Spatiotemporal Variations of Dryland Vegetation Phenology Revealed by Satellite-Observed Fluorescence and Greenness across the North Australian Tropical Transect

: Accurate characterization of spatial patterns and temporal variations in dryland vegetation is of great importance for improving our understanding of terrestrial ecosystem functioning under changing climates. Here, we explored the spatiotemporal variability of dryland vegetation phenology using satellite-observed Solar-Induced chlorophyll Fluorescence (SIF) and the Enhanced Vegetation Index (EVI) along the North Australian Tropical Transect (NATT). Substantial impacts of extreme drought and intense wetness on the phenology and productivity of dryland vegetation are observed by both SIF and EVI, especially in the arid/semiarid interior of Australia without detectable seasonality in the dry year of 2018–2019. The greenness-based vegetation index (EVI) can more accurately capture the seasonal and interannual variation in vegetation production than SIF (EVI r 2 : 0.47~0.86, SIF r 2 : 0.47~0.78). However, during the brown-down periods, the rate of decline in EVI is evidently slower than that in SIF and in situ measurement of gross primary productivity (GPP), due partially to the advanced seasonality of absorbed photosynthetically active radiation. Over 70% of the variability of EVI (except for Hummock grasslands) and 40% of the variability of SIF (except for shrublands) can be explained by the water-related drivers (rainfall and soil moisture). By contrast, air temperature contributed to 25~40% of the variability of the effective ﬂuorescence yield (SIF yield ) across all biomes. In spite of high retrieval noises and variable accuracy in phenological metrics (MAE: 8~60 days), spaceborne SIF observations, offsetting the drawbacks of greenness-based phenology products with a potentially lagged end of the season, have the promising capability of mapping and characterizing the spatiotemporal dynamics of dryland vegetation phenology.


Introduction
Vegetation phenology, the study of the periodic biological life cycle events of plants, is a critical regulator of carbon and water cycling in terrestrial ecosystems [1]. The trend of global warming has aroused great interest in understanding and monitoring the dynamics of vegetation phenology under the changing climate [2]. As a valuable indicator of climate variability and ecosystem responses [1,3], accurate measurement of land surface phenology (LSP) is crucial for better explicating the land-atmosphere-energy exchange and its representation in terrestrial biosphere models [4][5][6][7]. SIF oco2_005 was generated by the OCO-2 native SIF along with MODIS reflectance using machine learning [32], and it therefore showed an enhanced accuracy in determining the phenological transition of GPP derived from flux tower measurement as a consequence of it containing both structural and physiological information. However, whether the original SIF observation can accurately capture the phenology dynamics of Australia's dryland vegetation under climate variability remains unclear.
In arid and semiarid ecosystems, rainfall strongly affects the strength and occurrence of photosynthetic and respiratory activities and is the dominant driver for vegetation phenology and productivity [33][34][35]. The seasonality of Acacia-dominated woodlands (as measured by EVI) was found to show substantial responsiveness to hydroclimatic variability [4]. Additionally, approximately 80% of the variations in the length of the growing season of major dryland biomes along the NATT could be attributed to the variability of annual precipitation [4]. Nevertheless, there is a knowledge gap regarding how and to what extent major environmental and climatic drivers determine the phenological dynamics of fluorescence (as a surrogate of photosynthesis).
The objectives of this study were: (1) to evaluate the spatial patterns and seasonal variations of dryland vegetation phenology across NATT under a dynamic climate; (2) to contrast the differences in phenological metrics derived from satellite-observed SIF and EVI; (3) to determine the dominant factor that drives the interannual and seasonal variability of each vegetation variable among major biome types.

Study Area
This study was conducted at a regional scale between 12 • S and 23 • S and 128 • E and 138 • E, which is known as the Northern Australian Tropical Transect ( Figure 1). This region, particularly the northern NATT, has a classic monsoon climate pattern, which receives more than 80% of its annual precipitation during November to April [4]. From the northern mesic tropics to xeric central Australia, mean annual rainfall ranges from 1700 mm to approximately 300 mm [4].
Correspondingly, the vegetation follows a wet-dry gradient that shifts from Eucalyptusdominated forests, open forests, and woodlands in the coastal northern areas to Acaciadominated open woodlands, scattered shrubs, and Hummock grassland into the vast inland [4]. More detailed descriptions with respect to ecosystems, climate, and soils of the entire study region can be found in Ma et al. (2013) and Hutley et al. (2011) [4,36].
To contrast satellite observation with ground-based evidence, we selected five representative flux tower sites across the extensive study area: Howard Springs (AU-How

Satellite Data
In this study, we utilised satellite-based SIF records obtained from the Global Ozone Monitoring Experiment-2 (GOME-2) onboard the MetOp-B platform. This dataset is the retrieval of the far-red chlorophyll fluorescence peaking at 740 nm, based on a simplified radiative transfer model in the company of a principal component analysis [22]. Monthly global coverage of SIF data at a 0.5 • × 0.5 • spatial resolution (level 3, Version 28) from March 2013 to March 2019 was obtained from NASA Goddard Space Flight Centre (https://avdc.gsfc.nasa.gov/, accessed on 1 July 2020). The daily orbital data (level 2, Version) were also used to aggregate 16-day interval records for higher temporal resolution analysis. Given that the data provider of GOME-2 SIF suspended updating in March 2019, we introduced a novel SIF product (from July 2018 to June 2019) derived from TROPOMI

Satellite Data
In this study, we utilised satellite-based SIF records obtained from the Global Ozon Monitoring Experiment-2 (GOME-2) onboard the MetOp-B platform. This dataset is th retrieval of the far-red chlorophyll fluorescence peaking at 740 nm, based on a simplifie radiative transfer model in the company of a principal component analysis [22]. Monthl global coverage of SIF data at a 0.5° × 0.5° spatial resolution (level 3, Version 28) from March 2013 to March 2019 was obtained from NASA Goddard Space Flight Centr (https://avdc.gsfc.nasa.gov/, accessed on 1 July 2020). The daily orbital data (level 2, Ve sion) were also used to aggregate 16-day interval records for higher temporal resolutio analysis. Given that the data provider of GOME-2 SIF suspended updating in March 201 we introduced a novel SIF product (from July 2018 to June 2019) derived from TROPOM onboard the Sentinel-5 Precursor satellite as a supplementary in this study. A data-drive method was employed to retrieve the SIF signal using spectral measurements rangin from 743 nm to 758 nm. The daily orbital TROPOMI SIF at a 0.05° spatial resolution (ob tained from ftp://fluo.gps.caltech.edu/data/tropomi/, accessed on 1 August 2020) was like wise aggregated to 16-day series by the mean value.
We used the Moderate Resolution Imaging Spectroradiometer (onboard Aqua, Co lection 6) MYD13C1 (0.05°, 16-day) and MYD13C2 (0.05°, monthly) Vegetation Indice products from January 2007 to June 2019 downloaded from NASA Earth Observation dat (https://search.earthdata.nasa.gov/search, accessed on 1 August 2020). EVI is an opt mized version of vegetation indices that effectively reduces soil background influence and is widely used as a proxy of canopy greenness. The equation of EVI is: We used the Moderate Resolution Imaging Spectroradiometer (onboard Aqua, Collection 6) MYD13C1 (0.05 • , 16-day) and MYD13C2 (0.05 • , monthly) Vegetation Indices' products from January 2007 to June 2019 downloaded from NASA Earth Observation data (https://search.earthdata.nasa.gov/search, accessed on 1 August 2020). EVI is an optimized version of vegetation indices that effectively reduces soil background influences and is widely used as a proxy of canopy greenness. The equation of EVI is: where ρ blue , ρ red , and ρ N IR are reflectance in the blue, red, and near-infrared bands, respectively. To reduce noise and uncertainties, only the best-quality data were retained in this study by removing pixels for which the quality control flag of the first 2 bits was neither 00 nor 01, and pixelwise EVI time series data were smoothed using the Savitzky-Golay filter. MODIS daytime Land Surface Temperature (LST, MYD11C3, Version 6) at a monthly scale and a 0.05 • spatial resolution was included in this study, collected from NASA Earth Observation data (https://search.earthdata.nasa.gov/search, accessed on 1 August 2020). Similarly, bad-quality data were removed by eliminating pixels with a quality control flag.
To examine the impact of solar radiation on vegetation seasonality, monthly photosynthetic active radiation (PAR) at a 1 • spatial resolution was downloaded from the NASA Langley Research Centre, Cloud and Earth's Radiant Energy System (CERES, Ed4.1), including adjusted surface PAR, both direct and diffuse fluxes under all-sky conditions (http://ceres.larc.nasa.gov, accessed on 1 August 2020). The total PAR was computed as the sum of both direct and diffuse PAR [38]. As recent studies suggested EVI outperformed the MODIS fPAR (fraction of absorbed PAR) products in estimating the APAR [39], we refer to EVI × PAR as an alternative estimate of APAR:

Climate Data and Land Cover Map
To assess the interaction of environmental drivers and vegetation, monthly air temperature (at a 2 m height) and soil moisture content (surface 0-7 cm depth, root zone 28-100 cm depth) based on ERA-5 reanalysis data were downloaded from Copernicus Climate Change Service (https://cds.climate.copernicus.eu/, accessed on 1 July 2020).
Global monthly precipitation based on Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG, Version 6, Final run, 2007-2019) at a monthly scale and a 0.1 • spatial resolution was collected from the NASA Precipitation Processing System (https://pps.gsfc.nasa.gov/, accessed on 1 August 2020).
The National Dynamic Land Cover Dataset (DLCD) was used in this research, obtained from Geoscience Australia and the Bureau of Agricultural and Resource Economics and Sciences (http://www.ga.gov.au/scientific-topics/earth-obs/landcover, accessed on 1 July 2020). Given that some biome types were only covered by a few pixels over the study region, closed Tussock grassland, dense shrubland, and closed forest were, respectively, re-grouped into open/closed Tussock grassland, dense/open shrubland, and closed/open forest ( Figure 1). This dataset, validated with abundant field sites, was aggregated to a 0.5 • spatial resolution by the most frequent values.

Eddy Covariance Data
The original level 3 (AU-How, AU-Dry, AU-Stp) and level 6 (AU-ASM, AU-TTE) flux data provided by the OzFlux network (http://www.ozflux.org.au/, accessed on 1 October 2020) were used to pre-process, including quality control assessment, removal of outliers, and gap-filling [33]. In order to estimate daily mean GPP with hourly eddy covariance and meteorological data, flux partitioning for level 3 data was conducted in the open-source R scientific computation environment (Version 3.5.1) associated with the REddyProc package (Version 1.2) [40]. This tool used the gap-filling and flux partitioning algorithms to partition level 3 data into GPP and field ecosystem respiration [41]. The daily-estimated flux data were, respectively, aggregated into monthly and 16-day GPP to match with satellite-based observations.

Phenological Metrics
Owing to the fact that Ma et al. (2013) demonstrated the capability of singular spectrum analysis (SSA) in the analysis of nonlinear dynamics in NATT [4], we also employed the same method to smooth and reduce the noise in satellite-based SIF, EVI time series. Correspondingly, following Ma et al. (2013) [4], 37 composite periods of the window length and four leading components were selected to configure the parameters in the SSA implementation ( Figure 2). After interpolating to daily time series from SSA-reconstructed SIF, EVI series, we used the PhenoDeriv function from the "GreenBrown" package to derive the key phenological metrics. Five metrics were extracted: 1.
The start of the growing season (SOS), defined as the date halfway between the minimum value and the fastest greening rate; 2.
The peak of the growing season (POS), the date of the maximum value; 3.
The end of the growing season (EOS), the date halfway between the fastest browndown rate and minimum value;

4.
The rate of spring green-up (RSP), the amplitude of EVI or SIF between POS and SOS divided by the periods (days) between POS and SOS; 5.
The rate of autumn senescence (RAU), the amplitude of EVI or SIF between POS and EOS divided by the periods (days) between POS and EOS SIF, EVI series, we used the PhenoDeriv function from the "GreenBrown" package to derive the key phenological metrics. Five metrics were extracted: 1. The start of the growing season (SOS), defined as the date halfway between the minimum value and the fastest greening rate; 2. The peak of the growing season (POS), the date of the maximum value; 3. The end of the growing season (EOS), the date halfway between the fastest browndown rate and minimum value; 4. The rate of spring green-up (RSP), the amplitude of EVI or SIF between POS and SOS divided by the periods (days) between POS and SOS; 5. The rate of autumn senescence (RAU), the amplitude of EVI or SIF between POS and EOS divided by the periods (days) between POS and EOS To examine the interaction of environmental drivers and vegetation variables, the coefficient of determination (r 2 ) was calculated across four major biome types. A t-test was utilized to examine the statistical significance level of the relationships (p-value). To further explore the difference between SIF and EVI, we interpreted SIF with fPAR and SIFyield, expressed as: Data processing, analysis, and visualization were conducted in the open-source R scientific computation environment (Version 3.6.2) and the associated packages contributed by the R user community (http://cran.r-project.org, accessed on 1 August 2020). To examine the interaction of environmental drivers and vegetation variables, the coefficient of determination (r 2 ) was calculated across four major biome types. A t-test was utilized to examine the statistical significance level of the relationships (p-value). To further explore the difference between SIF and EVI, we interpreted SIF with fPAR and SIF yield , expressed as: Data processing, analysis, and visualization were conducted in the open-source R scientific computation environment (Version 3.6.2) and the associated packages contributed by the R user community (http://cran.r-project.org, accessed on 1 August 2020). Figure 3 shows the inter-annual variations in eddy-covariance-estimated GPP and satellite-based SIF, EVI over five selected flux sites during 2014-2019. Generally, both satellite variables, especially EVI, exhibited the capacity for capturing the seasonal and inter-annual dynamics of dryland vegetation as indicated by tower-based GPP (r 2 ranging from 0.47 to 0.86). Compared with northern mesic sites, the two southern semi-arid sites (AU-ASM, AU-TTE) displayed much higher inter-annual variability, of which there was no seasonality detected by satellite observation nor field measurement in 2018-2019 (Figure 3d,e). Likewise, there was significant hysteretic senescence in EVI as opposed to GPP and SIF over two southern sites, in particular in the wet year of 2016-2017 (Figure 3d,e). The POS of SIF was mostly advanced relative to those of EVI. Furthermore, we found that multi-year series of GOME-2 SIF were more erratic and deviated from the fitted SSAreconstructed curves than GPP and EVI across northern humid sites, as well as southern arid sites. Remote Sens. 2022, 14, x FOR PEER REVIEW 8  To assess the ability of SIF and EVI to track the seasonal dynamics of dryland vegetation as delineated by tower-based GPP, mean seasonal cycles of vegetation variables (normalized to unity at the maxima) over five selected tower sites during 2014-2019 are shown in Figure 4. For northern mesic site AU-How, GOME-2 SIF indicates a consistent growing season with tower data, in contrast to the 1-2-month lagged seasonal profile of EVI during both the green-up and senescent periods (Figure 4a). Conversely, a notably advanced springtime increase and earlier autumn drop are observed in SIF relative to tower-based GPP and EVI for the AU-Dry site (Figure 4b). For three semi-arid and arid sites (AU-Stp, AU-ASM, AU-TTE), both GPP and SIF show a slightly narrower mean seasonal profile as opposed to two northern sites (Figure 4c-e). Besides, there is substantial later senescence in EVI as compared with GPP, SIF, and APAR (≈EVI× PAR) over southern water-limited sites. Likewise, the seasonal profiles of APAR significantly shifted 1~2 months earlier relative to those of EVI over all selected sites.  The differences of key phenological metrics (SOS and EOS) derived from satelli observed SIF and EVI as compared with tower-based GPP over five flux tower sites du ing 2014-2019 are presented in Table 1. For SOS over three northern sites (AU-How, A Dry, and AU-Stp), the mean absolute errors (MAE) of both SIF and EVI ranging from 7 32 days were generally less than those over two southern arid sites (AU-ASM and A TTE, ranging from 31 to 54 days). On the contrary, there was a larger discrepancy in EO at AU-How and AU-Dry (MAE ranges from 39 to 60 days). Besides, for two southern si (AU-ASM and AU-TTE), the differences of EOS between EVI and GPP were notably larg than those between SIF and GPP.  The differences of key phenological metrics (SOS and EOS) derived from satelliteobserved SIF and EVI as compared with tower-based GPP over five flux tower sites during 2014-2019 are presented in Table 1. For SOS over three northern sites (AU-How, AU-Dry, and AU-Stp), the mean absolute errors (MAE) of both SIF and EVI ranging from 7 to 32 days were generally less than those over two southern arid sites (AU-ASM and AU-TTE, ranging from 31 to 54 days). On the contrary, there was a larger discrepancy in EOS at AU-How and AU-Dry (MAE ranges from 39 to 60 days). Besides, for two southern sites (AU-ASM and AU-TTE), the differences of EOS between EVI and GPP were notably larger than those between SIF and GPP.

Biogeographic Patterns of Vegetation Phenology
To contrast the difference in the timing of seasonal greening derived from two satellitebased vegetation indicators and assess spatial variability over the NATT, the region-wide phenology maps based on EVI ( Figure 5) and SIF ( Figure 6) were generated. Besides, three representative years, 2014-2015 (normal year), 2016-2017 (wet year), and 2018-2019 (dry year), were selected to evaluate the impact of inter-annual precipitation variability on vegetation phenology. We found large spatial and inter-annual variations in the timing of key phenology transition dates retrieved from EVI ( Figure 5), particularly in the southern arid/semi-arid NATT (18 • S~23 • S). In the extremely dry year of 2018-2019, EVI exhibited no seasonality over nearly the entire southern NATT as compared with detectable phenology in normal and wet years ( Figure 5). There is a remarkable lagging trend in key phenological metrics (especially POS and EOS) in 2014-2015 (normal year) and 2016-2017 (wet year) from north to south across the study area, of which Eucalyptus-dominated woodlands distributed in the humid northern NATT started to green-up earlier (September to October). By contrast, the SOS of Acacia-dominated woodlands and Hummock-dominated grasslands distributed in the arid inland was generally 1-2 months behind (November to December). Similarly, the peak and end of the growing season in the south were 2-3 months delayed compared to those in the north during the normal/wet years.
The spatial patterns of vegetation phenology derived from SIF across NATT are shown in Figure 6. Consistent with the in situ comparison, the key phenological metrics of most pixels derived from SIF were generally earlier than those derived from EVI. There was a contrary spatial pattern in the timing of transition dates between EVI and SIF, in which arid/semiarid ecosystems over the southern NATT exhibited advanced POS (December to January) and EOS (March to April) as opposed to POS (February to March) and EOS (April to May) over the northern humid area (Figure 6d-h). With reference to the normal/wet years, larger latitudinal changes in the peak of season between the north (11 • S-17 • S) and south (17 • S-23 • S) NATT under the extremely dry condition of 2018-2019 were observed, and fractional pixels in the south were likewise without detectable phenology (Figure 6c,f).
With the purpose of further contrasting the differences in the seasonal profile derived from SIF and EVI, the relationship between green-up rate and brown-down rate of two satellite-based indicators among four major vegetation types are shown in Figure 7. For the semiarid/arid biomes (Hummock grasslands and shrublands), the majority of pixels displayed considerably higher green-up rates than senescence rates (RSP EVI > |RAU EVI |), of which the absolute senescence rate (|RAU EVI |) of Hummock grasslands was mostly less than 0.002 d −1 (Figure 7a,b). By contrast, for northern sub-humid/humid biomes (Eucalyptus-dominated forests and woodlands and Tussock grasslands), there were notably rapid and comparable rates of green-up and senescence (0.0005~0.004 d −1 ) (Figure 7c,d).
On the other hand, RSP SIF and RAU SIF ranging from 0.002~0.010 nW m −2 nm −1 sr −1 d −1 had fewer discrepancies in spite of diverse biomes (Figure 7e-h).

Interaction between Environmental Drivers and Vegetation Variables
To investigate the dominant factors controlling the seasonal and inter-annual variations in vegetation, the biome-specific relationships of the time series of principle environmental drivers and vegetation variables (SIF, SIF PAR , SIF yield , and EVI) during 2014-2019 are shown in Figure 8. Apart from forests and woodlands, root zone soil moisture was most relevant for EVI (r 2 : 0.42~0.79) relative to other drivers; among those, temperature-related drivers were poorly correlated (r 2 < 0.16) (Figure 8a2-a4). By contrast, both moisture-related drivers, as well as land surface temperature highly corresponded with EVI over northern humid forests and woodlands (Figure 8a1). There were strong correlations (r 2 > 0.8) between SIF and EVI over forests and woodlands and Tussock grasslands as compared with moderate correlations (r 2 : 0.21, 0.38) over water-limited ecosystems (Figure 8b1-b4). Similarly, compared with temperature-related drivers (r 2 : 0~0.3), water-related factors (especially soil moisture) were more associated with SIF among all biomes. After removing the impact of solar radiation, there was an enhanced agreement between EVI and PAR-normalized SIF (SIF PAR ) over Tussock grassland, shrublands, and Hummock grasslands (r 2 : 0.51~0.86) (Figure 8c2-c4). Likewise, the correlation between root zone soil moisture and SIF PAR was strengthened relative to those with SIF regardless of different vegetation types. Precipitation and surface soil moisture agreed well with the effective fluorescence yield (SIF yield ) over forests and woodlands, as well as Tussock grasslands (r 2 : 0.38~0.62) (Figure 8d1-d2). In addition, there was a moderately increased correlation between air temperature and SIF yield (r 2 : 0.22~0.4) in comparison to those with SIF or SIF PAR (r 2 : 0~0.2).
(dry year), were selected to evaluate the impact of inter-annual precipitation variability on vegetation phenology. We found large spatial and inter-annual variations in the timing of key phenology transition dates retrieved from EVI ( Figure 5), particularly in the southern arid/semi-arid NATT (18°S~23°S). In the extremely dry year of 2018-2019, EVI exhibited no seasonality over nearly the entire southern NATT as compared with detectable phenology in normal and wet years ( Figure 5). There is a remarkable lagging trend in key phenological metrics (especially POS and EOS) in 2014-2015 (normal year) and 2016-2017 (wet year) from north to south across the study area, of which Eucalyptus-dominated woodlands distributed in the humid northern NATT started to green-up earlier (September to October). By contrast, the SOS of Acacia-dominated woodlands and Hummockdominated grasslands distributed in the arid inland was generally 1-2 months behind (November to December). Similarly, the peak and end of the growing season in the south were 2-3 months delayed compared to those in the north during the normal/wet years.  The spatial patterns of vegetation phenology derived from SIF across NATT are shown in Figure 6. Consistent with the in situ comparison, the key phenological metrics of most pixels derived from SIF were generally earlier than those derived from EVI. There was a contrary spatial pattern in the timing of transition dates between EVI and SIF, in which arid/semiarid ecosystems over the southern NATT exhibited advanced POS (December to January) and EOS (March to April) as opposed to POS (February to March) and EOS (April to May) over the northern humid area (Figure 6d-h). With reference to the normal/wet years, larger latitudinal changes in the peak of season between the north (11°S-17°S) and south (17°S-23°S) NATT under the extremely dry condition of 2018-2019 were observed, and fractional pixels in the south were likewise without detectable phenology (Figure 6c,f).
With the purpose of further contrasting the differences in the seasonal profile derived from SIF and EVI, the relationship between green-up rate and brown-down rate of two satellite-based indicators among four major vegetation types are shown in Figure 7. For the semiarid/arid biomes (Hummock grasslands and shrublands), the majority of pixels displayed considerably higher green-up rates than senescence rates (RSPEVI > |RAUEVI|), of which the absolute senescence rate (|RAUEVI|) of Hummock grasslands was mostly less than 0.002 d −1 (Figure 7a,b). By contrast, for northern sub-humid/humid biomes (Eucalyptus-dominated forests and woodlands and Tussock grasslands), there were notably rapid and comparable rates of green-up and senescence (0.0005~0.004 d −1 ) (Figure 7c,d). On the

Interaction between Environmental Drivers and Vegetation Variables
To investigate the dominant factors controlling the seasonal and inter-annual variations in vegetation, the biome-specific relationships of the time series of principle environmental drivers and vegetation variables (SIF, SIFPAR, SIFyield, and EVI) during 2014-2019 are shown in Figure 8. Apart from forests and woodlands, root zone soil moisture was most relevant for EVI (r 2 : 0.42~0.79) relative to other drivers; among those, temperature-related drivers were poorly correlated (r 2 < 0.16) (Figure 8a2-a4). By contrast, both moisture-related drivers, as well as land surface temperature highly corresponded with

Ground Interpretations of the Satellite-Observed Vegetation Phenology
Although the five selected sites exhibited distinct seasonality revealed by eddy covariance flux measurements of vegetation production (GPP), both satellite-based SIF and EVI generally captured seasonal dynamics and inter-annual variations over a variety of biomes (Figures 3 and 4). In comparison with the greenness-based vegetation index, GOME-2 SIF displayed more consistently a seasonal profile with tower-based GPP ( Figure  4). Especially, there was a significant "hysteresis effect" during the senescent period of EVI relative to that of GPP over water-limited ecosystems (Hummock grasslands and shrublands), consistent with previous findings [4]. In the wet year of 2016-2017, there was a considerably delayed senescence of EVI over two southern sites (AU-ASM and AU-TTE), relative to those of GPP and SIF ( Figure 3). Besides, we found that the autumn senescence rates of EVI were considerably slower than the spring green-up rates of EVI over these arid/semiarid biomes ( Figure 7); however, RAU and RSP of SIF were generally comparable. Aside from the slow chlorophyll degradation leading to a gradual decrease of EVI [42], another possible reason causing the discrepancies between EVI and SIF during

Ground Interpretations of the Satellite-Observed Vegetation Phenology
Although the five selected sites exhibited distinct seasonality revealed by eddy covariance flux measurements of vegetation production (GPP), both satellite-based SIF and EVI generally captured seasonal dynamics and inter-annual variations over a variety of biomes (Figures 3 and 4). In comparison with the greenness-based vegetation index, GOME-2 SIF displayed more consistently a seasonal profile with tower-based GPP (Figure 4). Especially, there was a significant "hysteresis effect" during the senescent period of EVI relative to that of GPP over water-limited ecosystems (Hummock grasslands and shrublands), consistent with previous findings [4]. In the wet year of 2016-2017, there was a considerably delayed senescence of EVI over two southern sites (AU-ASM and AU-TTE), relative to those of GPP and SIF ( Figure 3). Besides, we found that the autumn senescence rates of EVI were considerably slower than the spring green-up rates of EVI over these arid/semiarid biomes ( Figure 7); however, RAU and RSP of SIF were generally comparable. Aside from the slow chlorophyll degradation leading to a gradual decrease of EVI [42], another possible reason causing the discrepancies between EVI and SIF during the brown-down phase is that the rapid decline in solar radiation in the arid southern NATT gave rise to the swiftly dropping SIF signals (Figure 4f). After removing the impact of the PAR on SIF, there was a remarkably enhanced correlation between EVI and SIF PAR across southern water-limited biomes (Figure 8b3-b4).
In contrast to GOME-2 SIF, EVI had an improved capability (higher r 2 ) of tracking the interannual and seasonal variations in GPP over most sites (except AU-Stp, Figure 3), probably owing to the substantial footprint mismatch between flux tower measurement and satellite observations (EVI:~5 km, SIF:~50 km). Moreover, as compared with the reflectance-based vegetation index, the high retrieval noises over the low-productivity region resulted in more erratic SIF signals [43], giving rise to a relatively weaker correlation with tower-based GPP. Given the sparse spatial resolution of satellite-based SIF data, Wang et al. (2019) utilized a high-resolution contiguous SIF product (SIF OCO2_005 ) over the NATT and found that SIF OCO2_005 outperformed EVI at AU-ASM with a stronger temporal consistency with tower-based GPP [23]. However, this up-scaled dataset was generated by native OCO-2 SIF signals along with MODIS reflectance through a machine learning method [32], and it therefore contains information of canopy structure and chlorophyll content, like greenness-based indices. Even though EVI showed a tighter correlation with the GPP (higher r 2 ) than SIF OCO2_005 at AU-Dry and AU-Stp in Wang et al.'s (2019) studies, congruent with our results [23], nevertheless, the high spatiotemporal SIF data derived from TROPOMI onboard the Sentinel-5 Precursor launched in 2017 exhibiting striking consistency with field measurement has great potential of characterizing the phenological dynamics of dryland vegetation in the future [44].

Spatial Patterns of Vegetation Phenology
Distinct biogeographic patterns in the timing of transition dates, especially POS and EOS, derived from SIF and EVI were observed ( Figures 5 and 6), of which there was a considerable latitudinal shift in vegetation phenology (EVI) with a gradually delayed trend from north to south. This is in accordance with the in situ comparison that EVI displayed significantly late senescence at southern xeric sites as compared with GPP and SIF, particularly in the wet year of 2016-2017. In addition, the spatial phenomenon was in line with Ma et al.'s (2013) findings [4], that the majority of EOS based on EVI in wet years  Figure 5), probably as a consequence of the aforementioned "hysteresis effect". On the contrary, there was less difference in the spatial patterns of EOS based on SIF between the north and south NATT, as well as in normal/wet years ( Figure 6), of which both showed apparently earlier EOS, presumably due to the rapidly decreasing solar radiation over the southern inland. However, as an example of moderately inconsistent phenological metrics compared with field measurement GPP (Table 1), the coarse spatial and temporal resolution of GOME-2 SIF, as well as high retrieval noises, impeded the full potential of capturing the seasonal and interannual variations in vegetation. Considering the drawbacks of existing greenness-based phenology products, as well as the highly heterogeneous composition of dryland ecosystems, our findings imply that new spaceborne SIF with improved spatiotemporal resolution, such as TROPOMI, has great capability for advancing our understanding of phenological characterization in Australia.
Furthermore, SIF and EVI displayed a stronger temporal consistency over northern mesic biomes (forests and woodlands and Tussock grasslands) relative to southern xeric biomes (Hummock grasslands and shrublands) (Figure 8). Soil moisture can explain more than 60% of the seasonal and interannual variability in EVI over most biomes (except Hummock grasslands), suggesting it is the dominant factor controlling the dynamics of vegetation greenness across the NATT. By contrast, the temperature-related drivers could barely explain less than 5% of the variability (except forests and woodlands). Despite the fact that solar radiation has substantial impacts on the SIF signal, temperature-and moisture-related factors almost equally contributed to the process of light use efficiency for fluorescence (SIF yield , except forests and woodlands). This could partially explain the temporal discrepancies between SIF and EVI across diverse biomes.

Conclusions
In summary, we utilized satellite-based SIF and EVI observations to investigate the spatial patterns and seasonal dynamics of vegetation phenology across wet and dry years along the North Australian Tropical Transect. Considerable impacts of drought and wet extremes on the phenology and production of dryland vegetation were revealed by both SIF and EVI, especially in the arid/semiarid interior of Australia. Although EVI exhibited a considerably delayed senescence relative to SIF and GPP, phenological metrics derived from SIF had more variable accuracy in contrast to those derived from the greenness-based vegetation index. In addition, EVI exhibiting stronger correlation with tower-based GPP (r 2 : 0.47~0.86) can be a superior indicator to track the seasonal and interannual variation in dryland vegetation production than the spatially coarse SIF dataset (r 2 : 0.47~0.78). In spite of the sparse sampling and high retrieval noises of GOME-2 SIF products, which offset the drawbacks of greenness-based phenology products with a potentially lagged end of the season, spaceborne SIF retrieved from state-of-the-art instruments (such as TROPOMI, OCO-2) has the promising potential of assessing the characterization of the phenology dynamics of dryland ecosystems.  Data Availability Statement: All satellite-based data, as well as other datasets in this study are available from the corresponding author upon reasonable request.