Monitoring Mangrove Phenology Based on Gap Filling and Spatiotemporal Fusion: An Optimized Mangrove Phenology Extraction Approach (OMPEA)

作     者：Hong, Yu Zhou, Runfa Liu, Jinfu Que, Xiang Chen, Bo Chen, Ke He, Zhongsheng Huang, Guanmin 

作者机构：Fujian Agr & Forestry Univ Fuzhou 350002 Peoples R China Fujian Agr & Forestry Univ Key Lab Fujian Univ Ecol & Resource Stat Fuzhou 350002 Peoples R China Univ Idaho Dept Comp Sci Moscow ID 83844 USA Zhangjiangkou Natl Mangrove Nat Reserve Zhangzhou 363300 Peoples R China 

出 版 物：《REMOTE SENSING》 (Remote Sens.)

年 卷 期：2025年第17卷第3期

页      面：549-549页

核心收录：

学科分类：0830[工学-环境科学与工程（可授工学、理学、农学学位）] 1002[医学-临床医学] 070801[理学-固体地球物理学] 07[理学] 08[工学] 0708[理学-地球物理学] 0816[工学-测绘科学与技术] 

基　　金：National Natural Science Foundation of China Key Project of Scientific and Technological Innovation of Fujian Province [2021G02007] Fujian Provincial Science and Technology Innovation Project (Southeast Ecological Restoration) [4 KY-090000-04-2021-013] Science and Technology Innovation Project of Fujian Agriculture and Forestry University [KFB23044A, KFB23150] Forestry Technology Research Project of Fujian Province [2024FKJ17] 42202333 

主　　题：mangrove forests phenology monitoring OMPEA denoising algorithm spatiotemporal interpolation 

摘      要：Monitoring mangrove phenology requires frequent, high-resolution remote sensing data, yet satellite imagery often suffers from coarse resolution and cloud interference. Traditional methods, such as denoising and spatiotemporal fusion, faced limitations: denoising algorithms usually enhance temporal resolution without improving spatial quality, while spatiotemporal fusion models struggle with prolonged data gaps and heavy noise. This study proposes an optimized mangrove phenology extraction approach (OMPEA), which integrates Landsat and MODIS data with a denoising algorithm (e.g., Gap Filling and Savitzky-Golay filtering, GF-SG) and a spatiotemporal fusion model (e.g., Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model, ESTARFM). The key of OMPEA is that GF-SG algorithm filled data gaps from cloud cover and satellite transit gaps, providing high-quality input to ESTARFM and improving its accuracy of NDVI imagery reconstruction in mangrove phenology extraction. By conducting experiments on the GEE platform, OMPEA generates 1-day, 30 m NDVI imagery, from which phenological parameters (i.e., the start (SoS), end (EoS), length (LoS), and peak (PoS) of the growing season) are derived using the maximum separation (MS) method. Validation in four mangrove areas along the coastal China shows that OMPEA significantly improves the potential to capture mangrove phenology in the presence of incomplete data. The OMPEA significantly increased usable data, adding 7-33 Landsat images and 318-415 MODIS images per region. The generated NDVI series exhibits strong spatiotemporal consistency with original data (R2: 0.788-0.998, RMSE: 0.007-0.253) and revealed earlier SoS and longer LoS at lower latitudes. Cross-correlation analysis showed a 2-3 month lagged effects of temperature on mangroves growth, with precipitation having minimal impact. The proposed OMPEA improves the possibility of capturing mangrove phenology under non-continuous and low-resolution data, providing va