Seasonal variations in weather have significant impacts on crop yields.The accuracy of weather data is an important consideration for crop yield models.This study uses an independent in-situ weather station network to...Seasonal variations in weather have significant impacts on crop yields.The accuracy of weather data is an important consideration for crop yield models.This study uses an independent in-situ weather station network to validate the accuracy of monthly temperature and precipitation data from the in-situ weather station network operated by the Bureau of Meteorology(BOM),interpolated gridded data from this network,and satellite weather data for the South West Agricultural Region of Australia.This region covers five classes of the K?ppen-Geiger climate classification system and is responsible for 10 billion AUD of agricultural produce annually.A strong bias was found for the maximum temperatures in the Copernicus LST(land surface temperature)satellite product.This bias was linearly correlated with the in-situ temperature and exceeded 20℃in warmer months.Due to the bias’s linear nature,a linear correction was able to reduce the root-mean-square error(RMSE)of the Copernicus LST product by 82%.This process was tested for other regions of Australia and,despite some regional differences,a linear correction consistently reduced RMSE by 80%.The validation process demonstrated that the dataset with reliably the lowest RMSE is the gridded weather data calculated from BOM’s in-situ weather stations.Nearest neighbor in-situ weather stations generally had the next lowest RMSE,followed by weather-station corrected satellite products and lastly the non-weather station corrected satellite products.While the in-situ gridded product generally had the lowest RMSE,there were spatial and seasonal variations.Monthly maximum temperatures were more accurately measured by the bias-corrected Copernicus LST product in the northern and eastern extents(where there is a lower density of BOM in-situ stations).Monthly minimum temperatures from the Copernicus LST product had similar to slightly better RMSE than the Australian Water Availability Project(AWAP)product for the southern half of the study area and the rain-gauge corrected GSMaP(Global Satellite Mapping of Precipitation)product performed similarly to AWAP in the drier months(November-April).展开更多
文摘Seasonal variations in weather have significant impacts on crop yields.The accuracy of weather data is an important consideration for crop yield models.This study uses an independent in-situ weather station network to validate the accuracy of monthly temperature and precipitation data from the in-situ weather station network operated by the Bureau of Meteorology(BOM),interpolated gridded data from this network,and satellite weather data for the South West Agricultural Region of Australia.This region covers five classes of the K?ppen-Geiger climate classification system and is responsible for 10 billion AUD of agricultural produce annually.A strong bias was found for the maximum temperatures in the Copernicus LST(land surface temperature)satellite product.This bias was linearly correlated with the in-situ temperature and exceeded 20℃in warmer months.Due to the bias’s linear nature,a linear correction was able to reduce the root-mean-square error(RMSE)of the Copernicus LST product by 82%.This process was tested for other regions of Australia and,despite some regional differences,a linear correction consistently reduced RMSE by 80%.The validation process demonstrated that the dataset with reliably the lowest RMSE is the gridded weather data calculated from BOM’s in-situ weather stations.Nearest neighbor in-situ weather stations generally had the next lowest RMSE,followed by weather-station corrected satellite products and lastly the non-weather station corrected satellite products.While the in-situ gridded product generally had the lowest RMSE,there were spatial and seasonal variations.Monthly maximum temperatures were more accurately measured by the bias-corrected Copernicus LST product in the northern and eastern extents(where there is a lower density of BOM in-situ stations).Monthly minimum temperatures from the Copernicus LST product had similar to slightly better RMSE than the Australian Water Availability Project(AWAP)product for the southern half of the study area and the rain-gauge corrected GSMaP(Global Satellite Mapping of Precipitation)product performed similarly to AWAP in the drier months(November-April).
