Mapping Leaf Area Index with a Smartphone and Gaussian Processes

TL;DR

This paper demonstrates how combining smartphone-based LAI measurements with Gaussian process regression can produce accurate, spatially explicit LAI maps at a low cost, enabling broader environmental monitoring applications.

Contribution

It introduces a novel approach using smartphone data and Gaussian processes to generate LAI maps, comparing its accuracy to traditional instruments.

Findings

PocketLAI provides comparable LAI estimates to traditional tools.

Gaussian process regression yields reliable confidence intervals.

The method is cost-effective and suitable for large-scale applications.

Abstract

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.