Retrieval of leaf area index in alpine wetlands using a two-layer canopy reflectance model |
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| Institution: | 1. Program Officer, Grants-RUFORUM (Regional Universities Forum for Capacity Building in Agriculture), Makerere University, Department of Environmental Management, Kampala, Uganda;2. Executive Director, ESIPPS International Ltd, Kampala Uganda; Research Officer, National Livestock Resources Research Institute, Tororo, Uganda;3. Associate, ESIPPS International Ltd, Kampala Uganda; Research Officer, National Livestock Resources Research Institute, Tororo, Uganda;4. Social Economist, ESIPPS International Ltd, Kampala Uganda; Research Officer, National Livestock Resources Research Institute, Tororo, Uganda;5. GIS Specialist, ESIPPS International Ltd, Kampala Uganda; Research Officer, National Livestock Resources Research Institute, Tororo, Uganda;6. Makerere University, Department of Environmental Management, Kampala, Uganda;7. Collaborating Research Fellow, Makerere University, Department of Environmental Management, Kampala, Uganda;8. Regional Universities Forum for Capacity Building in Agriculture (RUFORUM), Wandegeya, Uganda;9. Principal Researcher, Council for Scientific and Industrial Research (CSIR), Pretoria and Department of Plant and Soil Science University of Pretoria |
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| Abstract: | In this paper, we focused on the retrieval of the LAI in an alpine wetland located in western part of China in late August and early July 2011. A two-layer canopy reflectance model (ACRM) was used to establish the relationships between the LAI and the reflectance of near-infrared (NIR) and red (RED) wavebands. The reflectance data were derived from Landsat TM L1T product and the Terra and Aqua MODIS 16-day and 8-day composite reflectance products (MOD/MYD09) at 250 m resolution. Due to the lack of the information about some major input parameters for ACRM, which are sensitive to model outputs in the reflectance of NIR and RED wavebands, the inverse problem was ill-posed. To overcome this problem, a method of increasing the sensitivity of the LAI while reducing the influence of other model free parameters based on the study of free parameters’ sensitivity to the ACRM outputs and the region’s features was studied. The area of interest was divided into two parts using the approximately statistic normalized difference vegetation index (NDVI) value around 0.5. One part was sparse vegetation (0.1 < NDVI < 0.5), which is more sensitive to soil background effects and less sensitive to the canopy biophysical and biochemical variables. The other part was dense vegetation (0.5 ≤ NDVI < 1.0), which is less sensitive to soil background effects and more sensitive to plant canopies and leaf parameters. Then, the relationships of ρnir–LAI and ρred–LAI were established using a look-up table algorithm for the two parts. Furthermore, a regularization technique for fast pixel-wise retrieval was introduced to reduce the elements of LUT sets while maintaining a relatively high accuracy. The results were very promising compared to the field measured LAI values that the correlation (R2) of the measured LAI values and retrieved LAI values reached 0.95, and the root-mean-square deviation (RMSD) was 0.33 for late August, 2011, while the R2 reached 0.82 and RMSD was 0.25 for early July 2011. |
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| Keywords: | Leaf area index ACRM radiative transfer model Look-up table algorithm Sensitive analysis of key model parameters Classification Regularization technique |
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