Sinha Sanjiv K, Padalia Hitendra, Dasgupta Anindita, Verrelst Jochem, Rivera Juan Pablo
Indian Institute of Remote Sensing, Indian Space Research Organisation (ISRO), 4-Kalidas Road, Dehradun, 248001, Uttarakhand, India.
Image Processing Laboratory (IPL), Parc Científic, Universitat de Valéncia, 46980 Paterna, Valéncia, Spain.
Int J Appl Earth Obs Geoinf. 2020 Apr;86:102027. doi: 10.1016/j.jag.2019.102027.
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 . 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 30m×30m 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 (R = 0.9, RMSE = 0.14), peak (R = 0.87, RMSE = 0.21) and senescence (R = 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.
森林在生物循环和环境调节中发挥着至关重要的作用。为了解森林冠层的关键过程（如光合作用、呼吸作用和蒸腾作用），关于叶面积指数（LAI）的空间变异性及其季节动态的可靠且准确的信息至关重要。在本研究中，我们评估了生物物理参数（LAI）反演方法——查找表（LUT）反演、机器学习回归算法 - 高斯过程回归（MLRA - GPR）和经验模型，利用自动辐射传输模型操作器（ARTMO）工具和哨兵 - 2卫星图像来估算热带落叶人工林的LAI。该研究在印度北阿坎德邦哈尔德瓦尼的中央塔莱森林分区进行。基于人工林林分组成和年龄的变异性，共建立了49个30米×30米大小的基本采样单元（ESU）。在叶片生长、高峰期和衰老期，使用植物冠层成像仪记录原位LAI。在用于预测LAI之前，用特定地点的生物物理和生化参数对PROSAIL模型进行了校准。还通过使用最优选择的哨兵 - 2植被指数的经验方法预测人工林LAI。此外，针对LAI测量对哨兵 - 2和中分辨率成像光谱仪（MODIS）的LAI产品进行了评估。MLRA - GPR在预测叶片生长季（R = 0.9，均方根误差RMSE = 0.14）、高峰期（R = 0.87，RMSE = 0.21）和衰老期（R = 0.86，RMSE = 0.31）的LAI时提供了最佳结果，而LUT反演模型优于基于植被指数（VI）的参数回归模型。源自哨兵 - 2的740纳米、783纳米和2190纳米波段组合的植被指数对LAI的预测效果最佳。
