NASA’s new technology is a breakthrough for farmers and agriculturalists throughout the world.

The Soil Moisture Active Passive (SMAP) satellite, launched in 2015, produces high-definition imagery of soil moisture levels around the world. This satellite measures water in the top two inches of topsoil anywhere on Earth. SMAP also detects whether ground is frozen in Earth’s cooler areas.

With updates every three days, the information SMAP collects is timely and impressive. The Crop Explorer tool on the USDA Foreign Agricultural Service website and Google Earth report data from SMAP.

The potential for SMAP data to help in agriculture is endless. SMAP data

  • Improve yield forecasts.
  • Help farmers make better irrigation plans.
  • Predict floods and drought.
  • Provide insight on crop health.
  • Report climate and temperature measurements.

SMAP data also help agricultural analysts predict markets. Combine SMAP data with soil sample data and drone images, and farmers can make more sustainable and informed decisions than ever before.

More Than Photography

Other satellite imagery technologies collect similar data, but SMAP changes the game. SMAP data is global and free, available to anyone, anywhere, with access to internet. This technology could mean big things for small farmers in countries with food insecurity issues.

The difference between satellite imagery and aerial photography is often overlooked. Satellites are large-scale and scientific, while aerial photography is small-scale and commercial. Satellite imagery has traditionally been used by governments, big corporations and educational facilities for research and military monitoring. But with SMAP, satellite data is easily accessible to farmers. It’s so much more than just a picture. SMAP captures the entire essence of the area, which means more information at farmers’ fingertips.

Drones and other forms of aerial data collection have their place in agriculture, but within this troposphere. Aerial imagery is typically expensive when farmers hire companies to collect the images. Recently, drones are becoming more affordable and more suited for personal or small business use.

However, with the increasing popularity of precision farming, satellites are moving into the agricultural sector to gather big data across big areas.

“Companies are working hard on downscaling techniques to produce moisture data at finer resolutions to show the farm/field variability,” says Viviane Faria, remote sensing and geographic information systems specialist at Trimble Agriculture. In other words, companies are trying to bring satellite-quality data back down to earth for a more close-up view of farms.

Putting the Data to Use

Satellite imagery helps farmers make data-driven decisions, leading to efficient management practices. Consumers are concerned now more than ever about their food and the environment, so a main goal of modern agriculture is sustainability, and satellite imagery helps farmers gather data to manage their farms in a sustainable way. Minimizing inputs and maximizing outputs is ideal. Knowing the problem areas of a field from satellite images and managing them properly makes this technology useful. For example, over-irrigating is a common efficiency issue on farms. But with SMAP soil moisture data, farmers know how much water is needed in specific areas of a field and can irrigate precisely.

Dr. Bruce Erickson, agronomy education distance and outreach director at Purdue University, lists specific pest identification using satellite imagery as an exciting goal for the future. “Incremental advances in this technology are more likely,” says Erickson. It’s understandable that things take time, and there is a demand for cutting-edge technology in agriculture, now that farmers have had a taste.

SMAP is just four years old and is already changing the way we see the world, literally. Farmers should be the next group of adapters who takes this technology and run with it. The insights and data SMAP can provide to farmers will be invaluable to operations around the world.