
In recent years, the use of remote sensing technology for agricultural monitoring has gained prominence. But when it comes to mapping and estimating carbon stocks in plantations like mango and rubber, remote sensing becomes even more crucial. Why? Because these crops, often overlooked, significantly contribute to terrestrial carbon pools, yet have not received the attention they deserve. In this article, we’ll break down how remote sensing, combined with machine learning, is being used to estimate carbon stocks in mango and rubber plantations in India. Let’s dive in!
Table of Contents-
Why Should You Care About Mango and Rubber Plantations?
Both mango and rubber are major commercial crops in India. Mango covers over 15.75 million hectares across the country, while rubber is rapidly expanding in states like Tripura. These plantations do more than just produce fruit and latex; they are critical carbon sinks. Understanding their carbon storage potential helps in shaping environmental policies and combating climate change.
Remote Sensing: The Eye in the Sky
Remote sensing allows us to monitor large areas of land from satellites. By analyzing different wavelengths of light reflected from the Earth’s surface, it can help identify tree species, measure biomass, and even assess the carbon content of soil. But it’s not just about pretty satellite pictures. Advanced sensors like Sentinel-2 and GEDI Lidar (used in this study) allow for precise measurements of tree height, canopy cover, and land use. These measurements, when combined with machine learning algorithms, help estimate carbon stocks in large plantations, such as those in Malihabad, Uttar Pradesh (for mango) and West Tripura, Tripura (for rubber).
How is Remote Sensing Used for Mango and Rubber?
1. Mapping Plantations
- Sentinel-2 satellite data at a 10-meter resolution was used to map out the mango and rubber plantations.
- The Random Forest algorithm (a machine learning technique) was employed to classify the land into mango orchards, rubber plantations, and other land uses.
- In Malihabad and West Tripura, high-resolution images were used to count individual trees in sample plots. This provided valuable data on tree density, which is crucial for accurate biomass and carbon stock estimation.
2. Measuring Tree Height and Biomass
- The GEDI Lidar sensor helped estimate tree heights by classifying trees into five height categories. Trees taller than 2 meters were classified and included in the analysis.
- Biomass, or the weight of the trees, was estimated using multi-sensor data. By combining this data with tree density measurements, researchers were able to assess the amount of carbon stored in the trees.
3. Estimating Soil Carbon
- Remote sensing isn’t just about trees! Soil Organic Carbon (SOC), which plays a vital role in carbon storage, was estimated using data from multiple sources, including the Soil Health Card system.
- Using vegetation maps, the research team extracted SOC data for different plantation types (mango, rubber, etc.), adding another layer to the carbon stock estimates.
The Role of Machine Learning
You may wonder, “How do they process all this data?” This is where machine learning, particularly the Random Forest algorithm, steps in. This technique helps to classify land based on satellite data and makes accurate predictions about what type of vegetation is present and how much carbon it holds.
Key Techniques:
- Random Forest: A machine learning method used for identifying and classifying mango and rubber plantations.
- GEDI Lidar: A satellite-based system that measures the height of tree canopies to estimate biomass.
- Multi-sensor Fusion: Combining data from multiple satellites and sensors to improve accuracy.
Actionable Tips: What Can You Do?
- For Farmers and Agricultural Planners: Remote sensing can help you manage your plantations more efficiently. From monitoring growth to predicting yields, this technology gives you the bigger picture.
- For Environmentalists: Use data on carbon stocks to advocate for policies that promote sustainable agriculture and carbon sequestration.
- For Tech Enthusiasts: Machine learning and remote sensing are transforming agriculture. If you’re into coding or data science, this is a growing field where your skills could make a real impact.

Conclusion: Key Takeaways for Quick Understanding
- Remote sensing is revolutionizing how we map and monitor carbon stocks in plantations like mango and rubber.
- Techniques like Sentinel-2, GEDI Lidar, and Random Forest machine learning provide accurate insights into tree height, density, and biomass.
- Mango and rubber plantations, often overlooked, are significant carbon sinks that play a vital role in climate mitigation.
- Remote sensing and machine learning offer a scalable, cost-effective way to monitor these plantations, ensuring that they are included in carbon management strategies.
Instagram Reels and Canva Infographics Summary:
- Introduction: Mango and rubber plantations are critical carbon sinks, and remote sensing helps map their carbon storage potential.
- Remote Sensing: Sentinel-2 satellite, GEDI Lidar, and machine learning (Random Forest) are key tools.
- Tree Height & Biomass: GEDI Lidar helps measure tree height and estimate carbon stored in trees.
- Soil Organic Carbon (SOC): SOC is crucial for carbon storage, and it can be estimated using satellite data and soil maps.
- Machine Learning: The Random Forest algorithm classifies plantations and estimates carbon stock.
- Conclusion: Remote sensing and machine learning are crucial for sustainable agriculture and environmental policy.
This summary is ideal for visually engaging reels and infographics that highlight the importance of using remote sensing in agriculture!
The research presents a detailed analysis of soil organic carbon (SOC) estimation, tree biomass, and land use mapping in mango and rubber plantations, integrating remote sensing and machine learning techniques. The methodology utilized Soil Health Card (SHC) data, Random Forest regression models, and vegetation indices such as NDVI from remote sensing data to estimate SOC density and biomass in two distinct areas: Malihabad and West Tripura. The study’s findings highlight the effectiveness of combining high-resolution satellite imagery with geospatial data to map plantation characteristics and estimate carbon storage in different land use systems.
Key points from the methodology and results:
- Data Acquisition and Quality Control: SHC data for the study areas included organic carbon (OC%) among other parameters. After data quality checks, 3069 and 9553 observations from Malihabad and West Tripura, respectively, were retained for analysis.
- SOC Estimation: SOC was predicted using NDVI data averaged over six years and modeled using the Random Forest regression algorithm. SOC concentration was converted to SOC density (Mg/ha) using Pedotransfer Functions (PTFs) and bulk density estimates.
- Biomass and Carbon Stock: Above-ground biomass (AGB) for mango orchards in Malihabad was estimated at 16,104.6 tonnes, while rubber plantations in West Tripura had 13,409.6 tonnes. Malihabad showed higher AGB (156 Mg/ha) compared to West Tripura (104 Mg/ha), indicating variability in carbon sequestration.
- Tree Density and Height: Tree densities for rubber and mango plantations were calculated, with 500 trees/ha for rubber and 71.1 trees/ha for mango. Tree heights, derived from GEDI Lidar data, ranged from 1 to 26 meters across both study sites, helping to discriminate between different vegetation types.
- SOC Spatial Distribution: SOC was higher under tea and rubber plantations in West Tripura, likely due to climatic factors such as higher rainfall and lower temperatures, which influence plant productivity and organic matter decomposition.

The study successfully demonstrates the integration of SHC data with remote sensing technologies for accurate estimation of soil carbon stocks and biomass in tropical plantation systems, offering significant insights for carbon cycle modeling and environmental management.
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