0000000000248362
AUTHOR
Lorenzo Busetto
showing 7 related works from this author
Intercomparison of instruments for measuring leaf area index over rice
2015
Leaf area index (LAI) is a key biophysical parameter used to determine foliage cover and crop growth in environmental studies in order to assess crop yield. LAI estimates can be classified as direct or indirect methods. Direct methods are destructive, time consuming, and difficult to apply over large fields. Indirect methods are non-destructive and cost-effective due to its portability, accuracy and repeatability. In this study, we compare indirect LAI estimates acquired from two classical instruments such as LAI-2000 and digital cameras for hemispherical photography, with LAI estimates acquired with a smart app (PocketLAI) installed on a mobile smartphone. In this work it is shown that LAI…
Performance of Spectral Fitting Methods for vegetation fluorescence quantification
2010
The Fraunhofer Line Discriminator (FLD) principle has long been considered as the reference method to quantify solar-induced chlorophyll fluorescence (F) from passive remote sensing measurements. Recently, alternative retrieval algorithms based on the spectral fitting of hyperspectral radiance observations, Spectral Fitting Methods (SFMs), have been proposed. The aim of this manuscript is to investigate the performance of such algorithms and to provide relevant information regarding their use. FLD and SFMs were used to estimate F starting from Top Of Canopy (TOC) fluxes at very high spectral resolution (0.12 nm) and sampling interval (0.1 nm), exploiting the O2-B (687.0 nm) and O2-A (760.6 …
Downstream Services for Rice Crop Monitoring in Europe: From Regional to Local Scale
2017
The ERMES agromonitoring system for rice cultivations integrates EO data at different resolutions, crop models, and user-provided in situ data in a unified system, which drives two operational downstream services for rice monitoring. The first is aimed at providing information concerning the behavior of the current season at regional/rice district scale, while the second is dedicated to provide farmers with field-scale data useful to support more efficient and environmentally friendly crop practices. In this contribution, we describe the main characteristics of the system, in terms of overall architecture, technological solutions adopted, characteristics of the developed products, and funct…
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…
Multitemporal Monitoring of Plant Area Index in the Valencia Rice District with PocketLAI
2016
Leaf area index (LAI) is a key biophysical parameter used to determine foliage cover and crop growth in environmental studies in order to assess crop yield. Frequently, plant canopy analyzers (LAI-2000) and digital cameras for hemispherical photography (DHP) are used for indirect effective plant area index (PAI(eff)) estimates. Nevertheless, these instruments are expensive and have the disadvantages of low portability and maintenance. Recently, a smartphone app called PocketLAI was presented and tested for acquiring PAI(eff) measurements. It was used during an entire rice season for indirect PAI(eff) estimations and for deriving reference high-resolution PAI(eff) maps. Ground PAI(eff) value…
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…
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…