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A Method for Retrieving Coarse-Resolution Leaf Area Index for Mixed Biomes Using a Mixed-Pixel Correction Factor

delete2023-01-01
delete11
PRE
AI
Y
Yadong Dong
J
Jing Li *
Z
Ziti Jiao
Q
Qinhuo Liu
赵菁 cover
赵菁 (Jing Zhao)
B
Baodong Xu
H
Hu Zhang
Z
Zhaoxing Zhang
C
Chang Liu
Y
Yuri Knyazikhin
R
Ranga B. Myneni
DOI:10.1109/TGRS.2023.3235949delete
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Abstract

Abstract

En 中文
The leaf area index (LAI) is a key structural parameter of vegetation canopies. Accordingly, several moderate-resolution global LAI products have been produced and widely used in the field of remote sensing. However, the accuracy of the current moderate-resolution global LAI products cannot satisfy the requirements recommended by the LAI application communities, especially in heterogeneous areas composed of mixed land cover types. In this study, we propose a mixed-pixel correction (MPC) method to improve the accuracy of LAI retrievals over heterogeneous areas by considering the influence of heterogeneity caused by the mixture of different biome types with the help of high-resolution land cover maps. The DART-simulated LAI, the aggregated Landsat LAI, and the site-based high-resolution LAI reference maps are used to evaluate the performance of the MPC method. The results indicate that the MPC method can reduce the influences of spatial heterogeneity and biome misclassification to obtain the LAI with much better accuracy than the Moderate Resolution Imaging Spectroradiometer (MODIS) main algorithm, given that the high-resolution land cover map is accurate. The root mean square error (RMSE) (bias) decreases from 0.749 (0.486) to 0.414 (0.087), while the R2 increases from 0.084 to 0.524, and the proportion of pixels that fulfill the uncertainty requirement of the GCOS increases from 38.2% to 84.6% for the results of site-based high-resolution LAI reference maps. Spatially explicit information about vegetation fractional cover can further reduce uncertainties induced by variations in canopy density for the results of DART simulated data. The proposed method shows potential for improving global moderate-resolution LAI products.
Keywords:
Biological system modeling
Vegetation mapping
Reflectivity
Remote sensing
MODIS
Indexes
Earth
Biome misclassification
land cover mixture
leaf area index (LAI)
Moderate Resolution Imaging Spectroradiometer (MODIS)

Journal

IEEE Transactions on Geoscience and Remote Sensing cover
IEEE Transactions on Geoscience and Remote Sensing
IF:
8.6
Papers:
2.1W
Citations:
10.7W

Organization

B
Beijing Normal University
Scholars:
3.3W
Papers: 2.7W
Citations: 4.2W
U
university of chinese academy of sciences, cas
Scholars:
4.1W
Papers: 3.8W
Citations: 75
H
Huazhong Agricultural University
Scholars:
3.2W
Papers: 1.8W
Citations: 3.5W
C
chinese academy of sciences
Scholars:
56.1W
Papers: 44.8W
Citations: 704
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