Do you really have to use Harris corners? There is many features developed after Harris corners, with better properties. A good overview can be found in this article:
Based on that article as well as my personal experience, I would suggest switching either to MSER (Maximally Stable Extermal Regions), or even combine them with DoG (Difference of Gaussians) -- the features first presented as a part of the SIFT pipeline.
If the problem really is in low contrast, then MSER features should really make you happy: they are (fairly) invariant to changes in lighting. In short, they are connected regions of the image stable through a series of different threshold binarizations.
The feature extraction process is independent from calculating the descriptors, so it shouldn't be too hard to integrate new ways of feature extraction in to your process.
Also, I've heard of (but never actually worked with) Multiscale Harris corners as an extension to Harris corners. I don't know much about them and personally can not recommend any reading materials on this topic, so I leave article search and picking the most interesting materials to you.
Furthermore, might I suggest that the image you posted might have other problems than low contrast. In my personal experience, vegetation like bushes or possibly the field you have, as well as the lovely bubbly clouds tend to produce "generic features" -- features which tend to have equally similar (or dissimilar) descriptors as a lot of other features.
Practically, this means that when doing feature matching on two images from a different perspective, features extracted from these kinds of surfaces tended to be falsely matched. I have done a Master thesis that in a large part deals with feature extraction to be used in feature matching further used to calculate a homography transformation between two images when I came across this problem. I didn't find any other articles describing this problem at the time, but my thesis might be helpful for your overall approach.
Lastly, as you have set, thresholds and techniques that work just fine on most images extract to little features in this kind of images, because of its mostly homogeneous areas. This kind of images present problems in feature matching (which can be extended to image stitching), content based image retrieval, and I would presume tracking as well as similar applications. No method currently works quite well on them.
Methods that work good on this kind of images as well as the typical cases are being explored and researched currently, such as an approach I started working on briefly described in this answer.