Improvement of mangrove soil carbon stocks estimation in north Vietnam using Sentinel-2 data and machine learning approach

Tien Dat Pham*, Naoto Yokoya, Thi Thu Trang Nguyen, Nga Nhu Le, Nam Thang Ha, Junshi Xia, Wataru Takeuchi, Tien Duc Pham*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

44 Citations (Scopus)


Quantifying total carbon (TC) stocks in soil across various mangrove ecosystems is key to understanding the global carbon cycle to reduce greenhouse gas emissions. Estimating mangrove TC at a large scale remains challenging due to the difficulty and high cost of soil carbon measurements when the number of samples is high. In the present study, we investigated the capability of Sentinel-2 multispectral data together with a state-of-the-art machine learning (ML) technique, which is a combination of CatBoost regression (CBR) and a genetic algorithm (GA) for feature selection and optimization (the CBR-GA model) to estimate the mangrove soil C stocks across the mangrove ecosystems in North Vietnam. We used the field survey data collected from 177 soil cores. We compared the performance of the proposed model with those of the four ML algorithms, i.e., the extreme gradient boosting regression (XGBR), the light gradient boosting machine regression (LGBMR), the support vector regression (SVR), and the random forest regression (RFR) models. Our proposed model estimated the TC level in the soil as 35.06–166.83 Mg ha−1 (average = 92.27 Mg ha−1) with satisfactory accuracy (R 2 = 0.665, RMSE = 18.41 Mg ha−1) and yielded the best prediction performance among all the ML techniques. We conclude that the Sentinel-2 data combined with the CBR-GA model can improve estimates of the mangrove TC at 10 m spatial resolution in tropical areas. The effectiveness of the proposed approach should be further evaluated for different mangrove soils of the other mangrove ecosystems in tropical and semi-tropical regions.

Original languageEnglish
Pages (from-to)68-87
Number of pages20
JournalGIScience and Remote Sensing
Issue number1
Publication statusPublished - 2021
Externally publishedYes


  • Soil carbon stocks
  • CatBoost
  • sentinel-2 MSI
  • machine learning
  • mangrove ecosystem
  • Vietnam


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