Abstract
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 on optical satellite features often exhibit low performance when transferred across geographies. 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 can reliably distinguish maize, a crop typically above 2 m 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 10 m 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.
Highlights
Crop type maps are a crucial step toward estimating crop area, mapping yield, studying local nutritional outcomes, and developing hydrological models (Boryan et al 2011, Jin et al 2019)
The results show that Global Ecosystem Dynamics Investigation (GEDI) features can distinguish a tall crop like maize from shorter crops, and that these features are highly transferable across geography
Our experiments transferring GEDI features and using GEDI to train wall-towall crop type maps in China, France, and the U.S show the robustness of lidar features across continents, despite the GEDI instrument being designed to monitor forests rather than cropland
Summary
Crop type maps are a crucial step toward estimating crop area, mapping yield, studying local nutritional outcomes, and developing hydrological models (Boryan et al 2011, Jin et al 2019). Recent years have seen significant progress in remote sensing-based crop type mapping, in high-income countries, with maps produced in the US (USDA-NASS 2020), Canada (Agriculture and Agri-Food Canada 2021), much of Europe (Defourny et al 2019, Belgiu & Csillik 2018), and parts of Asia (You et al 2021). While often high in accuracy, the models that produce these maps remain local, in the sense that model application is confined to the region where ground labels exist for
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