About Gro's Crop Masks

Accurately forecasting crop yields has broad implications for economic trading, food-production monitoring, and global food security. But creating predictive yield models for many country-crop pairs when reliable acreage data doesn’t exist presents a unique challenge—figuring out where crops are growing and exactly what the crops are.

To solve this problem, Gro Intelligence has built proprietary, high-resolution crop masks for a number of countries that lack good ground-based data, including Argentina, India, and Ukraine. By compiling satellite images of a given area, it’s possible to determine where a specific crop is growing, and just as importantly, where it is not growing.

To adequately assess the health of a crop using satellite-derived data, it is necessary to exclude signals that stem from extraneous plants like trees and grass and to focus solely on the information gathered from the crop of interest. In areas where reliable acreage data is readily available, this is a relatively painless process. Such is the case in the US where quality products like the USDA Cropland Data Layer (CDL) are robust and easily accessible.

However, identifying and differentiating crop acreage in different parts of the world is more arduous. Gro takes a two-step approach to define cropland boundaries in the absence of reliable data. The first step is identifying the vegetation that follows the typical crop growth cycle of the targeted crop for a given region. For example, if we know that planted wheat begins tillering in a given region over a period of weeks, we can begin to define acreage by looking at greening sections of cropland in satellite images taken for that specific region.

The second step attempts to discriminate between the target crop and any other vegetation that shares a similar life cycle. If ground-truthed data isn’t available to determine what crops are in a specific location, then an analog is used from other areas that do have reliable data. For example, we used CDL data in the US, along with other published journal articles, to isolate specific characteristics of winter wheat using visible, near-infrared, shortwave-infrared, and radar information from satellite imagery.

Crop yield models are valuable because they take a complex set of variables including acreage, weather patterns, and vegetative health, and synthesize them into an actionable piece of information for a variety of actors across the global supply chain. But a critical first step to building yield models is creating a crop mask to figure out what crops are growing where.