The accuracy achieved by applications employing hyperspectral data collected by hyperspectral cameras depends heavily on a proper estimation of the true spectral signal. Beyond question, a proper knowledge about the sensor response is key in this process. It is argued here that the common first order representation for hyperspectral NIR sensors does not represent accurately their thermal wavelength-dependent response, hence calling for more sophisticated and precise models. In this work, a wavelength-dependent, nonlinear model for a near infrared (NIR) hyperspectral camera is proposed based on its experimental characterization. Experiments have shown that when temperature is used as the input signal, the camera response is almost linear at low wavelengths, while as the wavelength increases the response becomes exponential. This wavelength-dependent behavior is attributed to the nonlinear responsivity of the sensors in the NIR spectrum. As a result, the proposed model considers different nonlinear input/output responses, at different wavelengths. To complete the representation, both the nonuniform response of neighboring detectors in the camera and the time varying behavior of the input temperature have also been modeled. The experimental characterization and the proposed model assessment have been conducted using a NIR hyperspectral camera in the range of 900 to 1700 [nm] and a black body radiator source. The proposed model was utilized to successfully compensate for both: (i) the nonuniformity noise inherent to the NIR camera, and (ii) the stripping noise induced by the nonuniformity and the scanning process of the camera while rendering hyperspectral images.