Regenerative Ag Verification 101 Pathway:
What is Remote Sensing in Agriculture?
At CIBO, we are focused on providing actionable information and insights regarding farmland, and we use many different sources of data to do so. Some of this data is available from public sources. Some we derive from our proprietary modeling technology. And other data comes to us from tens of thousands of miles in the air, via satellites soaring around the earth.
Remote sensing is the use of satellite images that take photos of a field over time so that the grower can analyze conditions based on the data and take action that will have a positive influence on crop yield. For instance, sensors can serve as an early warning system allowing a grower to intervene, early on, to counter disease before it has had a chance to spread widely. They can also perform a simple plant count, evaluate plant health, estimate yield, assess crop loss, manage irrigation, detect weeds, identify crop stress and map a field.
Today, remote sensing technologies continue to evolve and have become cheaper for capturing field level data. To understand crop progression throughout the growing season, maps of crop growth, crop diseases, weeds, crop nutrient deficiencies, and other crop and soil conditions are required. As a result, maps fro, remote sensed images showing crop and soil variability have become an integral part of agriculture.
According to Ohio State University, “Remote sensed imagery can be used for mapping soil properties, classification of crop species, detection of crop water stress, monitoring of weeds and crop diseases, and mapping of crop yield. Use of remote sensing is influenced by the type of platforms (satellite, air or ground) used for data collection; number and width of spectral bands captured by the sensor (multi versus hyperspectral); and spatial (high, medium and low), temporal (hourly, daily and weekly) and radiometric (8-, 12- and 16-bit) resolutions at which sensors collect data.”
“While using remote sensed images for agricultural decision-making, several issues must be carefully evaluated, including:
CIBO uses remote sensing to provide analyses of agricultural ecosystem scenarios and draws conclusions about why—down to the specific variable—certain outcomes will occur. CIBO’s scientists can do this even where there is limited or low-quality data. As the quality of remote sensed images improves, crop models will be able to improve the application of agricultural inputs while enhancing crop and farm efficiency. CIBO uses Performance Zone maps to showcase remotely sensed data.
According to CIBO Data Scientist Pankaj Bhambhani, “A Performance Zones map is a way to break up a parcel of farmland into different zones based on historical productivity. There are usually four major zones, namely Best Performance, Average Performance, Low Performance and Varying Performance. Sometimes an additional zone named Insufficient Data indicates that there wasn’t enough historical data available to reliably place the underlying portion of land into one of the four zones.”
“Land operators often use the performance zones map for their land as a validation tool, comparing its conclusions against their personal assessment of the field. But perhaps the biggest benefit of this product is the insight it provides for new, unfamiliar fields. We might estimate the risk associated with farming a new piece of farmland by looking at its performance zones map – in particular the regions of low and varying performance. This risk is quantified by CIBO Stability Score, which tells us what fraction of the field does not have low or varying performance. A higher stability score means that a smaller percentage of the land has low or varying performance, hence the risk of farming that land is lower. Finally, we note that in addition to telling us the risk, these regions of low and varying performance also represent opportunities for improving land productivity, perhaps through a change in management practices.”
Learn more by reading How Remotely Sensed Performance Zone Maps Improve Agriculture Assessments
Lead Computer Vision Scientist at CIBO Ernesto Brau sees an exciting future for remote sensing. According to Dr. Brau, “The vast (and growing) amount of remotely sensed data, in particular satellite imagery, collected over the past few years creates an opportunity for new and widespread insights into agricultural practices and land use. To this end, CIBO is building tools that combine cutting-edge technology from computer vision and artificial intelligence applied to multiple remote sensing applications, such as land cover classification, phenology analysis, irrigation detection, and many others. As a result, CIBO will continue to provide the deepest, most data-rich insight into every parcel of land in the United States.”
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