I would recommend
Array Signal Processing - Johnson, Dudgeon. This book covers classical spectral estimation, Minimum Variance Distortionless Response (MVDR), Linear Prediction, and subspace methods (e.g. MUSIC and ESPIRIT). It provides examples of the resolution capabilities between these methods. The are quite a number of good references in this book if you require additional detail.
MUSIC and ESPIRIT type algorithms are dicussed in most advanced DSP texts:
Advanced Digital Signal Processing, Proakis, Rader, Ling, Nikias
Discrete Random Signals and Statistical Signal Processing, Therrien
For parametric methods:
Spectral Analysis - A modern Perspective (Kay, Marple) Proc. IEEE Vol 6, No 11, 1981
Modern Spectral Estimation: Theory and Application, Steven Kay
Digital Spectral Analysis - S.L. Marple
SPECTRAL ANALYSIS OF SIGNALS - P Stoica and R Moses PDF is available here
The books by Marple and Kay tend to focus on algorithms i.e. implementations of various MA, AR, and ARMA approaches (e.g. fast Lattice filter implementations) rather than the performance of a basic approach
Compressive Sensing / Sparse Reconstruction techniques can also be used. There are too many references here to list. There are a few books by these authors Michael Elad, Yonina Eldar, and Holger Rauhut. These techniques are often quoted as "having super resolution properties"
The algorithms are often hard to compare directly because they all have twiddle factors which affect their performance. For example
1. For MA, AR, ARMA, what order of model to use?
2. For Compressive techniques - how closely to space the template vectors? What level of regularization to use? How many frequencies are there? How many samples do we have?