A new study titled ‘Photovoltaic panel surface temperature retrieval from MODIS through accounting for directional effects,’ has demonstrated a more accurate way to monitor the surface temperature of photovoltaic panels from space, addressing a long-standing gap in managing performance and thermal risk at utility scale solar plants.
As global solar capacity continues its rapid rise toward a projected 8,519 GW by 2050, operators are under increasing pressure to optimise output and manage heat related losses. Panel surface temperatures can reach 45–65 °C in summer, significantly reducing conversion efficiency and increasing the risk of equipment stress. Yet reliable, large scale temperature monitoring has remained difficult, with most existing methods limited to on-site sensors or intermittent aerial surveys.
Researchers have now developed a method using MODIS thermal infrared satellite data that more accurately captures panel temperatures across entire solar farms. The approach accounts for the complex physical structure of PV arrays, including the mix of hot panels and cooler shaded ground, as well as the way panels emit heat differently depending on viewing angle.
Traditional satellite land surface temperature products tend to underestimate panel temperatures because they treat solar farms like natural terrain. This leads to systematic cold bias and unreliable inputs for performance modelling. The new method corrects this by separating the different components within each satellite pixel and applying PV specific emissivity values.
Validation at two test sites with contrasting climates showed strong improvements. Temperature error was reduced from 10.8–18.9 °C to 3.7–8.6 °C during key daytime observation periods. The method also reduced systematic bias from as much as −17 °C to around −2 to −3 °C.
These gains translate directly into better operational insights. More accurate temperature data can reduce errors in power output simulations by around 3–5%, supporting improved forecasting, maintenance planning, and risk management during peak heat conditions.
The study highlights that the relatively low emissivity of solar panels, about 0.87 compared to more than 0.94 for natural surfaces, is the main factor influencing accuracy. Viewing geometry and array structure provide additional refinements.
While performance is strong during warmer months, the method is less reliable in winter due to extended shadows and possible snow cover, which introduce larger uncertainties. The researchers note that improving how shaded ground between panel rows is modelled will be key to extending the approach year-round.
The findings point to a practical pathway for continuous, large scale thermal monitoring of solar assets using existing satellite systems, a capability that could become increasingly valuable as solar deployment accelerates across high temperature regions including parts of Africa.
Link to the full paper HERE
Author: Bryan Groenendaal
May 20, 2026
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