甘蔗糖业2026，Vol.55Issue(4)：76-88,13.DOI:10.3969/j.issn.1005-9695.2026.04.007
基于2种数据源的糖料蔗固碳量定量估算模型研究——以上思县为例
Research on Prediction Method of Carbon Sequestration in Sugarcane Based on Two Data Sources:A Case Study of Shangsi County
摘要
Abstract
To improve the intelligent estimation level of sugarcane carbon sequestration in the area,this study applied observational data of sugarcane carbon sequestration,surface meteorological observation data,and FY3D-NDVI remote sensing data in Shangsi County from 2022 to 2024,a meteorological factor estimation model based on random forest and a remote sensing vegetation index estimation model based on multi-function fitting were constructed,respectively.Two estimation models were respectively constructed:a meteorological factor-based model using random forest(RF)algorithm,and a remote sensing vegetation index-based model employing multi-function fitting.The applicability of the two models was evaluated through comparative analysis of their fitting performance.The results indicate that:(1)Importance analysis of RF feature variables revealed that diffuse radiation and photosynthetically active radiation contributed more significantly to sugarcane carbon sequestration than conventional meteorological factors such as precipitation and sunshine duration.(2)A close relationship was observed between sugarcane carbon sequestration and NDVI;the NDVI values of the corresponding period in 2023 exhibited an optimal S-shaped relationship with the carbon sequestration of total aboveground biomass,stems,and leaves,respectively.This S-shaped growth pattern also proved applicable to the quantitative characterization of sugarcane carbon sequestration using NDVI.(3)The RF model demonstrated superior performance in prediction error control,with both root mean square error(RMSE)and mean absolute error(MAE)for total,stem,and leaf carbon sequestration being lower than those of the remote sensing-based estimation model.Following feature selection and weighted decision tree optimization,the RF model achieved an R2 of 0.655 and an absolute error of 2.820 t/hm2 making it the preferred model for carbon sequestration estimation based on meteorological data.In contrast,the remote sensing inversion model exhibited a higher coefficient of determination(R2),indicating its enhanced capability in capturing the overall trend of carbon sequestration dynamics.This study provides a scientific foundation for regional assessment of sugarcane carbon sequestration capacity and agricultural carbon accounting.关键词
糖料蔗/固碳量/随机森林/气象因子/植被指数Key words
Sugarcane/Carbon sequestration/Random forest/Meteorological factors/Vegetation index分类
农业科技引用本文复制引用
陆永顺,丁美花,潘春江,陆思宇,谭宗琨,孙明,方建芳,许彦灵,文徐能..基于2种数据源的糖料蔗固碳量定量估算模型研究——以上思县为例[J].甘蔗糖业,2026,55(4):76-88,13.基金项目
2025年度广西气象科研计划青年人才培养项目"基于多元数据的上思县糖料蔗生物固碳估算模型研究"(桂气科2025QN20) （桂气科2025QN20）
广西重点研发项目"广西糖料蔗碳汇气象评估关键技术与应用"(桂科AB22035069) （桂科AB22035069）
广西气象科研计划项目"甘蔗关键生育期土壤水分垂向响应机制的初步研究"(桂气科2025QN06) （桂气科2025QN06）
