1. A machine learning approach to estimation of downward solar radiation from satellite-derived data products: An application over a semi-arid ecosystem in the U.S
- Author
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Nancy F. Glenn, Bangshuai Han, Qingtao Zhou, Reggie D. Walters, and Alejandro N. Flores
- Subjects
Albedo ,Satellite Imagery ,Atmospheric Science ,010504 meteorology & atmospheric sciences ,0208 environmental biotechnology ,lcsh:Medicine ,Astronomical Sciences ,02 engineering and technology ,computer.software_genre ,01 natural sciences ,Remote Sensing ,Machine Learning ,lcsh:Science ,media_common ,Climatology ,Numerical Analysis ,Multidisciplinary ,Ecology ,Physics ,Electromagnetic Radiation ,Planetary Sciences ,Simulation and Modeling ,Satellite Communications ,Radiation flux ,Physical Sciences ,Engineering and Technology ,Solar Radiation ,Moderate-resolution imaging spectroradiometer ,Alternative Energy ,Algorithms ,Research Article ,Environmental Monitoring ,Computer and Information Sciences ,Watershed ,media_common.quotation_subject ,Terrain ,Machine learning ,Research and Analysis Methods ,Ecosystems ,Artificial Intelligence ,Solar Energy ,Humans ,Ecosystem ,0105 earth and related environmental sciences ,business.industry ,lcsh:R ,Ecology and Environmental Sciences ,Biology and Life Sciences ,United States ,020801 environmental engineering ,Interpolation ,Energy and Power ,Sky ,Remote Sensing Technology ,Earth Sciences ,Environmental science ,Satellite ,lcsh:Q ,Artificial intelligence ,business ,Shortwave ,computer ,Mathematics - Abstract
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.
- Published
- 2017