+ Site Statistics
References:
54,258,434
Abstracts:
29,560,870
PMIDs:
28,072,757
+ Search Articles
+ Subscribe to Site Feeds
Most Shared
PDF Full Text
+ PDF Full Text
Request PDF Full Text
+ Follow Us
Follow on Facebook
Follow on Twitter
Follow on LinkedIn
+ Translate
+ Recently Requested

Performance Evaluation of Machine Learning Methods for Leaf Area Index Retrieval from Time-Series MODIS Reflectance Data



Performance Evaluation of Machine Learning Methods for Leaf Area Index Retrieval from Time-Series MODIS Reflectance Data



Sensors 17(1)



Leaf area index (LAI) is an important biophysical parameter and the retrieval of LAI from remote sensing data is the only feasible method for generating LAI products at regional and global scales. However, most LAI retrieval methods use satellite observations at a specific time to retrieve LAI. Because of the impacts of clouds and aerosols, the LAI products generated by these methods are spatially incomplete and temporally discontinuous, and thus they cannot meet the needs of practical applications. To generate high-quality LAI products, four machine learning algorithms, including back-propagation neutral network (BPNN), radial basis function networks (RBFNs), general regression neutral networks (GRNNs), and multi-output support vector regression (MSVR) are proposed to retrieve LAI from time-series Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance data in this study and performance of these machine learning algorithms is evaluated. The results demonstrated that GRNNs, RBFNs, and MSVR exhibited low sensitivity to training sample size, whereas BPNN had high sensitivity. The four algorithms performed slightly better with red, near infrared (NIR), and short wave infrared (SWIR) bands than red and NIR bands, and the results were significantly better than those obtained using single band reflectance data (red or NIR). Regardless of band composition, GRNNs performed better than the other three methods. Among the four algorithms, BPNN required the least training time, whereas MSVR needed the most for any sample size.

(PDF emailed within 0-6 h: $19.90)

Accession: 060079784

Download citation: RISBibTeXText

PMID: 28045443

DOI: 10.3390/s17010081


Related references

Variational retrieval of leaf area index from MODIS time series data: examples from the Heihe river basin, north-west China. International Journal of Remote Sensing 33(3): 730-745, 2012

Evaluation of MODIS surface reflectance products for wheat leaf area index (LAI) retrieval. ISPRS Journal of Photogrammetry and Remote Sensing 63(6): 661-677, 2008

Use of General Regression Neural Networks for Generating the GLASS Leaf Area Index Product From Time-Series MODIS Surface Reflectance. IEEE Transactions on Geoscience and Remote Sensing 52(1): 209-223, 2014

A Framework for Consistent Estimation of Leaf Area Index, Fraction of Absorbed Photosynthetically Active Radiation, and Surface Albedo from MODIS Time-Series Data. IEEE Transactions on Geoscience and Remote Sensing 53(6): 3178-3197, 2015

Use of ensemble Kalman smoother algorithm for the time-series retrieval of leaf area index from remote sensing data. Guang Pu Xue Yu Guang Pu Fen Xi 31(9): 2485-2490, 2011

Leaf area index retrieval using IRS LISS-III sensor data and validation of the MODIS LAI product over Central India. Ieee Transactions on Geoscience and Remote Sensing 44(7): 1858-1865, 2006

Leaf area index retrieval using IRS LISS-III sensor data and validation of MODIS LAI product over Madhya Pradesh. Current Science 85(12): 1777-1782, 25 December, 2003

Assimilating leaf area index of three typical types of subtropical forest in China from MODIS time series data based on the integrated ensemble Kalman filter and PROSAIL model. Isprs Journal of Photogrammetry and Remote Sensing 126: 68-78, 2017

Estimation of paddy rice leaf area index using machine learning methods based on hyperspectral data from multi-year experiments. Plos One 13(12): E0207624, 2018

Retrospective retrieval of long-term consistent global leaf area index 1981–211 from combined AVHRR and MODIS data. Journal of Geophysical Research 117(G4): G04003, 2012

Retrieval of Leaf Area Index using IRS-P6, LISS-III data and validation of MODIS LAI product MOD15 V5 over trans Gangetic Plains of India. 2013

Retrospective retrieval of long-term consistent global leaf area index 1981–2011 from combined AVHRR and MODIS data. Journal of Geophysical Research 117(G4): G04003, 2012

Modeling and Predicting of MODIS Leaf Area Index Time Series Based on a Hybrid SARIMA and BP Neural Network Method. Guang Pu Xue Yu Guang Pu Fen Xi 37(1): 189-193, 2017

Winter wheat area estimation from MODIS-EVI time series data using the Crop Proportion Phenology Index. Remote Sensing of Environment 119(none): 0-242, 2012

A modified vegetation backscattering model for leaf area index retrieval from SAR time series. International Journal of Remote Sensing 37(24): 5884-5901, 2016