The reducer output names determine the names of the output bands: reducers with multiple inputs will use the output names directly, while reducers with a single input will prefix the output name with the input band name (e.g. '10_mean', '20_mean', etc.).
Reducers with weighted inputs can have the input weight based on the input mask, the kernel value, or the smaller of those two.
|this: ||Image||The input image.|
|Reducer||The reducer to apply to pixels within the neighborhood.|
|Kernel||The kernel defining the neighborhood.|
|String, default: "kernel"||One of 'mask', 'kernel', or 'min'.|
|Boolean, default: true||Mask output pixels if the corresponding input pixel is masked.|
|String, default: null||Optimization strategy. Options are 'boxcar' and 'window'. The 'boxcar' method is a fast method for computing count, sum or mean. It requires a homogeneous kernel, a single-input reducer and either MASK, KERNEL or no weighting. The 'window' method uses a running window, and has the same requirements as 'boxcar', but can use any single input reducer. Both methods require considerable additional memory.|