<p>Synthetic Aperture Radar (SAR) images face significant challenges with speckle noise when used in agricultural cropland planning and diversification. In agricultural SAR images, the image quality is compromised by the interference of radar signals, resulting in a disruptive noise that masks important land features like crop boundaries and vegetation patterns. The conventional methods for suppressing speckle-noise frequently result in losing essential intricacies needed for accurate land-use mapping, which hinders their efficacy in agricultural settings. An Iterative Variational Method (IVM) is presented in this research to efficiently suppress the speckle-noise without compromising the structural coherence of SAR images. The method relies on a variational optimization structure with an objective function that weighs two essential elements: suppressing speckle noise and preserving the fine details of the images. Experimental findings of IVM using SAR images from various agricultural areas showed that the proposed method is more effective than conventional and unconventional methods in suppressing noise and preserving details. To evaluate the effectiveness of the IVM method, it was compared with quite popular metrics such as Signal-to-Noise Ratio (SNR), Peak Signal-to-Noise Ratio (PSNR), Universal Image Quality Index (UIQI), and Structural Similarity Index (SSIM), and IVM demonstrated exceptional results, enhancing the usability of SAR images for land planning and crop diversification. The enhanced SAR images offer improved visualization of crop borders, easier recognition of different crop varieties, and more effective tracking of crop conditions, aiding in making informed choices in agricultural land planning. The suggested method could revolutionize agricultural monitoring using IVM technology, allowing for more accurate and data-focused approaches to crop rotation, crop variety, and eco-friendly land management, especially in areas lacking optical image resources.</p>