinjiang. Based on MODIS and Landsat images, we used ESTARFM to generate Landsat images with high spatial-temporal characteristics, which were compared with the real Landsat images. The application of ESTARFM algorithm in NDVI was performed for crop growth monitoring in Xinjiang. In conclusion ESTARFM can perform better image prediction in three different regional conditions, generate 30 m multi-temporal NDVI with good spatial resolution in Xinjiang, and monitor the growth of crops.