scholarly journals Seasonal variations in leaf area index, leaf chlorophyll, and water content; scaling-up to estimate fAPAR and carbon balance in a multilayer, multispecies temperate forest

1999 ◽  
Vol 19 (10) ◽  
pp. 673-679 ◽  
Author(s):  
V. Gond ◽  
D. G. G. de Pury ◽  
F. Veroustraete ◽  
R. Ceulemans
Keyword(s):  
Leaf Area Index ◽  
Water Content ◽  
Leaf Area ◽  
Seasonal Variations ◽  
Carbon Balance ◽  
Temperate Forest ◽  
Scaling Up ◽  
Area Index ◽  
Leaf Chlorophyll

scholarly journals A Canopy Transpiration Model Based on Scaling Up Stomatal Conductance and Radiation Interception as Affected by Leaf Area Index

Water ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 252
Author(s):  
Muhammad Shahinur Alam ◽  
David William Lamb ◽  
Nigel W. M. Warwick
Keyword(s):  
Stomatal Conductance ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Festuca Arundinacea ◽  
Radiation Energy ◽  
Scaling Up ◽  
Controlled Environment ◽  
Canopy Conductance ◽  
Individual Layer ◽  
Area Index

Estimating transpiration as an individual component of canopy evapotranspiration using a theoretical approach is extremely useful as it eliminates the complexity involved in partitioning evapotranspiration. A model to predict transpiration based on radiation intercepted at various levels of canopy leaf area index (LAI) was developed in a controlled environment using a pasture species, tall fescue (Festuca arundinacea var. Demeter). The canopy was assumed to be a composite of two indistinct layers defined as sunlit and shaded; the proportion of which was calculated by utilizing a weighted model (W model). The radiation energy utilized by each layer was calculated from the PAR at the top of the canopy and the fraction of absorbed photosynthetically active radiation (fAPAR) corresponding to the LAI of the sunlit and shaded layers. A relationship between LAI and fAPAR was also established for this specific canopy to aid the calculation of energy interception. Canopy conductance was estimated from scaling up of stomatal conductance measured at the individual leaf level. Other environmental factors that drive transpiration were monitored accordingly for each individual layer. The Penman–Monteith and Jarvis evapotranspiration models were used as the basis to construct a modified transpiration model suitable for controlled environment conditions. Specially, constructed self-watering tubs were used to measure actual transpiration to validate the model output. The model provided good agreement of measured transpiration (actual transpiration = 0.96 × calculated transpiration, R2 = 0.98; p < 0.001) with the predicted values. This was particularly so at lower LAIs. Probable reasons for the discrepancy at higher LAI are explained. Both the predicted and experimental transpiration varied from 0.21 to 0.56 mm h−1 for the range of available LAIs. The physical proportion of the shaded layer exceeded that of the sunlit layer near LAI of 3.0, however, the contribution of the sunlit layer to the total transpiration remains higher throughout the entire growing season.

scholarly journals Mapping leaf area index in a mixed temperate forest using Fenix airborne hyperspectral data and Gaussian processes regression

2021 ◽  
Vol 95 ◽  
pp. 102242 ◽  
Author(s):  
Rui Xie ◽  
Roshanak Darvishzadeh ◽  
Andrew K. Skidmore ◽  
Marco Heurich ◽  
Stefanie Holzwarth ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Gaussian Processes ◽  
Temperate Forest ◽  
Hyperspectral Data ◽  
Area Index

scholarly journals Selection of sowing date and biofertilization as alternatives to improve the yield and profitability of the F68 rice variety

2020 ◽  
Vol 38 (1) ◽  
pp. 61-72
Author(s):  
Yeison Mauricio Quevedo-Amaya ◽  
José Isidro Beltrán-Medina ◽  
José Álvaro Hoyos-Cartagena ◽  
John Edinson Calderón-Carvajal ◽  
Eduardo Barragán-Quijano
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Carbon Balance ◽  
Sowing Date ◽  
Dry Matter Accumulation ◽  
Phosphate Solubilizing Bacteria ◽  
Area Index ◽  
Use Of Resources ◽  
High Efficient ◽  
Selection Of

Multiple factors influence rice yield. Developing management practices that increase crop yield and an efficient use of resources are challenging to modern agriculture. Consequently, the aim of this study was to evaluate biological nitrogen fixation and bacterial phosphorous solubilization (biofertilization) practices with the selection of the sowing date. Three sowing dates (May, July and August) were evaluated when interacting with two mineral nutrition treatments using a randomized complete block design in a split-plot arrangement. Leaf carbon balance, leaf area index, interception and radiation use efficiency, harvest index, dry matter accumulation, nutritional status, and yield were quantified. Results showed that the maximum yield was obtained in the sowing date of August. Additionally, yield increased by 18.92% with the biofertilization treatment, reaching 35.18% of profitability compared to the local production practice. High yields were related to a higher carbon balance during flowering, which was 11.56% and 54.04% higher in August than in July and May, respectively, due to a lower night temperature. In addition, a high efficient use of radiation, which in August was 17.56% and 41.23% higher than in July and May, respectively, contributed to obtain higher yields and this behavior is related to the selection of the sowing date. Likewise, a rapid development of the leaf area index and an optimum foliar nitrogen concentration (>3%) were observed. This allowed for greater efficient use of radiation and is attributed to the activity of nitrogen-fixing and phosphate solubilizing bacteria that also act as plant growth promoters.

Estimation of leaf area index and leaf chlorophyll content in Sporobolus densiflorus using hyperspectral measurements and PROSAIL model simulations

2020 ◽  
Vol 42 (4) ◽  
pp. 1181-1200
Author(s):  
Estefanía Piegari ◽  
Juan I. Gossn ◽  
Francisco Grings ◽  
Verónica Barraza Bernadas ◽  
Ángela B. Juárez ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Chlorophyll Content ◽  
Area Index ◽  
Model Simulations ◽  
Leaf Chlorophyll Content ◽  
Leaf Chlorophyll

scholarly journals Relationship between Flag Leaf Characteristics and Main Yield Components in Oat (Avena sativa L.)

2019 ◽  
Vol 76 (1) ◽  
pp. 71
Author(s):  
Ionuṭ RACZ ◽  
Rozalia KADAR ◽  
Sorin VȂTCĂ ◽  
Ioana Virginia BERINDEAN ◽  
Adrian CECLAN ◽  
...  
Keyword(s):  
Grain Yield ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Avena Sativa ◽  
Yield Components ◽  
Flag Leaf ◽  
Chlorophyll Concentration ◽  
Area Index ◽  
Leaf Chlorophyll ◽  
Leaf Chlorophyll Concentration

The objective of this study was to investigate relationships between leaf area index, leaf chlorophyll concentration, yield components and grain yield in oat (Avena sativa L.). Ten oat varieties were analyzed in field conditions regarding those traits. Flag leaf chlorophyll concentration range between 451.51 and 747.79 units of μmol of chlorophyll per m2. Also, leaf area index range between 13.68 to 32.84 cm2. Significant correlation indices were highlighted between yield components and leaf area index, yield/yield components and chlorophyll concentration of flag leaf.

Analysis of water dynamics in the soil-plant-atmosphere continuum using a multi-sensor approach

2020 ◽  
Author(s):  
David Chaparro ◽  
Thomas Jagdhuber ◽  
Dara Entekhabi ◽  
María Piles ◽  
Anke Fluhrer ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Leaf Area Index ◽  
Water Content ◽  
Leaf Area ◽  
Optical Depth ◽  
Time Lag ◽  
Water Storage ◽  
Water Dynamics ◽  
Area Index ◽  
L Band

&lt;p&gt;Changing climate patterns have increased hydrological extremes in many regions [1]. This impacts water and carbon cycles, potentially modifying vegetation processes and thus terrestrial carbon uptake. It is therefore crucial to understand the relationship between the main water pools linked to vegetation (i.e., soil moisture, plant water storage, and atmospheric water deficit), and how vegetation responds to changes of these pools. Hence, the goal of this research is to understand the water pools and fluxes in the soil-plant-atmosphere continuum (SPAC) and their relationship with vegetation responses.&lt;/p&gt;&lt;p&gt;Our study spans from April 2015 to March 2019 and is structured in two parts:&lt;/p&gt;&lt;p&gt;Firstly, relative water content (RWC) is estimated using a multi-sensor approach to monitor water storage in plants. This is at the core of our research approach towards water pool monitoring within SPAC. Here, we will present a RWC dataset derived from gravimetric moisture content (&lt;em&gt;mg&lt;/em&gt;) estimates using the method first proposed in [2], and further validated in [3]. This allows retrieving RWC and &lt;em&gt;mg&lt;/em&gt; independently from biomass influences. Here, we apply this method using a sensor synergy including (i) vegetation optical depth from SMAP L-band radiometer (L-VOD), (ii) vegetation height (VH) from ICESat-2 Lidar and (iii) vegetation volume fraction (d) from AQUARIUS L-band radar. RWC status and temporal dynamics will be discussed.&lt;/p&gt;&lt;p&gt;Secondly, water dynamics in the SPAC and their impact on leaf changes are analyzed. We will present a global, time-lag correlation analysis among: (i) the developed RWC maps, (ii) surface soil moisture from SMAP (SM), (iii) vapor pressure deficit (VPD; from MERRA reanalysis [4]), and (iv) leaf area index (LAI; from MODIS [5]). Resulting time-lag and correlation maps, as well as analyses of LAI dynamics as a function of SPAC, will be presented at the conference.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;References&lt;/p&gt;&lt;p&gt;[1] IPCC. (2013). Annex I: Atlas of global and regional climate projections. In: van Oldenborgh, et al. (Eds.) Climate Change 2013: The Physical Science Basis (pp. 1311-1393). Cambridge University Press.&lt;/p&gt;&lt;p&gt;[2] Fink, A., et al. (2018). Estimating Gravimetric Moisture of Vegetation Using an Attenuation-Based Multi-Sensor Approach. In IGARSS 2018 (pp. 353-356). IEEE.&lt;/p&gt;&lt;p&gt;[3] Meyer, T., et al. Estimating Gravimetric Water Content of a Winter Wheat Field from L-Band Vegetation Optical Depth, Remote Sens.&amp;#160;2019,&amp;#160;11(20), 2353&lt;/p&gt;&lt;p&gt;[4] NASA (2019). Modern-Era Retrospective analysis for Research and Applications, Version 2. Accessed 2020-01-14 from https://gmao.gsfc.nasa.gov/reanalysis/MERRA-2/.&lt;/p&gt;&lt;p&gt;[5] Myneni, R., et al. (2015). MOD15A2H MODIS/Terra Leaf Area Index/FPAR 8-Day L4 Global 500m SIN Grid V006. Accessed 2020-01-14 from https://doi.org/10.5067/MODIS/MOD15A2H.006.&lt;/p&gt;

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