A Framework for High-Resolution Mapping of Soil Organic Matter (SOM) by the Integration of Fourier Mid-Infrared Attenuation Total Reflectance Spectroscopy (FTIR-ATR), Sentinel-2 Images, and DEM Derivatives

• Published in: Remote sensing (Basel, Switzerland) Vol. 15; no. 4; p. 1072
• Main Authors: Xu, Xuebin, Du, Changwen, Ma, Fei, Qiu, Zhengchao, Zhou, Jianmin
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.02.2023
• Subjects: Accuracy; Agricultural production; Analytical chemistry; Artificial neural networks; Attenuation; Calibration; Carbon; Carbon cycle; Carbon sequestration; Chemical analysis; China; climate; Climate change; data fusion; digital soil mapping; Fourier transforms; global carbon budget; Global climate; High resolution; Image enhancement; Infrared spectroscopy; kriging; Machine learning; Mapping; Neural networks; Organic matter; Performance evaluation; Performance prediction; prediction; proximal sensing; Reflectance; reflectance spectroscopy; regression analysis; Remote sensing; satellite sensing; Satellites; soil; Soil organic matter; Spatial discrimination; Spatial resolution; Spectroscopy; Spectrum analysis; Support vector machines; Terrestrial environments; Topography; China
• DOI: 10.3390/rs15041072
• ISSN: 2072-4292

Soil organic matter (SOM), as the greatest carbon storage in the terrestrial environment, is inextricably related to the global carbon cycle and global climate change. Accurate estimation and mapping of SOM content are crucial for guiding agricultural output and management, as well as controlling the climate issue. Traditional chemical analysis is unable to satisfy the dynamic estimation of SOM due to its low timeliness. Remote and proximal sensing have significant advantages in terms of ease of use, estimation accuracy, and geographical resolution. In this study, we developed a framework based on machine learning to estimate SOM with high accuracy and resolution using Fourier mid-infrared attenuation total reflectance spectroscopy (FTIR-ATR), Sentinel-2 images, and DEM derivatives. This framework’s performance was evaluated on a regional scale using 245 soil samples from northeast China. Results indicated that the calibration size could be shrunk to 50% while achieving a fair prediction performance for SOM content. The Lasso, partial least squares (PLS), support vector regression (SVR), and convolutional neural networks (CNN) performed well in predicting SOM from FTIR-ATR spectra, and the performance was enhanced further by using Sentinel-2 images and DEM derivates. The PLS, SVR, and CNN models created SOM maps with higher spatial resolution and variation than the Kriging approach. The PLS and SVR models provided enough variety and were more realistic in the local SOM map, making them usable at the field scale, and the suggested framework took a fresh look at high-resolution SOM mapping.