Mapping Leaf Area Index With a Smartphone and Gaussian Processes

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.

[1]  N. Breda.  Ground-based measurements of leaf area index: a review of methods, instruments and current controversies. , 2003, Journal of experimental botany.

[2]  Gérard Dedieu,et al.  Estimation of leaf area and clumping indexes of crops with hemispherical photographs , 2008 .

[3]  Luis Alonso,et al.  Retrieval of Vegetation Biophysical Parameters Using Gaussian Process Techniques , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[4]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[5]  T. Black,et al.  Foliage area and architecture of plant canopies from sunfleck size distributions , 1992 .

[6]  Gustavo Camps-Valls,et al.  Retrieval of Biophysical Parameters With Heteroscedastic Gaussian Processes , 2014, IEEE Geoscience and Remote Sensing Letters.

[7]  J. Chen,et al.  Defining leaf area index for non‐flat leaves , 1992 .

[8]  Lorenzo Busetto,et al.  Intercomparison of instruments for measuring leaf area index over rice , 2015, 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

[9]  Sancho Salcedo-Sanz,et al.  Prediction of Daily Global Solar Irradiation Using Temporal Gaussian Processes , 2014, IEEE Geoscience and Remote Sensing Letters.

[10]  Luis Gómez-Chova,et al.  Biophysical Parameter Estimation With a Semisupervised Support Vector Machine , 2009, IEEE Geoscience and Remote Sensing Letters.

[11]  Gustavo Camps-Valls,et al.  Gaussian processes uncertainty estimates in experimental Sentinel-2 LAI and leaf chlorophyll content retrieval , 2013 .

[12]  Naif Alajlan,et al.  Improved Estimation of Water Chlorophyll Concentration With Semisupervised Gaussian Process Regression , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[13]  Luis Gómez-Chova,et al.  Remote Sensing Image Processing , 2011, Remote Sensing Image Processing.

[14]  Raffaele Casa,et al.  Development of an app for estimating leaf area index using a smartphone. Trueness and precision determination and comparison with other indirect methods , 2013 .

[15]  B. Martínez,et al.  Accuracy assessment of fraction of vegetation cover and leaf area index estimates from pragmatic methods in a cropland area , 2009 .

[16]  A. Atiya,et al.  Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond , 2005, IEEE Transactions on Neural Networks.

[17]  M. Campos-Taberner.  Development of an Earth observation processing chain for crop biophysical parameters at local and global scale , 2017 .

[18]  Christopher K. I. Williams,et al.  Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning) , 2005 .

[19]  J. Welles,et al.  Canopy structure measurement by gap fraction analysis using commercial instrumentation , 1996 .