## 1 Introduction  
Pheology refers to the study of the recurring stages of plant and animal life [1]. Vegetation phenology studies specific plant life cycle events such as bud burst, canopy growth, flowering, and senescence, and their relation to environmental factors. Phenology is a robust ecological indicator of climate change [2, 3] and environmental variation impacts, across individual species to entire landscapes [4]. Most phenological events are receptive to temperature, rainfall, and human activities that affect vegetation growth and functions. For example, previous studies have linked shifts in rainfall and temperature to delayed leaf senescence and abscission in rubber trees [5], earlier start of the growing season across species in Ethiopia [6], advances in leaf unfolding of several woody species [7], and reduced litter components in three Acacia species [8]. Thus, phenology facilitates an improved understanding of the interactions among biotic and abiotic factors that impact life on Earth. Two commonly employed techniques to study vegetation phenology are ground observation [5; 9] and remote sensing (e.g., [10; 11]). Ground observation data are used for conducting species-specific phenological analyses at limited scales [9; 12]. This technique is time and resource expensive. Alternately, general inter-annual vegetation changes can be interpreted via spatial and spectral resolutions of remote sensing imagery, although this does not include specific phenological events [13]. This is called land surface phenology. It captures larger landscapes [14] over longer timeframes [15] in order to define the land surface phenological metrics of growing seasons. The most commonly used metrics are start, end, and length of growing season (SOS, EOS, and LOS, respectively) [16; 17]. The SOS and EOS are important phenological metrics as they are the termini wherein vegetation phenological events occur. Various types of satellite data have been used to monitor land surface phenology. These include Deep Space Climate Observatory (DSCOVR) satellites data [18], geostationary satellites data (e.g., GEOS-16 and -17 [19]), polar orbit satellites data (e.g., MODIS [11; 20; 21] and Landsat [22; 23; 24]) and solar-induced fluorescence (SIF) data [25; 26; 27]. Geostationary satellites provide the highest temporal resolution of 30 to 2.5 min, followed by MODIS at 1-day, and SIF at 16-days. Although higher temporal resolutions are crucial in phenology monitoring, the selection of satellite data is subject to a trade-off between spatial and temporal resolution and is limited by its coverage. Most land surface phenology models are derived from vegetation indices extracted from satellite data. The most widely used vegetation indices for this purpose are Normalised Difference Vegetation Index (NDVI) [28; 29] and Enhanced Vegetation Index (EVI) [30; 31]. For remote sensing, vegetation phenology is the integrative environmental indicator of climate change, which reveals the impacts of climate change on vegetation phenology over space and time. [PERSON] et al. [10] studied the land surface phenology response to climate variability across Australia. They modelled the phenological cycle curves from EVI-measured greening and browning, and extracted the key phenological metrics from it. Their result indicates that phenological metrics of cycle peak and integrated greenness were significantly correlated to the standardised difference in air pressure as opposed to rainfall. On the other hand, [PERSON] et al. [20] found that rainfall influences land surface phenology metrics during the green-up period but not in the senescence period in the semi-arid savanna of South Africa. In their study, the SOS and EOS phenological metrics were derived from analysis of a time series of leaf area index. Similarly, using NDVI data, [PERSON] et al.