Abstract Soil inorganic carbon (SIC) is critical for carbon sequestration, infiltration, and climate modeling, yet quantifying its precise spatial distribution at continental scales remains challengings. We introduce a high‐resolution (30 m) CONUS SIC map using machine learning (ML) models trained on the ISRIC World Soil Information Service (WoSIS ...
Zahra Ghahremani +4 more
wiley +1 more source
Geostatistics for functional data: an ordinary kriging approach
We present a methodology to perform spatial prediction when measured data are curves. In particular, we propose both an estimator of the spatial correlation and a functional kriging predictor. We adapt an optimization criterium used in multi- variable spatial prediction in order to estimate the kriging parameters.
Giraldo, Ramón +2 more
openaire +1 more source
Spatially correlated curves are present in a wide range of applied disciplines. In this paper we describe the R package geofd which implements ordinary kriging prediction for this type of data.
RAMÓN GIRALDO +2 more
doaj
Benchmarking Spatial Interpolation Methods for Long-Term Meteorological Exposure Assessment in China: Comparing Inverse Distance Weighting and Ordinary Kriging in Climate-Health Research. [PDF]
Zhang R +10 more
europepmc +1 more source
GIS-based soil bearing capacity zonation maps for the Dhaka metropolitan development plan (DMDP) area, Bangladesh. [PDF]
Rahman MS, Alajlan ZS, Ansary MA.
europepmc +1 more source
Evaluating Sentinel-2 gap filling techniques for cloud removal and data reconstruction. [PDF]
Grich S +5 more
europepmc +1 more source
A neural network-based automatic semi-variogram modeling approach for geomagnetic map construction in multi-source indoor and outdoor navigation. [PDF]
Zhan C, Huang P, Xue B, Lan B.
europepmc +1 more source
Lognormal Ordinary Kriging Metamodel in Simulation Optimization
Muzaffer Balaban, Berna Dengiz
openaire +1 more source
Spatial prediction of dog population distribution in Kenya. [PDF]
Das M +4 more
europepmc +1 more source

