  Digital surface model generation using traditional multi-view stereo matching
(MVS) performs poorly over non-Lambertian surfaces, with asynchronous
acquisitions, or at discontinuities. Neural radiance fields (NeRF) offer a new
paradigm for reconstructing surface geometries using continuous volumetric
representation. NeRF is self-supervised, does not require ground truth geometry
for training, and provides an elegant way to include in its representation
physical parameters about the scene, thus potentially remedying the challenging
scenarios where MVS fails. However, NeRF and its variants require many views to
produce convincing scene's geometries which in earth observation satellite
imaging is rare. In this paper we present SparseSat-NeRF (SpS-NeRF) - an
extension of Sat-NeRF adapted to sparse satellite views. SpS-NeRF employs dense
depth supervision guided by crosscorrelation similarity metric provided by
traditional semi-global MVS matching. We demonstrate the effectiveness of our
approach on stereo and tri-stereo Pleiades 1B/WorldView-3 images, and compare
against NeRF and Sat-NeRF. The code is available at
https://github.com/LulinZhang/SpS-NeRF
