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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
- Source :
- PLoS ONE, PLoS ONE, Vol 12, Iss 8, p e0180239 (2017)
- Publication Year :
- 2017
- Publisher :
- Public Library of Science, 2017.
-
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.
- 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
Subjects
Details
- Language :
- English
- ISSN :
- 19326203
- Volume :
- 12
- Issue :
- 8
- Database :
- OpenAIRE
- Journal :
- PLoS ONE
- Accession number :
- edsair.doi.dedup.....11933ccaf0519ab42f44158187184d85