Search results for "leaf area"

Development of an earth observation processing chain for crop bio-physical parameters at local scale

2015

This paper proposes a full Earth observation processing chaing for biophysical parameter estimation at local scales. In particular, we focus on the Leaf Area Index (LAI) as an essential climate variable required for the monitoring and modeling of land surfaces at local scale. The main goal of this study is tied to the use of optical satellite images to retrieve Earth Observation (EO) biophysical parameters able to describe the spatio-temporal changes in agro-ecosystems at local scale. The objective of this work is two-fold: (i) to set up and update the EO products processing chain at high resolution (local) scale; and (ii) derive multitemporal LAI maps at 30 m resolution to be fed into a cr…

Green LAI Mapping and Cloud Gap-Filling Using Gaussian Process Regression in Google Earth Engine

2021

For the last decade, Gaussian process regression (GPR) proved to be a competitive machine learning regression algorithm for Earth observation applications, with attractive unique properties such as band relevance ranking and uncertainty estimates. More recently, GPR also proved to be a proficient time series processor to fill up gaps in optical imagery, typically due to cloud cover. This makes GPR perfectly suited for large-scale spatiotemporal processing of satellite imageries into cloud-free products of biophysical variables. With the advent of the Google Earth Engine (GEE) cloud platform, new opportunities emerged to process local-to-planetary scale satellite data using advanced machine …

Testing Multi-Sensors Time Series of Lai Estimates to Monitor Rice Phenology: Preliminary Results

2018

Timely and accurate information on crop growth and seasonal dynamics are increasingly needed to develop monitoring systems aimed to detect seasonal anomalies, support site specific management and estimate crop yield at the end of the season. In particular, frequent decametric information nowadays being provided exploiting the new generation of Earth Observation (EO) platforms are fundamental for farm level monitoring. This study presents an analysis aimed at fully exploiting dense time series of EO data derived from the combined use of ESA Sentinel-2A and NASA Landsat-7/8 imageries for crop phenological monitoring. Decametric Leaf Area Index (LAI) maps were generated for the year 2016 by in…

FRUIT PRODUCTION OF CULTIVATED CACTI: A SHORT OVERVIEW ON PLANT ECOPHYSIOLOGY AND C BUDGET

2009

Vegetation and soil – related parameters for computing solar radiation exchanges within green roofs: Are the available values adequate for an easy mo…

2016

Several studies analyze the thermal performance of vegetated roofs, presenting either mathematical models or experimental quantifications of heat transfer process through them, also showing the effect of vegetation and soil parameters on the thermal and energy performance of this type of roofing system. However, presently the level of availability of these parameters, has not been enough considered. This work intends to investigate this underestimated issue of the green roofs’ thermal modeling, through the consideration of the availability of parameters pertinent to the shortwave radiation exchange, which are adopted by models based on leaf area index (LAI) and on the fractional vegetation …

Design, Building up and First Results of Three Monitored Green Coverings Over a University Department Building

2015

Abstract A viable solution to reducing the buildings’ energy demand is the implementation of green coverings that have been demonstrated to improve the envelope performance, especially in summer season. Anyway, to apply computer models analyzing the energy performance of buildings equipped with such components, the knowledge of specific parameters is needed. Among these, the fractional vegetation coverage and leaf area index are of great importance. By means of an experimental facility, first results of the evaluation of these foliage-related parameters for five types of vegetation are presented.

DATimeS: A machine learning time series GUI toolbox for gap-filling and vegetation phenology trends detection

2020

Abstract Optical remotely sensed data are typically discontinuous, with missing values due to cloud cover. Consequently, gap-filling solutions are needed for accurate crop phenology characterization. The here presented Decomposition and Analysis of Time Series software (DATimeS) expands established time series interpolation methods with a diversity of advanced machine learning fitting algorithms (e.g., Gaussian Process Regression: GPR) particularly effective for the reconstruction of multiple-seasons vegetation temporal patterns. DATimeS is freely available as a powerful image time series software that generates cloud-free composite maps and captures seasonal vegetation dynamics from regula…

Mapping evapotranspiration on vineyards: A comparison between Penman-Monteith and energy balance approaches for operational purposes

2012

Estimation of evapotranspiration (ET) in Sicilian vineyard is an emerging issue since these agricultural systems are more and more converted from rainfed to irrigated conditions, with significant impacts on the management of the scarce water resources of the region. The choice of the most appropriate methodology for assessing water use in these systems is still an issue of debating, due to the complexity of canopy and root systems and for their high spatial fragmentation. In vineyards, quality and quantity of the final product are dependent on the controlled stress conditions to be set trough irrigation. This paper reports an application of the well-known Penman-Monteith approach, applied i…

Accuracy assessment of fraction of vegetation cover and leaf area index estimates from pragmatic methods in a cropland area

2009

The fraction of vegetation cover (FVC) and the leaf area index (LAI) are important parameters for many agronomic, ecological and meteorological applications. Several in-situ and remote sensing techniques for estimating FVC and LAI have been developed in recent years. In this paper, the uncertainty of in-situ FVC and LAI measurements was evaluated by comparing estimates from LAI-2000 and digital hemispherical photography (DHP). The accuracy achieved with a spectral mixture analysis algorithm and two vegetation indices-based methods was assessed using atmospherically corrected Landsat Thematic Mapper (TM) data over the Barrax cropland area where the European Space Agency (ESA) SENtinel-2 and …

Direct validation of FVC, LAI and FAPAR VEGETATION/SPOT derived products using LSA SAF methodology

2007

The aim of this work is to perform a direct validation of fraction of vegetation cover (FVC), leaf area index (LAI) and fraction of absorbed photosynthetically active radiation (FAPAR) resulting products from applying the LSA SAF methodology to VEGETATION BRDF data. LSA SAF adapted algorithms were tested in adequate test sites comprising different continental biomes covering a wide range of FVC, LAI and FAPAR values. Results seem to indicate the competitiveness of LSA SAF proposed methodology to retrieve remotely sensed biophysical parameters. A noticeable good agreement regarding the ground measurements was found. The overall accuracy (RAISE) is around 20% for FVC and FAPAR and around 15% …