Estimating Crop Primary Productivity with Sentinel-2 and Landsat 8 using Machine Learning Methods Trained with Radiative Transfer Simulations
Estimating Crop Primary Productivity with Sentinel-2 and Landsat 8 using Machine Learning Methods Trained with Radiative Transfer Simulations
复制标题
DOI：
10.1016/j.rse.2019.03.002
复制
发表时间：
2019-05
期刊：
影响因子：
--
通讯作者：
Aleksandra Wolanin;Gustau Camps-Valls;L. Gómez-Chova;Gonzalo Mateo-García;C. Tol;Yongguang Zhang;L. Guanter
中科院分区：
文献类型：
--
作者：
Aleksandra Wolanin;Gustau Camps-Valls;L. Gómez-Chova;Gonzalo Mateo-García;C. Tol;Yongguang Zhang;L. Guanter
在过去的几十年里，卫星遥感被广泛用于农业应用，既用于评估植被状况，也用于随后的产量预测。总初级生产力(GPP)是指示作物光合作用功能和逆境的重要变量，现有的基于遥感的估算方法通常依赖于经验或半经验方法，这往往过于简化光合作用机制。在这项工作中，我们利用机械光合作用模型和卫星数据可用性方面的所有平行发展，对作物生产力进行高级监测。特别是，我们将基于过程的建模与土壤-冠层能量平衡辐射传输模型(SCOPE)、Sentinel-2和Landsat 8光学遥感数据和机器学习方法相结合，以估算作物GPP。使用这种方法，我们绕过了检索精确建模光合作用所需的一组植被生物物理参数的中间步骤，同时仍然考虑了原始基于物理的模型的复杂过程。使用模拟和基于通量塔的GPP数据对机器学习模型的几种实现进行了测试和验证。我们最终的神经网络模型能够估计测试通量塔站点的GPP，r2为0.92，均方根误差为1.38 GC d−1M−2，优于基于植被指数的经验模型。该模型对Landsat 8数据的适用性首次测试结果良好(r2值为0.82，均方根误差为1.97 GC d−1M−2)，这表明我们的方法可以进一步应用于其他传感器。在本研究中，建模和测试仅限于C3作物，但可以通过产生一个新的训练数据集来扩展到C4作物，该数据集的范围可以解释不同的光合作用途径。我们的模型成功地估计了各种C3作物类型和环境条件下的GPP，即使它没有使用来自相应站点的任何本地信息。这突出了它在当前地球观测云计算平台的帮助下，在全球范围内利用新的卫星传感器绘制作物生产力地图的潜力。
Satellite remote sensing has been widely used in the last decades for agricultural applications, both for assessing vegetation condition and for subsequent yield prediction. Existing remote sensing-based methods to estimate gross primary productivity (GPP), which is an important variable to indicate crop photosynthetic function and stress, typically rely on empirical or semi-empirical approaches, which tend to over-simplify photosynthetic mechanisms. In this work, we take advantage of all parallel developments in mechanistic photosynthesis modeling and satellite data availability for an advanced monitoring of crop productivity. In particular, we combine process-based modeling with the soil-canopy energy balance radiative transfer model (SCOPE) with Sentinel-2 and Landsat 8 optical remote sensing data and machine learning methods in order to estimate crop GPP. With this approach, we by-pass the need for an intermediate step to retrieve the set of vegetation biophysical parameters needed to accurately model photosynthesis, while still accounting for the complex processes of the original physically-based model. Several implementations of the machine learning models are tested and validated using simulated and flux tower-based GPP data. Our final neural network model is able to estimate GPP at the tested flux tower sites withr2of 0.92 and RMSE of 1.38 gC d−1m−2, which outperforms empirical models based on vegetation indices. The first test of applicability of this model to Landsat 8 data showed good results (r2of 0.82 and RMSE of 1.97 gC d−1m−2), which suggests that our approach can be further applied to other sensors. Modeling and testing is restricted to C3 crops in this study, but can be extended to C4 crops by producing a new training dataset with SCOPE that accounts for the different photosynthetic pathways. Our model successfully estimates GPP across a variety of C3 crop types and environmental conditions even though it does not use any local information from the corresponding sites. This highlights its potential to map crop productivity from new satellite sensors at a global scale with the help of current Earth observation cloud computing platforms.