Soil, due to agricultural and mining activities, can pose significant threats from heavy metal contamination, impacting both environmental health and agricultural productivity.
Detecting the severity of soil contamination is crucial. Traditional ground-based methods involve designing regular soil sampling strategies with a measurement depth of 0-5 cm, followed by chemical analysis and geostatistical interpolation of the data to generate spatial distribution maps. However, these methods may be limited by their complete dependence on field-based information.
Hyperspectral remote sensing has emerged as a game-changer in facilitating the estimation of soil contamination. These sensors capture a vast spectrum of light, revealing unique “fingerprints” of heavy metals like copper, arsenic, or cadmium. This data is then used in a process called spatial interpolation kriging to generate detailed maps, providing a comprehensive picture of contamination.
The benefits of using hyperspectral data are clear:
- Greater coverage: Mapping vast areas efficiently and economically.
- Non-intrusive: Protecting soil and minimizing disruption.
- Valuable insights: Guiding informed land management and agricultural optimization.
However, challenges remain in making accurate predictions, particularly with elements like arsenic. Nonetheless, ongoing research is paving the way for more refined data analysis.
Beyond heavy metals, hyperspectral data unlocks insights into other vital soil properties, enabling precision agriculture practices like targeted fertilizer application.
This revolutionary technology is shedding light on the invisible, paving the way for a healthier and more sustainable future for our planet and its resources.
References:
Liu, Z., Lu, Y., Peng, Y., Zhao, L., Wang, G., & Hu, Y. (2019). Estimation of soil heavy metal content using hyperspectral data. Remote Sensing, 11(12), 1464.
Mulder, V. L., De Bruin, S., Schaepman, M. E., & Mayr, T. R. (2011). The use of remote sensing in soil and terrain mapping—A review. Geoderma, 162(1-2), 1-19.
Sun, W., Liu, S., Wang, M., Zhang, X., Shang, K., & Liu, Q. (2023). Soil copper concentration map in mining area generated from AHSI remote sensing imagery. Science of The Total Environment, 860, 160511.
