中国农学通报2025，Vol.41Issue(34)：147-156,10.
西南复杂地形区10m级马铃薯叶面积指数反演研究
Research on Inversion of 10-meter Potato Leaf Area Index in Complex Terrain Area of Southwest China
摘要
Abstract
To achieve high-accuracy inversion of potato leaf area index(LAI)in the complex terrain of Southwest China and explain factors potentially affecting retrieval accuracy from the perspectives of surface spatial heterogeneity and model parameters,this study focused on potato cultivation areas in Zhaojue and Butuo Counties,Liangshan Prefecture,Sichuan Province.Based on Sentinel-2 imagery,an LAI inversion algorithm integrating the PROSAIL model and a neural network(NN)within SNAP was employed to achieve simple,efficient,and small-sample 10-meter LAI inversion with field validation.A comparison with the 500-m MODIS LAI product was further conducted to assess the advantages of high-resolution LAI in revealing spatial heterogeneity.The results showed that:(1)the 10-meter LAI retrieved using the PROSAIL+NN algorithm exhibited high overall accuracy,with a coefficient of determination(R2)of 0.86 and a root mean square error(RMSE)of 0.28 when compared with field measurements.Regional differences were evident—the retrieval accuracy in Butuo County sample area(RMSE=0.15)exceeded that in Zhaojue County sample area(RMSE=0.38),primarily associated with more complex topography(slopes 1°-14°)and higher surface heterogeneity.(2)Within 500-m pixels,the 10-meter LAI revealed an average within-pixel LAI variance of 2.17,which was 1.6 times the mean LAI of the study area.Furthermore,the maximum and minimum values of the MODIS LAI product were lower by 0.1 and 0.47,respectively,than the corresponding values from this study,and partial null pixels were present.This confirmed that the 10-meter LAI significantly surpasses the traditional 500-meter MODIS LAI product in quantifying spatial heterogeneity and ensuring data completeness.(3)Retrieval accuracy was also constrained by model input parameters:the SNAP default LAI range significantly exceeded the actual potato LAI range,resulting in some anomalous values.Additionally,insufficient representativeness of soil reflectance(ρsoil)and overestimation of the Band 12 reflectance after atmospheric correction(maximum value 0.7,exceeding the model's valid input range of 0.5)increased uncertainty.The PROSAIL+NN inversion method used in this study enables simple and efficient acquisition of 10-meter LAI products,allowing for precise characterization of the spatial heterogeneity in potato growth across the complex topography of Southwest China.With minimal ground measurements,strong mechanistic underpinnings and transferability,this approach can serve as an effective pathway for crop information acquisition in the mountainous regions of Southwest China,supporting digital agriculture,crop monitoring,and agricultural management.关键词
叶面积指数/遥感反演/马铃薯/Sentinel-2/精度验证/西南山地/数字农业Key words
leaf area index/remote sensing inversion/potatoes/Sentinel-2/precision validation/southwest mountainous region/digital agriculture分类
农业科技引用本文复制引用
卢晓宁,徐丹丹,刘轲,徐维新,徐保东..西南复杂地形区10m级马铃薯叶面积指数反演研究[J].中国农学通报,2025,41(34):147-156,10.基金项目
西南科技大学博士基金项目"耦合数据与知识的四川水稻参数遥感高效监测技术攻关"(22zx7169) （22zx7169）
风云卫星先行计划"川渝复杂地形区风云卫星高精度土壤水分产品研发及干旱监测预警"(FY-APP-2024.0404). （FY-APP-2024.0404）
