面向小样本数据的水体总磷浓度Stacking集成遥感反演模型研究
Research on Stacking ensemble remote sensing retrieval model for total phosphorus concentration in water with few-shot data
摘要
[目的]总磷(TP)浓度是衡量水体富营养化与生态健康的关键指标,传统人工采样方法成本高、空间覆盖有限,基于卫星遥感的反演技术虽为有效替代途径,却在样本量不足时面临模型拟合与泛化能力弱的挑战.[方法]为突破小样本条件下的精度限制,本文提出了面向小样本数据的Stacking集成遥感反演模型,通过构建多维度光谱特征集挖掘有效信息,结合递归特征消除(RFE)与浊度中介模型优化输入特征结构及物理一致性,再集成XGBoost、LightGBM、随机森林、CatBoost和偏最小二乘回归(PLSR)等异质基学习器,由元学习器进行高阶融合.[结果]基于Sentinel-2影像的试验表明,该模型实现了R2 优于0.92 的反演精度.[结论]本文方法能够显著提升小样本条件下模型的稳健性与泛化能力,为水体总磷遥感监测提供了可靠方法.
[Purposes]Total phosphorus(TP)concentration is a key indicator for measuring eutrophication and ecological health in water bodies.Traditional manual sampling methods are costly and have limited spatial coverage.Although satellite remote sensing inversion technology is an effective alternative,it faces challenges in model fitting and generalization ability when the sample size is insufficient.[Methods]To overcome the accuracy limitations under small sample conditions,this study proposes a Stacking integrated remote sensing inversion model for small sample data.By constructing a multidimensional spectral feature set to mine effective information,combined with recursive feature elimination(RFE)and turbidity mediation model to optimize input feature structure and physical consistency,heterogeneous base learners such as XGBoost,LightGBM,random forest,CatBoost,and partial least squares regression(PLSR)are integrated,and high-order fusion is performed by meta learners.[Findings]In the Sentinel-2 image experiment,the model achieved an inversion accuracy of R2 better than 0.92.[Conclusions]This method significantly improves the robustness and generalization ability of the model under small sample conditions,providing a reliable method for remote sensing monitoring of total phosphorus in water bodies.
年，卷(期)：
2026, (9)
分类号：
P237
机标分类号：
X524
TP391.4
X832
在线出版日期：
2026年10月28日
基金项目：
2024Z007:院自主科研项目SKS-2022072:水利部重大科技项目，
