论文标题
使用GEDI和Sentinel-2，全球尺度上高大的作物的年度田间尺度地图
Annual field-scale maps of tall and short crops at the global scale using GEDI and Sentinel-2
论文作者
论文摘要
作物类型地图对于跟踪农业土地的使用和估计作物生产至关重要。事实证明，遥感已成为一种有效且可靠的工具，用于在具有丰富地面标签的地区创建这些地图以用于模型培训，但是在许多地区和年份中，这些标签仍然很难获得。 NASA的全球生态系统动力学调查（GEDI）Spaceborne LiDAR仪器最初是为森林监测而设计的，已显示出有望区分高作物和短作物的希望。在当前的研究中，我们利用GEDI在2019 - 2021年以10 m的分辨率下在全球范围内开发短与高庄稼的壁墙地图。 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.然后，全世界的独立参考数据用于评估这些GEDI-S2地图。我们发现，GEDI-S2的表现几乎和对数千个本地参考培训点训练的模型一样，精度至少为87％，在整个美洲，欧洲和东亚通常超过90％。在农作物经常表现出较低的生物量（即非洲和南亚）的区域中观察到了高大的农作物区域的系统低估，并且在这些系统中需要进一步的工作。尽管GEDI-S2方法仅与短作物区分开来，但在许多景观中，这种区别在绘制主要的单个农作物类型方面有很长的路要走。因此，GEDI和Sentinel-2的组合为全球作物映射提供了非常有前途的途径，对地面数据的依赖最少。
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.
