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 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 Estimation of Terrestrial Global Gross Primary Production (GPP) with Satellite Data-Driven Models and Eddy Covariance Flux Data

2018 ◽  
Vol 10 (9) ◽  
pp. 1346 ◽  
Author(s):  
Joanna Joiner ◽  
Yasuko Yoshida ◽  
Yao Zhang ◽  
Gregory Duveiller ◽  
Martin Jung ◽  
...  
Keyword(s):  
Primary Production ◽  
Eddy Covariance ◽  
Satellite Data ◽  
Gross Primary Production ◽  
Satellite Observation ◽  
Data Driven ◽  
Spatial And Temporal Variability ◽  
Light Use Efficiency ◽  
High Productivity ◽  
Use Efficiency

We estimate global terrestrial gross primary production (GPP) based on models that use satellite data within a simplified light-use efficiency framework that does not rely upon other meteorological inputs. Satellite-based geometry-adjusted reflectances are from the MODerate-resolution Imaging Spectroradiometer (MODIS) and provide information about vegetation structure and chlorophyll content at both high temporal (daily to monthly) and spatial (∼1 km) resolution. We use satellite-derived solar-induced fluorescence (SIF) to identify regions of high productivity crops and also evaluate the use of downscaled SIF to estimate GPP. We calibrate a set of our satellite-based models with GPP estimates from a subset of distributed eddy covariance flux towers (FLUXNET 2015). The results of the trained models are evaluated using an independent subset of FLUXNET 2015 GPP data. We show that variations in light-use efficiency (LUE) with incident PAR are important and can be easily incorporated into the models. Unlike many LUE-based models, our satellite-based GPP estimates do not use an explicit parameterization of LUE that reduces its value from the potential maximum under limiting conditions such as temperature and water stress. Even without the parameterized downward regulation, our simplified models are shown to perform as well as or better than state-of-the-art satellite data-driven products that incorporate such parameterizations. A significant fraction of both spatial and temporal variability in GPP across plant functional types can be accounted for using our satellite-based models. Our results provide an annual GPP value of ∼140 Pg C year - 1 for 2007 that is within the range of a compilation of observation-based, model, and hybrid results, but is higher than some previous satellite observation-based estimates.

A Pear and Vegetable Interculture System: Land Equivalent Ratio, Light Use Efficiency and Productivity

1986 ◽  
Vol 22 (4) ◽  
pp. 383-392 ◽  
Author(s):  
S. M. Newman
Keyword(s):  
Photosynthetically Active Radiation ◽  
Dry Matter ◽  
Spatial Arrangement ◽  
Yield Reduction ◽  
Light Use Efficiency ◽  
Land Equivalent Ratio ◽  
Active Radiation ◽  
Use Efficiency ◽  
Pear Trees ◽  
Efficiency And Productivity

SUMMARYThe productivity, land equivalent ratios (LERs) and light use efficiency of a pear and radish interculture system were assessed. Pear yield was unaffected by intercropping. Relative yields for the radish component varied between 0.5–1.01 depending upon the yield index and spatial arrangement employed. This gave LER values for the system of 1.5–2.01. The overall trans-missivity of the pear canopy was 73%. A 47% reduction in photosynthetically active radiation (PAR) gave a yield reduction of 65% in terms of number of saleable radish, but did not affect total dry matter productivity. Reductions in radish yield directly beneath pear trees was thought to be due to other factors besides PAR. The total dry matter productivity of a system containing five successive radish crops was estimated at 26.25 tonnes ha−1 yr−1.

scholarly journals Light Use Efficiency of Aboveground Biomass Production of Norway Spruce Stands

2017 ◽  
Vol 65 (1) ◽  
pp. 9-16
Author(s):  
Michal Bellan ◽  
Irena Marková ◽  
Andrii Zaika ◽  
Jan Krejza
Keyword(s):  
Norway Spruce ◽  
Aboveground Biomass ◽  
Photosynthetically Active Radiation ◽  
Climatic Conditions ◽  
Light Use Efficiency ◽  
Active Radiation ◽  
Spruce Stands ◽  
Use Efficiency ◽  
Study Sites ◽  
Radiation Use

Light use efficiency (LUE or photosynthetically active radiation use efficiency) in production of young spruce stands aboveground biomass was determined at the study sites Rájec (the Drahanská vrchovina Highland) and Bílý Kříž (the Moravian‑Silesian Beskids Mountains) in 2014 and 2015. The LUE value obtained for the investigated spruce stands were in the range of 0.45 – 0.65 g DW MJ–1. The different LUE values were determined for highland and mountain spruce stand. The differences were caused by growth and climatic conditions and by the amount of assimilatory apparatus (LAI).

scholarly journals Global cropland monthly Gross Primary Production in the year 2000

2014 ◽  
Vol 11 (2) ◽  
pp. 3465-3488
Author(s):  
T. Chen ◽  
G. R. van der Werf ◽  
N. Gobron ◽  
E. J. Moors ◽  
A. J. Dolman
Keyword(s):  
Primary Production ◽  
Survey Data ◽  
Land Surface ◽  
Gross Primary Production ◽  
Co2 Exchange ◽  
Light Use Efficiency ◽  
Natural Ecosystems ◽  
Use Efficiency ◽  
Crop Types ◽  
Year 2000

Abstract. Croplands cover about 12% of the ice-free terrestrial land surface. Compared with natural ecosystems, croplands have distinct characteristics due to anthropogenic influences. Their global gross primary production (GPP) is not well constrained and estimates vary between 8.2 and 14.2 Pg C yr−1. We quantified global cropland GPP using a light use efficiency (LUE) model, employing satellite observations and survey data of crop types and distribution. A novel step in our analysis was to assign a maximum light use efficiency estimate (ϵ*GPP) to each of the 26 different crop types, instead of taking a uniform value as done in the past. These ϵ*GPP values were calculated based on flux tower CO2 exchange measurements and a literature survey of field studies, and ranged from 1.20 g CMJ−1 to 2.96 g CMJ−1. Global cropland GPP was estimated to be 11.05 Pg C yr−1 in the year 2000. Maize contributed most to this (1.55 Pg C yr−1), and the continent of Asia contributed most with 38.9% of global cropland GPP. In the continental United States, annual cropland GPP (1.28 Pg C yr−1) was close to values reported previously (1.24 Pg C yr−1) constrained by harvest records, but our estimates of ϵ*GPP values were much higher. Our results are sensitive to satellite information and survey data on crop type and extent, but provide a consistent and data-driven approach to generate a look-up table of ϵ*GPP for the 26 crop types for potential use in other vegetation models.

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