Modifying Geometric-Optical Bidirectional Reflectance Model for Direct Inversion of Forest Canopy Leaf Area Index
Abstract
:1. Introduction
2. Data and Pre-Processing
2.1. Study Area
2.2. Data Foundation
2.2.1. Field-Measured Data
2.2.2. MODIS, MISR, and SPOT Reflectance Data
2.2.3. Airborne LiDAR data
3. Method
3.1. Theoretical Foundation
3.1.1. Geometric-Optic Mutual Shadowing Model
3.1.2. Gap Probability Model
3.2. Modifying GOMS Model Using the Gap Probability Model
3.3. LAI Inversion by the MGOMS Model
3.3.1. Input Parameters in the Inversion Process of GOMS and MGOMS Models
Parameter | Initial Value | Lower Limit | Upper Limit |
---|---|---|---|
NIRG | 0.4225 | 0 | 1 |
NIRC | 0.384 | 0 | 1 |
NIRZ | 0.146 | 0 | 0.3 |
RG | 0.12 | 0 | 1 |
RC | 0.09 | 0 | 1 |
RZ | 0.015 | 0 | 0.05 |
0.25 | 0.1 | 0.8 | |
LAI | 2.88 | 0 | 7 |
n | 0.15 | 0 | 1 |
q | 0.2 | 0 | 1 |
b/R | 1.9525 | 0.74 | 7.5 |
h/b | 2.049 | 1 | 10 |
5.5766 | 0 | 50 |
3.3.2. Model Inversion Strategy
4. Results
4.1. MGOMS Model Accuracy Validation
4.1.1. Tendency of the BRF Simulated by MGOMS Model along with the Variation of LAI
4.1.2. Comparison of the Simulated BRF and MOD09GA BRF
4.2. MGOMS Model Application Validation
4.2.1. Uncertainty and Sensitivity Analysis of the Parameters in MGOMS Model
VZA (°) | VAA (°) | NIRG | NIRC | NIRZ | LAI | n | q | b/R | h/b | Δh/b |
---|---|---|---|---|---|---|---|---|---|---|
27.88 | 282.92 | 0.81 | 0.96 | 0.59 | 0.58 | 0.05 | 0.04 | 0.59 | 0.02 | 0.03 |
6.39 | 276.92 | 0.95 | 1.00 | 0.44 | 0.48 | 0.05 | 0.04 | 0.54 | 0.04 | 0.07 |
17.22 | 99.05 | 0.86 | 1.27 | 0.31 | 0.35 | 0.04 | 0.03 | 0.38 | 0.07 | 0.14 |
50.74 | 95.61 | 0.50 | 1.64 | 0.28 | 0.33 | 0.00 | −0.01 | 0.25 | 0.04 | 0.12 |
27.58 | 99.47 | 0.72 | 1.44 | 0.29 | 0.33 | 0.03 | 0.03 | 0.31 | 0.07 | 0.17 |
44.27 | 284.89 | 0.58 | 1.08 | 0.70 | 0.63 | 0.04 | 0.03 | 0.61 | 0.01 | 0.08 |
36.65 | 98.06 | 0.59 | 1.56 | 0.31 | 0.34 | 0.03 | 0.02 | 0.25 | 0.06 | 0.14 |
36.86 | 283.61 | 0.70 | 1.02 | 0.65 | 0.61 | 0.05 | 0.04 | 0.60 | 0.01 | 0.06 |
- (1)
- Invert NIRC using the NIR band reflectance datasets with backward-viewing directions and large viewing zenith angles;
- (2)
- Invert NIRG using the NIR band reflectance datasets with small viewing zenith angles;
- (3)
- Invert LAI (LAIJune) using the reflectance datasets for June with forward-viewing directions and large viewing zenith angles.
4.2.2. LAI Inversion Based on Retrieved Parameters by MGOMS Model
4.2.3. Precision of the Retrieved LAI by MGOMS Model
5. Discussions
6. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Li, C.; Song, J.; Wang, J. Modifying Geometric-Optical Bidirectional Reflectance Model for Direct Inversion of Forest Canopy Leaf Area Index. Remote Sens. 2015, 7, 11083-11104. https://doi.org/10.3390/rs70911083
Li C, Song J, Wang J. Modifying Geometric-Optical Bidirectional Reflectance Model for Direct Inversion of Forest Canopy Leaf Area Index. Remote Sensing. 2015; 7(9):11083-11104. https://doi.org/10.3390/rs70911083
Chicago/Turabian StyleLi, Congrong, Jinling Song, and Jindi Wang. 2015. "Modifying Geometric-Optical Bidirectional Reflectance Model for Direct Inversion of Forest Canopy Leaf Area Index" Remote Sensing 7, no. 9: 11083-11104. https://doi.org/10.3390/rs70911083