Understanding the sensitivity of remote sensing (RS) observation to land surface parameters (e.g., reflectance and temperature) is very important for estimating the accuracy of RS products and optimizing inversion algorithms. The most precise method for quantifying this sensitivity is physical modelling of derivative propagation in simulated 3D landscapes. However, to our knowledge, present land surface radiative transfer models (RTM) do not simulate derivative propagation. This paper proposes an original “differentiable radiative transfer modelling” that simulates the derivative propagation in natural and urban landscapes, for reflectance. It is integrated in the framework of DART RTM. We validated it both analytically and with a finite difference method applied to a 3D landscape. This new modelling extends the efficiency of 3D RTMs for sensitivity studies. It is implemented in the DART version freely available for research and education (https://dart.omp.eu). •First derivative (Jacobian) propagation modelling in land surface RT models.•Validation of this derivative modelling by the finite difference method.•Discussion of the major factors that influence the derivative.•Outlook of potential applications of the derivative modelling.