Refining landsat-based annual NDVImax estimation using shape model fitting and phenological metrics

作者：Lihao Zhang, Miaogen Shen, Licong Liu, Xuehong Chen, Ruyin Cao, Qi Dong, Yang Chen, Jin Chen · 发表于：Ecological Informatics · 年份：2025 · DOI：10.1016/j.ecoinf.2025.103107 · 被引用次数：8 · 研究领域：Remote Sensing in Agriculture、Remote Sensing and LiDAR Applications、Land Use and Ecosystem Services

The annual maximum normalized difference vegetation index (NDVI max ) is widely used as a surrogate for annual aboveground net primary productivity (ANPP) of summer-green vegetation. Landsat data, with its 30-m spatial resolution and high temporal consistency, have revealed long-term changes in NDVI max and ANPP. However, in cloudy regions with summer-green vegetation, such as the Tibetan Plateau, the scarcity of cloud-free Landsat NDVI observations complicates NDVI max estimation, particularly due to interannual variations in phenology and NDVI max . This study proposed a shape model fitting method that integrates interannual phenological similarity to estimate Landsat NDVI max , using the Tibetan Plateau as an example. For a given target year, an annual NDVI shape model was constructed using all cloud-free Landsat NDVI observations from that year and phenologically similar years, identified using phenological metrics derived from MODIS and GIMMS NDVI datasets. The model was then fitted to the target year's cloud-free NDVI time series to correct seasonal biases in NDVI observations. Validations with simulated and real images indicated that the proposed method outperformed several commonly used approaches in estimating NDVI max and detecting temporal trends across various conditions. The method more accurately captured the true annual NDVI trajectory and NDVI max date for the target year. It enabled the retrieval of long-term high-resolution NDVI max series for summer-green v...