scholarly journals Improved estimations of gross primary production using satellite-derived photosynthetically active radiation

2014 ◽  
Vol 119 (1) ◽  
pp. 110-123 ◽  
Author(s):  
Wenwen Cai ◽  
Wenping Yuan ◽  
Shunlin Liang ◽  
Xiaotong Zhang ◽  
Wenjie Dong ◽  
...  
Keyword(s):  
Primary Production ◽  
Photosynthetically Active Radiation ◽  
Gross Primary Production ◽  
Active Radiation

scholarly journals Influência entre relação de radiação fotossinteticamente ativa e radiação global na produtividade primária bruta para Santa Rita do Passa Quatro - SP

2018 ◽  
Vol 40 ◽  
pp. 52
Author(s):  
Celina Cândida Ferreira Rodrigues ◽  
Maria Do Rosário Alves Patriota ◽  
Bernardo Barbosa da Silva ◽  
André Bezerra Oliveira
Keyword(s):  
Data Analysis ◽  
Primary Production ◽  
Linear Equation ◽  
Photosynthetically Active Radiation ◽  
Gross Primary Production ◽  
Global Radiation ◽  
Active Radiation ◽  
The Relationship

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.

scholarly journals 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 

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.

scholarly journals 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

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.

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

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.

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

2012 ◽  
Vol 9 (7) ◽  
pp. 2565-2584 ◽  
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.

scholarly journals Improving Remote Sensing-based Estimation of Mangrove Forest Gross Primary Production by Quantifying Environmental Stressors: Sea Surface Temperature, Salinity, and Photosynthetic Active Radiation

2021 ◽  
Author(s):  
Yuhan Zheng ◽  
Wataru Takeuchi
Keyword(s):  
Remote Sensing ◽  
Primary Production ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Gross Primary Production ◽  
Sea Surface ◽  
Photosynthetic Active Radiation ◽  
Mangrove Forests ◽  
Active Radiation ◽  
Mangrove Ecosystems

Abstract Mangrove ecosystems play an important role in global carbon budget, however, the quantitative relationships between environmental drivers and productivity in these forests remain poorly understood. This study presented a remote sensing (RS)-based productivity model to estimate the light use efficiency (LUE) and gross primary production (GPP) of mangrove forests in China. Firstly, LUE model considered the effects of tidal inundation and therefore involved sea surface temperature (SST) and salinity as environmental scalars. Secondly, the downscaling effect of photosynthetic active radiation (PAR) on the mangrove LUE was quantified according to different PAR values. Thirdly, the maximum LUE varied with temperature and was therefore determined based on the response of daytime net ecosystem exchange and PAR at different temperatures. Lastly, GPP was estimated by combining the LUE model with the fraction of absorbed photosynthetically active radiation from Sentinel-2 images. The results showed that the LUE model developed for mangrove forests has higher overall accuracy (RMSE = 0.0051, R2 = 0.64) than the terrestrial model (RMSE = 0.0220, R2 = 0.24). The main environmental stressor for the photosynthesis of mangrove forests in China was PAR. The estimated GPP was, in general, in agreement with the in-situ measurement from the two carbon flux towers. Compared to the MODIS GPP product, the derived GPP had higher accuracy, with RMSE improving from 39.09 to 19.05 g C/m2/8 days in 2012, and from 33.76 to 19.51 g C/m2/8 days in 2015. The spatiotemporal distributions of the mangrove GPP revealed that GPP was most strongly controlled by environmental conditions, especially temperature and PAR, as well as the distribution of mangroves. These results demonstrate the potential of the RS-based productivity model for scaling up GPP in mangrove forests, a key to explore the carbon cycle of mangrove ecosystems at national and global scales.

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