A global service for the estimation of soil organic carbon with spectra.
Spectroscopy, particularly near-infrared (NIR) and visible-near-infrared (Vis-NIR) spectroscopy(Shen et al., 2022; Shen & Viscarra Rossel, 2021), has become a valuable tool for predicting soil organic carbon (SOC) due to its rapid, non-destructive, and accurate nature. Here are some key insights from recent studies on this application:
This web app integrates data science pipelines for the preprocessing, modelling, and prediction on spectra data to estimate organic carbon content in soil.
I was the lead developer of this platform. I developed a R package for spectroscopic modelling for soil organic carbon, data processing pipelines in Python, and backend APIs with Django and Django REST framework. I also developed early version of the frontend, and contributed to the latest frontend. The app was deployed to AWS.
This project will be publicly released soon.
References
2022
P&RS
Deep transfer learning of global spectra for local soil carbon monitoring
Zefang Shen, Leonardo Ramirez-Lopez, Thorsten Behrens, and 8 more authors
ISPRS Journal of Photogrammetry and Remote Sensing, 2022