ID Class Name Description RGB Code 1 Water bodies andchannels Natural and artificial inland water bodies. Lakes, lagoons, waterholes and wide/accumulation sectors of rivers and streams. Includes agricultural and mining reservoirs/ponds. 0, 92, 230 2 Beachs, dunes andsandbanks Coastal and riverine beaches and dunes; inland in abandoned and/or transported dunes, sandbanks in clogged rivers and other sandy sectors at altitude. 255, 235, 190 3 Mediterranean sclerophyll Native Forest Mediterranean sclerophyll forest sectors with mature native trees and dense thickets (woody plants greater than two meters in height), or with a coverage of over 80% withr espect to the Landsat grid. 56, 168, 0 4 Temperate Native Forest Temperate forested sectors with mature native trees (woody plants greater than three meters in height), or with a coverage over 90% with respect to Landsat grid. 38, 115, 0 5 Broad leaf plantation Forest plantations of exotic broad leaf species (mainly Eucalyptus sp.) 255, 0, 197 6 Fruit trees Fruit tree plantations (fruit growing). Class generated from training points obtained from the fruit tree cadastre (vineyards, avocado, citrus, grapefruit, etc). 200, 0, 0 7 Glacier and snow Snow-covered surfaces and eternal ice. 255, 255, 255 8 Riparian vegetation Flooded surfaces (permanent and seasonal) covered with vegetation, adjacent to watercourses, bodies of water and water outcrops. 0, 255, 197 9 Shrub and bushvegetation Sectors covered with woody plants less than 3 meters high. Canopy projection covering from 5% to 80% with respect to Landsat grid. 255, 120, 0 10 Pynophyte plantation Forest plantations of exotic acriculate and imbricate/scaly/spiral-eaved species (mainly Pinus radiata). 115, 40, 0 11 Grasslands, pastures and annual crops Annual natural (un-managed) grasslands and pastures and annual irrigated and rained (non-fruit) agricultural crops. Herbaceous cover with high seasonal variation in early spring vs. late summer. 255, 255, 0 12 Meadows and evergreen grasslands Managed and unmanaged evergreen grasslands and pastures. More common in southern Chile, associated with animal grazing areas. Herbaceous cover with low seasonal variation in early spring vs. late summer. 50, 230, 0 13 Bare soil and non-vegetated areas Decapitated soils, excavations, landfills, roads and mine stockpiles, rocky outcrops, faults, landslides and rock slides and sectors above the altitudinal limit of vegetation. 199, 215, 158 14 Peatland Soils covered by peatlands. 104, 104, 104 15 Impermeable surface Surfaces covered with impervious human infrastructure: Buildings, paved roads, housing and industrial sectors. 0, 0, 0 16 Harvested plantation Estimated harvested areas of tree plantations 186, 85, 211

The CLDynamicLandCover dataset is a dynamic land use dataset for Central Chile, covering latitudes -31.61 to -43.50. It provides land cover maps on a five-year scale from 1990 to 2018, created by a semi-automatic algorithm initially developed for the coastal sector of south-central Chile. This method was later expanded to cover areas from the southern Coquimbo region to the southern Los Lagos region. The dataset uses a supervised classification approach with satellite data (Landsat and SRTM) to represent reflectivity and topography, and auxiliary cartographic information. It includes training points for 15 land cover classes, photointerpreted and georeferenced in 2018, to create a spectral signature for each class. Predictive variables are selected using machine learning algorithms and expert criteria. The 2018 endmember is used to estimate the Jeffries-Matusita Distance for points from other years (1990, 1999, 2004, 2009, 2013), determining whether a pixel's land cover class changed or remained constant. A separability cutoff threshold optimizes class discrimination between years. Points below the threshold are retained (indicating no change), and those above are eliminated (indicating change). These points train a Random Forest classification model to generate land cover maps for each year. Land trajectories were corrected based on ecological transitions and economic cost criteria, with a new classification for harvested plantations. The product shows high accuracy, with values ranging from 0.894 to 0.950 and Kappa coefficients from 0.877 to 0.943 across the years.

 Management of global change impacts on hydrological extremes by coupling remote sensing data and an interdisciplinary modeling approach (Chi)2, Award: PCI ANID Chile-NSFC China NSFC190018. The catchment's memory: understanding how hydrological extremes are modulated by antecedent soil moisture conditions in a warmer climate, Award: ANID Fondecyt 1212071. Improving forest water yield and productivity quantification at the catchment scale by mapping root depth and ecophysiological thresholds with remote sensing and water transfer modeling, Award: ANID Fondecyt 1210932. Center for Climate and Resilience Research, Award: CR2, CONICYT/ FONDAP/15110009.