Estimation of Chlorophyll-a From Oceanographic Properties - An Indirect Approach

Tiwari, Surya Prakash; Adhikary, Subhrangshu; Banerjee, Saikat

doi:10.1109/igarss46834.2022.9883294

Cited by 3 publications

(1 citation statement)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“… Adhikary et al (2021) also applied various machine learning regression algorithms (random forest, extra trees, bagged, and gradient boosted regressors) to forecast phytoplankton levels globally, which found that the extra tree regression model performed the best, with an R² of 0.96. Machine learning and deep learning techniques were used to study the relationship between marine chlorophyll and physicochemical features, and found that random forests performed the best among all features, with a classification accuracy of 93.92% ( Tiwari, Adhikary & Banerjee, 2022 ).…”

Section: Introductionmentioning

confidence: 99%

Soil organic carbon estimation using remote sensing data-driven machine learning

Chen,

Wang,

Zhu

2024

PeerJ

View full text Add to dashboard Cite

Soil organic carbon (SOC) is a crucial component of the global carbon cycle, playing a significant role in ecosystem health and carbon balance. In this study, we focused on assessing the surface SOC content in Shandong Province based on land use types, and explored its spatial distribution pattern and influencing factors. Machine learning methods including random forest (RF), extreme gradient boosting (XGBoost), and support vector machine (SVM) were employed to estimate the surface SOC content in Shandong Province using diverse data sources like sample data, remote sensing data, socio-economic data, soil texture data, topographic data, and meteorological data. The results revealed that the SOC content in Shandong Province was 8.78 g/kg, exhibiting significant variation across different regions. Comparing the model error and correlation coefficient, the XGBoost model showed the highest prediction accuracy, with a coefficient of determination (R²) of 0.7548, root mean square error (RMSE) of 7.6792, and relative percentage difference (RPD) of 1.1311. Elevation and Clay exhibited the highest explanatory power in clarifying the surface SOC content in Shandong Province, contributing 21.74% and 13.47%, respectively. The spatial distribution analysis revealed that SOC content was higher in forest-covered mountainous regions compared to cropland-covered plains and coastal areas. In conclusion, these findings offer valuable scientific insights for land use planning and SOC conservation.

show abstract

Section: Introductionmentioning

confidence: 99%