Enhanced Prediction of Soil Carbon via Encoder-Decoder Neural Networks for a Boreal Study Area in Northern Ontario

Enhanced Prediction of Soil Carbon via Encoder-Decoder Neural Networks for a Boreal Study Area in Northern Ontario

Addressing the impacts of carbon in connection with land cover conversion and climate change is of predominant interest for boreal realms. Consequently, boosting accuracy for the prediction of total carbon (C) with soil mapping is a crucial objective, particularly for a boreal study area under risk...

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Bibliographic Details
Main Authors: Rory Pittman, Baoxin Hu
Format: Article
Language:English
Published: MDPI AG 2025-04-01
Series:Sensors
Subjects:
soil carbon
digital soil mapping
encoder-decoder
convolutional neural network
quantile mapping
uncertainty maps
Online Access:https://www.mdpi.com/1424-8220/25/8/2583
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