Agricultural remote sensing refers to the acquisition and analysis of information about agricultural land, crops, soils, and related environmental conditions without direct physical contact, using sensors mounted on satellites, aircraft, unmanned aerial vehicles, and ground-based platforms ^[1][2]. It employs optical, multispectral, hyperspectral, thermal infrared, and microwave sensors to capture electromagnetic signals reflected or emitted by agricultural surfaces. These observations are processed to derive information on vegetation characteristics, crop distribution, biomass, soil moisture, land surface temperature, evapotranspiration, and agricultural land-use changes ^[1][3]. Common analytical approaches include vegetation indices, spectral analysis, image classification, geospatial modeling, and machine learning. Agricultural remote sensing supports crop growth monitoring, yield estimation, drought assessment, irrigation management, soil and water monitoring, and the detection of agricultural environmental changes ^[1][2]. By integrating remotely sensed observations with geographic information systems, field measurements, and environmental datasets, it enables spatially explicit and repeated monitoring of agricultural systems across field, regional, and global scales.