农业机械学报2025，Vol.56Issue(8)：21-31,11.DOI:10.6041/j.issn.1000-1298.2025.08.002
多源遥感数据尺度转换的夏玉米蒸散发融合模型研究
Fusion Model of Evapotranspiration of Summer Maize for Scale Conversion of Multi-source Remote Sensing Data
摘要
Abstract
Evapotranspiration(ET)is a core element of crop water requirement and serves as a key basis for optimizing regional water resource allocation.Focusing on summer maize in the Baojixia Irrigation District located in the Guanzhong Plain of Shaanxi Province,four machine learning algorithms were employed,back propagation neural network(BPNN),support vector machine(SVM),extreme learning machine(ELM),and eXtreme gradient boosting(XGBoost),to develop a collaborative correction model utilizing multisource remote sensing data from unmanned aerial vehicles(UAVs)and satellites.Subsequently,the model constructed by the optimal algorithm was selected to correct satellite multispectral data,ultimately achieving scale conversion between UAV and satellite data.The calibrated high-precision satellite data were utilized to retrieve the leaf area index(LAI)and crop height(hc)of summer maize,providing essential data inputs for the evapotranspiration(ET)model.Evapotranspiration(ET)of summer maize was estimated using three distinct approaches:the dual crop coefficient method,the mapping evapotranspiration at high resolution with internalized calibration(METRIC)model,and the Penman-Monteith(P-M)canopy resistance model.Subsequently,the Bayesian model averaging(BMA)method was introduced to dynamically assign weights to each method/model across different growth stages.Ultimately,this process led to the development of a robust BMA-merged ET model for the maize growing period spanning from the jointing stage to physiological maturity.The results demonstrated that the XGBoost algorithm consistently achieved the highest modeling accuracy for B/G/R/NIR bands during the jointing to maturity stages of summer maize,with R2 values in the four-band modeling outperforming the suboptimal ELM algorithm by 8.43％,8.67％,6.79％,and 10.41％respectively.The retrieval of LAI and hc using the calibrated satellite multispectral data exhibited an average improvement in R2 of 97％and 67.5％,respectively,compared with retrievals based on the original satellite data.Compared with the single best-performing method/model(the METRIC model),the BMA-merged model significantly reduced the root mean squared error(RMSE)by 39.3％to 58.5％during both the jointing-tasseling stage and the dough-physiological maturity stage of summer maize.The"collaborative calibration-dynamic fusion"framework proposed significantly improved the accuracy of remote sensing-based evapotranspiration(ET)monitoring,thereby providing theoretical support for precision water resource management.关键词
蒸散发/无人机/卫星遥感/尺度转换/协同校正模型/贝叶斯模型平均Key words
evapotranspiration/unmanned aerial vehicle(UAV)/satellite remote sensing/scale conversion/collaborative calibration model/Bayesian model averaging(BMA)分类
农业科技引用本文复制引用
胡笑涛,刘畅,王亚昆,李高良,代秦,陈洪..多源遥感数据尺度转换的夏玉米蒸散发融合模型研究[J].农业机械学报,2025,56(8):21-31,11.基金项目
国家自然科学基金项目(U2243235)和陕西省科协青年人才托举计划项目(20240439) （U2243235）
