Search results for "leaf area"
showing 10 items of 124 documents
Influencia del ángulo de observación en la estimación del índice de área foliar (LAI) mediante imágenes PROBA/CHRIS
2016
La estimación de variables biofísicas como el Índice de Área Foliar (LAI) mediante técnicas de teledetección es objeto de numerosos estudios, ya que de su conocimiento se puede extraer valiosa información sobre el estado de la vegetación. En este trabajo se estudia la estimación del LAI mediante imágenes multiangulares PROBA/CHRIS, analizando el comportamiento de la reflectividad medida en sus 5 ángulos de observación, en las longitudes de onda de 665 y 705 nm correspondientes a la banda de absorción de la clorofila y la reflectividad de la vegetación en el Red-Edge, respectivamente. El Índice de Diferencia Normalizada (NDI) calculado en estas longitudes de onda, mostró una buena correlació…
A high-resolution, integrated system for rice yield forecasting at district level
2019
Abstract To meet the growing demands from public and private stakeholders for early yield estimates, a high-resolution (2 km × 2 km) rice yield forecasting system based on the integration of the WARM model and remote sensing (RS) technologies was developed. RS was used to identify rice-cropped area and to derive spatially distributed sowing dates, and for the dynamic assimilation of RS-derived leaf area index (LAI) data within the crop model. The system—tested for the main European rice production districts in Italy, Greece, and Spain—performed satisfactorily; >66% of the inter-annual yield variability was explained in six out of eight combinations of ecotype × district, with a maximum of 8…
Global Sensitivity Analysis of Leaf-Canopy-Atmosphere RTMs: Implications for Biophysical Variables Retrieval from Top-of-Atmosphere Radiance Data.
2019
Knowledge of key variables driving the top of the atmosphere (TOA) radiance over a vegetated surface is an important step to derive biophysical variables from TOA radiance data, e.g., as observed by an optical satellite. Coupled leaf-canopy-atmosphere Radiative Transfer Models (RTMs) allow linking vegetation variables directly to the at-sensor TOA radiance measured. Global Sensitivity Analysis (GSA) of RTMs enables the computation of the total contribution of each input variable to the output variance. We determined the impacts of the leaf-canopy-atmosphere variables into TOA radiance using the GSA to gain insights into retrievable variables. The leaf and canopy RTM PROSAIL was coupled with…
Biomass and volume modeling in Olea europaea L. cv "Leccino"
2017
Key message: This work demonstrates that the Olive tree, which is managed and pruned as a fruit tree, can be treated as a forest tree using allometric equations, to estimate both biomass production and volumes. Abstract: The Olive tree (Olea europaea L.) is an evergreen tree that can grow and accumulate a relatively high amount of dry matter, even in dry environmental conditions common in the Mediterranean basin and typical of traditional rain-fed agriculture. The objective of this research was to develop a tool to predict woody biomass and tree component volume for the olive tree, to be used for different agricultural and environmental purposes. The study was carried out in six olive grove…
A unified vegetation index for quantifying the terrestrial biosphere
2021
[EN] Empirical vegetation indices derived from spectral reflectance data are widely used in remote sensing of the biosphere, as they represent robust proxies for canopy structure, leaf pigment content, and, subsequently, plant photosynthetic potential. Here, we generalize the broad family of commonly used vegetation indices by exploiting all higher-order relations between the spectral channels involved. This results in a higher sensitivity to vegetation biophysical and physiological parameters. The presented nonlinear generalization of the celebrated normalized difference vegetation index (NDVI) consistently improves accuracy in monitoring key parameters, such as leaf area index, gross prim…
Data synergy between leaf area index and clumping index Earth Observation products using photon recollision probability theory
2018
International audience; Clumping index (CI) is a measure of foliage aggregation relative to a random distribution of leaves in space. The CI can help with estimating fractions of sunlit and shaded leaves for a given leaf area index (LAI) value. Both the CI and LAI can be obtained from global Earth Observation data from sensors such as the Moderate Resolution Imaging Spectrometer (MODIS). Here, the synergy between a MODIS-based CI and a MODIS LAI product is examined using the theory of spectral invariants, also referred to as photon recollision probability ('p-theory'), along with raw LAI-2000/2200 Plant Canopy Analyzer data from 75 sites distributed across a range of plant functional types.…
Spatial Variation of Leaf Optical Properties in a Boreal Forest Is Influenced by Species and Light Environment
2017
Leaf Optical Properties (LOPs) convey information relating to temporally dynamic photosynthetic activity and biochemistry. LOPs are also sensitive to variability in anatomically related traits such as Specific Leaf Area (SLA), via the interplay of intra-leaf light scattering and absorption processes. Therefore, variability in such traits, which may demonstrate little plasticity over time, potentially disrupts remote sensing estimates of photosynthesis or biochemistry across space. To help to disentangle the various factors that contribute to the variability of LOPs, we defined baseline variation as variation in LOPs that occurs across space, but not time. Next we hypothesized that there wer…
A Methodology to Derive Global Maps of Leaf Traits Using Remote Sensing and Climate Data
2018
This paper introduces a modular processing chain to derive global high-resolution maps of leaf traits. In particular, we present global maps at 500 m resolution of specific leaf area, leaf dry matter content, leaf nitrogen and phosphorus content per dry mass, and leaf nitrogen/phosphorus ratio. The processing chain exploits machine learning techniques along with optical remote sensing data (MODIS/Landsat) and climate data for gap filling and up-scaling of in-situ measured leaf traits. The chain first uses random forests regression with surrogates to fill gaps in the database (> 45% of missing entries) and maximizes the global representativeness of the trait dataset. Plant species are then a…
Downscaling rice yield simulation at sub-field scale using remotely sensed LAI data
2019
Abstract Crop modeling and remote sensing are key tools to gain deeper understanding on cropping system dynamics and, ultimately, to increase the sustainability of agricultural productions. This study presents a system to estimate rice yields at sub-field scale based on the integration of a biophysical model and remotely sensed products. Leaf area index (LAI) data derived from decametric optical imageries (i.e., Landsat-8, Landsat-7 and Sentinel–2A) were assimilated into the WARM rice model via automatic recalibration of crop parameters at a fine spatial resolution (30 m × 30 m), targeting the lowest error between simulated and remotely sensed LAI. The performance of the system was evaluate…
Plant functional trait response to environmental drivers across European temperate forest understorey communities
2020
Functional traits respond to environmental drivers, hence evaluating trait-environment relationships across spatial environmental gradients can help to understand how multiple drivers influence plant communities. Global-change drivers such as changes in atmospheric nitrogen deposition occur worldwide, but affect community trait distributions at the local scale, where resources (e.g. light availability) and conditions (e.g. soil pH) also influence plant communities. We investigate how multiple environmental drivers affect community trait responses related to resource acquisition (plant height, specific leaf area (SLA), woodiness, and mycorrhizal status) and regeneration (seed mass, lateral s…