Estimation of Green Area Index (GAI) and fraction of Absorbed Photosynthetically Active Radiation (fAPAR) from decametric satellites was investigated in this study using a large database of ground measurements over croplands. It covers six main crop types including rice, corn, wheat and barley, sunflower, soybean and other types of crops. Ground measurements were completed using either digital hemispherical cameras, LAI-2000 or AccuPAR devices over sites representative of a decametric pixel. Sites were spread over the globe and the data collected at several growth stages concurrently to the acquisition of Landsat-8 images. Several machine learning techniques were investigated to retrieve GAI and fAPAR from the Landsat-8 top of canopy reflectance values, either using empirical or simulated calibration databases and generic or crop-type specific algorithms.

Results show that using the six Landsat-8 bands together provided the best estimates of GAI and fAPAR. Machine learning techniques trained over a dataset simulated by the PROSAIL model provided less accurate estimates of GAI and fAPAR as compared to machine learning techniques trained over the ground data collected and applied over a similar dataset (best-case scenario). All machine learning techniques performed similarly when calibrated on PROSAIL simulations. The Gaussian process regression (GPR) was the best performing machine learning technique as compared to artificial neural networks (ANN), support vector machine regression (SVM) and as compared to NDVI simple model when calibrated over the ground dataset in the best-case scenario. However, the performance of the GPR trained over ground data is notably degraded when applied to crop types excluded from the calibration (worst-case scenario), and models based on simulations performs similarly of even better. Furthermore, training the GPR over specific crop types was performing slightly better than training over all the crop types together if at least 100 well distributed data points were available in the training dataset. Similar conclusions were obtained for the other machine learning techniques, with crop specific empirical models providing slightly better performances with a few exceptions. Finally, GAI was estimated by GPR with a RMSE varying between 0.45 and 1.19 and fAPAR with RMSE varying between 0.07 and 0.15 depending on the crop type.

在这项研究中，使用庞大的农田地面测量数据库，对绿地指数（GAI）和来自十进制卫星的吸收的光合有效辐射（fAPAR）的比例进行了估算。它涵盖了六种主要农作物类型，包括水稻，玉米，小麦和大麦，向日葵，大豆和其他农作物。使用数字半球摄像机，LAI-2000或AccuPAR设备在代表十进制像素的位置上完成了地面测量。站点遍布全球，在采集Landsat-8图像的同时，在多个增长阶段收集数据。对几种机器学习技术进行了研究，以从Landsat-8顶篷反射率值的顶部检索GAI和fAPAR，

结果表明，一起使用六个Landsat-8波段可提供GAI和fAPAR的最佳估计。与在类似数据集上收集和应用的地面数据上训练的机器学习技术（最佳情况）相比，在PROSAIL模型模拟的数据集上训练的机器学习技术提供的GAI和fAPAR估计值较不准确。在PROSAIL仿真中进行校准时，所有机器学习技术的表现都相似。与人工神经网络（ANN），支持向量机回归（SVM）和NDVI简单模型（在最佳情况下通过地面数据集进行校准）相比，高斯过程回归（GPR）是性能最佳的机器学习技术设想。但是，将GPR训练的地面数据的性能应用于除校准之外的农作物类型时（最坏的情况），其性能会显着下降，并且基于模拟的模型的性能甚至更好。此外，如果在训练数据集中至少有100个分布良好的数据点，则针对特定农作物类型进行GPR训练比一起针对所有农作物类型进行训练要好一些。对于其他机器学习技术，也得出了类似的结论，其中特定作物的经验模型提供了更好的性能，但有一些例外。最后，根据作物类型，通过GPR估算的GAI的均方根误差（RMSE）在0.45至1.19之间，而fAPAR的均方根误差（RMSE）在0.07至0.15之间。