遥感技术能被用于大尺度作物化学喷药效果检测, 这为精准农业航空施药发展提供了重要的技术支撑。 利用M-18B农用飞机在4米的飞行高度喷施化学农药混合剂(杀菌剂和植物生长调节剂), 去控制水稻爆发性疾病--叶片纹枯病和促进水稻植株的生长。 施药一周后, 喷药区的卫星影像被获取并计算植被指数, 同时采集了地面化学农药的药液沉积量。 分析了药液雾滴沉积量和植被指数的关系, 结果显示, 单相光谱特征(NDVI)和液滴沉积点密度(DDPD点·cm-2) 的相关系数是0.315, p-value为0.035; 时间变化特征 (MSAVI)和液滴沉积体积密度(DDVD μL·cm-2)之间的相关系数是0.312, p-value为0.038。 另外, 水稻生长活力最旺盛的范围都出现在喷洒区域内, 植株活力随着药液漂移距离的增加逐步减少。 同时, 相同的变化趋势也出现在雾滴沉积量与光谱特征的空间变化插值图中。 由此得知, 从卫星图像中计算的植被指数NDVI和MSAVI, 可以用来评估大尺度农田的农用航空药液喷洒效果。

Remote sensing technique can be used to examine the effects of agrichemical application on the performance of field crops at a large scale in an effort to develop precision agricultural aerial spraying technology. In this study, an airplane M-18B at the 4-m flight height was used to spray a mix of agrichemicals (a fungicide and a plant growth regulator) to control rice leaf blast disease and improve the growth vigor of rice plants in the field. After the aerial spraying, satellite imagery of tested area was acquired and processed to calculate vegetation indices (VIs). Ground agrichemical concentration data were also collected. The relationships between droplets deposition and VIs were analyzed. The results indicated that the highest correlation coefficient between single phase spectral feature (NDVI) and droplets deposition points density (DDPD, points·cm-2) was 0.315 with P-value of 0.035 while the highest correlation coefficient between temporal change characteristic (MSAVI) and droplets deposition volume density (DDVD, μL·cm-2) was 0.312 with P-value of 0.038). Rice plants with the greatest growth vigor were all detected within the spraying swath, with a gradual decrease in the vigor of rice plants with the increase of droplets drift distance. There were similar trend patterns in the changes of the spraying effects based on the spatial interpolation maps of droplets deposition data and spectral characteristics. Therefore, vegetation indexes, NDVI and MSAVI calculated from satellite imagery can be used to determine the aerial spraying effects in the field on a large scale.