大气纠正的目的是从遥感影像中去除大气影响,并反演获取地物真实反射率。介绍了一种逐像元对遥感影像进行大气纠正的算法,该算法基于6S(Second Simulation of the Satellite Signal in the Solar Spectrum)大气辐射传输模型计算建立的查找表(look-up table),并利用地面暗目标(dark object)进行陆地气溶胶光学厚度的自动反演,由于气溶胶的分布具有空间连续性,在获取地面暗目标气溶胶光学厚度值后,通过空间插值的方法计算影像中非暗目标像元的气溶胶光学厚度值,经过查找表二次插值计算,逐像元进行大气纠正并获取像元地表反射率值。以Landsat5遥感影像为例,介绍了算法流程,展示了大气纠正的结果。结果显示,利用查找表逐像元大气纠正的算法,能够在一定程度上去除云雾对影像的影响,更加精确的对遥感影像进行大气纠正并获取地物的真实反射率。
In this study, precision agriculture management zones were delineated using yield data over four years from the combine harvester equipped with yield monitor and DGPS receiver. Relative yields measured during each year were interpolated to 4 m2 grid size using ordinary kriging. The resultant interpolated yield maps were averaged across years to create a map of the mean relative yield, which was then used for cluster analysis. The mean yield map of post-classification was processed by applying majority filtering with window sizes that were equivalent to the grid sizes of 12, 20, 28, 36, 44, 52 and 60 m. The scale effect of management zones was evaluated using relative variance reduction, test of significant differences of the means of yield zones, spatial fragmentation, and spatial agreement. The results showed that the post-classification majority filtering （PCMF） eliminated lots of isolated cells or patches caused by random variation while preserving yield means, high variance reduction, general yield patterns, and high spatial agreement. The zoned result can be used as yield goal map for preplant or in-season fertilizer recommendation in precision agriculture.
