Mapping Soil Organic Carbon Dynamics in Taiwan’s Agricultural Land Using Field and Remote Sensing Data.

Soil organic carbon (SOC) stocks represent the second-largest natural carbon reservoir globally, surpassed only by the oceans. SOC plays a vital role in maintaining ecosystem health, offering numerous benefits such as enhancing soil structure, increasing nutrient availability, and boosting water retention capacity. Beyond its ecological significance, SOC is integral to climate change mitigation, given its ability to sequester atmospheric carbon dioxide effectively. Additionally, SOC contributes to improving the physical, chemical, and biological properties of soil, making it indispensable for sustainable land management. Taiwan, an island in the western Pacific Ocean, spans an area of approximately 35,800 square kilometers. Shaped like a tobacco leaf, the island extends 400 kilometers in length and 150 kilometers at its widest point. Taiwan’s landscape is characterized by a Central Mountain Range running north to south, steep slopes, and geologically fragile formations. In recent decades, Taiwan has experienced significant changes in land use and land cover, particularly in urban areas where cropland and forest land on city outskirts have been replaced by infrastructure development. These transformations have directly impacted SOC levels across the island, underscoring the need for accurate mapping to estimate SOC stocks and assess soil functionality, particularly in agricultural regions. Traditional ground sampling methods for estimating SOC, though precise, are often costly and labor-intensive. To address these limitations, alternative approaches, such as remote sensing, offer cost-effective solutions. Among various predictive modeling techniques, machine learning algorithms like Random Forest (RF) have emerged as highly effective tools for SOC estimation. RF models excel due to their ability to minimize correlation among individual decision trees and provide reliable error estimates, ensuring robust predictions.

In this study, we combined field sampling data (2010–2021) with remote sensing, topographic, and climatic datasets to estimate SOC stocks in the topsoil layer (0–30 cm) of Taiwan’s agricultural areas. Using the RF algorithm, we initially employed 23 explanatory variables and subsequently refined the model by eliminating less significant predictors, reducing the final set to 12 variables. The refined model demonstrated strong predictive accuracy, with R² values exceeding 0.70 for agriculture land in Taiwan. Our findings revealed spatial variations in SOC levels, with mountainous regions exhibiting higher SOC stocks compared to suburban and low-lying agricultural areas, where values were notably lower. SOC levels for agricultural lands ranged from a maximum of 7.14 kg/m² to a minimum of 2.55 kg/m², with an average value of 3.43 kg/m². Agricultural practices incorporating agroforestry techniques showed relatively higher SOC stocks, emphasizing the role of sustainable practices in enhancing soil carbon storage. The results of this study hold significant implications for long-term monitoring of SOC in Taiwan and provide a crucial reference for policymakers aiming to develop effective carbon sequestration strategies. By integrating field data with advanced modeling and remote sensing technologies, this research contributes to a deeper understanding of SOC dynamics and supports efforts to promote sustainable land management and climate resilience.