Sensible heat flux (H) plays an important role in characterizations of land surface water and heat balance. There are various types of H measurement methods that depend on observation scale, from local-area-scale eddy covariance (EC) to regional-scale large aperture scintillometer (LAS) and remote sensing (RS) products. However, methods of converting one H scale to another to validate RS produc...

abstract: converting point data to continuous one is the first step in order to use them in scorpan flowchart. for this purpose, different geostatistic methods are available which at present research regression kriging with local variogram was applied. for mapping apparent electrical conductivity at the area covering 72000ha located in ardakan region, 700 readings in horizontal and vertical mod...

This paper addresses the issue of incorporating a digital elevation model into the mapping of Ž . annual and monthly erosivity values in the Algarve region Portugal . Besides linear regression of erosivity against elevation, three geostatistical algorithms are introduced: simple kriging with Ž . Ž . varying local means SKlm , kriging with an external drift KED and colocated cokriging. Cross val...

This paper presents three multivariate geostatistical algorithms for incorporating a digital elevation model into the spatial prediction of rainfall: simple kriging with varying local means; kriging with an external drift; and colocated cokriging. The techniques are illustrated using annual and monthly rainfall observations measured at 36 climatic stations in a 5000 km region of Portugal. Cross...

This paper discusses the estimation and plug-in kriging prediction of a non-stationary spatial process assuming a smoothly varying variance function with an additive independent measurement error. A difference-based kernel smoothing estimator of the variance function and a modified likelihood estimator of the measurement error variance are used for parameter estimation. Asymptotic properties of...

In precision agriculture regression has been used widely to quantify the relationship between soil attributes and other environmental variables. However, spatial correlation existing in soil samples usually violates a basic assumption of regression: sample independence. In this study, a regression-kriging method was attempted in relating soil properties to the remote sensing image of a cotton f...

The spatial variability of soil organic carbon (SOC) and total nitrogen (STN) levels is important in both global carbon-nitrogen cycle and climate change research. There has been little research on the spatial distribution of SOC and STN at the watershed scale based on geographic information systems (GIS) and geostatistics. Ninety-seven soil samples taken at depths of 0-20 cm were collected dur...