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首页> 外文期刊>Geoscience and Remote Sensing Letters, IEEE >Mapping Leaf Area Index With a Smartphone and Gaussian Processes
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Mapping Leaf Area Index With a Smartphone and Gaussian Processes

机译：使用智能手机和高斯过程映射叶面积指数

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摘要

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, which is 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 its solid Bayesian foundations that offer 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 letter compares LAI predictions and confidence intervals of the retrievals obtained with PocketLAI with those obtained with classical instruments, such as digital hemispheric photography (DHP) and LI-COR LAI-2000. This letter shows that all three instruments obtained comparable results, but PocketLAI is far cheaper. The proposed methodology hence opens a wide range of possible applications at moderate cost.
机译：叶面积指数（LAI）是在环境研究中用于确定叶面覆盖和作物生长的关键生物物理参数。如今，智能手机已成为无处不在的传感器设备，具有高计算能力，适中的成本和高质量的传感器。最近介绍了一款名为PocketLAI的智能手机应用程序，并对其进行了测试，以获取地面LAI估算值。在这封信中，我们使用PocketLAI和Landsat 8影像的地面数据，探索了使用最新的非线性高斯过程回归（GPR）来获得水稻的空间明确LAI估计的方法。近年来，由于GPR坚实的贝叶斯基础，不仅提供了很高的准确性，而且提供了检索的置信区间，因此GPR受到了广泛欢迎。我们展示了第一张LAI地图，这些地图是通过智能手机结合先进的机器学习技术从地面数据获得的。这封信将用PocketLAI获得的检索结果与使用经典仪器（如数字半球摄影（DHP）和LI-COR LAI-2000）获得的检索结果的LAI预测和置信区间进行了比较。这封信表明，所有三种仪器均获得了可比的结果，但PocketLAI便宜得多。因此，所提出的方法以适中的成本开辟了广泛的可能应用。

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