With the rapid expansion of multispectral satellite imagery (MSSI) in precision agriculture, efficiently compressing vast datasets while preserving critical information is paramount. This paper presents a novel region-aware compression approach tailored to agricultural applications, leveraging the Normalized Difference Vegetation Index (NDVI) to classify image regions into Urban and Field areas. Urban regions are subjected to high-loss compression using JPEG2000, achieving a compression ratio (CR) of up to 72:1, while Field regions, critical for agricultural research, are compressed using adaptive techniques, including Discrete Wavelet Transform (DWT), Huffman coding, and Principal Component Analysis (PCA), resulting in a CR of 68:1. The proposed method preserves vital spectral and spatial information, as evidenced by high Peak Signal-to-Noise Ratio (PSNR) values of 85.74 and 79.68 and Structural Similarity Index (SSIM) scores of 0.95 and 0.93 for Landsat 8 and Sentinel-2 L2A datasets, respectively. Additionally, deep learning models (VGG16 and EfficientNet) fine-tuned on compressed data achieve classification accuracies of up to 98.47%, demonstrating that critical information is retained even after compression. These results underline the method’s ability to balance high compression efficiency with data integrity, making it a valuable tool for precision agriculture and other domains requiring high-fidelity multispectral data analysis.