论文标题
用智能手机和高斯流程制图叶子区域指数
Mapping Leaf Area Index with a Smartphone and Gaussian Processes
论文作者
论文摘要
叶面积指数（LAI）是用于确定环境研究中叶子覆盖和作物生长的关键生物物理参数。如今，智能手机是无处不在的传感器设备，具有较高的计算能力，中等成本和高质量的传感器。最近介绍并测试了一个名为Pocketlai的智能手机应用程序，以获取地面LAI估算值。在这封信中，我们探讨了使用最先进的非线性高斯工艺回归（GPR）使用Pocketlai和Landsat 8 Imagery的地面数据来得出空间上明确的LAI估计。近年来，GPR由于其坚实的贝叶斯基金会不仅提供了很高的准确性，而且还提供了检索的置信区间，因此GPR越来越受欢迎。我们显示了第一个带有从智能手机和高级机器学习的地面数据获得的LAI地图。这项工作比较了用PocketLai获得的检索的LAI预测和置信区间与使用经典仪器（例如数字半球摄影（DHP）和LI-COR LAI-200000）获得的检索。这封信表明所有三种乐器都得到了可比的结果，但是Pocketlai便宜得多。因此，提出的方法学以中等成本打开了广泛的可能应用。
Leaf area index (LAI) is a key biophysical parameter used to determine foliage cover and crop growth in environmental studies. Smartphones are nowadays ubiquitous sensor devices with high computational power, moderate cost, and high-quality sensors. A smartphone app, called PocketLAI, was recently presented and tested for acquiring ground LAI estimates. In this letter, we explore the use of state-of-the-art nonlinear Gaussian process regression (GPR) to derive spatially explicit LAI estimates over rice using ground data from PocketLAI and Landsat 8 imagery. GPR has gained popularity in recent years because of their solid Bayesian foundations that offers not only high accuracy but also confidence intervals for the retrievals. We show the first LAI maps obtained with ground data from a smartphone combined with advanced machine learning. This work compares LAI predictions and confidence intervals of the retrievals obtained with PocketLAI to those obtained with classical instruments, such as digital hemispheric photography (DHP) and LI-COR LAI-2000. This letter shows that all three instruments got comparable result but the PocketLAI is far cheaper. The proposed methodology hence opens a wide range of possible applications at moderate cost.
