Satellite estimates of airborne particles can be inaccurate when light reflected by land is poorly estimated. We developed a method for China's DaQi-1 satellite that uses machine learning to correct this land signal while retaining a physics-based calculation. Comparisons with ground observations showed reduced errors in spring and autumn–winter. The method also reduced errors the following spring without retraining, suggesting more reliable monitoring over complex land surfaces.