| 24 Apr 2026
Spurious seasonality of Earth observation LAI across three northern evergreen needleleaf forests: Implications for analyses of the carbon cycle
Abstract. Leaf area index (LAI) is a key biophysical variable which quantifies the surface area for light capture and photosynthetic activity per unit ground area, giving a first order constraint on potential photosynthesis. LAI is tightly coupled to the carbon, energy, and water cycles of the global terrestrial system. Numerous Earth observation (EO) products provide estimates of LAI over time (LAIEO), delivering valuable information on plant phenology and canopy dynamics. Widely-used LAIEO, however, consistently exhibit unrealistic seasonality in evergreen needleleaf forests at the northern latitudes. Taking a model-data fusion approach, we show that naïvely assimilating biased, whole-year LAIEO (i.e., the business-as-usual (BAU) approach) at three well-studied evergreen needleleaf forests in Fennoscandia implies an ecosystem carbon cycle which is unrealistic and inconsistent with independent lines of evidence. We further demonstrate that the model-data fusion framework, CARDAMOM, is capable of diagnosing realistic seasonal amplitudes of LAI by assimilating localised information on leaf lifespan coupled with summer-only LAIEO (i.e., the alternative (ALT) approach). Important differences arise from the BAU and ALT experiments. The BAU experiment showed highly seasonal canopy dynamics and diagnostic leaf traits erroneously consistent with deciduous species. Conversely, the ALT experiment displayed canopy dynamics and functional characteristics more reflective of evergreen needleleaf species. For BAU, biases in LAIEO propagated throughout the carbon cycle, especially in the southern, more productive sites. This investigation highlights the need for improved LAIEO estimates in northern evergreen forests to enhance understanding of carbon cycle processes in this region of rapid warming and large carbon stores, and provides a mechanism for improvement using independent leaf trait data.
I find the overall motivation of the manuscript somewhat unclear. The authors argue that EO-derived LAI exhibits biased seasonality in evergreen needleleaf forests (ENFs). Based on this premise, I would expect the study to clearly demonstrate how such biases propagate into carbon fluxes, and how the proposed method improves those estimates.
However, based on Fig. 3, while LAI may show seasonal biases (though this itself depends on resolving issues such as leaf-wood separation in field measurements), the differences in key carbon fluxes (NEE, GPP, and Reco) between the BAU approach (direct use of satellite LAI) and the authors’ alternative method (ALT) appear minimal. This raises the question of whether LAI seasonal bias meaningfully affects the simulated fluxes in practice.
Moreover, the alternative approach is relatively complex and heavily parameterized, yet the resulting improvements in fluxes are not clearly demonstrated. As such, it remains unclear whether the added model complexity is justified by a substantial gain in predictive performance.
Major comments:
The abstract (Lines 10–16) is somewhat confusing. It is unclear what is meant by “seasonal canopy dynamics”. Does this refer to LAI, carbon fluxes, or other processes? While LAI seasonality is known to be uncertain in ENF biomes, the extent to which these biases propagate to canopy processes remains debatable. For example, based on Fig. 3, the differences in NEE, GPP, and Reco between the BAU (direct use of satellite LAI) and ALT (the proposed method) simulations appear relatively small. This raises the question of whether the impact of LAI seasonality bias on ecosystem fluxes is important.
I think the accuracy of LAI should be evaluated in the context of the specific application. If the goal is to simulate carbon or water fluxes, LAI accuracy may not be critically important (see your Fig 3). For example, EO-derived LAI is known to have limitations, including saturation effects, differences between effective and true LAI (e.g., due to canopy clumping), and biases under snow cover. However, these issues may not strongly affect estimates of fAPAR, which are typically retrieved simultaneously from satellite reflectance and thus more directly reflect the canopy radiative transfer state. Since simulations of carbon fluxes primarily depend on APAR (APAR = fAPAR × PAR), uncertainties in LAI may not substantially propagate into flux estimates. Or it may depend on the upscaling scheme of the model itself (some models depend on using LAI to do the scaling, some using the fAPAR).
Instead, greater importance may lie in the biogeochemical properties of the leaves, such as light-harvesting pigment content and efficiency, Rubisco content and activity, and stomatal regulation. In Fig. 3, all simulated NEE are similar across experiments but differ from the eddy covariance (EC) observations. This suggests that improving the representation of biogeochemical processes in the model may be more important than refining LAI seasonality alone.
Detailed comments:
~ Line 15: “This investigation highlights the need for improved EO LAI estimates in northern evergreen forests…”. I agree that there are biases in EO-derived LAI seasonality in northern evergreen forests. However, the authors should be more specific about how such improvements could be achieved. If the bias primarily arises from snow obscuring satellite reflectance, then it may be fundamentally difficult to resolve within a purely satellite-based framework. On the other hand, if improvement requires incorporating more complex modeling or data assimilation approaches, this should be clearly stated, as it moves beyond a strictly observation-based (EO-only) methodology.
~ Line 40: “LAI EO estimates are typically derived from surface or top-of-atmosphere reflectances measured by passive optical sensors onboard satellites.” This statement is somewhat confusing and depends on how “EO” is defined. If EO refers to satellite-based observations, then all satellite measurements originate from TOA reflectance. However, many LAI products (e.g., MODIS LAI) are actually derived from surface reflectance, which is obtained after atmospheric correction of TOA measurements through upstream processing. The authors should clarify this distinction and use more precise terminology.
~ Line 50: The cited studies (e.g., Cohen et al., 2006; Tian et al., 2004) may be outdated. For example, MODIS LAI products have evolved substantially (currently at Collection 6), whereas these cited were based on much earlier versions (e.g., Collection 1 or 3). There might have been improvements between these versions. The authors should consider citing more recent literature.
Reference: Yan, K., Park, T., Yan, G., Liu, Z., Yang, B., Chen, C., Nemani, R., Knyazikhin, Y., & Myneni, R. (2016). Evaluation of MODIS LAI/FPAR Product Collection 6. Part 2: Validation and Intercomparison. Remote Sensing, 8(6), 460–26. https://doi.org/10.3390/rs8060460
Line 52-54: The Fang et al (2021) analysis is spurious. If you look at Yan et al 2016, their figure 4c shows the seasonality of ENF (their B7). It shows that the seasonal minimum is always above 1 LAI. It is perhaps that Fang et al (2021) did not strictly filtering low quality data based on the satellite QA.
~ Line 80: The manuscript compares two experiments (BAU and ALT), but it is unclear whether the results have been evaluated against other independent benchmarks. Have the authors compared their simulated GPP and ET with products such as MODIS GPP/ET, or with outputs from satellite-driven terrestrial biosphere models (such as BESS, BEPS)?
~ 90: For your hypothesis, is it possible to implement the leaf life span into large scale studies? It seems the leaf life span is based on some strong assumptions, and how to reflect the geographical differences and within species plasticity?
Fig. 1: Field LAI is derived from DHP measurements. How do the authors account for the leaf-to-wood ratio in this conversion? Is this ratio assumed to be constant throughout the year? If the DHP measurements represent plant area index (PAI), they may exhibit relatively weak seasonality, as suggested by Fig. 1. In that case, separating leaf area from woody components becomes critical, and the assumed leaf-to-wood ratio introduces additional uncertainty that should be discussed or quantified. From my experience, the SE-Htm panel shows many extremely low values and abrupt changes in MCD15A2H LAI. This is likely due to incorrect use of the QA flags, or possibly no QA filtering at all. You should exclude all back-up algorithm retrievals as that likely means cloud or snow presents and the reflectance is corrupted (also possible due to sun-sensor poor geometry, but it should be more like to happen during the winter seasons, while your spurious data spread all over the year).
~Lines 123-125: (1) rooting depth is an additional uncertainty. (2) Is CUE temporally constant?
~ Line 129: It appears that other variables (not LAI) with strong seasonal dynamics may be driving much of the variability in the simulated carbon fluxes, LAI and other related outputs, ultimately contributing to the improved seasonality of the results. Could the authors clarify which variables are primarily responsible for this seasonal control? For example, are these driven by meteorological forcings (e.g., radiation, temperature, VPD) or by internal model parameters/processes (stomata, quantum yield efficiency)?
L315: should LMA be a dynamic variable, spatially and temporally?