Prediction of Soil Properties with VIS-NIR-SWIR Reflectance Spectroscopy and Artificial Neural Networks : A Case Study on three Pedoenvironments of the Campania Region, Italy (Contributo in volume (capitolo o saggio))

Type

• Contributo in volume (capitolo o saggio) (Classe)
• Prodotto della ricerca (Classe)

Label

• Prediction of Soil Properties with VIS-NIR-SWIR Reflectance Spectroscopy and Artificial Neural Networks : A Case Study on three Pedoenvironments of the Campania Region, Italy (Contributo in volume (capitolo o saggio)) (literal)

Anno

• 2008-01-01T00:00:00+01:00 (literal)

Alternative label

• Leone A.P.; Calabrò G.; Coppola E.; Maffei C; Menenti M.; Tosca M.; Vella M.; Buondonno A. (2008)
  Prediction of Soil Properties with VIS-NIR-SWIR Reflectance Spectroscopy and Artificial Neural Networks : A Case Study on three Pedoenvironments of the Campania Region, Italy
  in The soils of tomorrow, 2008
  (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori

• Leone A.P.; Calabrò G.; Coppola E.; Maffei C; Menenti M.; Tosca M.; Vella M.; Buondonno A. (literal)

Pagina inizio

• 685 (literal)

Pagina fine

• 698 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#titoloVolume

• The soils of tomorrow (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#volumeInCollana

• 39 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#pagineTotali

• 744 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni

• Coppola E.; Buondonno A. (Unina 2); Calabrò G. (Unisannio) (literal)

Titolo

• Prediction of Soil Properties with VIS-NIR-SWIR Reflectance Spectroscopy and Artificial Neural Networks : A Case Study on three Pedoenvironments of the Campania Region, Italy (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#isbn

• 978-3-923381-56-2 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#curatoriVolume

• Dazzi C.; Costantini E. (literal)

Abstract

• A study was carried out to investigate whether basic soil properties can be predicted by using reflectance spectrometry GS) in the visible-near infrared- shortwave infrared (VIS-NIR-SWIR, 350-2500 nm) region using an artificial neural network (AI.IN) approach. Over 330 soil samples from three agro- forestry areas, representative of the pedo-environmental variability of the Campania region, Southern Italy, were used. The soil properties determined using conventional analyses were sand, silt, clay, organic carbon (OC), and calcium carbonate (CaCO3). Spectral reflectance (SR) measurements on soil samples were carried out under laboratory conditions, using a high resolution ASD FieldSpec spectroradiometer. Relationships between soil properties and soil SR were determined using ANN algorithms. The results obtained showed that clay content and OC can be predicted with high accuracy, while Sand and CaCO3 can be predicted with moderate to relatively high accuracy, respectively, and silt with relatively low accuracy. Improvement in the prediction of soil properties is expected using a larger number of soil samples for the training of the ANN algorithm, in combination with statistically based methods. (literal)

Prodotto di

• Institute for Agricultural and Forest Systems in the Mediterranean (ISAFoM) (Istituto)

Autore CNR

• Massimo Menenti (Persona)
• MAURO VELLA (Persona)
• ANTONIO PASQUALE LEONE (Unità di personale interno)
• MAURIZIO TOSCA (Unità di personale interno)

Insieme di parole chiave

• Keywords of "Prediction of Soil Properties with VIS-NIR-SWIR Reflectance Spectroscopy and Artificial Neural Networks : A Case Study on three Pedoenvironments of the Campania Region, Italy" (Insieme di parole chiave)

Incoming links:

Prodotto

• Institute for Agricultural and Forest Systems in the Mediterranean (ISAFoM) (Istituto)

Autore CNR di

• Massimo Menenti (Persona)
• ANTONIO PASQUALE LEONE (Unità di personale interno)
• MAURO VELLA (Persona)
• MAURIZIO TOSCA (Unità di personale interno)

Insieme di parole chiave di

• Keywords of "Prediction of Soil Properties with VIS-NIR-SWIR Reflectance Spectroscopy and Artificial Neural Networks : A Case Study on three Pedoenvironments of the Campania Region, Italy" (Insieme di parole chiave)