kundun14

Bare-soil-detection_LandSat

Synthetic soil composite derived from LandSat time series in Google Earth Engine

glacier_mapping_peru

Spatial validation of machine learning algorithms for main glacier regions in Peru.

Parlange-Haverkamp-1D-infiltration

Code for solving the 1D analytical Haverkamp infiltration equation using Newton method

smap_downscaling

Notebooks for downscaling SMAP soil moisture and validation against in situ data

Soil-carbon-content-modeling-using-machine-learning

soil-remote-sensing-system

Google Earth Engine algorithm for generating synthetic soil composites from Landsat catalog.

soil_moisture_SMAP_machine_learning

Scripts for the spatial analysis, processing and regression modeling of soil moisture retrieved from SMAP satellite using R

Spatial-validation-of-glacier-classification-algorithms

spatial_data_analysis_agriculture_R

Code in R for spatial analysis of agricultural and soil data.

terrain_clustering_gaussian_mixture_modeling

Clustering of geomorphometric atributes for soil clasificación purposes on andean landscapes based on Gaussian mixture modeling. Implementation of the code of Dyba, K., & Jasiewicz, J. (2022). Toward geomorphometry of plains—Country-level unsupervised classification of low-relief areas (Poland). Geomorphology, 413, 108373. https://doi.org/10.1016/j.geomorph.2022.108373

kundun14/soil_moisture_SMAP_machine_learning

Scripts for the spatial analysis, processing and regression modeling of soil moisture retrieved from SMAP satellite using R

kundun14/smap_downscaling

Notebooks for downscaling SMAP soil moisture and validation against in situ data

kundun14/Bare-soil-detection_LandSat

Synthetic soil composite derived from LandSat time series in Google Earth Engine

kundun14/Soil-carbon-content-modeling-using-machine-learning

kundun14/soil-remote-sensing-system

Google Earth Engine algorithm for generating synthetic soil composites from Landsat catalog.

kundun14/Spatial-validation-of-glacier-classification-algorithms

kundun14/terrain_clustering_gaussian_mixture_modeling

Clustering of geomorphometric atributes for soil clasificación purposes on andean landscapes based on Gaussian mixture modeling. Implementation of the code of Dyba, K., & Jasiewicz, J. (2022). Toward geomorphometry of plains—Country-level unsupervised classification of low-relief areas (Poland). Geomorphology, 413, 108373. https://doi.org/10.1016/j.geomorph.2022.108373

kundun14/glacier_mapping_peru

Spatial validation of machine learning algorithms for main glacier regions in Peru.

kundun14/Parlange-Haverkamp-1D-infiltration

Code for solving the 1D analytical Haverkamp infiltration equation using Newton method

kundun14/spatial_data_analysis_agriculture_R

Code in R for spatial analysis of agricultural and soil data.

kundun14/Cubic_Spline_interpolation_MODIS_time_series

kundun14/full_stack_submission-repository

kundun14/landSat_Sentinel_Harmonized_R

kundun14/latin_hypercube_sampling_R

Case study of Latin Hypercube Sampling in soil sampling with R

kundun14/leaflet_r_photo_tagging

kundun14/mlr3_spatial_cross_validation_R

Code for apply new approaches for spatial cross validation of machine learning predictions of spatial variables, based on MLR3 library and Schratz, P., Becker, M., Lang, M., & Brenning, A. (2021). Mlr3spatiotempcv: Spatiotemporal resampling methods for machine learning in R. ArXiv:2110.12674 [Cs, Stat]. http://arxiv.org/abs/2110.12674

kundun14/overlay_sp

kundun14/simulate_cluster_raster

simulate_cluster_raster

kundun14/super_learner_machine_learning_soil_mapping

Code and data from Mario Guevara´s Digital Soil Mapping 2021 course. SuperLearner approach for soil carbon mapping.