基于无人机平台多模态数据融合的小麦产量估算研究

Wheat yield estimation from UAV platform based on multi-modal remote sensing data fusion

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。

Crop yield estimations are important for national food security,people,and the environment.Timely and accurate esti-mation of crop yield at the field scale is of great significance for crop management,harvest and trade.It ultimately enables farmers to optimize inputs and economic return.We selected an irrigated wheat field in a region near Kaifeng,Henan province,for this study.The terrain in that region is undulating and spatial differences.We used a low-altitude unmanned aerial vehicle(UAV)remote sensing platform equipped with a multi-spectral camera,thermal infrared camera,and RGB camera to simultaneously obtain different remote sensing parameters during the key growth stages of wheat.Based on the extracted spectral reflectivity,thermal infrared temperature,and digital elevation information,we calculated the spatial variability of remote sensing parameters,and growth indices under different terrain characteristics.We also analyzed the correlations between vegetation indices,temperature parameters and wheat yield.By means of four machine learning methods,including multiple linear regression method(MLR),partial least squares regression method(PLSR),support vector machine regression method(SVR),and random forest regression method(RFR),we compared the yield estimation capability of single-modal data versus multimodal data fusion frameworks.The results showed that slope was an important factor affecting crop growth and yield.We observed significant differences in remote sensing parameters under different slope grades.Soil water content,water content of plants,and above-ground biomass at the three growth stages were significantly correlated with slope.Most of the vegetation indices and temperature parameters of three growth stages were significantly correlated with yield as well.Based on the strength of their correlation with yield,seven vegeta-tion indices(NDVI,GNDVI,EVI2,OSAVI,SAVI,NDRE,and WDRVI)and two temperature parameters(NRCT,CTD)were selected as the final input variables for the model.For the single-modal data framework,the model constructed with the vegetation indices was better than the yield model constructed with the temperature parameters,and the highest accuracy was obtained with a RFR model based on vegetation indices at filling stage(R2=0.724,RMSE=614.72 kg hm^(-2),MAE=478.08 kg hm^(-2)).For the double modal data fusion approach,the highest accuracy resulted at flowering stage,using the temperature parameters combined with the vegetation indices of RFR model(R2=0.865,RMSE=440.73 kg hm^(-2),MAE=374.86 kg hm^(-2)).Even higher accuracies were obtained,using the multimodal data fusion approach with a RFR model based on vegetation indices,temperature parameters and slope information at flowering stage(R2=0.893,RMSE=420.06 kg hm^(-2),MAE=352.69 kg hm^(-2)),and the highest valida-tion model(R2=0.892,RMSE=423.55 kg hm^(-2),MAE=334.43 kg hm^(-2))for fusion of the flowering stage.The results revealed that by using a multimodal data fusion framework of terrain factors combined with RFR,we can fully exploit the complementary and synergistic roles of different remote sensing information sources.This effectively improves the accuracy and stability of the yield estimation model,and provides a reference and support for crop growth monitoring and yield estimation.