Variations in Phenology Identification Strategies across the Mongolian Plateau Using Multiple Data Sources and Methods

Abstract

Satellite data and algorithms directly affect the accuracy of phenological estimation; therefore, it is necessary to compare and verify existing phenological models to identify the optimal combination of data and algorithms across the Mongolian Plateau (MP). This study used five phenology fitting algorithms—double logistic (DL) and polynomial fitting (Poly) combined with the dynamic threshold method at thresholds of 35% and 50% (DL-G35, DL-G50, Poly-G35, and Poly-G50) and DL combined with the cumulative curvature extreme value method (DL-CUM)—and two data types—the enhanced vegetation index (EVI) and solar-induced chlorophyll fluorescence (SIF)—to identify the start (SOS), peak (POS), and end (EOS) of the growing season in alpine meadow (ALM), desert steppe (DRS), forest vegetation (FV), meadow grassland (MEG), and typical grassland (TYG) of the MP. The optimal methods for identifying the SOS, POS, and EOS of typical grassland areas were Poly-G50 (NSE = 0.12, Pbias = 0.22%), DL-G35/50 (NSE = −0.01, Pbias = −0.06%), and Poly-G35 (NSE = 0.02, Pbias = 0.08%), respectively, based on SIF data. The best methods for identifying the SOS, POS, and EOS of desert steppe areas were Poly-G35 (NSE = −0.27, Pbias = −1.49%), Poly-G35/50 (NSE = −0.58, Pbias = −1.39%), and Poly-G35 (NSE = 0.29, Pbias = −0.61%), respectively, based on EVI data. The data source explained most of the differences in phenological estimates. The accuracy of polynomial fitting was significantly greater than that of the DL method, while all methods were better at identifying SOS and POS than they were at identifying EOS. Our findings can help to facilitate the establishment of a phenological estimation system suitable for the Mongolian Plateau and improve the observation methods of vegetation phenology.