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UID:pretalx-foss4g-it-2023-8F3CAF@talks.staging.osgeo.org
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DESCRIPTION:Authors: Margherita De Peppo\, Francesco Nutini\, Alberto Crema
 \, Gabriele Candiani\, Giovanni Antonio Re\, Federico Sanna\, Carla Cesara
 ccio\, Beniamino Gioli\, Mirco Boschetti\n\nSpatio-temporal estimation of 
 crop bio-parameters (BioPar) is required for agroecosystem management and 
 monitoring. BioPar such as Canopy Chlorophyll Content (CCC) and Leaf Area 
 Index (LAI) contribute to assess plant physiological status and health at 
 leaf and canopy level. Remote sensing provides an effective way to spatial
 ly explicitly retrieve CCC and LAI at different spatial and temporal scale
 s. Several studies demonstrated how Machine Learning (ML) techniques outpe
 rform traditional empirical approaches based on Vegetation Index in BioPar
  estimations from RS data. Among the different available algorithms Gaussi
 an processes regression (GPR) is considered promising for LAI and CCC mapp
 ing. However\, few of these studies have examined the performance of GPR i
 n predicting crop parameters when applied to different site\, season and c
 rop typology (i.e. validation using independent dataset). The specific obj
 ectives of this study conducted in the framework of E-CROPS project were: 
 (i) develop a transferable GPR algorithm for LAI and CCC estimation by exp
 loiting a robust multi-crop\, multi-year and site dataset\; (ii) assess GP
 R BioPar retrieval performance against ground measurements acquired over i
 ndependent dataset\; (iii) compare result with other methods including emp
 irically based VI models and operational product embedded in SNAP. In tota
 l\, 209 (CCC) and 301 (LAI) observations were used to train GPR models. Th
 en\, over the unseen dataset (LAI n=820 and CCC n=305) the GPR was validat
 ed. The results showed that for both LAI and CCC GPR retrieval are reliabl
 e and comparable with SNAP estimates despite CCC show a consistent underes
 timation. LAI (CCC) estimation metrics ranges for the different data sets 
 as follows: R2 0.2 to 0.75 (0.2 -0.7) and MAE 0.1 to 0.75 (0.5-3). Overall
  the results demonstrated the potentiality of GPR machine learning approac
 h in LAI and CCC estimations when a robust training set is exploited\, suc
 h condition guarantee a spatial-temporal transferability of the developed 
 model. GPR BioPar estimation from Sentinel 2 can produce decametric quasi-
 weekly quantitative information for crop spatio-temporal monitoring. Such 
 maps are a fundamental input for decision support systems devoted to smart
  crop management and early warning indication. Many precision agriculture 
 techniques could thus benefit from information generated with ideal qualit
 y and frequency for site-specific practices aimed at reducing inputs and i
 mproving the use-efficiency of fertilizers.
DTSTAMP:20260516T111429Z
LOCATION:Sala Biblioteca @ PoliBa
SUMMARY:Assessing transferability of Gaussian Process Regression for Canopy
  Chlorophyll Content and Leaf Area Index estimation from Sentinel-2 data e
 xploiting a multi-site\, year and crop dataset - Mirco Boschetti\, Carla C
 esaraccio\, Beniamino Gioli
URL:https://talks.staging.osgeo.org/foss4g-it-2023/talk/8F3CAF/
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