Time-series 10-m leaf chlorophyll inversion data from 2016 to 2025 in Gonghe County, Qinghai Province, China (CHL_GH)

The Gonghe County Leaf Chlorophyll Inversion Dataset (CHL_GH) provides high-accuracy, 10-m resolution time-series retrieval results of leaf chlorophyll content (Chl) across Gonghe County, Qinghai Province, China, from 2016 to 2025, with a specific focus on the photovoltaic (PV) power station areas within the region. Generated using in-situ chlorophyll field measurements and Sentinel-2 multispectral imagery obtained from the Google Earth Engine (GEE) platform, this dataset is produced by coupling the PROSAIL-5B leaf-canopy radiative transfer model and the eXtreme Gradient Boosting (XGBoost) machine learning algorithm, with a canopy structural correction framework applied to enhance retrieval robustness. Validation against independent simulated test subsets and field-measured Chl observations demonstrated high reliability of the dataset, yielding a coefficient of determination (R²) of 0.87, root mean square error (RMSE) of 4.15 μg·cm⁻², and relative root mean square error (RRMSE) of 16.73% for simulation-based validation, as well as an R² of 0.66, RMSE of 4.04 μg·cm⁻², and RRMSE of 19.44% for independent field-based validation.