For passive ranging in environments subject to unknown fluctuating spatial coherence losses, we derive a new lucky maximum likelihood estimator based on the probabilistic assumption that each collected data snapshot is either coherent or purely incoherent with some probability. Our lucky range estimator can be interpreted as first ranking the coherence quality of each data snapshot according to an array gain-like quantity during the parameter search, followed by accumulation of likelihood surfaces out of data snapshots of high spatial-coherence. This effectively avoids the wash-out or the smearing results encountered in the traditional procedures of utilizing a long integration time without a prior screening for the data spatial-coherence. A connection to the adaptive coherence estimator (ACE) is revealed. Also established is an equivalence between the lucky MLE and the Kullback-Leibler (KL) divergence based spatial coherence test.