Next Article in Journal
Examination of the Potential of Terrestrial Laser Scanning and Structure-from-Motion Photogrammetry for Rapid Nondestructive Field Measurement of Grass Biomass
Next Article in Special Issue
Effect of Solar-Cloud-Satellite Geometry on Land Surface Shortwave Radiation Derived from Remotely Sensed Data
Previous Article in Journal
Possibility of Estimating Seasonal Snow Depth Based Solely on Passive Microwave Remote Sensing on the Greenland Ice Sheet in Spring
Article Menu
Issue 6 (June) cover image

Export Article

Open AccessArticle
Remote Sens. 2017, 9(6), 533; doi:10.3390/rs9060533

Extended Data-Based Mechanistic Method for Improving Leaf Area Index Time Series Estimation with Satellite Data

1
The State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
2
Beijing Engineering Research Center for Global Land Remote Sensing Products, Beijing Normal University, Beijing 100875, China
3
Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Sangram Ganguly and Prasad Thenkabail
Received: 23 March 2017 / Revised: 20 May 2017 / Accepted: 24 May 2017 / Published: 26 May 2017
(This article belongs to the Special Issue Quantitative Remote Sensing of Land Surface Variables)
View Full-Text   |   Download PDF [2152 KB, uploaded 27 May 2017]   |  

Abstract

Leaf area index (LAI) is one of the key parameters in crop growth monitoring and global change studies. Multiple LAI products have been generated from satellite observations, many of which suffer from data discontinuities due to persistent cloud contamination and retrieval algorithm inaccuracies. This study proposes an extended data-based mechanistic method (EDBM) for estimating LAI time series from Moderate Resolution Imaging Spectroradiometer (MODIS) data. The data-based mechanistic model is universalized to supply the LAI background information, and then the vegetation canopy radiative-transfer model (PROSAIL) is coupled to calculate reflectances with the same observation geometry as MODIS reflectance data. The ensemble Kalman filter (ENKF) is introduced to improve LAI estimation based on the difference between simulated and observed reflectances. Field measurements from seven Benchmark Land Multisite Analysis and Intercomparison of Products (BELMANIP) sites and reference maps from the Imagine-S project La Albufera, Spain site were used to validate the model. The results demonstrate that when compared with field measurements, the LAI time-series estimates obtained using this approach were superior to those obtained with the MODIS 500 m resolution LAI product. The root mean square errors (RMSE) of the MODIS LAI product and of the LAI estimated with the proposed method were 1.26 and 0.5, respectively. When compared with reference LAI maps, the results indicate that the estimated LAI is spatially and temporally consistent with LAI reference maps. The average differences between EDBM and the LAI reference map on the selected four days was 0.32. View Full-Text
Keywords: LAI time series; data-based mechanistic method; radiative transfer model LAI time series; data-based mechanistic method; radiative transfer model
Figures

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Zhou, H.; Wang, J.; Liang, S.; Xiao, Z. Extended Data-Based Mechanistic Method for Improving Leaf Area Index Time Series Estimation with Satellite Data. Remote Sens. 2017, 9, 533.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top