Soil water depletion induces discrepancies between in situ measured vegetation indices and photosynthesis in a temperate heathland

Maleki, Maral; Arriga, Nicola; Roland, Marilyn; Wieneke, Sebastian; Barrios, José Miguel; Hoolst, Roel Van; Peñuelas, Josep; Janssens, Ivan A.; Balzarolo, Manuela

doi:10.1016/j.agrformet.2022.109110

Cited by 7 publications

(4 citation statements)

References 91 publications

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“…The constraint function of the VPD in this study corrected for bias in the estimated GPP during drought due to the incorporation of radiative effects, and the resulting GPP response to drought was more consistent with the observed GPP (Figure 5), indicating the necessity of considering meteorological constraints when estimating GPP under drought conditions. Similar results were recently reported in a study on the relationship between vegetation indices and GPP in a temperate heathland site that found that all vegetation indices failed to accurately estimate GPP during extreme drought events, and the inclusion of drought indicators in regression models was required to further improve estimation accuracy (Maleki et al, 2022). Taking a step beyond the current study, we extend the applicability of our conclusions from a single site to multiple sites and regions.…”

Section: Necessity Of Meteorological Constraintssupporting

confidence: 88%

Vegetation Index‐Based Models Without Meteorological Constraints Underestimate the Impact of Drought on Gross Primary Productivity

Chen,

Liu

et al. 2024

JGR Biogeosciences

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Recently developed solar‐induced chlorophyll fluorescence‐related vegetation indices (e.g., near infrared reflectance of vegetation (NIRv) and kernel normalized difference vegetation index (kNDVI)) have been reported to be appropriate proxies for vegetation photosynthesis. These vegetation indices can be used to estimate gross primary productivity (GPP) without considering meteorological constraints. However, it is not clear whether such a statement holds true under various environmental conditions. In this study, we explored whether these vegetation indices require meteorological constraints to better characterize GPP under extreme drought conditions using three extreme drought cases in Europe in 2003, 2010, and 2018. According to the long‐term series of observations, vegetation indices (NIRv and kNDVI) alone explained 60% and 57%, respectively, of the weekly GPP variation across the 66 flux sites. The explained variation increased to 69% and 64%, respectively, for the models that take into account radiative effects (NIRv and kNDVI multiplied by radiation). However, without considering meteorological constraints, these vegetation index‐based estimations severely underestimated negative GPP anomalies under drought stress, especially in models that incorporate radiative effects. After incorporating vapor pressure deficit (VPD)‐based meteorological constraints, the GPP estimations exhibited more pronounced negative anomalies during drought periods while maintaining model accuracy (at 70% and 65%, respectively). In addition, the GPP models based on site observations were applied at the regional scale (Europe). Our results indicated that the models without meteorological constraints again underestimated the impact of drought on GPP. This study emphasizes the importance of meteorological constraints in the estimation of GPP, especially under extreme drought conditions.

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Section: Necessity Of Meteorological Constraintssupporting

confidence: 88%

Vegetation Index‐Based Models Without Meteorological Constraints Underestimate the Impact of Drought on Gross Primary Productivity

Chen,

Liu

et al. 2024

JGR Biogeosciences

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“…This was the case, with most days in July and August showing low to very low actual/potential evapotranspiration ratios. These values of Ri and soil water content are low but not unrealistic for this area and the same soil [ 33 ], especially considering that we did not irrigate to ensure that plant experienced drought (as Si foliar sprays are known to protect plants against drought stress).…”

Section: Discussionmentioning

confidence: 99%

Limited effects of crop foliar Si fertilization on a marginal soil under a future climate scenario

Rineau,

Groh,

Claes

et al. 2024

Heliyon

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“…The indirect observation of soil moisture stress in VI allowed accurate sub-seasonal-scale modeling of GPP in these strongly water-limited biomes (AghaKouchak et al, 2015). In other biomes, the combination with a drought indicator is required to simulate GPP in such conditions (Maleki et al, 2022).…”

Section: Soil Moisturementioning

confidence: 99%

Temporal variability of observed and simulated gross primary productivity, modulated by vegetation state and hydrometeorological drivers

Pue

Wieneke

Bastos

et al. 2023

Preprint

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Abstract. The gross primary production (GPP) of the terrestrial biosphere is a key source of variability in the global carbon cycle. It is modulated by hydrometeorological drivers (i.e., shortwave radiation, air temperature, vapor pressure deficit and soil moisture) and the vegetation state (i.e., canopy greenness, leaf area index) at instantaneous to interannual timescales. In this study, we set out to evaluate the ability of GPP-models to capture this variability. 11 models were considered, which rely purely on remote sensing data (RS-driven), meteorological data (meteo-driven, e.g., dynamic global vegetation models; DGVMs) or a combination of both (hybrid, e.g., light-use efficiency models; LUE). They were evaluated using in situ observations at 61 eddy covariance sites, covering a broad range of herbaceous and forest biomes. The results illustrated how the determinant of temporal variability shifts from meteorological variables at sub-seasonal timescales to biophysical variables at seasonal and interannual scale. RS-driven models lacked the sensitivity to the dominant drivers at short timescales (i.e., shortwave radiation and vapor pressure deficit), and failed to capture the decoupling of photosynthesis and canopy greenness (e.g., in evergreen forests). Conversely, meteo-driven models accurately captured the variability accross timescales, despite the challenges in the prognostic simulation of the vegetation state. Largest errors were found in water-limited sites, where the accuracy of the soil moisture dynamics determines the quality of the GPP estimates. In arid herbaceous sites, canopy greenness and photosynthesis were more tightly coupled, resulting in improved results with RS-driven models. Hybrid models capitalized on the combination of RS observations and meteorological information. LUE models were among the most accurate models to monitor GPP across all biomes, despite their simple architecture. Overall, we conclude that the combination of meteorological drivers and remote sensing observations is required to yield an accurate reproduction of the spatio-temporal variability of GPP. To further advance the performance of DGVMs, improvements in the soil moisture dynamics and vegetation evolution are needed.

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Soil water depletion induces discrepancies between in situ measured vegetation indices and photosynthesis in a temperate heathland

Cited by 7 publications

References 91 publications

Vegetation Index‐Based Models Without Meteorological Constraints Underestimate the Impact of Drought on Gross Primary Productivity

Vegetation Index‐Based Models Without Meteorological Constraints Underestimate the Impact of Drought on Gross Primary Productivity

Limited effects of crop foliar Si fertilization on a marginal soil under a future climate scenario

Temporal variability of observed and simulated gross primary productivity, modulated by vegetation state and hydrometeorological drivers

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