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Estimating Sugarcane Aboveground Biomass and Carbon Stock Using the Combined Time Series of Sentinel Data with Machine Learning Algorithms
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Document Title
Estimating Sugarcane Aboveground Biomass and Carbon Stock Using the Combined Time Series of Sentinel Data with Machine Learning Algorithms
Author
Suwanlee S.R. Pinasu D. Som-ard J. Borgogno-Mondino E. Sarvia F.
Affiliations
Department of Geography Faculty of Humanities and Social Sciences Mahasarakham University Maha Sarakham 44150 Thailand; Technology and Informatics Institute for Sustainability National Metal and Materials Technology Center National Science and Technology Development Agency Thailand Science Park Pathum Thani 12120 Thailand; Department of Agricultural Forest and Food Sciences University of Turin Torino 10095 Italy
Type
Article
Source Title
Remote Sensing
ISSN
20724292
Year
2024
Volume
16
Issue
5
Open Access
All Open Access Gold
Publisher
Multidisciplinary Digital Publishing Institute (MDPI)
DOI
10.3390/rs16050750
Abstract
Accurately mapping crop aboveground biomass (AGB) in a timely manner is crucial for promoting sustainable agricultural practices and effective climate change mitigation actions. To address this challenge the integration of satellite-based Earth Observation (EO) data with advanced machine learning algorithms offers promising prospects to monitor land and crop phenology over time. However achieving accurate AGB maps in small crop fields and complex landscapes is still an ongoing challenge. In this study the AGB was estimated for small sugarcane fields (<1 ha) located in the Kumphawapi district of Udon Thani province Thailand. Specifically in order to explore estimate and map sugarcane AGB and carbon stock for the 2018 and 2021 years ground measurements and time series of Sentinel-1 (S1) and Sentinel-2 (S2) data were used and random forest regression (RFR) and support vector regression (SVR) applied. Subsequently optimized predictive models used to generate large-scale maps were adapted. The RFR models demonstrated high efficiency and consistency when compared to the SVR models for the two years considered. Specifically the resulting AGB maps displayed noteworthy accuracy with the coefficient of determination (R2) as 0.85 and 0.86 with a root mean square error (RMSE) of 8.84 and 9.61 t/ha for the years 2018 and 2021 respectively. In addition mapping sugarcane AGB and carbon stock across a large scale showed high spatial variability within fields for both base years. These results exhibited a high potential for effectively depicting the spatial distribution of AGB densities. Finally it was shown how these highly accurate maps can support as valuable tools sustainable agricultural practices government policy and decision-making processes. ? 2024 by the authors.
Industrial Classification
Knowledge Taxonomy Level 1
Knowledge Taxonomy Level 2
Knowledge Taxonomy Level 3
License
CC BY
Rights
Authors
Publication Source
Scopus