Evaluation of NASA Satellite‐ and Model‐Derived Weather Data for Simulation of Maize Yield Potential in China

Use of crop models is frequently constrained by lack of the required weather data. This paper evaluates satellite-based solar radiation and model-derived air temperature (maximum temperature, T max ; minimum temperature, T min ) from NASA and their utility for simulating maize (Zea mays L.) yield potential at 39 locations in China's major maize-producing regions. The reference data in this evaluation were the corresponding ground-observed weather data and simulated yield using these data. NASA weather data were closely correlated with data from ground weather stations with an r 2 > 0.8, but a systematic underestimation of air temperature was found (T max of -2.8°C; T min of -1.4°C). As a result, use of NASA data alone for yield simulation gave poor agreement with simulated yields using ground weather data (r 2 = 0.2). The simulations of yield potential using satellite-derived solar radiation, coupled with temperature data from ground stations, agreed well with simulated results using complete ground weather data in three of the five regions (r 2 > 0.9). The agreement in the other two regions was relatively poor (r 2 = 0.62 and 0.64). Across all 710 site-years evaluated, the agreement was shown with a mean error (ME) = 0.2 t ha -1 , a root mean square error (RMSE) = 0.6 t ha -1 , and r 2 = 0.9. Our results indicate that combining NASA solar radiation with ground-station temperature data provides an option for filling geospatial gaps in weather data for estimating maize yield potential in China.

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