EOS-4 dual-polarised and Hybrid polarimetric data utilization for field crops and plantation monitoring

In the humid, cloud-choked monsoons of India, watching a crop grow from space has long been a game of chance. Optical satellites, which rely on visible light to take pictures, are often blinded by thick fog, haze, or the relentless rain that defines the growing season. When the sky is gray, the view from above disappears, leaving farmers and scientists in the dark about the health of their fields. To solve this, researchers turned to a different kind of eye: radar. Unlike a camera that needs light, radar sends out invisible microwave pulses that bounce off the ground and return to the satellite, seeing right through clouds and darkness. This technology is particularly sensitive to the physical structure of plants and the water they hold, making it a reliable tool for tracking agriculture even when the weather is at its worst. For years, scientists have used these radar signals to map rice and other crops, but a new satellite has recently offered a sharper, more versatile way to look at the earth, promising to reveal details about plant growth that were previously hidden.

A team of researchers from India's remote sensing and space science institutes recently put this new capability to the test with the launch of EOS-4, a satellite designed to carry advanced radar instruments. Their goal was to see if this new data could do more than just identify what crops were growing; they wanted to know if it could track the daily life of a field, from the moment seeds are sown to the final harvest, and even monitor the complex, multi-year cycles of tea plantations. The study focused on two very different landscapes: the flat, semi-arid plains of Agra, where farmers grow rice and pearl millet, and the misty, hilly tea gardens of Palampur. By combining wide-area scans that cover entire regions with high-resolution snapshots of specific fields, the team mapped how the radar signals changed as the crops grew, matured, and were harvested.

In the fields of Agra, the researchers watched the rice and pearl millet through the monsoon season. Rice, which is often planted in flooded paddies, presented a unique signature. When the fields were first flooded and the young plants were just being transplanted, the radar signal was very weak because the smooth water surface acted like a mirror, reflecting the signal away from the satellite. As the rice grew taller and the leaves began to fill the air, the signal grew stronger, bouncing back more vigorously. The team found that by looking at these changes over time, they could distinguish between rice that was planted early, on time, or late, creating a clear picture of the harvest schedule across the district. Pearl millet, a hardy grain that thrives in drier conditions, showed a different pattern. As the plants grew from bare soil into dense, tall stalks, the radar signal increased steadily, peaking when the crop was at its fullest. The researchers used a specific index, a calculation based on how the radar bounced off the plants, to track this growth. They found that this index rose as the crop became lush and then dropped again as the plants dried out and were harvested, leaving behind only the stubble or bundled stalks. This allowed them to pinpoint exactly when the crops were ready to be cut and when the fields were empty.

To understand the plants in greater detail, the team also used a high-resolution mode of the satellite that could see individual fields with incredible clarity. They collected physical samples from the ground, measuring the weight of the plants and their height, and compared these numbers to the radar data. They discovered that the new radar signals were highly sensitive to the amount of living plant material, known as biomass, and the leaf area, even when the crops were very dense. By breaking down the radar signal into different types of bounces, they could tell the difference between the smooth surface of a harvested field, the double bounce that happens when a signal hits the ground and then a vertical stem, and the volume bounce that occurs when a signal scatters through a thick canopy of leaves. This level of detail meant they could not only see that a crop was there, but also estimate how much of it there was and how healthy it was, all without stepping foot in the field.

The study also turned its attention to the tea gardens of Palampur, a crop that behaves very differently from annual grains. Tea is a perennial plant, meaning it grows year after year, and its value depends on the timing of picking the youngest leaves and buds. The researchers found that the radar could detect the specific moments when the bushes were pruned and when the leaves were harvested. When the tea bushes were pruned, the radar saw a change in the structure of the plants, and as new leaves grew back, the signal shifted again. They observed that the presence of shade trees in the gardens created a consistent signal, but the tea bushes themselves showed distinct changes depending on whether they were being harvested or were in a dormant winter phase. The data revealed that timely picking and pruning created specific patterns in the radar return, allowing for the monitoring of these economic stages even during the heavy rains of the monsoon, a time when traditional observation is nearly impossible.

The findings suggest that this new satellite system is a powerful tool for managing agriculture in regions where weather often obscures the view. It proved capable of distinguishing between different types of crops, tracking their growth stages with precision, and estimating their physical condition. The ability to see through clouds and fog means that farmers and planners can make decisions based on real-time data rather than guesswork. While the study focused on rice, millet, and tea, the methods developed here point toward a future where radar can monitor a wide variety of crops and plantations, providing a continuous, reliable eye on the food supply regardless of the weather above.