Estimation of leaf area index using PROSAIL based LUT inversion, MLRA-GPR and empirical models: Case study of tropical deciduous forest plantation, North India
| Autor: | Jochem Verrelst, Sanjiv K. Sinha, Juan Pablo Rivera, Hitendra Padalia, Anindita Dasgupta |
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| Rok vydání: | 2020 |
| Předmět: |
Global and Planetary Change
010504 meteorology & atmospheric sciences 0211 other engineering and technologies Empirical modelling Regression analysis 02 engineering and technology 15. Life on land Management Monitoring Policy and Law Atmospheric sciences 01 natural sciences Regression Atmospheric radiative transfer codes Deciduous Spatial variability Computers in Earth Sciences Leaf area index 021101 geological & geomatics engineering 0105 earth and related environmental sciences Earth-Surface Processes Mathematics Transpiration |
| Zdroj: | International Journal of Applied Earth Observation and Geoinformation |
| ISSN: | 1569-8432 |
| DOI: | 10.1016/j.jag.2019.102027 |
| Popis: | Forests play a vital role in biological cycles and environmental regulation. To understand the key processes of forest canopies (e.g., photosynthesis, respiration and transpiration), reliable and accurate information on spatial variability of Leaf Area Index (LAI), and its seasonal dynamics is essential. In the present study, we assessed the performance of biophysical parameter (LAI) retrieval methods viz. Look-Up Table (LUT)-inversion, MLRA-GPR (Machine Learning Regression Algorithm- Gaussian Processes Regression) and empirical models, for estimating the LAI of tropical deciduous plantation using ARTMO (Automated Radiative Transfer Models Operator) tool and Sentinel-2 satellite images. The study was conducted in Central Tarai Forest Division, Haldwani, located in the Uttarakhand state, India. A total of 49 ESUs (Elementary Sampling Unit) of 30 m × 30 m size were established based on variability in composition and age of plantation stands. In-situ LAI was recorded using plant canopy imager during the leaf growing, peak and senescence seasons. The PROSAIL model was calibrated with site-specific biophysical and biochemical parameters before used to the predicted LAI. The plantation LAI was also predicted by an empirical approach using optimally chosen Sentinel-2 vegetation indices. In addition, Sentinel-2 and MODIS LAI products were evaluated with respect to LAI measurements. MLRA-GPR offered best results for predicting LAI of leaf growing (R2 = 0.9, RMSE = 0.14), peak (R2 = 0.87, RMSE = 0.21) and senescence (R2 = 0.86, RMSE = 0.31) seasons while LUT inverted model outperformed VI’s based parametric regression model. Vegetation indices (VIs) derived from 740 nm, 783 nm and 2190 nm band combinations of Sentinel-2 offered the best prediction of LAI. |
| Databáze: | OpenAIRE |
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