“Does remote-sensing information, such as that from Landsat and similar Earth-observing satellites, provide economic benefits to society, and can this value be estimated? Using satellite data for northeastern Iowa, U.S. Geological Survey scientists modeled the relations among land uses, agricultural production, and dynamic nitrate (NO3-) contamination of aquifers. They demonstrated that information from such modeling can allow more efficient management of agricultural production without sacrificing groundwater quality. Just for northeastern Iowa, the value of such remote-sensing information was shown to be as much as $858 million ± $197 million per year, which corresponds to a current value of $38.1 billion ± $8.8 billion for that flow of benefits into the foreseeable future.” That’s the summary of a recent USGS article by R. Raunikar, W. Forney, and S. Benjamin titled What is the Economic Value of Satellite Imagery?”
The USGS case study in a 35-county region in northeastern Iowa demonstrated the economic value of remote-sensing data by using improved agricultural production and nitrate-leaching estimates made possible by Earth-observing satellites, such as Landsat. Corn is a primary crop of Iowa. Its production often includes heavy application of nitrogenous fertilizer that can affect groundwater quality. In Iowa, groundwater pumped from aquifers, and often stored in water towers, is used as the source for drinking water by as many as 80 percent of residents.
The article ends by stating: “In this analysis, we have demonstrated that moderate resolution land imagery (MRLI) can be used operationally in a regulatory application. Although the example is an abstraction from an actual implementation of an environmental regulation, more constraints can be added to the DSS for regulating the impacts of agriculture on a wider range of ecosystem services. The modeling tools developed here were designed to analyze the ramifications of agricultural production on groundwater vulnerability. The case study demonstrated the VOI of MRLI by providing more accurate agricultural production and nitrate leaching estimates and additional benefits to society by more efficiently allocating production without sacrificing groundwater quality in an agricultural region. Thus, this use of MRLI—in conjunction with other scientific datasets and process models—provides an increase in potential economic value and, hence there is a positive VOI attributed to archived Earth observation data. Our analysis shows that the benefits of the Landsat archive and MRLI could be large over space and time as an operational Earth observation tool for managing land and natural resources.”
