<p>Transfer learning is especially interesting for the accuracy if you don't have enough data. For example, <a href="https://arxiv.org/abs/1811.08883" rel="nofollow noreferrer">this paper</a> compared training with and without pretraining on imagenet. They claim that after 10k images, pretraining does not give better results but still allows to train faster.<br>
Then if you have a small dataset, your question still holds whether you should pretrain on imagenet or on another dataset. I think the answer to this question is given in the following paragraph (the references there are probably of interest to you):</p>

<blockquote>
  <p><strong>Do we need big data?</strong> Yes. But  a  generic  large-scale, classification-level pre-training set is not ideal if we take into account the extra effort of collecting and cleaning data—the cost of collecting ImageNet has been largely ignored,  but  the  ‘pre-training’  step  in  the  ‘pre-training  +fine-tuning’ paradigm is in fact not free when we scale out this paradigm.  If the gain of large-scale classification-level pre-training becomes exponentially diminishing [44, 30], it would be more effective to collect data in the target domain.</p>
</blockquote>

<p>Therefore, you also need to consider the quality of your satellite image dataset. Since it should be closer to your data than Imagenet it is probably better. </p>
Transfer learning needs to be from more relevant domain?
<p>I am in search of a reference paper where I can find out that transfer learning needs to be from domain specific source model rather than using generalise model i.e., imagenet</p>

<p>For example Source dataset satellite/drone hyper/multi spectral images of plants and target dataset of hyper/multi spectral images of plants captured using agricultural robot</p>

<p>As compared to  </p>

<p>Source dataset ImageNet model and target dataset images of plants captured using agricultural robot</p>