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

• Published in: Ecological informatics Vol. 87; p. 103107
• Main Authors: Zhang, Lihao, Shen, Miaogen, Liu, Licong, Chen, Xuehong, Cao, Ruyin, Dong, Qi, Chen, Yang, Chen, Jin
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.07.2025; Elsevier
• Subjects: Aboveground net primary productivity (ANPP); China; data collection; global change; Landsat; Landsat NDVI; net primary productivity; phenology; Shape model fitting; Tibetan plateau; time series analysis; vegetation; vegetation index; Vegetation phenology; China; Vegetation phenology; Tibetan plateau; Landsat NDVI; Shape model fitting; Aboveground net primary productivity (ANPP)
• ISSN: 1574-9541
• DOI: 10.1016/j.ecoinf.2025.103107

The annual maximum normalized difference vegetation index (NDVImax) 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 NDVImax and ANPP. However, in cloudy regions with summer-green vegetation, such as the Tibetan Plateau, the scarcity of cloud-free Landsat NDVI observations complicates NDVImax estimation, particularly due to interannual variations in phenology and NDVImax. This study proposed a shape model fitting method that integrates interannual phenological similarity to estimate Landsat NDVImax, 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 NDVImax and detecting temporal trends across various conditions. The method more accurately captured the true annual NDVI trajectory and NDVImax date for the target year. It enabled the retrieval of long-term high-resolution NDVImax series for summer-green vegetation on the Tibetan Plateau and provided a reference for Landsat NDVImax extraction in other summer-green vegetation regions. Additionally, by addressing the observational biases, the method corrected previous overestimates of greening on Tibetan Plateau, thereby improving global change studies on summer-green vegetation. •We integrate phenology to refine shape model fitting method to estimate 30-m NDVI.•RMSE of annual peak NDVI (NDVImax) estimates was reduced by 26.1 %–74.6 %.•RMSE of estimates of temporal trends in NDVImax was reduced by 17.5 %–67.0 %.•Our method generates 30-m long-term NDVImax time series for summer–green vegetation.•If uncalbriated, the NDVImax increase over 1986–2020 will be severely overestimated.