Predicting gross primary production from the enhanced vegetation index and photosynthetically active radiation: Evaluation and calibration

This work aims to establish a relationship between the photosynthetically active radiation (PAR) and the global radiation (Rg) for Santa Rita do Passa Quatro (SP), during the period from February 2005 to February 2006 and evaluate its impact on gross primary production (GPP). Data of Rg (Wm-2) and PAR (μmols s-1m-2) were obtained from the mirometeorological tower located in Gleba Cerrado Giant Foot. Data analysis allowed the establishment of a linear equation to express the relationship between PAR (MJ m-2) and Rg (MJ m-2) as: PAR = 0.3787 Rg or PAR = 1.742 Rg, for PAR (μmols s-1m-2) and Rg (MJ m-2). This relationship is indicated for the GPP determination when there is no PAR measurements.

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The Response of the Amazon Ecosystem to the Photosynthetically Active Radiation Fields: Integrating Impacts of Biomass Burning Aerosol and Clouds in the NASA GEOS ESM

10.5194/acp-2021-138 ◽

2021 ◽

Author(s):

Huisheng Bian ◽

Eunjee Lee ◽

Randal D. Koster ◽

Donifan Barahona ◽

Mian Chin ◽

...

Keyword(s):

Primary Production ◽

Biomass Burning ◽

Photosynthetically Active Radiation ◽

Net Primary Production ◽

Gross Primary Production ◽

Cloud Amount ◽

Ecosystem Productivity ◽

Active Radiation ◽

The Impact ◽

Fertilizer Effect

Abstract. The Amazon experiences fires every year, and the resulting biomass burning aerosols, together with cloud particles, influence the penetration of sunlight through the atmosphere, increasing the ratio of diffuse to direct photosynthetically active radiation (PAR) reaching the vegetation canopy and thereby potentially increasing ecosystem productivity. In this study, we use the NASA Goddard Earth Observing System (GEOS) model running with coupled aerosol, cloud, radiation, and ecosystem modules to investigate the impact of Amazon biomass burning aerosols on ecosystem productivity, as well as the role of the Amazon’s clouds in tempering the impact. The study focuses on a seven-year period (2010–2016) during which the Amazon experienced a variety of dynamic environments (e.g., La Niña, normal years, and El Niño). The radiative impacts of biomass burning aerosols on ecosystem productivity – call here the aerosol light fertilizer effect – are found to increase Amazonian Gross Primary Production (GPP) by 2.6 % via a 3.8 % increase in diffuse PAR (DFPAR) despite a 5.4 % decrease in direct PAR (DRPAR) on multiyear average. On a monthly basis, this increase in GPP can be as large as 9.9 % (occurring in August 2010). Consequently, the net primary production (NPP) in the Amazon is increased by 1.5 %, or ~92 TgCyr−1– equivalent to ~37 % of the carbon lost due to Amazon fires over the seven years considered. Clouds, however, strongly regulate the effectiveness of the aerosol light fertilizer effect. The efficiency of the fertilizer effect is highest for cloud-free conditions and linearly decreases with increasing cloud amount until the cloud fraction reaches ~0.8, at which point the aerosol-influenced light changes from being a stimulator to an inhibitor of plant growth. Nevertheless, interannual changes in the overall strength of the aerosol light fertilizer effect are primarily controlled by the large interannual changes in biomass burning aerosols rather than by changes in cloudiness during the studied period.

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The response of the Amazon ecosystem to the photosynthetically active radiation fields: integrating impacts of biomass burning aerosol and clouds in the NASA GEOS Earth system model

Atmospheric Chemistry and Physics ◽

10.5194/acp-21-14177-2021 ◽

2021 ◽

Vol 21 (18) ◽

pp. 14177-14197

Author(s):

Huisheng Bian ◽

Eunjee Lee ◽

Randal D. Koster ◽

Donifan Barahona ◽

Mian Chin ◽

...

Keyword(s):

Primary Production ◽

Biomass Burning ◽

Photosynthetically Active Radiation ◽

Net Primary Production ◽

Gross Primary Production ◽

Diffuse Radiation ◽

Ecosystem Productivity ◽

Active Radiation ◽

Fertilization Effect ◽

The Impact

Abstract. The Amazon experiences fires every year, and the resulting biomass burning aerosols, together with cloud particles, influence the penetration of sunlight through the atmosphere, increasing the ratio of diffuse to direct photosynthetically active radiation (PAR) reaching the vegetation canopy and thereby potentially increasing ecosystem productivity. In this study, we use the NASA Goddard Earth Observing System (GEOS) model with coupled aerosol, cloud, radiation, and ecosystem modules to investigate the impact of Amazon biomass burning aerosols on ecosystem productivity, as well as the role of the Amazon's clouds in tempering this impact. The study focuses on a 7-year period (2010–2016) during which the Amazon experienced a variety of dynamic environments (e.g., La Niña, normal years, and El Niño). The direct radiative impact of biomass burning aerosols on ecosystem productivity – called here the aerosol diffuse radiation fertilization effect – is found to increase Amazonian gross primary production (GPP) by 2.6 % via a 3.8 % increase in diffuse PAR (DFPAR) despite a 5.4 % decrease in direct PAR (DRPAR) on multiyear average during burning seasons. On a monthly basis, this increase in GPP can be as large as 9.9 % (occurring in August 2010). Consequently, the net primary production (NPP) in the Amazon is increased by 1.5 %, or ∼92 Tg C yr−1 – equivalent to ∼37 % of the average carbon lost due to Amazon fires over the 7 years considered. Clouds, however, strongly regulate the effectiveness of the aerosol diffuse radiation fertilization effect. The efficiency of this fertilization effect is the highest in cloud-free conditions and linearly decreases with increasing cloud amount until the cloud fraction reaches ∼0.8, at which point the aerosol-influenced light changes from being a stimulator to an inhibitor of plant growth. Nevertheless, interannual changes in the overall strength of the aerosol diffuse radiation fertilization effect are primarily controlled by the large interannual changes in biomass burning aerosols rather than by changes in cloudiness during the studied period.

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Remote sensing-based estimation of gross primary production in a subalpine grassland

Biogeosciences Discussions ◽

10.5194/bgd-9-1711-2012 ◽

2012 ◽

Vol 9 (2) ◽

pp. 1711-1758

Author(s):

M. Rossini ◽

S. Cogliati ◽

M. Meroni ◽

M. Migliavacca ◽

M. Galvagno ◽

...

Keyword(s):

Primary Production ◽

Chlorophyll Content ◽

Photosynthetically Active Radiation ◽

Gross Primary Production ◽

Light Use Efficiency ◽

Near Surface ◽

Active Radiation ◽

Vegetation Indexes ◽

Green Vegetation ◽

Use Efficiency

Abstract. This study investigates the performances in a terrestrial ecosystem of gross primary production (GPP) estimation of a suite of spectral vegetation indexes (VIs) that can be computed from currently orbiting platforms. Vegetation indexes were computed from near-surface field spectroscopy measurements collected using an automatic system designed for high temporal frequency acquisition of spectral measurements in the visible near-infrared region. Spectral observations were collected for two consecutive years in Italy in a subalpine grassland equipped with an Eddy Covariance (EC) flux tower which provides continuous measurements of net ecosystem carbon dioxide (CO2) exchange (NEE) and the derived GPP. Different VIs were calculated based on ESA-MERIS and NASA-MODIS spectral bands and correlated with biophysical (Leaf Area Index, LAI; fraction of photosynthetically active radiation intercepted by green vegetation, fIPARg), biochemical (chlorophyll concentration) and ecophysiological (green light-use efficiency, LUEg) canopy variables. In this study, the normalized difference vegetation index (NDVI) showed better correlations with LAI and fPARg (r = 0.90 and 0.95, respectively), the MERIS terrestrial chlorophyll index (MTCI) with leaf chlorophyll content (r = 0.91) and the Photochemical Reflectance Index (PRI551), computed as (R531−R551)/(R531+R551) with LUEg (r = 0.64). Subsequently, these VIs were used to estimate GPP using different modelling solutions based on the light-use efficiency model describing the GPP as driven by the photosynthetically active radiation absorbed by green vegetation (APARg) and by the efficiency (ε) with which plants use the absorbed radiation to fix carbon via photosynthesis. Results show that GPP can be successfully modelled with a combination of VIs and meteorological data or VIs only. Vegetation indexes designed to be more sensitive to chlorophyll content explained most of the variability in GPP in the ecosystem investigated, characterized by a strong seasonal dynamic of GPP. Accuracy in GPP estimation slightly improves when taking into account high frequency modulations of GPP driven by incident PAR or modelling LUEg with the PRI in model formulation. Similar results were obtained for both measured daily VIs and VIs obtained as 16-day composite time series and then downscaled from the compositing period to daily scale (resampled data). However, the use of resampled data rather than measured daily input data decreases the accuracy of the total GPP estimation on an annual basis.

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Remote sensing-based estimation of gross primary production in a subalpine grassland

Biogeosciences ◽

10.5194/bg-9-2565-2012 ◽

2012 ◽

Vol 9 (7) ◽

pp. 2565-2584 ◽

Cited By ~ 79

Author(s):

M. Rossini ◽

S. Cogliati ◽

M. Meroni ◽

M. Migliavacca ◽

M. Galvagno ◽

...

Keyword(s):

Primary Production ◽

Chlorophyll Content ◽

Photosynthetically Active Radiation ◽

Gross Primary Production ◽

Light Use Efficiency ◽

Near Surface ◽

Active Radiation ◽

Vegetation Indexes ◽

Green Vegetation ◽

Use Efficiency

Abstract. This study investigates the performances in a terrestrial ecosystem of gross primary production (GPP) estimation of a suite of spectral vegetation indexes (VIs) that can be computed from currently orbiting platforms. Vegetation indexes were computed from near-surface field spectroscopy measurements collected using an automatic system designed for high temporal frequency acquisition of spectral measurements in the visible near-infrared region. Spectral observations were collected for two consecutive years in Italy in a subalpine grassland equipped with an eddy covariance (EC) flux tower that provides continuous measurements of net ecosystem carbon dioxide (CO2) exchange (NEE) and the derived GPP. Different VIs were calculated based on ESA-MERIS and NASA-MODIS spectral bands and correlated with biophysical (Leaf area index, LAI; fraction of photosynthetically active radiation intercepted by green vegetation, fIPARg), biochemical (chlorophyll concentration) and ecophysiological (green light-use efficiency, LUEg) canopy variables. In this study, the normalized difference vegetation index (NDVI) was the index best correlated with LAI and fIPARg (r = 0.90 and 0.95, respectively), the MERIS terrestrial chlorophyll index (MTCI) with leaf chlorophyll content (r = 0.91) and the photochemical reflectance index (PRI551), computed as (R531-R551)/(R531+R551) with LUEg (r = 0.64). Subsequently, these VIs were used to estimate GPP using different modelling solutions based on Monteith's light-use efficiency model describing the GPP as driven by the photosynthetically active radiation absorbed by green vegetation (APARg) and by the efficiency (ε) with which plants use the absorbed radiation to fix carbon via photosynthesis. Results show that GPP can be successfully modelled with a combination of VIs and meteorological data or VIs only. Vegetation indexes designed to be more sensitive to chlorophyll content explained most of the variability in GPP in the ecosystem investigated, characterised by a strong seasonal dynamic of GPP. Accuracy in GPP estimation slightly improves when taking into account high frequency modulations of GPP driven by incident PAR or modelling LUEg with the PRI in model formulation. Similar results were obtained for both measured daily VIs and VIs obtained as 16-day composite time series and then downscaled from the compositing period to daily scale (resampled data). However, the use of resampled data rather than measured daily input data decreases the accuracy of the total GPP estimation on an annual basis.

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Can vegetation index track the interannual variation in gross primary production of temperate deciduous forests?

Ecological Processes ◽

10.1186/s13717-021-00324-2 ◽

2021 ◽

Vol 10 (1) ◽

Author(s):

Fan Liu ◽

Chuankuan Wang ◽

Xingchang Wang

Keyword(s):

Primary Production ◽

Vegetation Index ◽

Near Infrared ◽

Deciduous Forest ◽

Gross Primary Production ◽

Growing Season ◽

Temperate Deciduous Forest ◽

Near Infrared Reflectance ◽

Modis Ndvi ◽

Temperate Deciduous

Abstract Background Vegetation indices (VIs) by remote sensing are widely used as simple proxies of the gross primary production (GPP) of vegetation, but their performances in capturing the inter-annual variation (IAV) in GPP remain uncertain. Methods We evaluated the performances of various VIs in tracking the IAV in GPP estimated by eddy covariance in a temperate deciduous forest of Northeast China. The VIs assessed included the normalized difference vegetation index (NDVI), the enhanced vegetation index (EVI), and the near-infrared reflectance of vegetation (NIRv) obtained from tower-radiometers (broadband) and the Moderate Resolution Imaging Spectroradiometer (MODIS), respectively. Results We found that 25%–35% amplitude of the broadband EVI tracked the start of growing season derived by GPP (R2: 0.56–0.60, bias < 4 d), while 45% (or 50%) amplitudes of broadband (or MODIS) NDVI represented the end of growing season estimated by GPP (R2: 0.58–0.67, bias < 3 d). However, all the VIs failed to characterize the summer peaks of GPP. The growing-season integrals but not averaged values of the broadband NDVI, MODIS NIRv and EVI were robust surrogates of the IAV in GPP (R2: 0.40–0.67). Conclusion These findings illustrate that specific VIs are effective only to capture the GPP phenology but not the GPP peak, while the integral VIs have the potential to mirror the IAV in GPP.

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Modelling spatial and temporal dynamics of gross primary production in the Sahel from earth-observation-based photosynthetic capacity and quantum efficiency

Biogeosciences ◽

10.5194/bg-14-1333-2017 ◽

2017 ◽

Vol 14 (5) ◽

pp. 1333-1348 ◽

Cited By ~ 9

Author(s):

Torbern Tagesson ◽

Jonas Ardö ◽

Bernard Cappelaere ◽

Laurent Kergoat ◽

Abdulhakim Abdi ◽

...

Keyword(s):

Primary Production ◽

Quantum Efficiency ◽

Vegetation Index ◽

Photosynthetic Capacity ◽

Gross Primary Production ◽

Earth Observation ◽

Arid Ecosystems ◽

Spatially Explicit ◽

Equivalent Water Thickness ◽

Semi Arid

Abstract. It has been shown that vegetation growth in semi-arid regions is important to the global terrestrial CO2 sink, which indicates the strong need for improved understanding and spatially explicit estimates of CO2 uptake (gross primary production; GPP) in semi-arid ecosystems. This study has three aims: (1) to evaluate the MOD17A2H GPP (collection 6) product against GPP based on eddy covariance (EC) for six sites across the Sahel; (2) to characterize relationships between spatial and temporal variability in EC-based photosynthetic capacity (Fopt) and quantum efficiency (α) and vegetation indices based on earth observation (EO) (normalized difference vegetation index (NDVI), renormalized difference vegetation index (RDVI), enhanced vegetation index (EVI) and shortwave infrared water stress index (SIWSI)); and (3) to study the applicability of EO upscaled Fopt and α for GPP modelling purposes. MOD17A2H GPP (collection 6) drastically underestimated GPP, most likely because maximum light use efficiency is set too low for semi-arid ecosystems in the MODIS algorithm. Intra-annual dynamics in Fopt were closely related to SIWSI being sensitive to equivalent water thickness, whereas α was closely related to RDVI being affected by chlorophyll abundance. Spatial and inter-annual dynamics in Fopt and α were closely coupled to NDVI and RDVI, respectively. Modelled GPP based on Fopt and α upscaled using EO-based indices reproduced in situ GPP well for all except a cropped site that was strongly impacted by anthropogenic land use. Upscaled GPP for the Sahel 2001–2014 was 736 ± 39 g C m−2 yr−1. This study indicates the strong applicability of EO as a tool for spatially explicit estimates of GPP, Fopt and α; incorporating EO-based Fopt and α in dynamic global vegetation models could improve estimates of vegetation production and simulations of ecosystem processes and hydro-biochemical cycles.

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