2026-10-07T21:44:57Z https://api.figshare.com/v2/oai

oai:figshare.com:article/33802834 2026-09-15T15:43:37Z category_25771 item_type_3 month_year_09_2026

Cross-validation performance of machine-learning models for global cropland GPP upscaling under observation-, year-, site-, and region-holdout schemes (1994–2014 FLUXNET training data) yuxing sang (24703510) Chenzhi Wang (11603041) Liangliang Zhang (19645186) Mingzhu He (24372645) Yean-UK Kim (24943060) Quanbo Zhao (19854957) Yue He (24307106) Xuhui Wang (8635403) Exploration geochemistry cropland primary productivity FLUXNET data machine learning (stat.ML) cross-validation approach upscaling technique <p dir="ltr">This dataset contains the full cross-validation results underlying the model-performance assessment reported in Observation-constrained assessment of cropland productivity trends and drivers at the global scale<br></p><p dir="ltr">Three machine-learning algorithms (random forest, boosted regression trees, artificial neural network) were trained on monthly FLUXNET2015 GPP (GPP_NT_VUT_MEAN, QC > 0.8; 9,567 observations from 166 sites, 1994–2014), using monthly meteorological variables and one of three satellite vegetation indices (GIMMS3g NDVI, MODIS13C2 V6 NDVI, MODIS13C2 V6 EVI) as predictors.</p><p dir="ltr">Four holdout schemes of increasing stringency were applied: leave-observation-out (10% of records withheld at random), leave-year-out (10% of years withheld), leave-site-out (10% of sites withheld), and leave-region-out (one of six continents withheld at a time). The first three were repeated ten times with independent random draws.</p><p dir="ltr">Reported metrics are the correlation coefficient (R, dimensionless), predictive coefficient of determination (R², dimensionless), root mean square error (RMSE, g C m⁻² month⁻¹) and bias (g C m⁻² month⁻¹).</p> 2026-09-15T15:43:37Z Dataset Dataset 10.6084/m9.figshare.33802834.v1 https://figshare.com/articles/dataset/Cross-validation_performance_of_machine-learning_models_for_global_cropland_GPP_upscaling_under_observation-_year-_site-_and_region-holdout_schemes_1994_2014_FLUXNET_training_data_/33802834 CC BY 4.0