Zhou Qingtao, Flores Alejandro, Glenn Nancy F, Walters Reggie, Han Bangshuai
Department of Geosciences, Boise State University, Boise, Idaho, United States of America.
Department of Natural Resources and Environmental Management, Muncie, Indiana, United States of America.
PLoS One. 2017 Aug 4;12(8):e0180239. doi: 10.1371/journal.pone.0180239. eCollection 2017.
Shortwave solar radiation is an important component of the surface energy balance and provides the principal source of energy for terrestrial ecosystems. This paper presents a machine learning approach in the form of a random forest (RF) model for estimating daily downward solar radiation flux at the land surface over complex terrain using MODIS (MODerate Resolution Imaging Spectroradiometer) remote sensing data. The model-building technique makes use of a unique network of 16 solar flux measurements in the semi-arid Reynolds Creek Experimental Watershed and Critical Zone Observatory, in southwest Idaho, USA. Based on a composite RF model built on daily observations from all 16 sites in the watershed, the model simulation of downward solar radiation matches well with the observation data (r2 = 0.96). To evaluate model performance, RF models were built from 12 of 16 sites selected at random and validated against the observations at the remaining four sites. Overall root mean square errors (RMSE), bias, and mean absolute error (MAE) are small (range: 37.17 W/m2-81.27 W/m2, -48.31 W/m2-15.67 W/m2, and 26.56 W/m2-63.77 W/m2, respectively). When extrapolated to the entire watershed, spatiotemporal patterns of solar flux are largely consistent with expected trends in this watershed. We also explored significant predictors of downward solar flux in order to reveal important properties and processes controlling downward solar radiation. Based on the composite RF model built on all 16 sites, the three most important predictors to estimate downward solar radiation include the black sky albedo (BSA) near infrared band (0.858 μm), BSA visible band (0.3-0.7 μm), and clear day coverage. This study has important implications for improving the ability to derive downward solar radiation through a fusion of multiple remote sensing datasets and can potentially capture spatiotemporally varying trends in solar radiation that is useful for land surface hydrologic and terrestrial ecosystem modeling.
短波太阳辐射是地表能量平衡的重要组成部分，也是陆地生态系统的主要能量来源。本文提出了一种以随机森林（RF）模型形式的机器学习方法，用于利用中分辨率成像光谱仪（MODIS）遥感数据估算复杂地形下陆地表面的日太阳辐射向下通量。该模型构建技术利用了美国爱达荷州西南部半干旱的雷诺兹溪实验流域和关键带观测站中16个太阳通量测量的独特网络。基于在流域内所有16个站点的每日观测数据构建的复合RF模型，太阳辐射向下通量的模型模拟与观测数据匹配良好（r2 = 0.96）。为了评估模型性能，从随机选择的16个站点中的12个站点构建RF模型，并针对其余4个站点的观测数据进行验证。总体均方根误差（RMSE）、偏差和平均绝对误差（MAE）较小（范围分别为：37.17 W/m2 - 81.27 W/m2、-48.31 W/m2 - 15.67 W/m2和26.56 W/m2 - 63.77 W/m2）。当外推到整个流域时，太阳通量的时空模式与该流域的预期趋势基本一致。我们还探索了太阳辐射向下通量的重要预测因子，以揭示控制太阳辐射向下的重要属性和过程。基于在所有16个站点构建的复合RF模型，估算太阳辐射向下通量的三个最重要预测因子包括近红外波段（0.858μm）的黑天空反照率（BSA）、可见光波段（0.3 - 0.7μm）的BSA和晴空覆盖率。本研究对于通过融合多个遥感数据集提高获取太阳辐射向下通量的能力具有重要意义，并且有可能捕捉太阳辐射的时空变化趋势，这对于陆地表面水文和陆地生态系统建模很有用。
