Development of an enhanced hybrid piecewise logistic model for retrieving land surface phenology in drylands

The accurate retrieval of land surface phenology (LSP) for drylands is extremely challenging. Drylands exhibit vegetation characteristics such as sparse and patchy vegetation cover, low seasonal greenness variability, and high spatial heterogeneity. The irregular rainy and dry episodes often complicate vegetation growth, leading to an irregular temporal trajectory with multiple growth stages during the greenup and senescence phases. Moreover, the heterogeneous phenological cycles among the vegetation species in a satellite pixel and other factors may lead to a long period with only a very slight increase or decrease in greenness before or after a vegetation growing cycle. Current phenological retrieval methods, however, commonly assume that vegetation greenness gradually increases in a greenup phase and decreases in a senescence phase, following a single sigmoidal growth trajectory, which is inadequate to describe the irregular growth in drylands. In this study, we developed a novel algorithm to improve on the hybrid piecewise logistic model (HPLM) for improving LSP retrievals, especially in drylands. Our enhanced HPLM (E-HPLM) algorithm addresses two characteristics of irregular growth trajectories: (1) the multiple plateau stages within a greenup or senescence phase, and (2) the long linear tail before the start or after the end of a growing season. Specifically, we identified multiple growth stages within a greenup or senescence phase in order to fit each stage separately with a logistic model, and added a linear parameter to the logistic model to eliminate long linear tails by adjusting the background values. We implemented this new algorithm to retrieve LSPs in global drylands using the 500 m Visible Infrared Imaging Radiometer Suite (VIIRS) dataset from 2013 to 2022. The results were then compared with those of HPLM-retrieved LSPs. We also evaluated the E-HPLM results using phenometrics derived from the PhenoCam observations at site levels and the fused Harmonized Landsat and Sentinel-2 (HLS)-PhenoCam dataset at regional levels. The E-HPLM was able to reduce the uncertainty by ∼10 days in the pixels with plateau stages from 2013 to 2022 in global drylands in comparison with the HPLM algorithm, where the plateau stage appeared in over 74 % of drylands. Compared with the HPLM, the E-HPLM improved overall phenology accuracy by two days for the PhenoCam sites and one to four days in HLS-PhenoCam areas, although the improvements varied with land cover types and aridity levels. The E-HPLM algorithm has the potential to replace the current HPLM algorithm, with improved ability to retrieve LSP in drylands and to generate global LSP products.