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

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 vegetation on the Tibetan Plateau and provided a reference for Landsat NDVI max 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 (NDVI max ) estimates was reduced by 26.1 %–74.6 %. • RMSE of estimates of temporal trends in NDVI max was reduced by 17.5 %–67.0 %. • Our method generates 30-m long-term NDVI max time series for summer–green vegetation. • If uncalbriated, the NDVI max increase over 1986–2020 will be severely overestimated.