  Crop type maps are critical for tracking agricultural land use and estimating
crop production. Remote sensing has proven an efficient and reliable tool for
creating these maps in regions with abundant ground labels for model training,
yet these labels remain difficult to obtain in many regions and years. NASA's
Global Ecosystem Dynamics Investigation (GEDI) spaceborne lidar instrument,
originally designed for forest monitoring, has shown promise for distinguishing
tall and short crops. In the current study, we leverage GEDI to develop
wall-to-wall maps of short vs tall crops on a global scale at 10 m resolution
for 2019-2021. Specifically, we show that (1) GEDI returns can reliably be
classified into tall and short crops after removing shots with extreme view
angles or topographic slope, (2) the frequency of tall crops over time can be
used to identify months when tall crops are at their peak height, and (3) GEDI
shots in these months can then be used to train random forest models that use
Sentinel-2 time series to accurately predict short vs. tall crops. Independent
reference data from around the world are then used to evaluate these GEDI-S2
maps. We find that GEDI-S2 performed nearly as well as models trained on
thousands of local reference training points, with accuracies of at least 87%
and often above 90% throughout the Americas, Europe, and East Asia. Systematic
underestimation of tall crop area was observed in regions where crops
frequently exhibit low biomass, namely Africa and South Asia, and further work
is needed in these systems. Although the GEDI-S2 approach only differentiates
tall from short crops, in many landscapes this distinction goes a long way
toward mapping the main individual crop types. The combination of GEDI and
Sentinel-2 thus presents a very promising path towards global crop mapping with
minimal reliance on ground data.
