Search results for "Leaf Area Index"
showing 10 items of 105 documents
Gaussian processes uncertainty estimates in experimental Sentinel-2 LAI and leaf chlorophyll content retrieval
2013
Abstract ESA’s upcoming Sentinel-2 (S2) Multispectral Instrument (MSI) foresees to provide continuity to land monitoring services by relying on optical payload with visible, near infrared and shortwave infrared sensors with high spectral, spatial and temporal resolution. This unprecedented data availability leads to an urgent need for developing robust and accurate retrieval methods, which ideally should provide uncertainty intervals for the predictions. Statistical learning regression algorithms are powerful candidats for the estimation of biophysical parameters from satellite reflectance measurements because of their ability to perform adaptive, nonlinear data fitting. In this paper, we f…
Derivation of high-resolution leaf area index maps in support of validation activities: Application to the cropland Barrax site
2009
The validation of coarse satellite-derived products from field measurements generally relies on up-scaling the field data to the corresponding satellite products. This question is commonly addressed through the generation of a reference high-resolution map of an area covering several moderate resolution pixels. This paper describes a method by which reference leaf area index (LAI) maps can be generated from ground-truth LAI measurements. The method is based on a multivariate ordinary least squares (OLS) algorithm which uses an iteratively re-weighted least squares (IRLS) algorithm. It provides an empirical relationship (i.e. a transfer function) between in situ measurements and concomitant …
Climate Data Records of Vegetation Variables from Geostationary SEVIRI/MSG Data: Products, Algorithms and Applications
2019
The scientific community requires long-term data records with well-characterized uncertainty and suitable for modeling terrestrial ecosystems and energy cycles at regional and global scales. This paper presents the methodology currently developed in EUMETSAT within its Satellite Application Facility for Land Surface Analysis (LSA SAF) to generate biophysical variables from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) on board MSG 1-4 (Meteosat 8-11) geostationary satellites. Using this methodology, the LSA SAF generates and disseminates at a time a suite of vegetation products, such as the leaf area index (LAI), the fraction of the photosynthetically active radiation absorbed …
A Critical Comparison of Remote Sensing Leaf Area Index Estimates over Rice-Cultivated Areas: From Sentinel-2 and Landsat-7/8 to MODIS, GEOV1 and EUM…
2018
Leaf area index (LAI) is a key biophysical variable fundamental in natural vegetation and agricultural land monitoring and modelling studies. This paper is aimed at comparing, validating and discussing different LAI satellite products from operational services and customized solution based on innovative Earth Observation (EO) data such as Landsat-7/8 and Sentinel-2A. The comparison was performed to assess overall quality of LAI estimates for rice, as a fundamental input of different scale (regional to local) operational crop monitoring systems such as the ones developed during the "An Earth obseRvation Model based RicE information Service" (ERMES) project. We adopted a multiscale approach f…
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.