Sugarcane Height Estimation

This dataset contains processed, plot-level data used for sugarcane height prediction based on multi-source satellite imagery and machine learning models. The dataset was derived from field observations and remote sensing data collected from three sugarcane plots located within a single agroecological zone over two consecutive planting seasons (2023–2024). Each record represents a plot-level observation aggregated at a specific observation date. The dataset includes ground-measured sugarcane height as the target variable, along with a set of predictor variables comprising vegetation indices extracted from Sentinel-2 and Landsat-9 imagery, morphological attributes (e.g., planting age), meteorological variables, and derived indicators used in model development. The data were preprocessed to ensure temporal consistency and were evaluated using a forward chaining strategy to preserve the chronological order between training and testing samples. This dataset supports reproducibility of the reported results and can be used for benchmarking machine learning approaches for crop height estimation under controlled agroecological conditions.