结合多模态数据的滨海湿地碱蓬叶面积指数无人机高光谱反演

Salt marsh vegetation is an important part of the blue carbon ecosystem, which has strong carbon sequestration and carbon storage capacity. The Leaf Area Index (LAI) determines its growth, photosynthetic active radiation absorption ratio, and biomass. LAI is often used to simulate the photosynthesis, respiration, and transpiration of vegetation, thus playing a crucial role in improving the yield of Suaeda salsa. An accurate estimation of Suaeda salsa can provide an important basis for judging the growth status of alkali ponies and thus provide an effective aid for monitoring salt marsh wetlands. To improve the accuracy of LAI estimation accurately and rapidly, the Yellow River Delta Suaeda salsa shoal wetland was selected, and the indigenous plant Suaeda salsa was used as the research object. A UAV hyperspectral remote sensing image was obtained, and the ground spectrum was measured in combination with regional soil factors, vegetation spectral characteristics, hyperspectral image texture characteristics, and vegetation coverage. Multimodal data, through Random Forest (RF) feature selection for multimodal data, and the RF-PSO-DELM algorithm with a dual optimization strategy was developed to construct an inversion model of the LAI of Suaeda salsa in coastal wetlands. The coefficient of determination (R²) and the Root Mean Square Error (RMSE) were 0.9546 and 0.1341, respectively. Compared with the inversion model accuracy of Suaeda salsa LAI constructed on the basis of the five algorithms of SVM, BP, ELM, DELM, and PSO-DELM, R² was increased by 0.2654 at most, and the RMSE was reduced by 0.0828 at most. Compared with the traditional inversion model (SVM), the RF-PSO-DELM model had better generalization; moreover, the fusion of multimodal data could effectively improve the accuracy of the inversion model. This study further enriched the theory and technology for the accurate monitoring of salt marsh vegetation based on UAV hyperspectral remote sensing technology. Multisource modal data, such as soil factors, texture features, spectral features, and vegetation cover affecting the growth of alkali ponies in coastal wetlands, were comprehensively considered, and the important influencing factors sensitive to the LAI of alkali ponies’ LAI were extracted by the random forest feature preference algorithm, which effectively reduced the complexity of model inversion and greatly improved the accuracy of model prediction.