Next Article in Journal
Assessing Performance of NDVI and NDVI3g in Monitoring Leaf Unfolding Dates of the Deciduous Broadleaf Forest in Northern China
Previous Article in Journal
Trends and Variability of AVHRR-Derived NPP in India
Remote Sens. 2013, 5(2), 830-844; doi:10.3390/rs5020830
Article

The Impact of Potential Land Cover Misclassification on MODIS Leaf Area Index (LAI) Estimation: A Statistical Perspective

1,* , 1
 and 2
Received: 18 December 2012; in revised form: 4 February 2013 / Accepted: 4 February 2013 / Published: 15 February 2013
View Full-Text   |   Download PDF [748 KB, uploaded 19 June 2014]   |   Browse Figures
Abstract: Understanding the impact of vegetation mixture and misclassification on leaf area index (LAI) estimation is crucial for algorithm development and the application community. Using the MODIS standard land cover and LAI products, global LAI climatologies and statistics were obtained for both pure and mixed pixels to evaluate the effects of biome mixture on LAI estimation. Misclassification between crops and shrubs does not generally translate into large LAI errors (<0.37 or 27.0%), partly due to their relatively lower LAI values. Biome misclassification generally leads to an LAI overestimation for savanna, but an underestimation for forests. The largest errors caused by misclassification are also found for savanna (0.51), followed by evergreen needleleaf forests (0.44) and broadleaf forests (~0.31). Comparison with MODIS uncertainty indicators show that biome misclassification is a major factor contributing to LAI uncertainties for savanna, while for forests, the main uncertainties may be introduced by algorithm deficits, especially in summer. The LAI climatologies for pure pixels are recommended for land surface modeling studies. Future studies should focus on improving the biome classification for savanna systems and refinement of the retrieval algorithms for forest biomes.
Keywords: leaf area index (LAI); uncertainty; land cover; biome type; subpixel mixture; biome misclassification; MODIS leaf area index (LAI); uncertainty; land cover; biome type; subpixel mixture; biome misclassification; MODIS
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.

Export to BibTeX |
EndNote


MDPI and ACS Style

Fang, H.; Li, W.; Myneni, R.B. The Impact of Potential Land Cover Misclassification on MODIS Leaf Area Index (LAI) Estimation: A Statistical Perspective. Remote Sens. 2013, 5, 830-844.

AMA Style

Fang H, Li W, Myneni RB. The Impact of Potential Land Cover Misclassification on MODIS Leaf Area Index (LAI) Estimation: A Statistical Perspective. Remote Sensing. 2013; 5(2):830-844.

Chicago/Turabian Style

Fang, Hongliang; Li, Wenjuan; Myneni, Ranga B. 2013. "The Impact of Potential Land Cover Misclassification on MODIS Leaf Area Index (LAI) Estimation: A Statistical Perspective." Remote Sens. 5, no. 2: 830-844.


Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert