Soil organic carbon (SOC) might be invisible to the naked eye, but it’s one of the major parameters driving soil health and fertility. Measuring and predicting its levels across landscapes, especially in vulnerable semi-arid regions, is critical for sustainable land management. This blog delves into the arena of SOC mapping, exploring various methods using remote sensing based inputs.
Why is it important to maintain healthy SOC levels?
A balanced SOC level is important for soil health. It’s particularly crucial in arid and semi-arid regions, where its loss can be devastating. Therefore, monitoring SOC near mining activities, which are known to deplete it, is essential for mitigating harmful effects and ensuring long-term land productivity.
What are the challenges observed in measuring SOC?
Traditionally, measuring SOC involved painstakingly collecting soil samples and lab analysis. This has further challenges such as:
- Soil disturbance: Sampling itself can alter the delicate soil structure, impacting measurements.
- Limited data: Sparsely collected data might not capture the full picture of SOC variation.
- Extensive field data collection: Gathering enough samples can be time-consuming and labor-intensive.
Modern approaches for measuring SOC
Technology is revolutionizing the process of measuring SOC. We can now tap into three main approaches:
1. Statistical and Machine Learning (ML) methods: These include algorithms like Multiple Linear Regression (MLR), Classification and Regression Tree (CART), Support Vector Machine (SVM) and Random Forest (RF). They use existing data to statistically estimate SOC levels, accounting for trends and patterns.
2. Geostatistical models: These include tools like Ordinary Kriging which utilize spatial dependence to interpolate SOC values between sampled points, creating detailed maps.
3. Hybrid methods: These methods combine the strengths of ML and geostatistics. Regression Kriging, for instance, can incorporate ML insights into kriging, boosting prediction accuracy.
Where technology is advancing?
We are now not limited to ground-based methods. Technological advancements offer exciting possibilities:
- Remote sensing data: Satellites like Landsat-9 can collect invaluable data on vegetation cover and land use, indirectly revealing SOC levels.
- Topographic data: Digital Elevation Models (DEMs) provide insights into slopes and drainage patterns, further informing SOC estimations.
- Advanced analytics: Combining remote sensing and topographic data with Support Vector Regression, a powerful ML algorithm, allows for accurate prediction of SOC across vast landscapes, applicable to both natural and agricultural ecosystems.
A Case Study: Determining SOC in a Semi-Arid Landscape
Researchers in a semi-arid region collected soil samples (0-10 cm deep) and demarcated the spatial distribution of SOC. They employed two kriging methods – Ordinary and Regression – to predict SOC values at unsampled locations. Regression Kriging was found to be more accurately predicting the SOC. Further, with the help of remote sensing inputs, they identified the following key factors influencing SOC levels:
- Topography: Valleys boast higher SOC than hillsides, revealing the interplay of gravity and erosion.
- Clay content: Clay acts like a sponge, holding onto valuable organic matter.
- Landscape features: Forests and grasslands naturally harbor higher SOC than bare soils.
- Land use: Agricultural practices can either deplete or build SOC, depending on management.
In conclusion, understanding and maintaining SOC is no longer a guessing game. Cutting-edge tools, from sophisticated statistical models to space-age technology, are empowering us to map, predict, and protect this invaluable soil treasure. By embracing these advancements, we can ensure the land’s health and fertility for generations to come. Soil organic carbon is important for driving healthy ecosystems. Let’s map soil organic carbon for a vibrant future!
References
Zhang, T., Li, Y., & Wang, M. (2024). Remote sensing-based prediction of organic carbon in agricultural and natural soils influenced by salt and sand mining using machine learning. Journal of Environmental Management, 352, 120107.
Boubehziz, S., Khanchoul, K., Benslama, M., Benslama, A., Marchetti, A., Francaviglia, R., & Piccini, C. (2020). Predictive mapping of soil organic carbon in Northeast Algeria. Catena, 190, 104539.
Keskin, H., Grunwald, S., & Harris, W. G. (2019). Digital mapping of soil carbon fractions with machine learning. Geoderma, 339, 40-58.
