The simulation of fields with varying spatial structures is an interesting strategy when it comes to testing or evaluating a specific processing method. The main advantage of simulations is that one is able to control the data distribution within the field so that the context under which the processing method is applied is well-known. For instance, one might Read more about Simulating spatial datasets with known spatial variability[…]

When entering the field of geostatistics, one is confronted almost instantaneously to the variogram tool. The definition and existence of the variogram relies on fundamental assumptions that are often presented from a theoretical point of view. These assumptions are almost always left apart because they are relatively difficult to understand. I sincerely admit that the mathematical Read more about Fundamental assumptions of the variogram : Second-order stationarity, intrinsic stationarity…. What is this all about ?[…]

Introducing the variogram It is nearly impossible to talk about the analysis of Precision Agriculture data without mentioning the variogram. Be aware that some people will refer to the term semi-variogram instead. There is some kind of confusion between these two terms (Bachmaier et Backes, 2008). Some authors talk about semi-variogram because of the factor 2 at the denominator Read more about Variogram and spatial autocorrelation[…]

Interpolation is the process of mapping a variable  at unsampled locations using a set of samples of known location and value (Fig. 1). These samples can come from a field campaign or can be the information measured by fixed or mobile sensors inside a field. More than often, in Precision Agriculture studies, one has a subset Read more about Spatial data interpolation : TIN, IDW, kriging, block kriging, co-kriging…. What are the differences ?[…]