A 21-Year Time Series of Global Leaf Chlorophyll Content Maps From MODIS Imagery

Xu, Mingzhu; Chen, Jing M.; Liu, Yang; Wolanin, Aleksandra; Croft, Holly; He, Li; Shang, Rong; Ju, Weimin; Zhang, Yongguang; He, Yuhong; Wang, Rong

doi:10.1109/tgrs.2022.3204185

Cited by 14 publications

(7 citation statements)

References 54 publications

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“…The most widely produced vegetation products are LAI and fAPAR but also fractional vegetation cover and to a lesser extent canopy chlorophyll content (e.g. Myneni et al, 2015;Yan et al, 2016;Fang et al, 2019;Xu et al, 2022). To obtain productivity metrics, TS data streams of the traits have been integrated into various GPP assimilation schemes (e.g.…”

Section: Quantitative Traitsmentioning

confidence: 99%

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Reviews and syntheses: Remotely sensed optical time series for monitoring vegetation productivity

Kooistra,

Berger,

Brede

et al. 2024

Biogeosciences

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Abstract. Vegetation productivity is a critical indicator of global ecosystem health and is impacted by human activities and climate change. A wide range of optical sensing platforms, from ground-based to airborne and satellite, provide spatially continuous information on terrestrial vegetation status and functioning. As optical Earth observation (EO) data are usually routinely acquired, vegetation can be monitored repeatedly over time, reflecting seasonal vegetation patterns and trends in vegetation productivity metrics. Such metrics include gross primary productivity, net primary productivity, biomass, or yield. To summarize current knowledge, in this paper we systematically reviewed time series (TS) literature for assessing state-of-the-art vegetation productivity monitoring approaches for different ecosystems based on optical remote sensing (RS) data. As the integration of solar-induced fluorescence (SIF) data in vegetation productivity processing chains has emerged as a promising source, we also include this relatively recent sensor modality. We define three methodological categories to derive productivity metrics from remotely sensed TS of vegetation indices or quantitative traits: (i) trend analysis and anomaly detection, (ii) land surface phenology, and (iii) integration and assimilation of TS-derived metrics into statistical and process-based dynamic vegetation models (DVMs). Although the majority of used TS data streams originate from data acquired from satellite platforms, TS data from aircraft and unoccupied aerial vehicles have found their way into productivity monitoring studies. To facilitate processing, we provide a list of common toolboxes for inferring productivity metrics and information from TS data. We further discuss validation strategies of the RS data derived productivity metrics: (1) using in situ measured data, such as yield; (2) sensor networks of distinct sensors, including spectroradiometers, flux towers, or phenological cameras; and (3) inter-comparison of different productivity metrics. Finally, we address current challenges and propose a conceptual framework for productivity metrics derivation, including fully integrated DVMs and radiative transfer models here labelled as “Digital Twin”. This novel framework meets the requirements of multiple ecosystems and enables both an improved understanding of vegetation temporal dynamics in response to climate and environmental drivers and enhances the accuracy of vegetation productivity monitoring.

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Section: Quantitative Traitsmentioning

confidence: 99%

“…To obtain productivity metrics, TS data streams of the traits have been integrated into various GPP assimilation schemes (e.g. Jung et al, 2007;Xie et al, 2019;Chen et al, 2022).…”

Section: Quantitative Traitsmentioning

confidence: 99%

Reviews and syntheses: Remotely sensed optical time series for monitoring vegetation productivity

Kooistra,

Berger,

Brede

et al. 2024

Biogeosciences

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“…We used an average yearly Vcmax25 for each pixel that varied from year to year (2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017)(2018), and it was further resampled to 0.05°×0.05° for driving the model. The Vcmax25 product was produced by 165 satellite-derived leaf chlorophyll content (LCC) (Xu et al, 2022) and a semi-mechanistic model (Lu et al, 2022). Lu et al, 2020;Wang et al, 2020b).…”

Section: Data and Processing (1) Flux Tower Datamentioning

confidence: 99%

“…Lu et al, 2020;Wang et al, 2020b). More mechanisms for deriving Vcmax25 from remote sensing data are available in Lu et al (2022), Luo et al (2018), andXu et al (2022).…”

Section: Data and Processing (1) Flux Tower Datamentioning

confidence: 99%

Elevated atmospheric CO₂ and vegetation structural changes contributed to GPP increase more than climate and forest cover changes in subtropical forests of China

Chen,

Meunier,

Peaucelle

et al. 2023

Preprint

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Abstract. Abstract: The subtropical forest gross primary productivity (GPP) plays a pivotal role in the global carbon cycle and in regulating the global climate. Quantifying the individual and combined effects of forest cover change (FCC), vegetation structural change (VSC, i.e., leaf area index (LAI)), CO2 fertilization, and climate change (CC) on annual GPP dynamics of various subtropical forest types are essential for mitigating carbon emissions and predicting climate changes, but these impacts remain unclear. In this study, we used a processed-based model to comprehensively investigate the impacts of these factors on GPP variations with a series of model experiments in China’s subtropical forests during 2001–2018. Simulated actual GPP showed a significant increasing trend (26.72 TgC year−1, p < 0.001) under the interaction effects of these factors. The CO2 fertilization (8.23 TgC year−1, p < 0.001) and VSC (4.55 TgC year−1, p = 0.005) were the two dominant drivers of total subtropical forest GPP increase, followed by the effect of FCC (1.35 TgC year−1, p < 0.001) and CC (1.11 TgC year−1, p = 0.08). We observed different responses to drivers depending on forest types. The evergreen broadleaved forests have a high carbon sink potential due to the positive effects of all drivers. Both the FCC (1.29 TgC year−1, p < 0.001) and CC (0.53 TgC year−1, p < 0.05) significantly decreased evergreen needleleaved forest GPP, while their negative effects were almost offset by the positive impact of VSC. Our results indicated that forest structural change outweighed the forest cover change in promoting GPP, which is an overlooked driver that needs to be accounted for in studies, as well as ecological and management programs. Overall, our study offers a novel perspective on different drivers of subtropical forest GPP changes, which provides valuable information for policy makers in forest management to mitigate climate change.

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“…We used the GLOBMAP LAI product derived from MODIS and AVHRR satellites, which has a global coverage from 1981 to 2020, at 16 day (1981)(1982)(1983)(1984)(1985)(1986)(1987)(1988)(1989)(1990)(1991)(1992)(1993)(1994)(1995)(1996)(1997)(1998)(1999)(2000) or 8 day (2001-2020) temporal intervals and a spatial resolution of 8 km (Liu et al, 2012). We used the Locally Adjusted Cubic-spline Capping (LACC) algorithm (Chen et al, 2006) properties and a canopy bidirectional re ectance model with remote sensing surface re ectance (Xu et al, 2022). The LCC series have a resolution of 500 m and 8-day intervals.…”

Section: Introductionmentioning

confidence: 99%

Declining global sensitivity of stomatal conductance to photosynthesis

Leng,

Chen,

et al. 2024

Preprint

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Terrestrial ecosystems play a pivotal role in mitigating climate change through photosynthesis and transpiration regulated by plant stomata. Terrestrial biosphere models (TBMs) commonly couple a biochemical photosynthesis model, i.e., the Farquhar model, and a semi-empirical stomatal conductance model, i.e., the Ball-Berry model, to estimate gross primary productivity (GPP) and evapotranspiration (ET). However, the key ecosystem parameters (i.e., maximum carboxylation rate Vcmax and the Ball-Berry slope m) are commonly prescribed as constant in TBMs, hindering accurate quantification of global GPP and ET. m depicts ‘compromise between the costs and benefits of stomatal conductance relative to the photosynthetic activity of the leaf’, namely the sensitivity of stomatal conductance to photosynthesis. However, although recent work has retrieved continuous seasonal V_cmax over the globe, there is a lack of an effective method to derive spatially and temporally explicit m in TBMs. In this study, we estimated monthly m through a Bayesian parameter optimization approach at 136 eddy covariance flux sites (809 site years) and upscaled site-level m to global gridded m during 2001-2020 via a machine learning model. We found a significant declining trend of global m (R2 = 0.72, P < 0.001) in 2001-2020. We found the largest decline of m in grassland (0.04 per year), moderate declines in deciduous broadleaf forests, mixed forests, and cropland (0.02 per year), the smallest decline in evergreen needleleaf forests and savannas (0.01 per year). We found the air temperature plays the predominant role in driving the declining trend of m. This study generates and analyzes a global timeseries of m by ‘handshaking’ the measured fluxes, terrestrial biosphere models, and remote sensing. Our results highlight the changing relationship between photosynthesis and stomatal conductance under climate change and identify the importance of incorporating m as a dynamic parameter in future GPP and ET simulations with terrestrial biosphere models.

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A 21-Year Time Series of Global Leaf Chlorophyll Content Maps From MODIS Imagery

Cited by 14 publications

References 54 publications

Reviews and syntheses: Remotely sensed optical time series for monitoring vegetation productivity

Reviews and syntheses: Remotely sensed optical time series for monitoring vegetation productivity

Elevated atmospheric CO₂ and vegetation structural changes contributed to GPP increase more than climate and forest cover changes in subtropical forests of China

Declining global sensitivity of stomatal conductance to photosynthesis

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A 21-Year Time Series of Global Leaf Chlorophyll Content Maps From MODIS Imagery

Cited by 14 publications

References 54 publications

Reviews and syntheses: Remotely sensed optical time series for monitoring vegetation productivity

Reviews and syntheses: Remotely sensed optical time series for monitoring vegetation productivity

Elevated atmospheric CO2 and vegetation structural changes contributed to GPP increase more than climate and forest cover changes in subtropical forests of China

Declining global sensitivity of stomatal conductance to photosynthesis

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Elevated atmospheric CO₂ and vegetation structural changes contributed to GPP increase more than climate and forest cover changes in subtropical forests of China