Figure: NIR sensor on a plant.

Have you ever wondered why most leaves appear green? The answer lies in chlorophyll, the main pigment used by plants for photosynthesis. Chlorophyll absorbs most of the blue and red wavelengths of sunlight to produce energy, but it reflects green light. That reflected green light is what our eyes see, making healthy leaves look green. However, leaves interact with much more than just the visible part of the light spectrum.

From a remote sensing perspective, leaves are even more interesting. Satellites and drones use sensors that can detect light beyond what humans can see, especially in the near-infrared (NIR) region. Healthy leaves strongly reflect NIR light because of their internal cell structure, while stressed or unhealthy leaves reflect less. By comparing the amount of red light absorbed with the amount of NIR light reflected, scientists can measure plant health using vegetation indices such as the Normalized Difference Vegetation Index (NDVI).

This is why remote sensing has become an important tool in agriculture, forestry, and environmental monitoring. Instead of checking plants one by one, researchers can quickly assess large areas from the air or space. Changes in leaf color and reflectance can reveal early signs of drought, disease, nutrient deficiency, or environmental stress before they are visible to the human eye. In simple terms, while our eyes only see green leaves, remote sensing allows us to "see" much more, providing valuable information about the health and condition of vegetation.

References:

Huang, S., Tang, L., Hupy, J. P., Wang, Y., & Shao, G. (2020). A commentary review on the use of normalized difference vegetation index (NDVI) in the era of popular remote sensing. Journal of Forestry Research, 32(1), 1–6. https://doi.org/10.1007/s11676-020-01155-1

Li, X., Zhang, Y., Wang, H., & Chen, J. (2025). A comprehensive review of crop chlorophyll mapping using remote sensing technologies. Remote Sensing, 17(8), 1456. https://doi.org/10.3390/rs17081456

Haque, M. A., Kim, J., & Lee, D. (2024). Effects of environmental conditions on vegetation indices derived from multispectral remote sensing: A review. Korean Journal of Remote Sensing, 40(5), 1073–1098.

Date of Input: 10/08/2026 | Updated: 10/08/2026 | hasniah