作物学报 ›› 2022, Vol. 48 ›› Issue (7): 1746-1760.doi: 10.3724/SP.J.1006.2022.11053
张少华1(), 段剑钊1,2, 贺利1, 井宇航1, 郭天财1, 王永华1, 冯伟1,*()
ZHANG Shao-Hua1(), DUAN Jian-Zhao1,2, HE Li1, JING Yu-Hang1, Urs Christoph Schulthess2,*(), Azam Lashkari1,2, GUO Tian-Cai1, WANG Yong-Hua1, FENG Wei1,*()
摘要:
作物产量估测关系到人民生活质量和国家粮食安全问题, 在田块尺度下及时准确估算产量, 对于农事操作管理、收获、销售及种植计划制定均具有重要意义。选择地势起伏及空间差异较大的农田为研究区, 利用低空无人机遥感平台搭载多光谱相机、热红外相机和RGB相机, 同步获取小麦关键生育时期的无人机遥感影像, 并提取光谱反射率、热红外温度和数字高程信息。首先统计不同地形特征下遥感参数和生长指标的空间变异情况, 分析植被指数和温度参数与小麦产量的相关性, 然后利用多元线性回归(multiple linear regression, MLR)、偏最小二乘回归(partial least squares regression, PLSR)、支持向量机回归(support vector machine regression, SVR)和随机森林回归(random forest regression, RFR) 4种机器学习方法以单模态数据和多模态遥感信息融合2种方式进行建模, 比较单模态数据和多模态数据融合的产量估测能力。结果表明, 坡度是影响作物生长和产量的重要因子, 3个生育期内, 不同坡度等级下遥感参数差异明显, 土壤含水量、植株含水量和地上部生物量与坡度的相关性均达显著水平, 植被指数和温度参数与产量的相关性均达显著水平。依据与产量的相关性, 筛选7个植被指数(NDVI、GNDVI、EVI2、OSAVI、SAVI、NDRE、WDRVI)和2个温度参数(NRCT、CTD)作为模型输入变量, 对于单模态数据而言, 对产量的估算效应为植被指数 > 温度参数, 以灌浆期植被指数的RFR模型效果最好(R2=0.724, RMSE=614.72 kg hm-2, MAE=478.08 kg hm-2); 对于双模态数据融合来说, 在植被指数基础上融入冠层温度参数表现最好, 开花期RFR模型效果进一步提高(R2=0.865, RMSE=440.73 kg hm-2, MAE=374.86 kg hm-2); 在双模态数据基础上引入坡度信息进行三模态数据融合, 其产量估算效果明显优于单模态和双模态数据融合, 其中以开花期植被指数、温度参数和坡度信息融合的RFR估算效果最好(R2=0.893, RMSE=420.06 kg hm-2, MAE=352.69 kg hm-2), 模型验证效果较好(R2= 0.892, RMSE=423.55 kg hm-2, MAE=334.43 kg hm-2)。可见, 在本试验条件下通过引入地形因子, 结合随机森林回归算法将多模态数据有效融合, 可充分发挥不同遥感信息源之间互补协同作用, 有效提高了产量估算模型的精度与稳定性, 为作物生长监测及产量估算提供思路参考和方法支持。
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