Mid‐to‐high latitude forests play an important role in the terrestrial carbon cycle, but the representation of photosynthesis in boreal forests by current modelling and observational methods is still challenging. In particular, the applicability of existing satellite‐based proxies of greenness to indicate photosynthetic activity is hindered by small annual changes in green biomass of the often evergreen tree population and by the confounding effects of background materials such as snow. As an alternative, satellite measurements of sun‐induced chlorophyll fluorescence (SIF) can be used as a direct proxy of photosynthetic activity. In this study, the start and end of the photosynthetically active season of the main boreal forests are analysed using spaceborne SIF measurements retrieved from the GOME‐2 instrument and compared to that of green biomass, proxied by vegetation indices including the Enhanced Vegetation Index (EVI) derived from MODIS data. We find that photosynthesis and greenness show a similar seasonality in deciduous forests. In high‐latitude evergreen needleleaf forests, however, the length of the photosynthetically active period indicated by SIF is up to 6 weeks longer than the green biomass changing period proxied by EVI, with SIF showing a start‐of‐season of approximately 1 month earlier than EVI. On average, the photosynthetic spring recovery as signalled by SIF occurs as soon as air temperatures exceed the freezing point (2–3 °C) and when the snow on the ground has not yet completely melted. These findings are supported by model data of gross primary production and a number of other studies which evaluated in situ observations of CO2 fluxes, meteorology and the physiological state of the needles. Our results demonstrate the sensitivity of space‐based SIF measurements to light‐use efficiency of boreal forests and their potential for an unbiased detection of photosynthetic activity even under the challenging conditions interposed by evergreen boreal ecosystems.

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Corresponding authors: C Wu (wucy@igsnrr.ac.cn), H Wang (wanghj@igsnrr.ac.cn) 38 and Q Ge (geqs@igsnrr.ac.cn). 39 2 Plant phenology is a sensitive indicator of climate change 1-4 , and plays a significant role in 40 regulating carbon uptake by plants [5][6][7] . Previous studies have focused on spring leaf-out by 41 daytime temperature and the onset of snowmelt time 8-9 , but the drivers controlling leaf 42 senescence date (LSD) in autumn remain largely unknown 10-12 . Using long-term ground 43 phenological records (14536 time series since the 1900s) and satellite greenness 44 observations dating back to the 1980s, we show that rising preseason maximum daytime 45 (Tday) and minimum nighttime (Tnight) temperatures had contrasting effects on the timing of 46 autumn LSD in the Northern Hemisphere (>20°N). If higher Tday leads to an earlier or later 47 leaf-out and flowering dates and hence extends the growing season 8,[14][15] . In contrast to 62 those extensive research efforts on spring phenology, autumn phenology, particularly leaf 63 senescence date (LSD), is more challenging to understand, and has not received 64 sufficient attention 16,17 , while also serving as an important indicator of changing foliar

[1] Land surface phenology (LSP) is an integrative indicator of vegetation dynamics under a changing environment. Increasing amounts of remote sensing measurements and CO 2 flux observations offer unprecedented opportunities to quantify LSP phases at landscape scale. LSP start of season (SOS) and end of season (EOS) estimates are often based on the use of a single-purpose vegetation index derived from optical satellite data, characterized by poor performances in decoupling soil and snow cover dynamics from LSP cycles, as well as contrasting responses of the needleleaf and broadleaf forests in boreal ecosystems. We propose a new remote-sensing-based phenology index (PI) which combines the merits of normalized difference vegetation index (NDVI) and normalized difference infrared index (NDII) by taking the difference of squared greenness and wetness to remove the soil and snow cover dynamics from key vegetation LSP cycles. We have cross-validated the remote-sensing-based LSP dates with those of CO 2 flux observations from 11 selected tower sites across Canada and the United States consisting of needleleaf forests, broadleaf forests, and croplands. The results indicate that PI estimates the SOS and EOS dates better than NDVI when compared to the LSP dates from CO 2 flux measurements (reduced RMSE, bias and dispersions, and higher correlation). PI-based SOS and EOS estimates are in good agreement with those derived from CO 2 flux measurements with mean bias comparable to the temporal resolution of the high-quality, 8-day composite satellite measurements. Finally, PI also shows a smoother time series compared to NDVI and NDII.

Leaf chlorophyll is central to the exchange of carbon, water and energy between the biosphere and the atmosphere, and to the functioning of terrestrial ecosystems. This paper presents the first spatially continuous view of terrestrial leaf chlorophyll content (ChlLeaf) across a global scale. Weekly maps of ChlLeaf were produced from ENIVSAT MERIS full resolution (300 m) satellite data with a two-stage physically-based radiative transfer modelling approach. Firstly, leaf-level reflectance was derived from top-of-canopy satellite reflectance observations using 4-Scale and SAIL canopy radiative transfer models 3 for woody and non-woody vegetation, respectively. Secondly, the modelled leaf-level reflectance was used in the PROSPECT leaf-level radiative transfer model to derive ChlLeaf. The ChlLeaf retrieval algorithm was validated with measured ChlLeaf data from sample measurements at field locations, and covering six plant functional types (PFTs). Modelled results show strong relationships with field measurements, particularly for deciduous broadleaf forests (R 2 = 0.67; RMSE = 9.25 µg cm -2 ; p<0.001), croplands (R 2 = 0.41; RMSE = 13.18 µg cm -2 ; p<0.001) and evergreen needleleaf forests (R 2 = 0.47; RMSE = 10.63 µg cm -2 ; p<0.001). When the modelled results from all PFTs were considered together, the overall relationship with measured ChlLeaf remained good (R 2 = 0.47, RMSE = 10.79 µg cm -2 ; p<0.001).This result was an improvement on the relationship between measured ChlLeaf and a commonly used chlorophyll-sensitive spectral vegetation index; the MERIS Terrestrial Chlorophyll Index (MTCI; R 2 = 0.27, p<0.001). The global maps show large temporal and spatial variability in ChlLeaf, with evergreen broadleaf forests presenting the highest leaf chlorophyll values with global annual median of 54.4 µg cm -2 . Distinct seasonal ChlLeaf phenologies are also visible, particularly in deciduous plant forms, associated with budburst and crop growth, and leaf senescence. It is anticipated that this global ChlLeaf product will make an important step towards the explicit consideration of leaf-level biochemistry in terrestrial water, energy and carbon cycle modelling.

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Aim To investigate the importance of autumn phenology in controlling interannual variability of forest net ecosystem productivity (NEP) and to derive new phenological metrics to explain the interannual variability of NEP. Location North America and Europe. Method Flux data from nine deciduous broadleaf forests (DBF) and 13 evergreen needleleaf forests (ENF) across North America and Europe (212 site‐years) were used to explore the relationships between the yearly anomalies of annual NEP and several carbon flux based phenological indicators, including the onset/end of the growing season, onset/end of the carbon uptake period, the spring lag (time interval between the onset of growing season and carbon uptake period) and the autumn lag (time interval between the end of the carbon uptake period and the growing season). Meteorological variables, including global shortwave radiation, air temperature, soil temperature, soil water content and precipitation, were also used to explain the phenological variations. Results We found that interannual variability of NEP can be largely explained by autumn phenology, i.e. the autumn lag. While variation in neither annual gross primary productivity (GPP) nor in annual ecosystem respiration (Re) alone could explain this variability, the negative relationship between annual NEP and autumn lag was due to a larger Re/GPP ratio in years with a prolonged autumn lag. For DBF sites, a longer autumn lag coincided with a significant decrease in annual GPP but showed no correlation with annual Re. However, annual GPP was insensitive to a longer autumn lag in ENF sites but annual Re increased significantly. Main conclusions These results demonstrate that autumn phenology plays a more direct role than spring phenology in regulating interannual variability of annual NEP. In particular, the importance of respiration may be potentially underestimated in deriving phenological indicators.

Evapotranspiration (ET) is commonly estimated using the Penman‐Monteith equation, which assumes that the plant canopy is a big leaf (BL) and the water flux from vegetation is regulated by canopy stomatal conductance (Gs). However, BL has been found to be unsuitable for terrestrial biosphere models built on the carbon‐water coupling principle because it fails to capture daily variations of gross primary productivity (GPP). A two‐big‐leaf scheme (TBL) and a two‐leaf scheme (TL) that stratify a canopy into sunlit and shaded leaves have been developed to address this issue. However, there is a lack of comparison of these upscaling schemes for ET estimation, especially on the difference between TBL and TL. We find that TL shows strong performance (r2 = 0.71, root‐mean‐square error = 0.05 mm/h) in estimating ET at nine eddy covariance towers in Canada. BL simulates lower annual ET and GPP than TL and TBL. The biases of estimated ET and GPP increase with leaf area index (LAI) in BL and TBL, and the biases of TL show no trends with LAI. BL miscalculates the portions of light‐saturated and light‐unsaturated leaves in the canopy, incurring negative biases in its flux estimation. TBL and TL showed improved yet different GPP and ET estimations. This difference is attributed to the lower Gs and intercellular CO2 concentration simulated in TBL compared to their counterparts in TL. We suggest to use TL for ET modeling to avoid the uncertainty propagated from the artificial upscaling of leaf‐level processes to the canopy scale in BL and TBL.