  High resolution crop type maps are an important tool for improving food
security, and remote sensing is increasingly used to create such maps in
regions that possess ground truth labels for model training. However, these
labels are absent in many regions, and models trained in other regions on
typical satellite features, such as those from optical sensors, often exhibit
low performance when transferred. Here we explore the use of NASA's Global
Ecosystem Dynamics Investigation (GEDI) spaceborne lidar instrument, combined
with Sentinel-2 optical data, for crop type mapping. Using data from three
major cropped regions (in China, France, and the United States) we first
demonstrate that GEDI energy profiles are capable of reliably distinguishing
maize, a crop typically above 2m in height, from crops like rice and soybean
that are shorter. We further show that these GEDI profiles provide much more
invariant features across geographies compared to spectral and phenological
features detected by passive optical sensors. GEDI is able to distinguish maize
from other crops within each region with accuracies higher than 84%, and able
to transfer across regions with accuracies higher than 82% compared to 64% for
transfer of optical features. Finally, we show that GEDI profiles can be used
to generate training labels for models based on optical imagery from
Sentinel-2, thereby enabling the creation of 10m wall-to-wall maps of tall
versus short crops in label-scarce regions. As maize is the second most widely
grown crop in the world and often the only tall crop grown within a landscape,
we conclude that GEDI offers great promise for improving global crop type maps.
