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UID:pretalx-foss4g-it-2023-8HU3SP@talks.staging.osgeo.org
DTSTART;TZID=GMT:20230613T171500
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DESCRIPTION:Non-photosynthetic vegetation (NPV) detection and quantificatio
 n represent a key variable in remote sensing of conservative agriculture\,
  and\, more recently\, in carbon farming due to its important role in wate
 r\, nutrient and carbon cycling. For this reason\, both mapping and charac
 terization of NPV represent a relevant topic in the exploitation of Earth 
 Observation (EO) data for agriculture monitoring.\nStudies on NPV mapping 
 by EO data benefit from the availability of hyperspectral data due to the 
 high spectral resolution particularly at wavelengths from 1.6 to 2.3m\,
  where the spectral features of carbon-based constituents of plants are di
 stinctive. The launch of new generation hyperspectral satellites\, as PRIS
 MA (PRecursore IperSpettrale della Missione Applicativa) and\, more recent
 ly\, EnMAP (Environmental Mapping and Analysis Program) offers research op
 portunities in the field\, which before was mainly investigated by proxima
 l and aerial sensing.\nEarly studies already proved the potential of PRISM
 A in NPV due to the prominence of the cellulose-lignin key absorption feat
 ure at 2.1m. More recent studies on PRISMA make use of machine learning
  regression algorithm (MLRA) trained on the basis of radiative transfer mo
 del simulations\, or on the basis of Exponential Gaussian Optimization (EG
 O) of specific absorption features on sensed data.\nThis second approach\,
  proposed in this study\, is aimed at the determination of Crop Residue Co
 ver (CRC) using PRISMA hyperspectral imagery by a two-step approach making
  use of: i) firstly\, an Exponential Gaussian Optimization to model pre-se
 lected absorption features\, also reducing the spectral dimension\; ii) se
 condly\, a Random Forest paradigm\, performing non-linear regression to fi
 nally predict and map CRC.\nThis study exploits for the training phase an 
 extensive and well documented spectral library\, namely “Reflectance spe
 ctra of agricultural field conditions supporting remote sensing evaluation
  of non-photosynthetic vegetation cover” made available online by USGS (
 https://doi.org/10.5066/P9XK3867). It consists of 916 in situ surface refl
 ectance spectra collected using a proximal full range spectroradiometer (3
 50 to 2500 nm). Spectra are annotated with the corresponding fractions of 
 NPV\, Soil and (if any) Green Vegetation\, as estimated by point sampling 
 digital photograph of the radiometer field-of-view. \nThis spectral librar
 y was resampled to PRISMA spectral resolution\, prior to the Gaussian Expo
 nential Optimization (EGO) on 4 spectral intervals of interest\, already t
 ested in previous studies\, and corresponding to absorption bands of: cell
 ulose-lignin\, plant pigments\, vegetation water content and clays.\nThe E
 GO algorithm optimizes continuum-removed spectra by 4 parameters - absorpt
 ion band depth\, center\, width and asymmetry – and since this is perfor
 med for each spectral interval\, it results in 16 parameters. This is a re
 duced space as compared to the one of the input spectra (around 230 bands)
 . This parameter space was used to train a Random Forest to model the regr
 ession between Crop Residue Cover percentage and EGO parameters\, achievin
 g a determination coefficient around 0.8 (RPD ˜2.1\; MSE ˜ 0.02) on the 
 test set. \nThe RF model was firstly validated against an independent spec
 tral library of around 100 spectra\, collected during a proximal sensing s
 urvey with a portable full range spectroradiometer\, conducted in a large 
 farm test site (3800ha) located in Jolanda di Savoia (Italy). Also in this
  case\, spectra are annotated with Crop Residue Cover percentages\, and re
 sampled to PRISMA spectral resolution. The model performance on this datas
 et is in agreement with the test on the USGS spectral library. \nFinally\,
  the regression model was applied to a PRISMA image \, acquired on the Jol
 anda di Savoia farm (June 21st 2021)\, for CRC mapping. The resulting map 
 was validated against field observations: the CRC map show values and patt
 erns in good agreement with ground data confirming encouraging prediction 
 capabilities of the model \nIn conclusion\, the proposed classification ap
 proach\, trained on a spectral library is predictive\, as proved on an ind
 ependent spectral data set and on the PRISMA image. Further work will enco
 mpass testing the robustness of the model by collecting field ground data 
 of Crop Residue Cover at the PRISMA scale\; monitoring CRC dynamics on PRI
 SMA time series\; and\, the use of Radiative Transfer Model simulations to
  enlarge the training set\, accounting also for different factors controll
 ing reflectance (e.g. soil moisture).
DTSTAMP:20260505T041137Z
LOCATION:Sala Videoconferenza @ PoliBa
SUMMARY:Spectroscopic Determination of Crop Residue Cover using Exponential
 -Gaussian Optimization of absorption features and Random Forest - Ramin He
 idarian Dehkordi\, Monica Pepe\, katayoun Fakherifard
URL:https://talks.staging.osgeo.org/foss4g-it-2023/talk/8HU3SP/
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